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12th North American Paleontological Convention June 17-21, 2024

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Society for the Preservation of Biodiversity Information Standards
(TDWG)
Natural History Collections
(SPNHC)

SPNHC & TDWG Joint Conference (Hybrid)

"Enhancing Local Capacity, Elevating Global Standards."

Okinawa Convention Center

2-6 September 2024

Save the date!

Please read the important information before you register!

Daily schedule

The conferece days will be Monday - Friday (2-6 September), but note that Monday is a half-day beginning after lunch with the welcoming remarks and plenary presentations. Specially arranged excursions will be offered on the preceding Sunday (1 September) and the following Saturday (7 September). An initial list of excursions on the bracketing days will be announced soon. We expect the excursions to be popular and space limited, so stay tuned. (Also be aware that early access will be granted to the first 120 registrants!)

Conference program

SPNHC TDWG 2024 will include symposia, panel discussion, workshop, lightning talks, demonstrations, general contributed papers, and plenary presentations. See the session descriptions on our Sessions page.

Abstracts

More than 390 abstracts for oral presentations and posters were reviewed by session organizers and members of the Program Committee between 22 April-12 May.

Notifications of acceptance for presentation at the conference were emailed to abstract submitters (not to all authors) on 15 May through the Oxford Abstracts conference platform. These notifications included an invitation to submitters to respond to reviewer comments/corrections by editing their abstracts ( deadline 29 May). Presenters were reminded to register for the conference to secure their place in the program. Presentations are now being organized into the final schedule. Please address questions to editor@tdwg.org

Authors please note: you will have an opportunity to submit extended abstracts to Biodiversity Information Science and Standards (BISS). Details to be announced in June.

By registering for and/or participating in conference sessions, workshops, panels, field trips, social events, or any other activity related to the SPNHC - TDWG 2024 Conference, you agree to abide by this Code of Conduct. This includes activities within the local community. As a participant in the conference, you represent our organizations and the shared values of our chosen professions. Read more...

Visa requirements

The Ministry of Foreign Affairs of Japan is the recommended site https://www.mofa.go.jp/j_info/visit/visa/index.html for information about visas for traveling to Japan.

Accommodations

The conference will be held at the Okinawa Convention Center (OCC), not a hotel, so we have not arranged for room-blocks to be held at one or more local hotels. We recommend looking at the hotels listed under Neighborhood Accommodations on the OCC website, or simply searching for nearby hotels on your favorite travel or mapping site.

Information not to miss from the Visit Okinawa Japan website:

About the SPNHC-TDWG joint conference

The Society for the Preservation of Natural History Collections (SPNHC) is an international organization devoted to the preservation, conservation and management of natural history collections.

Biodiversity Information Standards (TDWG) develops and maintains data standards that support the integration of primary biodiversity information across disciplines, organizations, and nations. Its annual conference has become a preeminent forum for advancing biodiversity informatics.

SPNHC and TDWG first met jointly in 2018 (Dunedin, New Zealand). The joint conference was widely viewed as beneficial to both organizations and something we should do periodically. TDWG last met in Asia in 2012 (Beijing), and SPNHC has never met in Asia. By holding our second joint meeting in Okinawa, Japan, our organizations welcome new participants from across Asia. Please join us and meet your colleagues from around the world!

Please send general questions about the conference to okinawa2024@spnhc.org.

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SPNHC & TDWG Joint Conference - A hybrid conference in Okinawa, Japan, 2-6 September 2024

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Toolkits & Guides for 5 core documents including Collection Management Policy and Disaster Preparedness.

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This toolkit offers guidance on the needed resources to successfully revise or draft your museum's core documents. With it comes a combination of tools, guidelines, and action plans to help you understand the importance of-and required elements for-each of the five documents required for Core Documents Verification.

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This wiki provides an update to iDigBio's 2012 resource, Biological Collections Databases, Tools, and Data Publication Portals. This page can be linked to using the shortlink, https://bit.ly/idigbio-cms.

iDigBio gathers information about collections management system (CMS, also referred to as a content management system) options to share with the natural history collections community, both as an online resource available to anyone, and as a reference specifically for participants in our Digitization Academy courses. We have asked representatives of each CMS to respond to a survey containing a standard set of questions, which we believe can be helpful for potential new users comparing CMS options. These surveys, as well as additional resources, are organized by CMS option (alphabetically) below.

If you are a CMS representative and wish to be added to this list, or to update the information here, please contact Erica Krimmel (ekrimmel@fsu.edu).

Arctos is a consortium of museums and organizations that collaborate to manage and serve data from natural and cultural history collections. Arctos is a web-based platform providing comprehensive, flexible tools for all collection management and data publishing needs. Arctos is a community of practice dedicated to sharing data vocabulary and standards, forming strong links between related catalog records across institutions, curating and improving the quality of shared data such as agents, taxonomy, and geography, enabling extended use, and developing new database tools. Data and media hosting is provided by the Texas Advanced Computing Center (TACC).

2021 Survey

Arctos 2-page Highlights

Arctos Handbook

Arctos Search Portal

Webinar recordings

BRAHMS is database software for managing natural history collections, botanic gardens, seed banks, field surveys, taxonomic research and biogeographic study.

2021 Survey

CollectionSpace

CollectionSpace is a community-supported collections management solution for museums and other collecting organizations, providing standards-based collections management workflows, robust tools to store and describe a wide variety of digital assets, unlimited user licenses, and a public collections browser. An organizational home for CollectionSpace is provided by LYRASIS, a unique non- profit membership organization and trusted service provider for hundreds of cultural heritage organizations. LYRASIS is home to 10 community-supported programs, providing support, strategic direction and community management, while also representing new programs for the future health of LYRASIS and the thousand-plus members and partners we represent through our Research & Innovation Division.

2021 Survey

Overview of CollectionSpace (video, 2021)

CollectiveAccess

CollectiveAccess is a highly configurable collections management system suitable for use in a wide range of disciplines, from anthropology to art history to paleontology to corporate archives. CollectiveAccess includes a range of preconfigured "profiles" for various use cases and standards, including DarwinCore, SPECTRUM and VRA.

2021 Survey

The EMu Collections Management System has been at the forefront of collections management for more than three decades. Comprehensive and flexible, EMu can accommodate the requirements of any collecting institution.

2021 Survey

Axiell presentation for iDigBio (video, 2021)

Specify Software handles the curatorial processing and publishing of data associated with specimens in biological collections, including museums, herbaria, and biorepositories of tissue and DNA samples. Specify manages data for collections management tasks associated with museum transactions and mobilizes collections data through the Specify Web Portal, and through IPT to caches such as GBIF and iDigBio. Specify 7 also integrates through APIs with aggregators and online services for retrieving records of information related to specimen catalog entries.

2021 Survey

Extending Specify for a New Biological Collections Computing Paradigm (video, 2021)

Symbiota is an adaptable, customizable software that enables self-defined and self-governed communities of practice to form, collaborate, and produce globally integrated, high- quality biodiversity data resources. Symbiota is ideal for small- to medium-sized collections that lack IT support and resources. Symbiota has also been used to create location-based data portals (e.g., http://www.cal-ibis.org/) and institutional management systems across collections (e.g. https://bellatlas.umn.edu). Data in web-based Symbiota portals can be instantly searchable via public search features, and there are many tools to connect these data to other collections and to aggregators (e.g., iDigBio, GBIF).

2021 Survey

Introduction to Symbiota (video, 2021)

TaxonWorks

TaxonWorks (TW) is a web-based workbench, an open-source platform serving taxonomists, biodiversity scientists, natural history collections and more. It allows you to capture, organize, and enrich your taxonomic, nomenclature, collections, and bibliographic source data; share it with collaborators; and package it for analysis and publication. The software has been in production for over 5 years and is very actively updated by a core team of 5 with numerous other community and internal contributions. Anyone in the world can and is encouraged to contribute to the open-source code base, https://github.com/SpeciesFileGroup/taxonworks. Talk with us and the TW community on gitter ( https://gitter.im/SpeciesFileGroup/taxonworks).

2021 Survey

The Museum System

Gallery Systems specializes in developing and implementing management systems for cultural collections, content and media. We currently work with almost 800 museums and cultural heritage collections worldwide of all sizes and types, managing collections ranging from art to natural history and costumes to archives.

2021 Survey

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iDigBio Wiki- Collections Management Systems

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This wiki provides an update to iDigBio's 2012 resource, Biological Collections Databases, Tools, and Data Publication Portals. This page can be linked to using the shortlink, https://bit.ly/idigbio-cms. iDigBio gathers information about collections management system (CMS, also referred to as a content management system) options to share with the natural history collections community, both as an online resource available to anyone, and as a reference specifically for participants in our Digitization Academy courses.

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Consultation on primary biodiversity data and the Post-2020 Global Biodiversity Framework

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Consultation on primary biodiversity data and the Post-2020 Global Biodiversity Framework

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In preparation for the Fifteenth Convention of the Parties (COP15) to the UN Convention on Biological Diversity (CBD) in Kunming, China, the _alliance for biodiversity knowledge_ is organizing a community consultation. With the help of key partners across the science-policy interface, the consultation will engage those involved in developing global and national indicators under the CBD’s post-2020 Global Biodiversity Framework (GBF) and Essential Biodiversity Variables (EBVs).

To start the discussion, we encourage community members to read the white paper, Primary biodiversity data and the Post-2020 Global Biodiversity Framework, which provides an overview of the dependence of previous and future indicator frameworks on primary biodiversity data.

This agenda for the webinar will form the basis of a two-week moderated consultation addressing the themes of:

Improving data models and standards for improved usability

Addressing spatial, temporal and taxonomic biases in primary biodiversity data

Increasing transparency in processes for indicator development

The discussion will explore how to improve workflows around the primary biodiversity data on which indicators and EBVs rely and highlight the key role of biodiversity observation networks and information facilities in achieving the goals of the GBF.

Register here

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Consultation on primary biodiversity data and the Post-2020 Global Biodiversity Framework | alliance for biodive

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In preparation for the Fifteenth Convention of the Parties (COP15) to the UN Convention on Biological Diversity (CBD) in Kunming, China, the _alliance for biodiversity knowledge_ is organizing a community consultation. With the help of key partners across the science-policy interface, the consultation will engage those involved in developing global and national indicators under the CBD's post-2020 Global Biodiversity Framework (GBF) and Essential Biodiversity Variables (EBVs).

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Computer Operated Photogrammetric Imaging System

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Computer Operated Photogrammetric Imaging System

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The goal of this project is to design and prototype a modular, multi-view imaging system to facilitate the acquisition of complex image sets from natural history specimens and cultural heritage artifacts.

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The COPIS project began as an outgrowth of a National Science Foundation Cyber-enabled Discovery and Innovation grant to explore the use of 2D imaging, 3D laser scanning and machine learning to address species identification and new species discovery within groups of fishes. Limitations discovered in laser scanning non-rigid, fluid-preserved specimens prompted us to evaluate photogrammetry as an alternative for 3D data acquisition. Results from photogrammetry were promising yet setting up a suitable environment to capture large numbers of high-resolution images from multiple viewpoints around an object still presented a significant challenge for 3D digitization at scale. This led to the development of a single-camera proof-of-concept for automated multi-view imaging and in 2015 the first multi-camera COPIS prototype was developed for imaging fishes at the Tulane University Fish Collection. More recently, a second phase of development has begun to improve modularity, incorporate structured light and move beyond fishes through a pilot project to 3D image a diverse sampling of anthropological artifacts from the Yale Peabody Museum. Upon completion of this project, open-hardware design specifications will be published through this website. If you are interested in contributing to the development or engaging as an "early adopter", feel free to contact us.

The primary function of COPIS is to produce large numbers of overlapping images from multiple viewpoints around a centrally mounted object for photogrammetric 3-D reconstruction; however, the system may also be programmed to capture imagery at varying focal depths along any direction and supports gridded imaging suitable for producing mosaics of whole specimen drawers, large documents, tapestries and paintings. COPIS comprises a custom-built, photographic light box equipped with one or more high-resolution DSLR cameras mounted on a five-axis gantry. The design was prototyped and tested to accommodate 6 DSLR cameras, but the modular nature of the system should support implementations that can scale from 1 to many cameras only constrained by available space, power and z-axis height.

Multi-Camera Five-Axis Gantry:

Imaging Modalities:

Lightbox with Dual Main Gantries:

The COPIS Client is a software desktop application used to communicate with the COPIS imaging instrument.

It is built on top of WxPython and OpenGL, and provides a user-friendly 3D interface for defining and managing imaging sessions as well as operating the device. The application allows users to define the imaging environment (dimensions, number of cameras and their types and modes of communication) and place one or more 3D proxy objects, within a virtual 3D space representing the imaging chamber. Tools are provided to allow end-users to automatically create and manually edit imaging paths. Supported paths include spherical, cylindrical, and helical positions.

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The COPIS project began as an outgrowth of a National Science Foundation Cyber-enabled Discovery and Innovation grant to explore the use of 2D imaging, 3D laser scanning and machine learning to address species identification and new species discovery within groups of fishes.

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We believe in the transformative power of engaging many different perspectives.

Different perspectives help us all to achieve more

Our mission is deeply inclusive: empower every person and every organization on the planet to achieve more. We expect each of us-no matter what our level, role or function is-to play an active role in creating environments where people of diverse backgrounds are excited to bring all of who they are and do their best work.

How we're advancing inclusion

Our communities inspire our progress

We strive to create an environment that brings the power of diversity to life-where people with different backgrounds and experiences thrive in both their professional and personal lives.

We reach out with purpose

Guided by a commitment to building diverse communities, we're intentional in who we reach out to, and how we connect with them. We increasingly do business with minority- and women-owned companies. We globally invest in developing the next tech industry leaders. And we pursue candidates of diverse backgrounds to help us do our best work yet.

Partnering to advance diversity

Inclusive thinking drives our innovation

We design for all human experiences and needs. So, we strive to gather, listen to, and include as many perspectives as possible, with the goal of discovering how to bring out the best in each other, and everyone who uses what we create.

Our inclusive design principles

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Microsoft Global Diversity and Inclusion

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We believe in the transformative power of engaging many different perspectives. Our mission is deeply inclusive: empower every person and every organization on the planet to achieve more. We expect each of us-no matter what our level, role or function is-to play an active role in creating environments where people of diverse backgrounds are excited to bring all of who they are and do their best work.

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Are you a biologist who studies the diversity of sex, gender, and sexualities in nature? Does your work explore inclusive and accurate ways to teach about these subjects? Are you thinking about attending the Evolution 2022 meeting in Cleveland, OH, USA? The organizers are seeking 3-4 evolutionary biologists to give short, engaging, general audience talks (akin to a TED talk) at an LGBTQ+ event called " Evolution's Rainbow" at this year's Evolution conference. The event will also include a panel Q&A session with our presenters. Our aim is to create a light-hearted and informative evening that inspires curiosity and facilitates dialogue on the exceptional diversity of sex, gender, and sexualities in nature. We are particularly excited to hear from a diversity of voices and encourage applications from speakers from historically excluded groups. This event will be open to all Evolution goers and the general public. Proceeds from the evening will be donated to the . LGBT Community Center of Greater Cleveland To apply, please send a 150-200 word brief pitch for your talk to diversity@evolutionsociety.org by April 29, 2022.

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Evolution's Rainbow Event at Evolution 2022

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Are you a biologist who studies the diversity of sex, gender, and sexualities in nature? Does your work explore inclusive and accurate ways to teach about these subjects? Are you thinking about attending the Evolution 2022 meeting in Cleveland, OH, USA?

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For fans of Alison Bechdel’s Fun Home and Meg-John Barker’s Queer, Fine is an essential graphic memoir about the intricacies of gender identity and expression. As Rhea Ewing neared college graduation in 2012, they became consumed by the question: What is gender? This obsession sparked a quest in their quiet Midwest town, where they anxiously approached both friends and strangers for interviews to turn into comics. A decade later, their project has exploded into a fantastical and informative portrait of a surprisingly vast community spread across the country. Questions such as How do you identify? invited deep and honest accounts of adolescence, taking hormones, changing pronouns—and how these experiences can differ depending on culture, race, and religion. Amidst beautifully rendered scenes emerges Ewing’s own visceral story growing up in rural Kentucky, grappling with their identity as a teenager, and ultimately finding themself through art—and by creating something this very fine.

FINE: a comic about gender was the product of thousands of miles traveled, dozens of interviews conducted, and nearly a decade of work. Learn more about how FINE came to be.

Resources

Looking for help? This page includes a hub of resources for health services, guides for allies, and emergency funds for transgender folks.

Support the Community

Connect with the amazing projects and brilliant works of those who participated in the project.

“It's a wonderful and ponderous thing to examine… done with such sensitivity and thought.”

— Lucy Knisley

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FINE: a comic about gender is a graphic novel by Rhea Ewing, based on interviews from transgender and cisgender people from across the United States. Questions such as How do you identify? invited deep and honest accounts of adolescence, taking hormones, changing pronouns – and how these experiences

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Biodiversity Literacy in Undergraduate Education

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Biodiversity Literacy in Undergraduate Education

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Welcome to the BLUE Data Network

Who is BLUE?

The RCN-UBE Biodiversity Literacy in Undergraduate Education - Data Initiative (BLUE Data) is a community of biodiversity, data, and education specialists who are working together to identify core biodiversity data competencies for undergraduates, develop strategies for integrating these competencies into the introductory biology curriculum, and build capacity for sustained development and implementation of biodiversity and data literacy education.

Over the five years of the RCN-UBE, BLUE Data has four goals:

1. Cultivate a diverse and inclusive network of biodiversity researchers, data scientists, and biology educators focused on undergraduate data-centric biodiversity education.

2. Build community consensus on core biodiversity data literacy competencies.

3. Develop strategies and exemplar materials to guide the integration of biodiversity data literacy competencies into introductory undergraduate biology curricula.

4. Extend the network to engage a broader community of undergraduate educators in biodiversity data literacy efforts.

What's NEW with BLUE?

Check out our recent publication in Bioscience !

​

We've added new education modules to our resources page, click here to view them now!

​

Stay up to date on all things BLUE related by following us on Twitter @BiodiversityEd!

​

​

Join our listserve to hear about new opportunities and resources.

​

This material is based upon work supported by the National Science Foundation under DBI 1730526. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Biodiversity Literacy in Undergraduate Education (BLUE)

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Welcome to the BLUE Data Network Who is BLUE? The RCN-UBE Biodiversity Literacy in Undergraduate Education - Data Initiative (BLUE Data) is a community of biodiversity, data, and education specialists who are working together to identify core biodiversity data competencies for undergraduates, develop strategies for integrating these competencies into the introductory biology curriculum, and build capacity for sustained development and implementation of biodiversity and data literacy education.

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Please be aware when registering, all times are in the Eastern Time Zone. If you have any questions about registration, please check out the Registration FAQ or email onlineseminars@saa.org.

Exploring Applications of 3-D Printing in Archaeology for Education, Public Outreach, and Museum Exhibits

Three-dimensional (3-D) printing is increasingly infiltrating all aspects of society, from manufacturing and medicine to STEM education on K-12 levels. This seminar will explore the basics of 3-D printing and how archaeologists can integrate 3-D models and printed materials into all aspects of their discipline, from the field to the laboratory, and into the classroom and the museum. Particular attention will be paid to the following areas:

how digital 3-D models enhance identification of artifacts and ecofacts in the field and laboratory over 2-D drawings or photographs

how 3-D printed replicas expand opportunities for teaching and research at all levels of education, but especially for undergraduate teaching

how 3-D printed replicas can be incorporated into public outreach programs, maximizing access to the past, while minimizing risks to fragile heritage

how 3-D printed replicas can be integrated into museum exhibits to create a more interactive and tactile element

The 3-D printed past is not something from the far-off archaeology future, but should be seen as very much a part of the archaeological present.

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Exploring Applications of 3-D Printing in Archaeology for Education, Public Outreach, and Museum Exhibits

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Please be aware when registering, all times are in the Eastern Time Zone. If you have any questions about registration, please check out the Registration FAQ or email onlineseminars@saa.org. Three-dimensional (3-D) printing is increasingly infiltrating all aspects of society, from manufacturing and medicine to STEM education on K-12 levels.

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Defining

A well-defined, robust, and consistent set of data types is essential for well-designed Web APIs, metadata schematics, and data dictionaries. When designing the NCMNS API, one of the key challenges is to select a suite of data types that simultaneously adhere to the following principles: maximize specificity, maintain interoperability, accommodate third party datasets, and sustain strict adherence once established. Nearly every programming language and database technology uses different data types. Interoperability is highly variable. Beginning with a simple common set of primitive data types, many types are further refined by specifying a standard set of formats, such as specific strings and composite types (e.g., arrays, dict). These difficulties are amplified when a wide array of datasets adopts a single response schema. A standardized response requires that data types in existing third-party datasets map to the standard set utilized by the API prior to ingesting.

NCMNS API Data Types

The data types in the NCMNS API are an extended set that is mainly based on the RFC Standards, OpenAPI Specification and the JSON Schema Specification. In addition, an extensive amount of literature and web-based resources were assessed to assure interoperability, refine format specifications, and create a comprehensive terminology taking ideas from across data communities. References to the support publications and resources are provided in the table labeled Resources. The table below outlines the NCMNS API data types, followed by a list of additional resources and source information.

The table below outlines the NCMNS API data types followed by a list of additional resources and source information.

Core (Primitive) Data Types

Format Specifications

Several data types may be further defined using an optional format option. These are extensions of the primitive core data type in the table above. Accordingly, they inherit the characteristics of the primitive then extended by a defined format usually by a globally recognized standard (RFC). The purpose for the format extesnions is to provide an additional level of specificity to a given attribute.

Structural/Composite/Complex Data Types

Value Domain

The set of valid textural values defined by one or more constraints within infite space

Types of Value Domains: Enumerated Domains,

Enumerated Domain

A type of value domain that contains a finite set of valid values

All values are string data type, also refered to as picklists, valid value sets

Array

An ordered (indexed), fixed-length set of values

Complex

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A well-defined, robust, and consistent set of data types is essential for well-designed Web APIs, metadata schematics, and data dictionaries. When designing the NCMNS API, one of the key challenges is to select a suite of data types that simultaneously adhere to the following principles: maximize specificity, maintain interoperability, accommodate third party datasets, and sustain strict adherence once established.

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Web Content Principles from Stackoverflow

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Web Content Principles from Stackoverflow

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Principles and tactics for writing at Stack Overflow. These will help produce clear, consistent content for users of different professions, identities, and levels of English fluency.

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Principles and tactics for writing at Stack Overflow. These will help produce clear, consistent content for users of different professions, identities, and levels of English fluency.

Write for a 7th grade reading level. Stack Overflow users are busy people with limited time and attention. They're also located all over the world with different levels of English fluency. You can help everyone follow along by choosing simple, consistent words and phrases, and being careful about jokes and cultural references.

If there's a simpler word to say what you mean, use it. Always choose the short, simple word over the long, complicated one. Don't complicate your ideas with business or insider jargon. Only use jargon when you have reason to believe it will improve user understanding.

Trim word bloat. Don't make people read more than they need to.

Be careful with jokes and cultural references. Jokes, idioms, slang, and regional or cultural references don't always translate well for our global audience. The next time you're making a reference, ask yourself if there's a more universal way to make your point.

Pick names early and implement them consistently. Consistent language reduces the number of things your users have to learn or remember. If multiple names already exist, identify and eliminate them.

Avoid exaggerated statements. When possible, back up your claims with facts and data. Be careful with adjectives and modifiers.

Endless possibilities

Choose from external hosting options or host on stackoverflow.com.

Innovative

Your privacy is our #1 priority.

Manage your privacy through simple settings.

Users are trying to get things done. Write and structure your content so that people can easily scan and take the most important actions. Avoid using negative language.

Use headings and bullets to make your content easier to scan.

Think about what people need to know right away, and move that information to the top.

Use the active voice, particularly in product content. Start every CTA with a strong verb.

Your company can use Stack Overflow to reach the world's developers. We're excited to help you start the developer hiring process.

Reach the world's developers.
Create job listing

For 14 days, you can try Teams for free. We make it easy for you to create a team.

Get started with a 14-day free trial.
Create Team

Be careful with negative language. It is usually longer, less friendly, and less active than positive language.

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Principles and tactics for writing at Stack Overflow. These will help produce clear, consistent content for users of different professions, identities, and levels of English fluency. Write for a 7th grade reading level. Stack Overflow users are busy people with limited time and attention.

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Biodiversity Information Standards (TDWG) is a non-profit organization and community dedicated to developing biodiversity information standards.

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Tools & Resources for Data Usage Metrics

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Tools & Resources for Data Usage Metrics

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Tools & Resources for Data Usage Metrics

Since the founding of the RDA Data Usage Metrics Working Group, the community has focused on accessible ways to standardize and build open data metrics. Join to hear about the latest tools and resources from DataCite and Make Data Count to support repositories in data usage metrics and where the larger RDA community can go to support researchers in data research assessment metrics.

Repositories and data infrastructure providers interested in standardizing data usage, and building for open data metrics, should join to hear about the new tools and services being developed through Make Data Count and DataCite.

Presenters

Daniella Lowenberg

ORCID ID: 0000-0003-2255-1869

University of California Office of the President

UC Curation Center (UC3)

Sr Data Publishing Product Mgr & Make Data Count Principal Investigator

United States

Daniella holds a variety of leadership positions: Chair of the Research Data Alliance (RDA) Data Usage Metrics Working Group, Principal Investigator of the Sloan Foundation-funded Make Data Count initiative, and Founding Chair of the joint FORCE11 & COPE Working Group on Ethics of Data Publishing.

Ian Bruno

0000-0003-4901-9936

Cambridge Crystallographic Data Centre (CCDC)

Director of Data Initiatives

United Kingdom

In his role as Director of Data Initiatives at the CCDC, Ian is responsible for establishing strategies that will make CCDC's data more readily discoverable and reusable by wider scientific communities. A specific focus is on the adoption and development of community principles, guidelines and standards to support these aims. He is an active participant in a range of global initiatives including the Research Data Alliance, the International Union of Pure and Applied Chemistry and the InChI Trust.

Matt Buys

ORCID ID: 0000-0001-7234-3684

DataCite

Executive Director

Germany

Matthew leads a passionate and committed team at DataCite who provide the means to create, find, cite, connect, and use research globally. Based in Amsterdam, Matthew focusses on building DataCite into a sustainable global community. Previously Matthew was the Director of Engagement at ORCID where he played an important role in growing the community into an international-scale research effort.

Kristian Garza

ORCID ID: 0000-0003-3484-6875

DataCite

Product Designer

Germany

Kristian is responsible for determining information architecture, user research, and design to support an intuitive user journey for all DataCite services and products. Previously, he contributed to the development and implementation of a robust technical architecture DataCite. Before he held different research positions within the MSSL and the ELIXIR Interoperability platform. Kristian has a Ph.D. degree in Computer Science from the University of Manchester

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Tools & Resources for Data Usage Metrics

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Since the founding of the RDA Data Usage Metrics Working Group, the community has focused on accessible ways to standardize and build open data metrics. Join to hear about the latest tools and resources from DataCite and Make Data Count to support repositories in data usage metrics and where the larger RDA community can go to support researchers in data research assessment metrics.

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Ukrainian librarians in the struggle against Russian invasion

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Ukrainian librarians in the struggle against Russian invasion

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"Ukrainian librarians in the struggle against Russian invasion"

- Weds, April 6, 11a-12:30p CST

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Voices from Ukraine: Ukrainian librarians in the struggle against Russian invasion

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Organized by the Coalition of Slavic Librarians for Peace (CSLP): Olha Aleksic (Ukrainian Research Institute at Harvard University), Ksenya Kiebuzinski (University of Toronto), Liladhar R. Pendse (University of California-Berkeley) and George Andrew Spencer (University of Wisconsin-Madison)

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PAWS: A Web Shell (PAWS) is a Jupyter notebook deployment hosted by Wikimedia.

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PAWS: A Web Shell (PAWS) is a Jupyter notebook deployment hosted by Wikimedia.

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PAWS: A Web Shell (PAWS) is a Jupyter notebook deployment hosted by Wikimedia. A Jupyter notebook is a popular Open Source tool that allows to create and share documents that contain live code.

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If you need to share documents that contain text alongside live code (e.g. OpenRefine cleanup steps) or visualizations, this may be worth a try. Try it out or read more background here

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PAWS: A Web Shell (PAWS) is a Jupyter notebook deployment hosted by Wikimedia. A Jupyter notebook is a popular Open Source tool that allows to create and share documents that contain live code.

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The importance of community engagement to data collaborations

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The importance of community engagement to data collaborations

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The ODI Microsoft Podcast: The importance of community engagement to data collaborations by The Open Data Institute

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In this podcast, we hear about the importance of community engagement from three participants of the second cohort of the ODI Microsoft peer learning network, an initiative which aims to give data collaborations working on significant societal challenges the opportunity to share and learn from each other. 

Host James Maddison, Senior Consultant at the ODI, leads the conversation with Laureen van Breen, Managing Director at WikiRate; Kisha Bwenge, Senior Community Building Manager at the Open Contracting Partnership; and Tim Robertson, Head of Informatics at GBIF.

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Darwin Core—the most commonly used data standard in the GBIF community—has provided a simple and effective framework for supporting the growth of species occurrence data available from the GBIF network. But this simplicity of this standard, established and maintained by Biodiversity Information Standards (TDWG), has come with significant limitations when it comes to shaping data from diverse sources.

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The Bioschemas community aims to make life sciences resources more discoverable on the Web. The community has developed Schema.org terms for describing life sciences related concepts, and provides usage guides and tools to help with the development. The ELIXIR Training Portal (TeSS) already harvests Bioschemas markup to populate their registry, and we have been working with different ELIXIR communities to generate community specific Knowledge Graphs over their resources. Another benefit is that the same markup can be used to feed content into general research knowledge graphs such as OpenAIRE.

In this talk, we will give an overview of Bioschemas and show how the markup can be consumed by others. imported into a triplestore, and exposed as a REST API.

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Date: 04 April 2022

Venue: Virtual
Contacts:
Title: (Bio)schemas - Making (not only life science) data resources more interoperable and discoverable on the Web

Date: 4 April 2022 @ 15:00 (BST)

Abstract: The content of web ressources can be enriched with machine processable metadata using the Schema.org vocabulary. The embedded markup is used by dedicated (metadata) search portals such as Google's Dataset Search, but also as general mechanism for Search engine optimization (SEO) and to display rich information snippets to users.

The Bioschemas community aims to build on these approaches to enable life sciences resources to become more discoverable on the Web. The community has developed Schema.org terms for describing life sciences related concepts, and provides usage guides and tools to help with the development.

Bioschemas markup can also be exploited by our own communities. The ELIXIR Training Portal (TeSS) already harvests Bioschemas markup to populate their registry, and we have been working with different ELIXIR communities to generate community specific Knowledge Graphs over their resources. Another benefit is that the same markup can be used to feed content into general research knowledge graphs such as OpenAIRE.

In this talk, we will give an overview of Bioschemas and show how the markup can be consumed by others. imported into a triplestore, and exposed as a REST API.

Back to top

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Date: 04 April 2022 Venue: Virtual Contacts: Title: (Bio)schemas - Making (not only life science) data resources more interoperable and discoverable on the Web Date: 4 April 2022 @ 15:00 (BST) Abstract: The content of web ressources can be enriched with machine processable metadata using the Schema.org vocabulary.

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BioGUID.org is a service for indexing and cross-linking identifiers for data objects within the realm of Biodiversity informatics.

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Overview

BioGUID.org is a service for indexing and cross-linking identifiers for data objects within the realm of Biodiversity informatics. The importance of reliable globally unique identifiers for mobilizing biodiversity data has been well established through multiple workshops, whitepapers, and publications spanning several decades. However, the biodiversity informatics community has made very little real progress in establishing broadly adopted standards and coordinating data providers towards common and sensible practice. BioGUID.org is a platform for indexing and cross-linking identifiers of all kinds, to better facilitate establishing relationships among digital biodiversity objects. The core items managed by BioGUID.org are "Identifier Domains" (sets of identifiers) and the Identifiers within those Domains. Each Identifier Domain may have one or more "Dereference Services" associated with it (e.g., http://dx.doi.org/ is a Dereference Service for the DOI Identifier Domain). Identifiers are cross-linked to each other by being anchored to the same "Identified Object". For example, the fish genus Odontanthias is represented by identifiers in , ITIS, and the Catalog of Fishes (among others), and all of these identifiers are anchored to the same Object within BioGUID. With a core set of web services, documentation and web tools (with more features on the way), BioGUID.org is designed to help tie Biodiversity data together.

Structure

The primary component of BioGUID.org is a database system that stores information on Identifiers, Identifier Domains (sets of identifiers), "Dereference Services" (web services that perform some sort of action on identifiers), and a set of support tables that together enable the indexing and cross-linking functions that BioGUID.org provides. To understand how it works, it's important to understand the basic components.

Identifiers

BioGUID.org considers identifiers in a broad sense. It certainly includes everything that modern informatics practitioners consider to be "good" identifiers (such as Digital Object Identifiers [DOIs], Universally Unique Identifiers [UUIDs], HTTP-URIs, and other kinds of robust identifiers). But it also accommodates other kinds of identifiers, such as ISSN and ISBN numbers, specimen catalog numbers, certain kinds of codes (e.g. country codes or language codes), and almost any text-string that a person might think of as an Identifier. One could argue that even things like text-string taxon names could be represented in BioGUID.org as identifiers (and we have considered it). However, in that case, because of the rich and complex metadata related to taxon names and their relationships with taxon concepts, we feel it's better to leave taxon name-strings out of the scope of identifiers indexed by BioGUID.org (for now, at least...) In general, however, almost anything that anyone might consider to be an identifier can be included within the BioGUID.org identifier system.

Identifier Domains

Identifier Domains represent sets of identifiers that are assigned to objects related to biodiversity. In theory, each Identifier Domain assigns one identifier to one object. In the real world, the same object may have been issued more than one identifier by the same Identifier Domain, but this is usually in error (e.g., when the same specimen is cataloged twice with two different catalog numbers, or when a duplicate record is discovered in a database of taxon names or literature citations). Also, there should generally be no duplicate identifiers assigned within the same Identifier Domain; but again, in the real world, mistakes sometimes happen (e.g., when the same catalog number is accidentally issued to two separate specimens). Identifier Domains my be very broad in scope. For example, Digital Object Identifiers, or DOIs, represent a single Identifier Domain because the identifiers are unique across all DOIs, and the intention is to issue one unique identifier for each object. Or, Identifier Domains may be very narrow in scope. For example, there is an Identifier Domain to represent the Language Codes assigned by the Biodiversity Heritage Library. In some cases, it may be arbitrary how finely to parse sets of identifiers into discrete Identifier Domains. For example, Bishop Museum issues catalog numbers to specimens in its collection. Considering only the catalog numbers themselves, there would be a separate Identifier Domain for each of the major specimen collections. There is a fish specimen with the catalog number "1234", and a bird specimen with the same number, and a plant specimen, and an insect, and so on. Each "1234" would be an identifier within a different Identifier Domain. However, if the Collection Code is considered part of the identifier along with the catalog number, then there may be only one Identifier Domain for all of the Bishop Museum specimen collections combined. In this context, the fish specimen would have the identifier "I-1234", and the bird would have "B-1234", and the plant would have "BISH 1234"... and so on. One could even take a further step back and establish a single Identifier Domain to represent ALL Darwin-core triplets (instiutionCode + collectionCode + catalogNumber). This would be somewhat impractical to manage, however. Although there is some wiggle-room for defining when to establish a separate Identifier Domain for a particular series of Identifiers, for the most part it is a relatively straightforward and obvious process.

Dereference Services

One of the confusing and contentious issues concerning modern identifiers for biodiversity data objects revolves around the conflation of the role of "identification" of objects with information about how to retrieve information about something. In particular, the Linked Data (Linked Open Data, or LOD) community follows strict conventions that all identifiers must be represented as HTTP-URIs (that is all LOD identifiers begin with the characters "http://", followed by a domain name and the normal template for HTTP URIs). One of the greatest values of doing this it that such identifiers are self-resolving; that is, they not only allow for the unique identification of objects (every HTTP-URI is unique), but the identifier itself includes information about a particular internet protocol (HTTP) and a particular internet address (via the DNS system) where information about the object can be accessed. There are costs and benefits to this approach (which would take far more text than this web page can afford to adequately address). However, BioGUID.org is designed to accomodate the LOD community standards (HTTP-URIs can certainly be indexed as identifiers), while simultaneously recognizing the need to index the multitude of identifiers that are not represented as HTTP-URIs.

The way BioGUID.org achieves this "best of both worlds" approach is by managing the concept of "Dereference Services". The easiest way to explain a Dereference Service in the context of identifiers is with the example of DOIs. To most people, a DOI (i.e., the DOI as an identifier) begins with the characters "10.". Another convention is to prefix the DOI with the letters "doi:" - sometimes in upper-case; sometimes lower-case. Sometimes with a space between the colon and the 10.; sometimes not. In either case, a "naked" DOI (that begins with 10.) is perfectly acceptable as an identifier, but by itself fails to meet the needs of the LOD community. To perform action on a naked DOI, one has to know what to do with it. By broad convention, a DOI becomes actionable when it is prefixed with the characters "http://doi.org/". Doing so enriches the DOI by converting it to an HTTP URI. Most people (including us at BioGUID.org) do not consider the text "http://doi.org/" to be part of the identifier itself; but rather it represents a form of dereferencing metadata - or, what we refer to as a "Dereference Service". Effectively the text "http://doi.org/" is a prefix that allows the DOI identifier suffix to be dereferenced. Again, it would take more space than this page allows to describe this in detail, but the approach taken at BioGUID.org is to track Dereference Services as independent objects with their own properties, that can be combined with Identifiers (and in some cases additional suffix text after the identifier) to make otherwise "naked" identifiers both actionable, and compatible with LOD requirements. Importantly a single Dereference Service may be able to perform action on identifiers from many different Identifier Domains, and likewise identifiers within a single Identifier Domain may be serviced by multiple Dereference Services. BioGUID.org supports this many-to-many relationship between Dereference Services and Identifier Domains (and the Identifiers they perform action on.)

Identified Objects

Another aspect of BioGUID that is much less visible, but equally important (if not more so) is the concept of Identified "Objects". These objects are generally conceptual in nature (e.g., a taxon name, or a literature citation, or a collecting event, or an organism occurrence); and even when they apply to a "physical" thing (like a specimen), it's better to think of the identifier as representing a conceptual object (note: there is also a long discussion concerning the application of identifiers to digital objects, such as media files and individual database records; but that is yet another topic that, if addressed adequately, would consume the space on this web page). If BioGUID.org did nothing other than index identifiers (and their associated Identifier Domains and Dereference Services), it would be performing a very valuable service. However, the true value of BioGUID.org lies in its ability to cross-link identifiers that have been assigned to the same object. For example, the bluefish ( Pomatomus saltatrix) has been registered on ZooBank (under its original establishment by Linnaeus in 1766). This species also exists in the WoRMS database, and it also has a record in the Catalog of Fishes database, and in FishBase. Indeed, these various identifiers are all cross-linked to each other on the ZooBank site. However, the WoRMS record also includes a link to the Barcode of Life database and the Encyclopedia of Life database, as well as GenBank (i.e., the NCBI taxonomy). Each of these other websites contains additional links to other external databases. Some of these cross-links were generated semi-automatically; and others were made painstakingly by hand. One of the main purposes of BioGUID.org is to serve as a global cross-linking service so that, rather than have every individual website individually attempt to establish these cross-links among related records, BioGUID.org provides a set of services and tools to consolidate all of these different records through anchoring identifiers to a common conceptual "object". That way, by establishing just one link (e.g., between a ZooBank record and the corresponding WoRMS record), the identifier equivalencies of each can be inherited not only by each other, but by every single cross-linked data system. The effect is a potential exponential expansion of cross-linking identifiers and their associated metadata.

Services

BioGUID.org is primarily a database system with a set of data services, and a web-service layer that support basic search and record creation functions. Although it has a simple website (which you are reading right now), this is primarily intended to serve as a repository for documentation, and to demonstrate the sorts of functionality that the web services are designed to fulfill. We plan to spend much more time in the near future expanding content and improving features and functionality. We will likely continue to make minor improvements to the website as well, but that is a lower priority. For more detailed documentation of the BioGUID.org web services and other information (such as the Data Model), please visit the API page.

Data Access and Licensing

All content available within the BioGUID.org site, including information on Identifier Domains, Dereference Services, Identifiers, and cross-linked objects are available under the Creative Commons Zero (CC0, Public Domain Dedication).

The GBIF Ebbe Nielsen Challenge

The general concept behind this website has been floating around in my head for nearly a decade, and I registered the domain name over a year ago. However, the real impetus to finally build the underlying database and associated services was inspired by the announcement of the GBIF Ebbe Nielsen Challenge. In the weeks leading up to the submission deadline, the database was built from scratch, with a rich set of data-layer services and a few basic web services (and even more basic web page). However, we are firmly committed to the continued expansion of BioGUID.org and its services, to demonstrate the power and potential of developing a universal identifier index and cross-linking system for biodiversity data.

All content within the BioGUID.org site is available under the Creative Commons Zero license (Public Domain).

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BioGUID.org is a service for indexing and cross-linking identifiers for data objects within the realm of Biodiversity informatics. The importance of reliable globally unique identifiers for mobilizing biodiversity data has been well established through multiple workshops, whitepapers, and publications spanning several decades.

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...in its larval stages. As of 2022, next steps are to index actual datasets, not just publications. More background on the NSF PAR development timeline here.
Related to the NSF FAIROS RCN program.

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Do you have questions about NSF-PAR content, procedures, or policies? More information is available at NSF's Public Access page and in our Frequently Asked Questions. You may also leave feedback by clicking on this .

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Do you have questions about NSF-PAR content, procedures, or policies? More information is available at NSF's Public Access page and in our Frequently Asked Questions. You may also leave feedback by clicking on this .

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Saving Ukrainian Cultural Heritage Online

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Saving Ukrainian Cultural Heritage Online

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Efforts under way to preserve data from Ukrainian museums, libraries, archives. Volunteer anytime or join the workshop on March 5 - https://bit.ly/datarescueforUA

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We are a group of cultural heritage professionals - librarians, archivists, researchers, programmers - working together to identify and archive at-risk sites, digital content, and data in Ukrainian cultural heritage institutions while the country is under attack. We are using a combination of technologies to crawl and archive sites and content, including the Internet Archive's Wayback Machine, the Browsertrix crawler and the ArchiveWeb.page browser extension and app of the Webrecorder project.

How you can get involved

1. Submit important URLs for collections in cultural heritage institutions in Ukraine

2. Volunteer to help identify and archive sites and content, run technology (Browsertrix) or let us know if you are a Ukrainian researcher in need of secure storage for research or data files.

You'll need to wait until we add you to the Slack to actually get started, but if you want to read about the process, here's our workflow and a orientation for new volunteers.

Team

Olha Aleksic, Ukrainian Research Institute, Harvard University

Kostyantyn Bondarenko, Ukrainian Research Institute, Harvard University

Kristina Conroy, Ukrainian Research Institute, Harvard University

Quinn Dombrowski, Stanford University

Andy Janco, Haverford College

Anna E. Kijas, Tufts University

Ilya Kreymer, Webrecorder Software

Sebastian Majstorovic, Austrian Centre for Digital Humanities and Cultural Heritage

Iryna Voloshyna, Department of Folklore and Ethnomusicology, Indiana University

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Saving Ukrainian Cultural Heritage Online (SUCHO)

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We are a group of cultural heritage professionals - librarians, archivists, researchers, programmers - working together to identify and archive at-risk sites, digital content, and data in Ukrainian cultural heritage institutions while the country is under attack.

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Explore Funding Opportunities in Research Resources at the Division of Biological Infrastructure (DBI)

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Explore Funding Opportunities in Research Resources at the Division of Biological Infrastructure (DBI)

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Digital Data 2022: Enhancing and advancing the quality of digitized data

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Digital Data 2022: Enhancing & advancing the quality of digitized data

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Digital Data 2022: Enhancing & advancing the quality of digitized data

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Sixth Annual Digital Data in Biodiversity Research Conference

The Field Museum in collaboration with iDigBio and the Natural Science Collections Alliance is pleased to announce the sixth annual Digital Data in Biodiversity Research Conference, to be hosted May 23-25, 2022. This year's event with be a virtual meeting.

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Digital Data 2022: Enhancing & advancing the quality of digitized data

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QUICK LINKS Registration Abstract Submission Themes Speakers New for 2022 CLICK HERE TO REGISTER Sixth Annual

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Microscopy Society of America Student Council and Northwestern University’s NU ANCE Center ​are excited to present a half-day conference to highlight women in microscopy in honor of International Women’s Day on March 8, 2022.

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Microscopy Society of America Student Council and Northwestern University's NU ANCE ​ are excited to present a half-day conference to highlight women in microscopy in honor of International Women's Day on March 8, 2022. ​

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With so many good things happening within RDA in the coming days/weeks, we put together a list of upcoming deadlines that will hopefully help you plan for all of your RDA-related activities.

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RDA Deadlines - Mark your Calendars!

With so many good things happening within RDA in the coming days/weeks, we put together a list of upcoming deadlines that will hopefully help you plan for all of your RDA-related activities.

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With so many good things happening within RDA in the coming days/weeks, we put together a list of upcoming deadlines that will hopefully help you plan for all of your RDA-related activities.

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Introduction to Python, the language for Data Science!

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Introduction to Python, the language for Data Science!

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Wed, February 2, 2022

6:00 AM – 7:30 AM CST

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Introduction to Python, the language for Data Science!

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Eventbrite - Le Wagon Dubai presents Introduction to Python, the language for Data Science! - Wednesday, February 2, 2022 - Find event and ticket information.

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Computer legacies: Narrating histories of digital media in museums

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Computer legacies: Narrating histories of digital media in museums

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Thu, January 27, 2022

9:30 AM – 12:30 PM CST

An online symposium questioning how museums can contribute to constructing and disseminating historical narratives about computing.

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Computer legacies: Narrating histories of digital media in museums

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An online symposium questioning how museums can contribute to constructing and disseminating historical narratives about computing.

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endoflife.date is a community-maintained project to document end-of-life dates, and support lifecycles of various products.

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End-of-life (EOL) and support information is often hard to track, or very badly presented. endoflife.date documents EOL dates and support lifecycles for various products.

endoflife.date aggregates data from various sources and presents it in an understandable and succinct manner. It also makes the data available using an easily accessible API and has iCalendar support.

endoflife.date currently tracks 317 products. Here are some of our most popular pages:

Contributing

Want to contribute? Great! We try to make it easy, and all contributions, even the smaller ones, are more than welcome. This includes new product suggestion or addition, existing product updates, feature request, bug reports, fixes... Take a look at our contribution guide for more information.

If you maintain release information for a product (end-of-life dates or support information), we also have a set of recommendations along with a checklist on some best practices for publishing this information.

And do not hesitate to play with our API. Here are a few awesome tools that already did it: norwegianblue, end_of_life, and cicada. Find more on our Known Users page.

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Check end-of-life, support schedule, and release timelines for more than 200 products at one place.

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GBIF North America Region Website Launched

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GBIF North America Region Website Launched

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The North America Region strives to bring together representatives from GBIF Nodes, IPT installations, and other organizations/stakeholders to inspire collaboration and discussion of shared goals, challenges, and opportunities.

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The North America Region strives to bring together representatives from GBIF Nodes, IPT installations, and other organizations/stakeholders to inspire collaboration and discussion of shared goals, challenges, and opportunities.

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[tdwg-content] Update: Diversifying the GBIF Model

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Dear all, (Sent to GBIF Nodes interest group and TDWG content. Apologies if you receive this more than once) I am happy to share an update on progress as we start to explore enriching the data model for GBIF.

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Google Analytics declared illegal in the EU.

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Google Analytics declared illegal in the EU.

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Will Google protect data of European better to comply with the GDPR?

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Google Analytics illegal in Europe

When the Privacy Shield legislation was invalidated in 2020, this had far-reaching consequences for US online services operating in Europe: They were no longer allowed to transfer data of European citizens to the US as this would make data of European citizens vulnerable to American mass surveillance - a clear violation of the European GDPR.

However, the Silicon Valley tech industry largely ignored the ruling. NOYB says:

"While this (=invalidation of Privacy Shield) sent shock waves through the tech industry, US providers and EU data exporters have largely ignored the case. Just like Microsoft, Facebook or Amazon, Google has relied on so-called "Standard Contract Clauses" to continue data transfers and calm its European business partners."

Now, the Austrian Data Protection Authority strikes the same chord as the European court when declaring Privacy Shield as invalid: It has decided that the use of Google Analytics violates the General Data Protection Regulation (GDPR). Google is "subject to surveillance by US intelligence services and can be ordered to disclose data of European citizens to them". Therefore, the data of European citizens may not be transferred across the Atlantic.
Original decision by the Austrian court. Machine translation of the original decision.
What was the case about?

On August 14, 2020, a Google user had accessed an Austrian website about health issues. This website used Google Analytics, and data about the user was transmitted to Google. Based on this data, Google was able to deduce who he or she was.

On August 18, 2020, the Google user complained to the Austrian data protection authority with the help of the data protection organization NOYB.
Now, the Austrian court has declared this data transfer as illegal.
The issue at hand is that due to the American CLOUD Act US authorities are able to demand personal data from Google, Facebook and other US providers, even when they are operating outside of the US, so in Europe for instance.

Thus, Google cannot provide an adequate level of protection under Article 44 GDPR - a clear violation of European data protection guarantees. The standard contractual clauses invoked by the website operator do not help, as recognized in 2020 by the European Court of Justice (ECJ) in its decision on the "Privacy Shield" (Schrems II).

The decisive factor for the legal assessment of the use of Google Analytics is not whether a U.S. intelligence agency actually obtained the data or whether Google actually identified the user. The mere fact that this was theoretically possible already was a violation of the GDPR.

Google users can, however, make a setting in their Google accounts to stop Google from evaluating their use of third-party websites in detail. But that this feature exists is proof that Google is able to merge usage data to with the individual.

Biggest success of NOYB

This ruling is one of the biggest successes of the data protection organization NOYB to date. Consequently, the NOYB and Max Schrems are very happy about the decision by the Austrian court:

"This is a very detailed and sound decision. The bottom line is: Companies can't use US cloud services in Europe anymore. It has now been 1.5 years since the Court of Justice confirmed this a second time, so it is more than time that the law is also enforced."

This ruling is the first among 101 lawsuits by Schrems' non-profit NOYB in most member states of the European Union. Similar decisions are now expected to drop in Germany, the Netherlands and other EU member states.

Remove Google Analytics?
Tutanota - as a secure email service that focuses on users' privacy - has never used Google Analytics.
But now, many companies in Europe must ask themselves whether they should remove Google Analytics from their websites or risk a penalty for violating the GDPR.

In the long run, there will be two options: Either the US changes its surveillance laws to strengthen their tech businesses, or US providers will have to host data of European users in Europe.

The Dutch Authority for Personal Data (AP) - where two decisions on the use of Google Analytics are still pending - has now updated its own guidance on the "privacy-friendly setup of Google Analytics".

With the update, the AP has issued a warning:

"Please note: The use of Google Analytics may soon no longer be allowed."

The Dutch Authority for Personal Data plans to decide on the pending Google Analytics cases in early 2022. Then the AP will issue a clear statement on whether the use of Google Analytics is illegal in Europe or not.

Conclusion

While Silicon Valley tech companies will find a way to still offer their services in Europe - one way or another - the approach that they took after the invalidation of Privacy Shield must raise several red flags to European businesses:

As a European company it is no longer possible to trust sensitive user data to companies such as Google that deliberately ignore European privacy legislation and risk hefty fines for their European business customers. (The fines against the Austrian health website in the discussed case have not been decided upon, yet, but we will follow the development closely.)

To the contrary - and as privacy is becoming increasingly important to consumers around the world - it is a logical step for any European business to choose services that focus on protecting their users' privacy.

Tutanota, for instance, is a secure German email provider that is in full compliance with the GDPR.

Recommended for further reading: Quick guide to take back your privacy online with lots of Google alternatives.

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Google Analytics declared illegal in the EU.

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When the Privacy Shield legislation was invalidated in 2020, this had far-reaching consequences for US online services operating in Europe: They were no longer allowed to transfer data of European citizens to the US as this would make data of European citizens vulnerable to American mass surveillance - a clear violation of the European GDPR.

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A Data Cleaner's Cookbook - third (final) version now online

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A Data Cleaner's Cookbook - third (final) version now online

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This final version from Bob Mesibov corrects a few errors and expands several sections, and is slightly reorganised. If you have used the Cookbook before, be sure to refresh your browser to ensure you go to the latest versions of the pages you visit.

The companion blog "BASHing data" is also winding up this year, with the last post (number 200!) to appear in mid-April. At that time I'll archive all blog posts and Cookbook 3 in Zenodo (https://doi.org/10.5281/zenodo.3722598), but I plan to keep both resources online as well, at datafix.com.au.

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Call for data papers describing datasets on vectors of human diseases

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Call for data papers describing datasets on vectors of human diseases

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TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization, GigaScience Press and GBIF are today announcing a new call for authors to submit Data Release papers on vectors of human disease in a thematic series to be published in GigaByte Journal.

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TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization, GigaScience Press and GBIF are today announcing a new call for authors to submit Data Release papers on vectors of human disease in a thematic series to be published in GigaByte Journal.

Watch the 25 January webinar about the call for data papers

Vector-borne diseases account for about one quarter of all infectious diseases. Although there has been significant progress for malaria, with a recent decrease in malaria morbidity and mortality rates, this progress is currently halting. Other diseases, such as those caused by arboviruses like dengue, chikungunya, yellow fever and more recently Zika, are expanding, with an increased number of cases and fatalities.

The necessity to develop new vector control strategies, approaches and tools was recognized through the Global Vector Control Response approved by the World Health Assembly in 2017. Among the mutually agreed objectives between GBIF and TDR is to work on a repository of data related to vectors and support design and identification of sources and contacts for data mobilization campaigns to improve data coverage to help research on human health. Within the framework of this collaboration, GigaByte will support a special issue on data papers on vectors.

The data papers submitted should describe datasets with the following criteria:

Data has clear relevance for research on vectors of human vector-borne diseases

Dataset contains more than 5,000 records that are new to GBIF.org in 2021/22 along with high-quality data and metadata

Data is dedicated to the public domain under an open CC0 designation

The call for manuscripts will be open until 28 February 2022.

The article processing fee-normally US$350-will be waived for 15 papers, provided that the publications are accepted and meet the above criteria.

Related news: Contractor sought to support mobilization of biodiversity data related to human health

Instructions for Authors

We recommend that authors start by preparing the dataset and publishing it through GBIF.org before turning to manuscript writing and editing.

The manuscript must be prepared in English and submitted in accordance with GigaByte's instructions to authors.

Authors may contribute to more than one manuscript, and Data Releases can support and be published alongside traditional analysis publications, but the work and data release described needs to stand-alone and justify itself by adding value.

GigaByte will publish a special issue including the selected papers by the first half of 2022. As a newly launched journal we are working to index our papers as widely as possible, and the journal is currently indexed in the Directory of Open Access Journals (DOAJ), CNKI and JGate. The authors contributing to the series will also be acknowledged and invited as contributing authors to an accompanying series announcement article in GigaByte's well established sister journal GigaScience, which is indexed by MEDLINE, PubMed, the Science Citation Index Expanded, CAS, CNKI, EMBASE and Scopus.

Manuscripts and datasets will go through an open and transparent peer-review process, including data auditing and curation from the GigaScience Press data team. To find out more about data publishing, see the GBIF.org explainer on data papers, the Quick guide to publishing data through GBIF.org and the GigaByte submission instructions. GigaByte Data Release articles have a simple easy-to-write format, and authors can make their submissions using Microsoft word (DOC, DOCX), PDF and TeX/LaTeX files (see Overleaf template.)

GigaByte's novel, end-to-end XML publishing platform, means publication can be done in a quicker and more cost-effective manner better designed for these more granular research objects that don't require such a labour intensive and detailed vehicle for sharing. It also allows additional interactivity and we can work with the authors to embed maps, video and imaging data plugins and other relevant tools for visualizing data and results in the final publications. Please discuss with the editors if you have any dynamic content you would like to highlight.

For questions, please contact Scott Edmunds, the Chief Editor of the thematic series at GigaByte, or health@gbif.org.

Definition of terms

Datasets with relevance for research on vectors of human vector-borne diseases
This sponsored call for data papers has a thematic focus onvectors of human vector-borne diseases. Authors can prepare data papers that describe checklist, occurrence or sampling-event datasets; this blog post will help authors determine which class of dataset is most suitable. Data on pathogens (viruses, bacteria and parasites) can be published as attributes of vector data in the associatedOccurrences field in occurrence and sampling event datasets, No human data can be included in the datasets. See examples of existing checklist, occurrence, and sampling-event datasets.

Datasets with more than 5,000 records that are new to GBIF.org
The 5,000occurrence records minimum threshold is merely a guiding number, not the target to publish a dataset which is cut to be just over the limit to pass. Data must be new to GBIF.org in 2021/22, providing high-quality data and metadata. Checklist and sampling-event datasets below the threshold will be considered eligible on the basis of exceptional value and handled case-by-case by the editor. Minimum publishable units, salami publishing and dataset version papers are discouraged: datasets should be published in their original, untrimmed state. Many datasets are, by nature, dynamic, and while a data paper promotes and describes the dataset at its current state at submission, the dataset link can and often will resolve to the evolving online resource. Therefore, a data paper should ideally be written in a way that it serves as a bibliographic citation, a showcase and a lasting "home" for the dataset. Any datasets with additional supporting data not suitable for GBIF will be curated and hosted in the GigaScience Press GigaDB repository or other domain-specific repositories.

Datasets with high-quality data and metadata
Authors should start by publishing a dataset comprising data and metadata that meets GBIF'sdata-quality requirements. This effort will involve work on an installation of the GBIF Integrated Publishing Toolkit (IPT). If the Darwin Core archive is constructed elsewhere, the use of the GBIF Data Validator is recommended before the publication.

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Call for data papers describing datasets on vectors of human diseases

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TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization, GigaScience Press and GBIF are today announcing a new call for authors to submit Data Release papers on vectors of human disease in a thematic series to be published in GigaByte Journal.

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We invite you to attend SPNHC 2022!
‘Through the door and through the web: releasing the power of natural history collections onsite and online’
The meeting of the Society for the Preservation of Natural History Collections (SPNHC), International Partner – BHL (Biodiversity Heritage Library) and National Partner – NatSCA (Natural Sciences Collections Association) will take place in Edinburgh, Scotland, UK from the 5th – 10th June 2022

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Findable Accessible Interoperable Reusable Open Science Research Coordination Networks (FAIROS RCN)

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Findable Accessible Interoperable Reusable Open Science Research Coordination Networks (FAIROS RCN)

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Beta site for NSF - National Science Foundation

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Findable Accessible Interoperable Reusable Open Science Research Coordination Networks (FAIROS RCN)

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Welcome to beta.nsf.gov, the new version of the National Science Foundation’s (NSF) website. We’re completely revamping our site and we’re doing it in the open so you can check on our progress. Learn more about the project

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An intro to GitHub & git -- One of the recorded workshop/seminars from the Berkeley folks here: https://ccb.berkeley.edu/outreach/workshops-bootcamps/#core-skills-seminars

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New feature enables search of occurrence data by global IUCN Red List Category

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New feature enables search of occurrence data by global IUCN Red List Category

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Users of GBIF.org and the GBIF API can now search and filter species occurrence records based on their global extinction risk as classified by The IUCN Red List of Threatened Species™ (Red List). The release of this improvement marks an important milestone under the 2019 memorandum of cooperation outlining areas of collaboration and common interest between GBIF and International Union for the Conservation of Nature (IUCN).

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Users of GBIF.org and the GBIF API can now search and filter species occurrence records based on their global extinction risk as classified by The IUCN Red List of Threatened Species™ (Red List). The release of this improvement marks an important milestone under the 2019 memorandum of cooperation outlining areas of collaboration and common interest between GBIF and International Union for the Conservation of Nature (IUCN).

The Red List is the world's most comprehensive source of information on the global extinction risks to individual animal, fungus and plant species. Given its role as an indicator of the health of the world's biodiversity and a tool for conservation action and policy change, the Red List is indispensable to any inventory of the world's scientific names.

IUCN has enabled the new search feature by providing GBIF access to prepare, process and publish updated versions of a taxonomic checklist version of the Red List. Having access to the latest Red List updates ensures that all assessed taxa appear in the GBIF Backbone Taxonomy.

"The release of the new Red List Category filter offers a clear demonstration of the value of information exchange between IUCN and GBIF," said Craig Hilton-Taylor, Head of the Red List Unit at IUCN. "Giving users of open data on biodiversity the ability to search for and filter records based on extinction risk offers a straightforward way of contributing more on-the-ground evidence into conservation decision-making and assessment processes."

Whether they use GBIF.org or the GBIF API, data users can filter and download occurrence records for any species or subspecies globally assessed by IUCN and classified into any Red List Category:

GBIF informatics staff have processed the global species occurrence index against the Red List to support the Not Evaluated (NE) facet. This filter displays records for all formally described species of animals, plants and fungi within the backbone taxonomy that have not yet reviewed for the Red List, providing the most complete view of assessment gaps at global levels (excluding fossils and micro-organisms, which are ineligible for assessment).

"The release of the global Red List filter simplifies the process of using a species' conservation status to access and apply open data in scientific research and conservation planning," said Andrew Rodrigues, programme officer for engagement and participation at the GBIF Secretariat. "This additional functionality also highlights the key role that organizations publishing data through the GBIF network play in feeding conservation-relevant data into decision-making processes."

The Red List filter improves the visibility of the latest species-level information relating to extinction risk for GBIF data users and offers just one example of how improved data and information exchange between the IUCN and GBIF networks adds value to their respective products and services. Species pages on GBIF.org already display the Red List status bar for all assessed species (as in this example), and GBIF has continued its support of the IUCN Invasive Species Specialist Group (first announced in 2018) by providing access to nearly 300 individual national and island checklists from the Global Register of Introduced and Invasive Species (GRIIS).

Meanwhile, experts from the IUCN Species Survival Commission have frequently consulted GBIF-mediated data while assessing and building knowledge on the conservation status of species. More than 15,000 individual Red List assessments have previously referenced GBIF; of those, 440 provide DOI-based citations that ensure the persistence, transparency and reproducibility of the experts' analyses (another explicit aim of the cooperation agreement between IUCN and GBIF). GBIF's training course on biodiversity data use likewise provide instruction in this common use case of preparing fit-for-purpose data and applying IUCN protocols in Red List-related activities.

The release of IUCN Red List occurrence search embodies the growing alignment between the two networks, which is also reflected in IUCN members' recently approval of an urgent call to share and use primary biodiversity in situ data through the IUCN World Conservation Congress. Other areas outlined for future collaboration in the GBIF-IUCN MOC include the Environmental Impact Classification for Alien Taxa (EICAT), Key Biodiversity Areas and the IUCN Species Survival Commission's Species Monitoring Specialist Group.

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New feature enables search of occurrence data by global IUCN Red List Category

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Users of GBIF.org and the GBIF API can now search and filter species occurrence records based on their global extinction risk as classified by The IUCN Red List of Threatened Species™ (Red List). The release of this improvement marks an important milestone under the 2019 memorandum of cooperation outlining areas of collaboration and common interest between GBIF and International Union for the Conservation of Nature (IUCN).

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Audubon core: open comment period begins 15 January. 

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Audubon core: open comment period begins 15 January. 

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The Audubon Core Maintenance Group is opening a public comment period for three term-change proposals starting on 15 January 2022. The comment period will continue until 15 February, or until consensus is reached on the proposal and there are no dissenting comments for 30 days. To view and comment on the proposals, visit https://github.com/tdwg/ac/milestone/5 and comment on the respective issues.  IMPORTANT NOTE: one of the proposals (https://github.com/tdwg/ac/issues/220) has technical implications that could set a precedent beyond Audubon Core (proposes using the W3C standard, "Semantic Annotation of Web Services Description Language and XML Schema," to document relationships between terms in different schemas). Another (https://github.com/tdwg/ac/issues/195) has potential implications for stability of existing implementations that use ac:subjectPart and ac:subjectOrientation. If these implications affect you, we strongly urge you to share your thoughts prior to the close of comment period.

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Term proposals for public comment starting 2022-01-15 Milestone · tdwg/ac

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Audubon Core. Contribute to tdwg/ac development by creating an account on GitHub.

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Georeferencing: a review of methods and applications

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Georeferencing: a review of methods and applications

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Georeferencing: a review of methods and applications

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(2014). Georeferencing: a review of methods and applications. Annals of GIS: Vol. 20, Web and Wireless GIS, pp. 61-69.

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Minority Serving Institutions Listening Session 

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Minority Serving Institutions Listening Session 

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The iDigBio team is interested in hearing from faculty, staff, and students from Minority Serving Intuitions regarding needs and opportunities for collaboration around digitizing collections and promoting research and teaching using collections. We are also interested to hear about needs and opportunities related to building authentic research experiences in biology for undergraduates.

Please join us on November 30th at 3:00 PM Eastern Time for a brief presentation about the iDigBio project, and some information from NSF followed by an open discussion on how iDigBio could help facilitate collections digitization programs as well as collections-based research and educational initiatives at Minority Serving Institutions. Our open discussion will include registered participants, iDigBio staff, NSF Program Directors, and researchers associated with existing digitization projects.

Register Here: https://ufl.zoom.us/meeting/register/tJYtdO6pqjwsEtAODpEhLqvyRJ335SGmnpVf

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The World Standard for RESTful APIs

The OpenAPI Specification was donated to the Linux Foundation under the OpenAPI Initiative in 2015. The specification creates a RESTful interface for easily developing and consuming an API by effectively mapping all the resources and operations associated with it.

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Version 3.0.3

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 RFC2119 RFC8174 when, and only when, they appear in all capitals, as shown here.

This document is licensed under The Apache License, Version 2.0.

Introduction

The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to RESTful APIs which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. When properly defined, a consumer can understand and interact with the remote service with a minimal amount of implementation logic.

An OpenAPI definition can then be used by documentation generation tools to display the API, code generation tools to generate servers and clients in various programming languages, testing tools, and many other use cases.

Table of Contents

Definitions

OpenAPI Document

A document (or set of documents) that defines or describes an API. An OpenAPI definition uses and conforms to the OpenAPI Specification.

Path Templating

Path templating refers to the usage of template expressions, delimited by curly braces ({}), to mark a section of a URL path as replaceable using path parameters.

Each template expression in the path MUST correspond to a path parameter that is included in the Path Item itself and/or in each of the Path Item's Operations.

Media type definitions are spread across several resources. The media type definitions SHOULD be in compliance with RFC6838.

Some examples of possible media type definitions:

 text/plain; charset=utf-8 application/json application/vnd.github+json application/vnd.github.v3+json application/vnd.github.v3.raw+json application/vnd.github.v3.text+json application/vnd.github.v3.html+json application/vnd.github.v3.full+json application/vnd.github.v3.diff application/vnd.github.v3.patch

HTTP Status Codes

The HTTP Status Codes are used to indicate the status of the executed operation. The available status codes are defined by RFC7231 and registered status codes are listed in the IANA Status Code Registry.

Specification

Versions

The OpenAPI Specification is versioned using Semantic Versioning 2.0.0 (semver) and follows the semver specification.

The major. minor portion of the semver (for example 3.0) SHALL designate the OAS feature set. Typically, versions address errors in this document, not the feature set. Tooling which supports OAS 3.0 SHOULD be compatible with all OAS 3.0.* versions. The patch version SHOULD NOT be considered by tooling, making no distinction between 3.0.0 and 3.0.1 for example.

Each new minor version of the OpenAPI Specification SHALL allow any OpenAPI document that is valid against any previous minor version of the Specification, within the same major version, to be updated to the new Specification version with equivalent semantics. Such an update MUST only require changing the openapi property to the new minor version.

For example, a valid OpenAPI 3.0.2 document, upon changing its openapi property to 3.1.0, SHALL be a valid OpenAPI 3.1.0 document, semantically equivalent to the original OpenAPI 3.0.2 document. New minor versions of the OpenAPI Specification MUST be written to ensure this form of backward compatibility.

An OpenAPI document compatible with OAS 3.*.* contains a required field which designates the semantic version of the OAS that it uses. (OAS 2.0 documents contain a top-level version field named and value "2.0".)

Format

An OpenAPI document that conforms to the OpenAPI Specification is itself a JSON object, which may be represented either in JSON or YAML format.

For example, if a field has an array value, the JSON array representation will be used:

All field names in the specification are case sensitive. This includes all fields that are used as keys in a map, except where explicitly noted that keys are case insensitive.

The schema exposes two types of fields: Fixed fields, which have a declared name, and Patterned fields, which declare a regex pattern for the field name.

Patterned fields MUST have unique names within the containing object.

In order to preserve the ability to round-trip between YAML and JSON formats, YAML version 1.2 is RECOMMENDED along with some additional constraints:

Note: While APIs may be defined by OpenAPI documents in either YAML or JSON format, the API request and response bodies and other content are not required to be JSON or YAML.

Document Structure

An OpenAPI document MAY be made up of a single document or be divided into multiple, connected parts at the discretion of the user. In the latter case, $ref fields MUST be used in the specification to reference those parts as follows from the JSON Schema definitions.

It is RECOMMENDED that the root OpenAPI document be named: openapi.json or openapi.yaml.

Data Types

Primitive data types in the OAS are based on the types supported by the JSON Schema Specification Wright Draft 00. Note that integer as a type is also supported and is defined as a JSON number without a fraction or exponent part. null is not supported as a type (see for an alternative solution). Models are defined using the Schema Object, which is an extended subset of JSON Schema Specification Wright Draft 00.

Primitives have an optional modifier property: format. OAS uses several known formats to define in fine detail the data type being used. However, to support documentation needs, the format property is an open string-valued property, and can have any value. Formats such as"email", "uuid", and so on, MAY be used even though undefined by this specification. Types that are not accompanied by a format property follow the type definition in the JSON Schema. Tools that do not recognize a specific format MAY default back to the type alone, as if the format is not specified.

The formats defined by the OAS are:

Rich Text Formatting

Throughout the specification description fields are noted as supporting CommonMark markdown formatting. Where OpenAPI tooling renders rich text it MUST support, at a minimum, markdown syntax as described by CommonMark 0.27. Tooling MAY choose to ignore some CommonMark features to address security concerns.

Relative References in URLs

Unless specified otherwise, all properties that are URLs MAY be relative references as defined by RFC3986. Relative references are resolved using the URLs defined in theServer Object as a Base URI.

Relative references used in $ref are processed as per JSON Reference, using the URL of the current document as the base URI. See also the Reference Object.

Schema

In the following description, if a field is not explicitly REQUIRED or described with a MUST or SHALL, it can be considered OPTIONAL.

OpenAPI Object

This is the root document object of the OpenAPI document.

Fixed Fields

This object MAY be extended with Specification Extensions.

Info Object

The object provides metadata about the API. The metadata MAY be used by the clients if needed, and MAY be presented in editing or documentation generation tools for convenience.

Fixed Fields

This object MAY be extended with Specification Extensions.

Info Object Example

{ "title": "Sample Pet Store App", "description": "This is a sample server for a pet store.", "termsOfService": "http://example.com/terms/", "contact": { "name": "API Support", "url": "http://www.example.com/support", "email": "support@example.com" }, "license": { "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0.html" }, "version": "1.0.1" }

title: Sample Pet Store App description: This is a sample server for a pet store. termsOfService: http://example.com/terms/ contact: name: API Support url: http://www.example.com/support email: support@example.com license: name: Apache 2.0 url: https://www.apache.org/licenses/LICENSE-2.0.html version: 1.0.1

Contact information for the exposed API.

Fixed Fields

This object MAY be extended with Specification Extensions.

{ "name": "API Support", "url": "http://www.example.com/support", "email": "support@example.com" }

name: API Support url: http://www.example.com/support email: support@example.com

License Object

License information for the exposed API.

Fixed Fields

This object MAY be extended with Specification Extensions.

License Object Example

{ "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0.html" }

name: Apache 2.0 url: https://www.apache.org/licenses/LICENSE-2.0.html

Server Object

An object representing a Server.

Fixed Fields

url

REQUIRED. A URL to the target host. This URL supports Server Variables and MAY be relative, to indicate that the host location is relative to the location where the OpenAPI document is being served. Variable substitutions will be made when a variable is named in {brackets}.

variables

Map[ string, Server Variable Object]

A map between a variable name and its value. The value is used for substitution in the server's URL template.

This object MAY be extended with Specification Extensions.

Server Object Example

A single server would be described as:

{ "url": "https://development.gigantic-server.com/v1", "description": "Development server" }

url: https://development.gigantic-server.com/v1 description: Development server

The following shows how multiple servers can be described, for example, at the OpenAPI Object's :

{ "servers": [ { "url": "https://development.gigantic-server.com/v1", "description": "Development server" }, { "url": "https://staging.gigantic-server.com/v1", "description": "Staging server" }, { "url": "https://api.gigantic-server.com/v1", "description": "Production server" } ] }

servers: - url: https://development.gigantic-server.com/v1 description: Development server - url: https://staging.gigantic-server.com/v1 description: Staging server - url: https://api.gigantic-server.com/v1 description: Production server

The following shows how variables can be used for a server configuration:

{ "servers": [ { "url": "https://{username}.gigantic-server.com:{port}/{basePath}", "description": "The production API server", "variables": { "username": { "default": "demo", "description": "this value is assigned by the service provider, in this example `gigantic-server.com`" }, "port": { "enum": [ "8443", "443" ], "default": "8443" }, "basePath": { "default": "v2" } } } ] }

servers: - url: https://{username}.gigantic-server.com:{port}/{basePath} description: The production API server variables: username: # note! no enum here means it is an open value default: demo description: this value is assigned by the service provider, in this example `gigantic-server.com` port: enum: - '8443' - '443' default: '8443' basePath: # open meaning there is the opportunity to use special base paths as assigned by the provider, default is `v2` default: v2

Server Variable Object

An object representing a Server Variable for server URL template substitution.

Fixed Fields

default

REQUIRED. The default value to use for substitution, which SHALL be sent if an alternate value is not supplied. Note this behavior is different than the Schema Object's treatment of default values, because in those cases parameter values are optional. If the is defined, the value SHOULD exist in the enum's values.

This object MAY be extended with Specification Extensions.

Components Object

Holds a set of reusable objects for different aspects of the OAS. All objects defined within the components object will have no effect on the API unless they are explicitly referenced from properties outside the components object.

Fixed Fields

This object MAY be extended with Specification Extensions.

All the fixed fields declared above are objects that MUST use keys that match the regular expression: ^[a-zA-Z0-9\.\-_]+$.

Field Name Examples:

User User_1 User_Name user-name my.org.User

Components Object Example

"components": { "schemas": { "GeneralError": { "type": "object", "properties": { "code": { "type": "integer", "format": "int32" }, "message": { "type": "string" } } }, "Category": { "type": "object", "properties": { "id": { "type": "integer", "format": "int64" }, "name": { "type": "string" } } }, "Tag": { "type": "object", "properties": { "id": { "type": "integer", "format": "int64" }, "name": { "type": "string" } } } }, "parameters": { "skipParam": { "name": "skip", "in": "query", "description": "number of items to skip", "required": true, "schema": { "type": "integer", "format": "int32" } }, "limitParam": { "name": "limit", "in": "query", "description": "max records to return", "required": true, "schema" : { "type": "integer", "format": "int32" } } }, "responses": { "NotFound": { "description": "Entity not found." }, "IllegalInput": { "description": "Illegal input for operation." }, "GeneralError": { "description": "General Error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/GeneralError" } } } } }, "securitySchemes": { "api_key": { "type": "apiKey", "name": "api_key", "in": "header" }, "petstore_auth": { "type": "oauth2", "flows": { "implicit": { "authorizationUrl": "http://example.org/api/oauth/dialog", "scopes": { "write:pets": "modify pets in your account", "read:pets": "read your pets" } } } } } }

components: schemas: GeneralError: type: object properties: code: type: integer format: int32 message: type: string Category: type: object properties: id: type: integer format: int64 name: type: string Tag: type: object properties: id: type: integer format: int64 name: type: string parameters: skipParam: name: skip in: query description: number of items to skip required: true schema: type: integer format: int32 limitParam: name: limit in: query description: max records to return required: true schema: type: integer format: int32 responses: NotFound: description: Entity not found. IllegalInput: description: Illegal input for operation. GeneralError: description: General Error content: application/json: schema: $ref: '#/components/schemas/GeneralError' securitySchemes: api_key: type: apiKey name: api_key in: header petstore_auth: type: oauth2 flows: implicit: authorizationUrl: http://example.org/api/oauth/dialog scopes: write:pets: modify pets in your account read:pets: read your pets

Paths Object

Holds the relative paths to the individual endpoints and their operations. The path is appended to the URL from the Server Object in order to construct the full URL. The Paths MAY be empty, due to ACL constraints.

Patterned Fields

/{path}
Path Item Object
A relative path to an individual endpoint. The field name MUST begin with a forward slash (/). The path is appended (no relative URL resolution) to the expanded URL from the Server Object's url field in order to construct the full URL. Path templating is allowed. When matching URLs, concrete (non-templated) paths would be matched before their templated counterparts. Templated paths with the same hierarchy but different templated names MUST NOT exist as they are identical. In case of ambiguous matching, it's up to the tooling to decide which one to use.

This object MAY be extended with Specification Extensions.

Path Templating Matching

Assuming the following paths, the concrete definition, /pets/mine, will be matched first if used:

 /pets/{petId} /pets/mine

The following paths are considered identical and invalid:

 /pets/{petId} /pets/{name}

The following may lead to ambiguous resolution:

 /{entity}/me /books/{id}

Paths Object Example

{ "/pets": { "get": { "description": "Returns all pets from the system that the user has access to", "responses": { "200": { "description": "A list of pets.", "content": { "application/json": { "schema": { "type": "array", "items": { "$ref": "#/components/schemas/pet" } } } } } } } } }

/pets: get: description: Returns all pets from the system that the user has access to responses: '200': description: A list of pets. content: application/json: schema: type: array items: $ref: '#/components/schemas/pet'

Path Item Object

Describes the operations available on a single path. A Path Item MAY be empty, due to ACL constraints. The path itself is still exposed to the documentation viewer but they will not know which operations and parameters are available.

Fixed Fields

This object MAY be extended with Specification Extensions.

Path Item Object Example

{ "get": { "description": "Returns pets based on ID", "summary": "Find pets by ID", "operationId": "getPetsById", "responses": { "200": { "description": "pet response", "content": { "*/*": { "schema": { "type": "array", "items": { "$ref": "#/components/schemas/Pet" } } } } }, "default": { "description": "error payload", "content": { "text/html": { "schema": { "$ref": "#/components/schemas/ErrorModel" } } } } } }, "parameters": [ { "name": "id", "in": "path", "description": "ID of pet to use", "required": true, "schema": { "type": "array", "items": { "type": "string" } }, "style": "simple" } ] }

get: description: Returns pets based on ID summary: Find pets by ID operationId: getPetsById responses: '200': description: pet response content: '*/*' : schema: type: array items: $ref: '#/components/schemas/Pet' default: description: error payload content: 'text/html': schema: $ref: '#/components/schemas/ErrorModel' parameters: - name: id in: path description: ID of pet to use required: true schema: type: array items: type: string style: simple

Operation Object

Describes a single API operation on a path.

Fixed Fields

This object MAY be extended with Specification Extensions.

Operation Object Example

{ "tags": [ "pet" ], "summary": "Updates a pet in the store with form data", "operationId": "updatePetWithForm", "parameters": [ { "name": "petId", "in": "path", "description": "ID of pet that needs to be updated", "required": true, "schema": { "type": "string" } } ], "requestBody": { "content": { "application/x-www-form-urlencoded": { "schema": { "type": "object", "properties": { "name": { "description": "Updated name of the pet", "type": "string" }, "status": { "description": "Updated status of the pet", "type": "string" } }, "required": ["status"] } } } }, "responses": { "200": { "description": "Pet updated.", "content": { "application/json": {}, "application/xml": {} } }, "405": { "description": "Method Not Allowed", "content": { "application/json": {}, "application/xml": {} } } }, "security": [ { "petstore_auth": [ "write:pets", "read:pets" ] } ] }

tags: - pet summary: Updates a pet in the store with form data operationId: updatePetWithForm parameters: - name: petId in: path description: ID of pet that needs to be updated required: true schema: type: string requestBody: content: 'application/x-www-form-urlencoded': schema: properties: name: description: Updated name of the pet type: string status: description: Updated status of the pet type: string required: - status responses: '200': description: Pet updated. content: 'application/json': {} 'application/xml': {} '405': description: Method Not Allowed content: 'application/json': {} 'application/xml': {} security: - petstore_auth: - write:pets - read:pets

External Documentation Object

Allows referencing an external resource for extended documentation.

Fixed Fields

This object MAY be extended with Specification Extensions.

External Documentation Object Example

{ "description": "Find more info here", "url": "https://example.com" }

description: Find more info here url: https://example.com

Parameter Object

Describes a single operation parameter.

A unique parameter is defined by a combination of a name and location.

Parameter Locations

There are four possible parameter locations specified by the in field:

path - Used together with Path Templating, where the parameter value is actually part of the operation's URL. This does not include the host or base path of the API. For example, in /items/{itemId}, the path parameter is itemId.

query - Parameters that are appended to the URL. For example, in /items?id=###, the query parameter is id.

header - Custom headers that are expected as part of the request. Note that RFC7230 states header names are case insensitive.

cookie - Used to pass a specific cookie value to the API.

Fixed Fields

The rules for serialization of the parameter are specified in one of two ways. For simpler scenarios, a and can describe the structure and syntax of the parameter.

For more complex scenarios, the property can define the media type and schema of the parameter. A parameter MUST contain either a schema property, or a content property, but not both. When example or examples are provided in conjunction with the schema object, the example MUST follow the prescribed serialization strategy for the parameter.

content

Map[ string, Media Type Object]

A map containing the representations for the parameter. The key is the media type and the value describes it. The map MUST only contain one entry.

Style Values

In order to support common ways of serializing simple parameters, a set of style values are defined.

Style Examples

Assume a parameter named color has one of the following values:

 string -> "blue" array -> ["blue","black","brown"] object -> { "R": 100, "G": 200, "B": 150 }

The following table shows examples of rendering differences for each value.

This object MAY be extended with Specification Extensions.

Parameter Object Examples

A header parameter with an array of 64 bit integer numbers:

{ "name": "token", "in": "header", "description": "token to be passed as a header", "required": true, "schema": { "type": "array", "items": { "type": "integer", "format": "int64" } }, "style": "simple" }

name: token in: header description: token to be passed as a header required: true schema: type: array items: type: integer format: int64 style: simple

A path parameter of a string value:

{ "name": "username", "in": "path", "description": "username to fetch", "required": true, "schema": { "type": "string" } }

name: username in: path description: username to fetch required: true schema: type: string

An optional query parameter of a string value, allowing multiple values by repeating the query parameter:

{ "name": "id", "in": "query", "description": "ID of the object to fetch", "required": false, "schema": { "type": "array", "items": { "type": "string" } }, "style": "form", "explode": true }

name: id in: query description: ID of the object to fetch required: false schema: type: array items: type: string style: form explode: true

A free-form query parameter, allowing undefined parameters of a specific type:

{ "in": "query", "name": "freeForm", "schema": { "type": "object", "additionalProperties": { "type": "integer" }, }, "style": "form" }

in: query name: freeForm schema: type: object additionalProperties: type: integer style: form

A complex parameter using content to define serialization:

{ "in": "query", "name": "coordinates", "content": { "application/json": { "schema": { "type": "object", "required": [ "lat", "long" ], "properties": { "lat": { "type": "number" }, "long": { "type": "number" } } } } } }

in: query name: coordinates content: application/json: schema: type: object required: - lat - long properties: lat: type: number long: type: number

Request Body Object

Describes a single request body.

Fixed Fields

content

Map[ string, Media Type Object]

REQUIRED. The content of the request body. The key is a media type or media type range and the value describes it. For requests that match multiple keys, only the most specific key is applicable. e.g. text/plain overrides text/*

This object MAY be extended with Specification Extensions.

Request Body Examples

A request body with a referenced model definition.

{ "description": "user to add to the system", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/User" }, "examples": { "user" : { "summary": "User Example", "externalValue": "http://foo.bar/examples/user-example.json" } } }, "application/xml": { "schema": { "$ref": "#/components/schemas/User" }, "examples": { "user" : { "summary": "User example in XML", "externalValue": "http://foo.bar/examples/user-example.xml" } } }, "text/plain": { "examples": { "user" : { "summary": "User example in Plain text", "externalValue": "http://foo.bar/examples/user-example.txt" } } }, "*/*": { "examples": { "user" : { "summary": "User example in other format", "externalValue": "http://foo.bar/examples/user-example.whatever" } } } } }

description: user to add to the system content: 'application/json': schema: $ref: '#/components/schemas/User' examples: user: summary: User Example externalValue: 'http://foo.bar/examples/user-example.json' 'application/xml': schema: $ref: '#/components/schemas/User' examples: user: summary: User Example in XML externalValue: 'http://foo.bar/examples/user-example.xml' 'text/plain': examples: user: summary: User example in text plain format externalValue: 'http://foo.bar/examples/user-example.txt' '*/*': examples: user: summary: User example in other format externalValue: 'http://foo.bar/examples/user-example.whatever'

A body parameter that is an array of string values:

{ "description": "user to add to the system", "content": { "text/plain": { "schema": { "type": "array", "items": { "type": "string" } } } } }

description: user to add to the system required: true content: text/plain: schema: type: array items: type: string

Each Media Type Object provides schema and examples for the media type identified by its key.

Fixed Fields

This object MAY be extended with Specification Extensions.

{ "application/json": { "schema": { "$ref": "#/components/schemas/Pet" }, "examples": { "cat" : { "summary": "An example of a cat", "value": { "name": "Fluffy", "petType": "Cat", "color": "White", "gender": "male", "breed": "Persian" } }, "dog": { "summary": "An example of a dog with a cat's name", "value" : { "name": "Puma", "petType": "Dog", "color": "Black", "gender": "Female", "breed": "Mixed" }, "frog": { "$ref": "#/components/examples/frog-example" } } } } }

application/json: schema: $ref: "#/components/schemas/Pet" examples: cat: summary: An example of a cat value: name: Fluffy petType: Cat color: White gender: male breed: Persian dog: summary: An example of a dog with a cat's name value: name: Puma petType: Dog color: Black gender: Female breed: Mixed frog: $ref: "#/components/examples/frog-example"

Considerations for File Uploads

In contrast with the 2.0 specification, file input/output content in OpenAPI is described with the same semantics as any other schema type. Specifically:

# content transferred with base64 encoding schema: type: string format: base64

# content transferred in binary (octet-stream): schema: type: string format: binary

These examples apply to either input payloads of file uploads or response payloads.

A requestBody for submitting a file in a POST operation may look like the following example:

requestBody: content: application/octet-stream: schema: # a binary file of any type type: string format: binary

In addition, specific media types MAY be specified:

# multiple, specific media types may be specified: requestBody: content: # a binary file of type png or jpeg 'image/jpeg': schema: type: string format: binary 'image/png': schema: type: string format: binary

To upload multiple files, a multipart media type MUST be used:

requestBody: content: multipart/form-data: schema: properties: # The property name 'file' will be used for all files. file: type: array items: type: string format: binary

Support for x-www-form-urlencoded Request Bodies

To submit content using form url encoding via RFC1866, the following definition may be used:

requestBody: content: application/x-www-form-urlencoded: schema: type: object properties: id: type: string format: uuid address: # complex types are stringified to support RFC 1866 type: object properties: {}

In this example, the contents in the requestBody MUST be stringified per RFC1866 when passed to the server. In addition, the address field complex object will be stringified.

When passing complex objects in the application/x-www-form-urlencoded content type, the default serialization strategy of such properties is described in the Encoding Object's property as form.

Special Considerations for multipart Content

It is common to use multipart/form-data as a Content-Type when transferring request bodies to operations. In contrast to 2.0, a schema is REQUIRED to define the input parameters to the operation when using multipart content. This supports complex structures as well as supporting mechanisms for multiple file uploads.

When passing in multipart types, boundaries MAY be used to separate sections of the content being transferred - thus, the following default Content-Types are defined for multipart:

If the property is a primitive, or an array of primitive values, the default Content-Type is text/plain

If the property is complex, or an array of complex values, the default Content-Type is application/json

If the property is a type: string with format: binary or format: base64 (aka a file object), the default Content-Type is application/octet-stream

Examples:

requestBody: content: multipart/form-data: schema: type: object properties: id: type: string format: uuid address: # default Content-Type for objects is `application/json` type: object properties: {} profileImage: # default Content-Type for string/binary is `application/octet-stream` type: string format: binary children: # default Content-Type for arrays is based on the `inner` type (text/plain here) type: array items: type: string addresses: # default Content-Type for arrays is based on the `inner` type (object shown, so `application/json` in this example) type: array items: type: '#/components/schemas/Address'

An encoding attribute is introduced to give you control over the serialization of parts of multipart request bodies. This attribute is only applicable to multipart and application/x-www-form-urlencoded request bodies.

Encoding Object

A single encoding definition applied to a single schema property.

Fixed Fields

This object MAY be extended with Specification Extensions.

Encoding Object Example

requestBody: content: multipart/mixed: schema: type: object properties: id: # default is text/plain type: string format: uuid address: # default is application/json type: object properties: {} historyMetadata: # need to declare XML format! description: metadata in XML format type: object properties: {} profileImage: # default is application/octet-stream, need to declare an image type only! type: string format: binary encoding: historyMetadata: # require XML Content-Type in utf-8 encoding contentType: application/xml; charset=utf-8 profileImage: # only accept png/jpeg contentType: image/png, image/jpeg headers: X-Rate-Limit-Limit: description: The number of allowed requests in the current period schema: type: integer

Responses Object

A container for the expected responses of an operation. The container maps a HTTP response code to the expected response.

The documentation is not necessarily expected to cover all possible HTTP response codes because they may not be known in advance. However, documentation is expected to cover a successful operation response and any known errors.

The default MAY be used as a default response object for all HTTP codes that are not covered individually by the specification.

The Responses Object MUST contain at least one response code, and it SHOULD be the response for a successful operation call.

Fixed Fields

Patterned Fields

HTTP Status Code
Response Object | Reference Object

Any HTTP status code can be used as the property name, but only one property per code, to describe the expected response for that HTTP status code. A Reference Object can link to a response that is defined in the OpenAPI Object's components/responses section. This field MUST be enclosed in quotation marks (for example, "200") for compatibility between JSON and YAML. To define a range of response codes, this field MAY contain the uppercase wildcard character X. For example, 2XX represents all response codes between [200-299]. Only the following range definitions are allowed: 1XX, 2XX, 3XX, 4XX, and 5XX. If a response is defined using an explicit code, the explicit code definition takes precedence over the range definition for that code.

This object MAY be extended with Specification Extensions.

Responses Object Example

A 200 response for a successful operation and a default response for others (implying an error):

{ "200": { "description": "a pet to be returned", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/Pet" } } } }, "default": { "description": "Unexpected error", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorModel" } } } } }

'200': description: a pet to be returned content: application/json: schema: $ref: '#/components/schemas/Pet' default: description: Unexpected error content: application/json: schema: $ref: '#/components/schemas/ErrorModel'

Response Object

Describes a single response from an API Operation, including design-time, static links to operations based on the response.

Fixed Fields

description

REQUIRED. A short description of the response. CommonMark syntax MAY be used for rich text representation.

headers

Maps a header name to its definition. RFC7230 states header names are case insensitive. If a response header is defined with the name "Content-Type", it SHALL be ignored.

content

Map[ string, Media Type Object]

A map containing descriptions of potential response payloads. The key is a media type or media type range and the value describes it. For responses that match multiple keys, only the most specific key is applicable. e.g. text/plain overrides text/*

This object MAY be extended with Specification Extensions.

Response Object Examples

Response of an array of a complex type:

{ "description": "A complex object array response", "content": { "application/json": { "schema": { "type": "array", "items": { "$ref": "#/components/schemas/VeryComplexType" } } } } }

description: A complex object array response content: application/json: schema: type: array items: $ref: '#/components/schemas/VeryComplexType'

Response with a string type:

{ "description": "A simple string response", "content": { "text/plain": { "schema": { "type": "string" } } } }

description: A simple string response content: text/plain: schema: type: string

Plain text response with headers:

{ "description": "A simple string response", "content": { "text/plain": { "schema": { "type": "string", "example": "whoa!" } } }, "headers": { "X-Rate-Limit-Limit": { "description": "The number of allowed requests in the current period", "schema": { "type": "integer" } }, "X-Rate-Limit-Remaining": { "description": "The number of remaining requests in the current period", "schema": { "type": "integer" } }, "X-Rate-Limit-Reset": { "description": "The number of seconds left in the current period", "schema": { "type": "integer" } } } }

description: A simple string response content: text/plain: schema: type: string example: 'whoa!' headers: X-Rate-Limit-Limit: description: The number of allowed requests in the current period schema: type: integer X-Rate-Limit-Remaining: description: The number of remaining requests in the current period schema: type: integer X-Rate-Limit-Reset: description: The number of seconds left in the current period schema: type: integer

Response with no return value:

{ "description": "object created" }

description: object created

Callback Object

A map of possible out-of band callbacks related to the parent operation. Each value in the map is a Path Item Object that describes a set of requests that may be initiated by the API provider and the expected responses. The key value used to identify the path item object is an expression, evaluated at runtime, that identifies a URL to use for the callback operation.

Patterned Fields

This object MAY be extended with Specification Extensions.

Key Expression

The key that identifies the Path Item Object is a runtime expression that can be evaluated in the context of a runtime HTTP request/response to identify the URL to be used for the callback request. A simple example might be$request.body#/url. However, using a runtime expression the complete HTTP message can be accessed. This includes accessing any part of a body that a JSON Pointer RFC6901 can reference.

For example, given the following HTTP request:

POST /subscribe/myevent?queryUrl=http://clientdomain.com/stillrunning HTTP/1.1 Host: example.org Content-Type: application/json Content-Length: 187 { "failedUrl" : "http://clientdomain.com/failed", "successUrls" : [ "http://clientdomain.com/fast", "http://clientdomain.com/medium", "http://clientdomain.com/slow" ] } 201 Created Location: http://example.org/subscription/1

The following examples show how the various expressions evaluate, assuming the callback operation has a path parameter named eventType and a query parameter named queryUrl.

Callback Object Examples

The following example uses the user provided queryUrl query string parameter to define the callback URL. This is an example of how to use a callback object to describe a WebHook callback that goes with the subscription operation to enable registering for the WebHook.

myCallback: '{$request.query.queryUrl}': post: requestBody: description: Callback payload content: 'application/json': schema: $ref: '#/components/schemas/SomePayload' responses: '200': description: callback successfully processed

The following example shows a callback where the server is hard-coded, but the query string parameters are populated from the id and email property in the request body.

transactionCallback: 'http://notificationServer.com?transactionId={$request.body#/id}&email={$request.body#/email}': post: requestBody: description: Callback payload content: 'application/json': schema: $ref: '#/components/schemas/SomePayload' responses: '200': description: callback successfully processed

Example Object

Fixed Fields

value

Any

Embedded literal example. The value field and externalValue field are mutually exclusive. To represent examples of media types that cannot naturally represented in JSON or YAML, use a string value to contain the example, escaping where necessary.

externalValue

A URL that points to the literal example. This provides the capability to reference examples that cannot easily be included in JSON or YAML documents. The value field and externalValue field are mutually exclusive.

This object MAY be extended with Specification Extensions.

In all cases, the example value is expected to be compatible with the type schema of its associated value. Tooling implementations MAY choose to validate compatibility automatically, and reject the example value(s) if incompatible.

Example Object Examples

In a request body:

requestBody: content: 'application/json': schema: $ref: '#/components/schemas/Address' examples: foo: summary: A foo example value: {"foo": "bar"} bar: summary: A bar example value: {"bar": "baz"} 'application/xml': examples: xmlExample: summary: This is an example in XML externalValue: 'http://example.org/examples/address-example.xml' 'text/plain': examples: textExample: summary: This is a text example externalValue: 'http://foo.bar/examples/address-example.txt'

In a parameter:

parameters: - name: 'zipCode' in: 'query' schema: type: 'string' format: 'zip-code' examples: zip-example: $ref: '#/components/examples/zip-example'

In a response:

responses: '200': description: your car appointment has been booked content: application/json: schema: $ref: '#/components/schemas/SuccessResponse' examples: confirmation-success: $ref: '#/components/examples/confirmation-success'

The Link object represents a possible design-time link for a response. The presence of a link does not guarantee the caller's ability to successfully invoke it, rather it provides a known relationship and traversal mechanism between responses and other operations.

Unlike dynamic links (i.e. links provided in the response payload), the OAS linking mechanism does not require link information in the runtime response.

For computing links, and providing instructions to execute them, a runtime expression is used for accessing values in an operation and using them as parameters while invoking the linked operation.

Fixed Fields

This object MAY be extended with Specification Extensions.

A linked operation MUST be identified using either an operationRef or operationId. In the case of an operationId, it MUST be unique and resolved in the scope of the OAS document. Because of the potential for name clashes, the operationRef syntax is preferred for specifications with external references.

Examples

Computing a link from a request operation where the $request.path.id is used to pass a request parameter to the linked operation.

paths: /users/{id}: parameters: - name: id in: path required: true description: the user identifier, as userId schema: type: string get: responses: '200': description: the user being returned content: application/json: schema: type: object properties: uuid: # the unique user id type: string format: uuid links: address: # the target link operationId operationId: getUserAddress parameters: # get the `id` field from the request path parameter named `id` userId: $request.path.id # the path item of the linked operation /users/{userid}/address: parameters: - name: userid in: path required: true description: the user identifier, as userId schema: type: string # linked operation get: operationId: getUserAddress responses: '200': description: the user's address

When a runtime expression fails to evaluate, no parameter value is passed to the target operation.

Values from the response body can be used to drive a linked operation.

links: address: operationId: getUserAddressByUUID parameters: # get the `uuid` field from the `uuid` field in the response body userUuid: $response.body#/uuid

Clients follow all links at their discretion. Neither permissions, nor the capability to make a successful call to that link, is guaranteed solely by the existence of a relationship.

OperationRef Examples

As references to operationId MAY NOT be possible (the operationId is an optional field in an Operation Object), references MAY also be made through a relative operationRef:

links: UserRepositories: # returns array of '#/components/schemas/repository' operationRef: '#/paths/~12.0~1repositories~1{username}/get' parameters: username: $response.body#/username

or an absolute operationRef:

links: UserRepositories: # returns array of '#/components/schemas/repository' operationRef: 'https://na2.gigantic-server.com/#/paths/~12.0~1repositories~1{username}/get' parameters: username: $response.body#/username

Note that in the use of operationRef, the escaped forward-slash is necessary when using JSON references.

Runtime Expressions

Runtime expressions allow defining values based on information that will only be available within the HTTP message in an actual API call. This mechanism is used by Link Objects and Callback Objects.

The runtime expression is defined by the following ABNF syntax

 expression = ( "$url" / "$method" / "$statusCode" / "$request." source / "$response." source ) source = ( header-reference / query-reference / path-reference / body-reference ) header-reference = "header." token query-reference = "query." name path-reference = "path." name body-reference = "body" ["#" json-pointer ] json-pointer = *( "/" reference-token ) reference-token = *( unescaped / escaped ) unescaped = %x00-2E / %x30-7D / %x7F-10FFFF ; %x2F ('/') and %x7E ('~') are excluded from 'unescaped' escaped = "~" ( "0" / "1" ) ; representing '~' and '/', respectively name = *( CHAR ) token = 1*tchar tchar = "!" / "#" / "$" / "%" / "&" / "'" / "*" / "+" / "-" / "." / "^" / "_" / "`" / "|" / "~" / DIGIT / ALPHA

Here, json-pointer is taken from RFC 6901, char from RFC 7159 and token from RFC 7230.

The name identifier is case-sensitive, whereas token is not.

The table below provides examples of runtime expressions and examples of their use in a value:

Examples

Runtime expressions preserve the type of the referenced value. Expressions can be embedded into string values by surrounding the expression with {} curly braces.

The Header Object follows the structure of the Parameter Object with the following changes:

1. name MUST NOT be specified, it is given in the corresponding headers map.

2. in MUST NOT be specified, it is implicitly in header.

3. All traits that are affected by the location MUST be applicable to a location of header (for example, ).

A simple header of type integer:

{ "description": "The number of allowed requests in the current period", "schema": { "type": "integer" } }

description: The number of allowed requests in the current period schema: type: integer

Tag Object

Adds metadata to a single tag that is used by the Operation Object. It is not mandatory to have a Tag Object per tag defined in the Operation Object instances.

Fixed Fields

This object MAY be extended with Specification Extensions.

Tag Object Example

{ "name": "pet", "description": "Pets operations" }

name: pet description: Pets operations

Reference Object

A simple object to allow referencing other components in the specification, internally and externally.

The Reference Object is defined by JSON Reference and follows the same structure, behavior and rules.

For this specification, reference resolution is accomplished as defined by the JSON Reference specification and not by the JSON Schema specification.

Fixed Fields

This object cannot be extended with additional properties and any properties added SHALL be ignored.

Reference Object Example

{ "$ref": "#/components/schemas/Pet" }

$ref: '#/components/schemas/Pet'

Relative Schema Document Example

{ "$ref": "Pet.json" }

$ref: Pet.yaml

Relative Documents With Embedded Schema Example

{ "$ref": "definitions.json#/Pet" }

$ref: definitions.yaml#/Pet

Schema Object

The Schema Object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. This object is an extended subset of the JSON Schema Specification Wright Draft 00.

For more information about the properties, see JSON Schema Core and JSON Schema Validation. Unless stated otherwise, the property definitions follow the JSON Schema.

Properties

The following properties are taken directly from the JSON Schema definition and follow the same specifications:

title

multipleOf

maximum

exclusiveMaximum

minimum

exclusiveMinimum

maxLength

minLength

pattern (This string SHOULD be a valid regular expression, according to the Ecma-262 Edition 5.1 regular expression dialect)

maxItems

minItems

uniqueItems

maxProperties

minProperties

required

enum

The following properties are taken from the JSON Schema definition but their definitions were adjusted to the OpenAPI Specification.

type - Value MUST be a string. Multiple types via an array are not supported.

allOf - Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema.

oneOf - Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema.

anyOf - Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema.

not - Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema.

items - Value MUST be an object and not an array. Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema. items MUST be present if the type is array.

properties - Property definitions MUST be a Schema Object and not a standard JSON Schema (inline or referenced).

additionalProperties - Value can be boolean or object. Inline or referenced schema MUST be of a Schema Object and not a standard JSON Schema. Consistent with JSON Schema, additionalProperties defaults to true.

description - CommonMark syntax MAY be used for rich text representation.

format - See Data Type Formats for further details. While relying on JSON Schema's defined formats, the OAS offers a few additional predefined formats.

default - The default value represents what would be assumed by the consumer of the input as the value of the schema if one is not provided. Unlike JSON Schema, the value MUST conform to the defined type for the Schema Object defined at the same level. For example, if type is string, then default can be "foo" but cannot be 1.

Alternatively, any time a Schema Object can be used, a Reference Object can be used in its place. This allows referencing definitions instead of defining them inline.

Additional properties defined by the JSON Schema specification that are not mentioned here are strictly unsupported.

Other than the JSON Schema subset fields, the following fields MAY be used for further schema documentation:

Fixed Fields

This object MAY be extended with Specification Extensions.

Composition and Inheritance (Polymorphism)

The OpenAPI Specification allows combining and extending model definitions using the allOf property of JSON Schema, in effect offering model composition. allOf takes an array of object definitions that are validated independently but together compose a single object.

While composition offers model extensibility, it does not imply a hierarchy between the models. To support polymorphism, the OpenAPI Specification adds the discriminator field. When used, the discriminator will be the name of the property that decides which schema definition validates the structure of the model. As such, the discriminator field MUST be a required field. There are two ways to define the value of a discriminator for an inheriting instance.

Use the schema name.

Override the schema name by overriding the property with a new value. If a new value exists, this takes precedence over the schema name. As such, inline schema definitions, which do not have a given id, cannot be used in polymorphism.

XML Modeling

The xml property allows extra definitions when translating the JSON definition to XML. The XML Object contains additional information about the available options.

Schema Object Examples

Primitive Sample

{ "type": "string", "format": "email" }

type: string format: email

Simple Model

{ "type": "object", "required": [ "name" ], "properties": { "name": { "type": "string" }, "address": { "$ref": "#/components/schemas/Address" }, "age": { "type": "integer", "format": "int32", "minimum": 0 } } }

type: object required: - name properties: name: type: string address: $ref: '#/components/schemas/Address' age: type: integer format: int32 minimum: 0

Model with Map/Dictionary Properties

For a simple string to string mapping:

{ "type": "object", "additionalProperties": { "type": "string" } }

type: object additionalProperties: type: string

For a string to model mapping:

{ "type": "object", "additionalProperties": { "$ref": "#/components/schemas/ComplexModel" } }

type: object additionalProperties: $ref: '#/components/schemas/ComplexModel'

Model with Example

{ "type": "object", "properties": { "id": { "type": "integer", "format": "int64" }, "name": { "type": "string" } }, "required": [ "name" ], "example": { "name": "Puma", "id": 1 } }

type: object properties: id: type: integer format: int64 name: type: string required: - name example: name: Puma id: 1

Models with Composition

{ "components": { "schemas": { "ErrorModel": { "type": "object", "required": [ "message", "code" ], "properties": { "message": { "type": "string" }, "code": { "type": "integer", "minimum": 100, "maximum": 600 } } }, "ExtendedErrorModel": { "allOf": [ { "$ref": "#/components/schemas/ErrorModel" }, { "type": "object", "required": [ "rootCause" ], "properties": { "rootCause": { "type": "string" } } } ] } } } }

components: schemas: ErrorModel: type: object required: - message - code properties: message: type: string code: type: integer minimum: 100 maximum: 600 ExtendedErrorModel: allOf: - $ref: '#/components/schemas/ErrorModel' - type: object required: - rootCause properties: rootCause: type: string

Models with Polymorphism Support

{ "components": { "schemas": { "Pet": { "type": "object", "discriminator": { "propertyName": "petType" }, "properties": { "name": { "type": "string" }, "petType": { "type": "string" } }, "required": [ "name", "petType" ] }, "Cat": { "description": "A representation of a cat. Note that `Cat` will be used as the discriminator value.", "allOf": [ { "$ref": "#/components/schemas/Pet" }, { "type": "object", "properties": { "huntingSkill": { "type": "string", "description": "The measured skill for hunting", "default": "lazy", "enum": [ "clueless", "lazy", "adventurous", "aggressive" ] } }, "required": [ "huntingSkill" ] } ] }, "Dog": { "description": "A representation of a dog. Note that `Dog` will be used as the discriminator value.", "allOf": [ { "$ref": "#/components/schemas/Pet" }, { "type": "object", "properties": { "packSize": { "type": "integer", "format": "int32", "description": "the size of the pack the dog is from", "default": 0, "minimum": 0 } }, "required": [ "packSize" ] } ] } } } }

components: schemas: Pet: type: object discriminator: propertyName: petType properties: name: type: string petType: type: string required: - name - petType Cat: ## "Cat" will be used as the discriminator value description: A representation of a cat allOf: - $ref: '#/components/schemas/Pet' - type: object properties: huntingSkill: type: string description: The measured skill for hunting enum: - clueless - lazy - adventurous - aggressive required: - huntingSkill Dog: ## "Dog" will be used as the discriminator value description: A representation of a dog allOf: - $ref: '#/components/schemas/Pet' - type: object properties: packSize: type: integer format: int32 description: the size of the pack the dog is from default: 0 minimum: 0 required: - packSize

Discriminator Object

When request bodies or response payloads may be one of a number of different schemas, a discriminator object can be used to aid in serialization, deserialization, and validation. The discriminator is a specific object in a schema which is used to inform the consumer of the specification of an alternative schema based on the value associated with it.

When using the discriminator, inline schemas will not be considered.

Fixed Fields

mapping

An object to hold mappings between payload values and schema names or references.

The discriminator object is legal only when using one of the composite keywords oneOf, anyOf, allOf.

In OAS 3.0, a response payload MAY be described to be exactly one of any number of types:

MyResponseType: oneOf: - $ref: '#/components/schemas/Cat' - $ref: '#/components/schemas/Dog' - $ref: '#/components/schemas/Lizard'

which means the payload MUST, by validation, match exactly one of the schemas described by Cat, Dog, or Lizard. In this case, a discriminator MAY act as a "hint" to shortcut validation and selection of the matching schema which may be a costly operation, depending on the complexity of the schema. We can then describe exactly which field tells us which schema to use:

MyResponseType: oneOf: - $ref: '#/components/schemas/Cat' - $ref: '#/components/schemas/Dog' - $ref: '#/components/schemas/Lizard' discriminator: propertyName: petType

The expectation now is that a property with name petType MUST be present in the response payload, and the value will correspond to the name of a schema defined in the OAS document. Thus the response payload:

{ "id": 12345, "petType": "Cat" }

Will indicate that the Cat schema be used in conjunction with this payload.

In scenarios where the value of the discriminator field does not match the schema name or implicit mapping is not possible, an optional mapping definition MAY be used:

MyResponseType: oneOf: - $ref: '#/components/schemas/Cat' - $ref: '#/components/schemas/Dog' - $ref: '#/components/schemas/Lizard' - $ref: 'https://gigantic-server.com/schemas/Monster/schema.json' discriminator: propertyName: petType mapping: dog: '#/components/schemas/Dog' monster: 'https://gigantic-server.com/schemas/Monster/schema.json'

Here the discriminator value of dog will map to the schema #/components/schemas/Dog, rather than the default (implicit) value of Dog. If the discriminator value does not match an implicit or explicit mapping, no schema can be determined and validation SHOULD fail. Mapping keys MUST be string values, but tooling MAY convert response values to strings for comparison.

When used in conjunction with the anyOf construct, the use of the discriminator can avoid ambiguity where multiple schemas may satisfy a single payload.

In both the oneOf and anyOf use cases, all possible schemas MUST be listed explicitly. To avoid redundancy, the discriminator MAY be added to a parent schema definition, and all schemas comprising the parent schema in an allOf construct may be used as an alternate schema.

For example:

components: schemas: Pet: type: object required: - petType properties: petType: type: string discriminator: propertyName: petType mapping: dog: Dog Cat: allOf: - $ref: '#/components/schemas/Pet' - type: object # all other properties specific to a `Cat` properties: name: type: string Dog: allOf: - $ref: '#/components/schemas/Pet' - type: object # all other properties specific to a `Dog` properties: bark: type: string Lizard: allOf: - $ref: '#/components/schemas/Pet' - type: object # all other properties specific to a `Lizard` properties: lovesRocks: type: boolean

a payload like this:

{ "petType": "Cat", "name": "misty" }

will indicate that the Cat schema be used. Likewise this schema:

{ "petType": "dog", "bark": "soft" }

will map to Dog because of the definition in the mappings element.

XML Object

A metadata object that allows for more fine-tuned XML model definitions.

When using arrays, XML element names are not inferred (for singular/plural forms) and the name property SHOULD be used to add that information. See examples for expected behavior.

Fixed Fields

This object MAY be extended with Specification Extensions.

XML Object Examples

The examples of the XML object definitions are included inside a property definition of a Schema Object with a sample of the XML representation of it.

No XML Element

Basic string property:

{ "animals": { "type": "string" } }

animals: type: string

Basic string array property ( is false by default):

{ "animals": { "type": "array", "items": { "type": "string" } } }

animals: type: array items: type: string

XML Name Replacement

{ "animals": { "type": "string", "xml": { "name": "animal" } } }

animals: type: string xml: name: animal

XML Attribute, Prefix and Namespace

In this example, a full model definition is shown.

{ "Person": { "type": "object", "properties": { "id": { "type": "integer", "format": "int32", "xml": { "attribute": true } }, "name": { "type": "string", "xml": { "namespace": "http://example.com/schema/sample", "prefix": "sample" } } } } }

Person: type: object properties: id: type: integer format: int32 xml: attribute: true name: type: string xml: namespace: http://example.com/schema/sample prefix: sample

XML Arrays

Changing the element names:

{ "animals": { "type": "array", "items": { "type": "string", "xml": { "name": "animal" } } } }

animals: type: array items: type: string xml: name: animal

<animal>value</animal> <animal>value</animal>

The external name property has no effect on the XML:

{ "animals": { "type": "array", "items": { "type": "string", "xml": { "name": "animal" } }, "xml": { "name": "aliens" } } }

animals: type: array items: type: string xml: name: animal xml: name: aliens

<animal>value</animal> <animal>value</animal>

Even when the array is wrapped, if a name is not explicitly defined, the same name will be used both internally and externally:

{ "animals": { "type": "array", "items": { "type": "string" }, "xml": { "wrapped": true } } }

animals: type: array items: type: string xml: wrapped: true

<animals> <animals>value</animals> <animals>value</animals> </animals>

To overcome the naming problem in the example above, the following definition can be used:

{ "animals": { "type": "array", "items": { "type": "string", "xml": { "name": "animal" } }, "xml": { "wrapped": true } } }

animals: type: array items: type: string xml: name: animal xml: wrapped: true

<animals> <animal>value</animal> <animal>value</animal> </animals>

Affecting both internal and external names:

{ "animals": { "type": "array", "items": { "type": "string", "xml": { "name": "animal" } }, "xml": { "name": "aliens", "wrapped": true } } }

animals: type: array items: type: string xml: name: animal xml: name: aliens wrapped: true

<aliens> <animal>value</animal> <animal>value</animal> </aliens>

If we change the external element but not the internal ones:

{ "animals": { "type": "array", "items": { "type": "string" }, "xml": { "name": "aliens", "wrapped": true } } }

animals: type: array items: type: string xml: name: aliens wrapped: true

<aliens> <aliens>value</aliens> <aliens>value</aliens> </aliens>

Security Scheme Object

Defines a security scheme that can be used by the operations. Supported schemes are HTTP authentication, an API key (either as a header, a cookie parameter or as a query parameter), OAuth2's common flows (implicit, password, client credentials and authorization code) as defined in RFC6749, and OpenID Connect Discovery.

Fixed Fields

description

Any

A short description for security scheme. CommonMark syntax MAY be used for rich text representation.

name

REQUIRED. The name of the header, query or cookie parameter to be used.

scheme

REQUIRED. The name of the HTTP Authorization scheme to be used in the Authorization header as defined in RFC7235. The values used SHOULD be registered in the IANA Authentication Scheme registry.

bearerFormat

A hint to the client to identify how the bearer token is formatted. Bearer tokens are usually generated by an authorization server, so this information is primarily for documentation purposes.

flows
OAuth Flows Object
REQUIRED. An object containing configuration information for the flow types supported.

openIdConnectUrl
openIdConnect
REQUIRED. OpenId Connect URL to discover OAuth2 configuration values. This MUST be in the form of a URL.

This object MAY be extended with Specification Extensions.

Security Scheme Object Example

Basic Authentication Sample

{ "type": "http", "scheme": "basic" }

type: http scheme: basic

API Key Sample

{ "type": "apiKey", "name": "api_key", "in": "header" }

type: apiKey name: api_key in: header

JWT Bearer Sample

{ "type": "http", "scheme": "bearer", "bearerFormat": "JWT", }

type: http scheme: bearer bearerFormat: JWT

Implicit OAuth2 Sample

{ "type": "oauth2", "flows": { "implicit": { "authorizationUrl": "https://example.com/api/oauth/dialog", "scopes": { "write:pets": "modify pets in your account", "read:pets": "read your pets" } } } }

type: oauth2 flows: implicit: authorizationUrl: https://example.com/api/oauth/dialog scopes: write:pets: modify pets in your account read:pets: read your pets

OAuth Flows Object

Allows configuration of the supported OAuth Flows.

Fixed Fields

This object MAY be extended with Specification Extensions.

OAuth Flow Object

Configuration details for a supported OAuth Flow

Fixed Fields

authorizationUrl

REQUIRED. The authorization URL to be used for this flow. This MUST be in the form of a URL.

tokenUrl

oauth2 ("password", "clientCredentials", "authorizationCode")

REQUIRED. The token URL to be used for this flow. This MUST be in the form of a URL.

refreshUrl

The URL to be used for obtaining refresh tokens. This MUST be in the form of a URL.

scopes

REQUIRED. The available scopes for the OAuth2 security scheme. A map between the scope name and a short description for it. The map MAY be empty.

This object MAY be extended with Specification Extensions.

OAuth Flow Object Examples

{ "type": "oauth2", "flows": { "implicit": { "authorizationUrl": "https://example.com/api/oauth/dialog", "scopes": { "write:pets": "modify pets in your account", "read:pets": "read your pets" } }, "authorizationCode": { "authorizationUrl": "https://example.com/api/oauth/dialog", "tokenUrl": "https://example.com/api/oauth/token", "scopes": { "write:pets": "modify pets in your account", "read:pets": "read your pets" } } } }

type: oauth2 flows: implicit: authorizationUrl: https://example.com/api/oauth/dialog scopes: write:pets: modify pets in your account read:pets: read your pets authorizationCode: authorizationUrl: https://example.com/api/oauth/dialog tokenUrl: https://example.com/api/oauth/token scopes: write:pets: modify pets in your account read:pets: read your pets

Security Requirement Object

Lists the required security schemes to execute this operation. The name used for each property MUST correspond to a security scheme declared in the Security Schemes under the Components Object.

Security Requirement Objects that contain multiple schemes require that all schemes MUST be satisfied for a request to be authorized. This enables support for scenarios where multiple query parameters or HTTP headers are required to convey security information.

When a list of Security Requirement Objects is defined on the OpenAPI Object or Operation Object, only one of the Security Requirement Objects in the list needs to be satisfied to authorize the request.

Patterned Fields

{name}

Each name MUST correspond to a security scheme which is declared in the Security Schemes under the Components Object. If the security scheme is of type "oauth2" or "openIdConnect", then the value is a list of scope names required for the execution, and the list MAY be empty if authorization does not require a specified scope. For other security scheme types, the array MUST be empty.

Security Requirement Object Examples

Non-OAuth2 Security Requirement

OAuth2 Security Requirement

{ "petstore_auth": [ "write:pets", "read:pets" ] }

petstore_auth: - write:pets - read:pets

Optional OAuth2 Security

Optional OAuth2 security as would be defined in an OpenAPI Object or an Operation Object:

{ "security": [ {}, { "petstore_auth": [ "write:pets", "read:pets" ] } ] }

security: - {} - petstore_auth: - write:pets - read:pets

Specification Extensions

While the OpenAPI Specification tries to accommodate most use cases, additional data can be added to extend the specification at certain points.

The extensions properties are implemented as patterned fields that are always prefixed by "x-".

The extensions may or may not be supported by the available tooling, but those may be extended as well to add requested support (if tools are internal or open-sourced).

Security Filtering

Some objects in the OpenAPI Specification MAY be declared and remain empty, or be completely removed, even though they are inherently the core of the API documentation.

The reasoning is to allow an additional layer of access control over the documentation. While not part of the specification itself, certain libraries MAY choose to allow access to parts of the documentation based on some form of authentication/authorization.

Two examples of this:

1. The Paths Object MAY be empty. It may be counterintuitive, but this may tell the viewer that they got to the right place, but can't access any documentation. They'd still have access to the Info Object which may contain additional information regarding authentication.

2. The Path Item Object MAY be empty. In this case, the viewer will be aware that the path exists, but will not be able to see any of its operations or parameters. This is different from hiding the path itself from the Paths Object, because the user will be aware of its existence. This allows the documentation provider to finely control what the viewer can see.

Appendix A: Revision History

# data[].attributes.attachment_link.description
Version 3.0.3 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 RFC2119 RFC8174 when, and only when, they appear in all capitals, as shown here.

# data[].attributes.headline
TDWG November 2021, Working Sessions schedule is out

# data[].attributes.subject
TDWG November 2021, Working Sessions schedule is out

# data[].attributes.body
TDWG will host working sessions of the Interest and Task Groups (IG/TG) each Wednesday and Thursday in November. These sessions will be hosted in Zoom by TDWG and be free but require registration.

Working sessions will include introductions, overviews of IG/TG use cases, solutions, collaboration resources, and current work practices. The sessions will be interactive, with the participants being able to see each other and raise hands to interact with the conveners, speakers and other participants.

# data[].attributes.attachment_link.readable_text
TDWG will host working sessions of the Interest and Task Groups (IG/TG) each Wednesday and Thursday in November. These sessions will be hosted in Zoom by TDWG and be free but require registration.

Working sessions will include introductions, overviews of IG/TG use cases, solutions, collaboration resources, and current work practices. The sessions will be interactive, with the participants being able to see each other and raise hands to interact with the conveners, speakers and other participants.

Below you can find the list of sessions, including workshops on a specific subject and meetings of the respective Interest Groups and Task Groups (ITG) and Functional Groups (FG) to progress on their activities and planning.

The latest schedule (21 Oct.) has been posted as a Google spreadsheet. See the TDWG 2021 November Working Sessions Schedule. Three time zones are listed for start times of each session. Time zone differences across our global community make it unavoidable that some sessions of interest may occur well outside of normal working hours or during the middle of the night for some.

Check back for more repeated sessions to be added soon. Each session will be recorded and made available for later viewing. Session organizers are encouraged to use shared notes to enable asynchronous participation. Links will be added to the descriptions below after the live session.

Registration is available through Eventbrite. Registration is required to participate in the online events (so we can arrange sufficient capacity), but registration is free.
Register for November Working Sessions
The services required to run an online conference, however, are not free. We encourage all participants to join TDWG as an individual member or through an institutional membership.

WS01: GeoSchemes (Terrestrial Domain)

Date/Time: November 24, 13:00 UTC ( Convert to your time zone)

Session Type: Workshop

Organizers: Francisco Pando, Real Jardín Botánico-CSIC / GBIF Spain, Madrid, Spain | Nicolas Bailly, FishBase/SeaLifeBase, Q‑quatics, Philippines, Beaty Biodiversity Museum and Sea Around Us, Vancouver, British Columbia, Canada | Visotheary Ung, Museum Nacional d'Histoire Natural, Paris

Description: This workshop will focus on reviewing the work done so far in relation to bringing up-to-date the "World Geographic Scheme for Recording Plant Distributions" in the context of the newly created "Geographic Schemes", and to gather more input and participation in the current developments. We will specifically address the terrestrial component. More information at: https://github.com/tdwg/geoschemes

WS02: Plinian Core

Date/Time: November 24, 17:30 UTC ( Convert to your time zone)

Session Type: Workshop

Organizers: Francisco Pando, Real Jardín Botánico-CSIC / GBIF Spain, Madrid, Spain

Description: With this workshop we aim to: 1. Present the recent developments and point to future directions of Plinian Core, specifically the works to make Plinian core compliant with the TDWG's SDS (Standards Documentation Standard. 2. Maintain an open discussion on different aspects of Plinian Core implementation, documentation and evolution as to gather feed-back and additional participation to tackle the Plinian Core specification maintenance and future developments. More information at: https://github.com/tdwg/PlinianCore

WS03: Hands-on session on standardization of biological interactions data

Date/Time: November 10, 14:00 UTC ( Convert to your time zone)

Session Type: Workshop

Organizers: José Augusto Salim, University of São Paulo, São Paulo, SP, Brazil | Maarten Trekels, Meise Botanic Garden, Meise, Belgium

Description: Primary data about biological interactions (e.g. predator-prey, host-parasite, pollinator-plant, frugivore-plant) are becoming increasingly available online and can be accessed from a great number of data repositories. However, the lack of a formal standard to support biological interaction data sharing and interoperability, makes data aggregation a laborious task and therefore it has hampered the development of wider, more integrative studies. Even the terminology itself is not well established. Different terms have been used to designate different or same concepts related to biological interactions. Terms like "species interaction", "biotic interaction", "ecological interaction", "interspecific interaction" and "biological interaction" are frequently used interchangeably in the literature. Despite the variety of methods to collect, record and store biological interaction data, there is a great need and interest in making these datasets comparable and interoperable, considering the importance of understanding such processes' role in sustaining biodiversity. Thus, the aim of this hands-on session is to gather people with different views and expertise on how biological interactions data can be organized and which are the most relevant elements for sharing biological interactions data - probably, there is a minimum set of attributes and properties common to all or most interaction data use cases. The hands-on session will be a practical exercise to guide participants on how to document biological interaction data using current biodiversity data standards (e.g., Darwin Core), and will promote discussions among the participants for the development of a best practice guide for documenting biological interactions. Since there are many different use cases and due to time constraints, during the session we will cover a subset of use cases proposed by the participants and all the session's activities will be focused on the selected use cases. We will evaluate the suggested use cases and select a subset based on the demand, complexity and diversity. The objective is to cover the most common and yet different - in terms of type of interaction - use cases that fits the session duration. However, we will create discussion on the TDWG Species Interaction Data Interest Group GitHub repository for all use cases (including the use cases not selected to be handled during the session). In the discussions we will encourage people to provide examples of interaction data and collaboratively elaborate a standardized version of the working datasets.

WS04: People in Biodiversity Data: Sharing the Disambiguation Work

Date/Time: November 17, 20:00 UTC ( Convert to your time zone)

Session Type: Workshop

Organizers: David P. Shorthouse, Agriculture & Agri-Food Canada, Ottawa, Ontario, Canada | Quentin Groom, Meise Botanical Garden, Meise, Belgium | Elspeth Haston, Royal Botanic Garden Edinburgh, Edinburgh, Scotland | Anne Thessen, Oregon State University, Oregon, USA

Description: The People in Biodiversity Task Group within the Joint Research Data Alliance - TDWG Attribution Interest Group has a Charter with twelve deliverables, https://www.tdwg.org/community/attribution/ all of which are nearing completion. We will provide an update of our accomplishments and will also look to the future. One of the goals of Task Group and that of the Interest Group is to democratize the activities of disambiguating people names and attributing data objects and products to reconciled identities. We will use this workshop to discuss and provide examples of how these linkages may participate in open, curatorial round trips to data providers and aggregators and to highlight the role that newly ratified Darwin Core terms, identifiedByID and recordedByID play in this emerging ecosystem.

FS01: Technical Architecture Group (TAG)

Date/Time: November 10, 17:30 UTC ( Convert to your time zone)

Session Type: Functional Subcommittee meeting

Organizers: James Macklin, Agriculture and Agri-Food Canada, Ottawa, ON, Canada | Nicky Nicolson, Royal Botanic Gardens, Kew, London, UK

Description: This session will involve an overview of the responsibilities of the Technical Architecture Group and a discussion and prioritization of relevant issues.

Repeated Session (FS01-R): November 17, 23:00 UTC ( Convert to your time zone)

FS02: TDWG Outreach and Communication

Date/Time: November 24, 14:30 UTC ( Convert to your time zone)

Session Type: Functional Subcommittee meeting

Organizers: Mareike Petersen, Museum für Naturkunde (MfN) Berlin, Berlin, Germany

Description:

ITG01: Earth Sciences and Paleobiology Interest Group

Date/Time: November 4, 17:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Holly Little, Smithsonian National Museum of Natural History, Washington, DC, USA | Mareike Petersen, Museum für Naturkunde, Berlin, Germany

Description: This meeting of the Earth Sciences and Paleobiology Interest Group will include updates from and discussions about coordinating work and information sharing across related efforts like DiSSCo, COST Mobilize, and the Paleo Data Working Group. We will also delve into assessments of terms from TDWG data standards (e.g. DwC and EFG) and how to expand their use and applicability for earth sciences and paleobiology data.

Repeated Session (ITG01-R): November 10, 23:00 UTC ( Convert to your time zone)

ITG02: Taxon Names and Concepts Interest Group (TNC)

Date/Time: November 4, 21:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Niels Klazenga, Royal Botanic Gardens Victoria/Atlas of Living Australia, Melbourne, Victoria, Australia | Johan Liljeblad, SLU Swedish Species Information Centre, Uppsala, Sweden

Description: This year a TCS 2 Task Group was established whose job is to bring the Taxon Concept Schema (TCS) into a state in which it can be maintained, which means taking it out of the XML Schema it is currently in and turning it into a vocabulary standard similar to Darwin Core and Audubon Core. While the Task Group is doing its work, the Interest Group has suspended most other activities. Therefore, while at last year's Interest Group meeting we had more general discussions about what we are looking for in a standard for dealing with taxon information, in this meeting we will do a deep dive into TCS, discussing where we have gotten to so far and which issues need further discussion. As always, we very much like input from people outside the Interest Group, partly because knowing how the wider community feels about things might help resolve differences of opinion within the Interest Group and partly because it is better for issues we have not thought of to come up now than during review. All information can be found in the TCS 2 Task Group's GitHub repository https://github.com/tdwg/tcs2/.

Repeated Session (ITG02-R): November 18, 9:00 UTC ( Convert to your time zone)

ITG03: Citizen Science Interest Group - PPSR core

Date/Time: November 18, 20:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Rob Stevenson, University of Massachusetts Boston, Boston, USA | Libby Ellwood, iDigBio: Global Communications Manager; La Brea Tar Pits, Los Angles, California, USA

Description: The citizen-science IG facilitates the development and application of standards and best practices for collection and use of biodiversity data in partnership with our TDWG colleagues, the broader citizen science community via collaboration with the Data and Metadata Working Groups of the Citizen Science Association (CSA), information scientists and a host of other interested parties, including biodiversity scientists, resource managers, indigenous groups, natural history museums, GBIF, government agencies, and NGOs. The scope of this group includes all aspects of data collection, data quality, metadata and social computing as it pertains to the biodiversity domain.

In this working session of the IG, we will present relevant updates to the above as well as discuss the following: 1) How can we harmonize TDWG standard with PPSR Core? How do we promote and use the strength of both? 2) Using the forum as a mechanism to gage others' thoughts. Summarize the state of affairs - look towards the longer term.

ITG04: Machine Observations Interest Group

Date/Time: November 3, 21:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Peggy Newman, Atlas of Living Australia, Melbourne, Victoria, Australia | Peter Desmet

Description: In this session we will tackle three discussion topics related to the work of the Machine Observations Interest Group: 1) Camtrap DP as an example of a Frictionless community standard, 2) Lessons from the standardisation framework for bio-logging data to facilitate exchange between manufacturers and repositories and 3) How to document Darwin Core transformation guides in a human- and machine-readable way? Each of the three topics will be introduced with a five minute primer and allow 25 minutes for discussion and questions. The last half hour of the session is reserved for open discussion. Anyone is welcome to join.

ITG05: Genomic Biodiversity Interest Group

Date/Time: November 24, 19:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Gabi Droege, Botanic Garden and Botanical Museum Berlin, Berlin, Germany | Katie Barker, Smithsonian National Museum of Natural History, Washington, DC, USA

Description: This session will be a regular working meeting of the Genomic Biodiversity Interest Group, focussing on its current activities: updates TG "Sustainable DarwinCore MIxS Interoperability" and the recently established TG "Environmental Samples and eDNA". For the latter we will present the roadmap for the next two years and discuss use cases and priorities. There will be also time to discuss other topics within the Genomic Biodiversity scope if needed.

ITG06: Access to Biological Collection Data (ABCD) Interest Group

Date/Time: November 18, 17:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Anton Güntsch, Botanic Garden and Botanical Museum Berlin, Berlin, Germany | David Fichtmüller, Botanic Garden and Botanical Museum Berlin, Berlin, Germany

Description: The main topic of the meeting will be the discussion of the status of ABCD 3.0 ratification.

ITG07: Darwin Core Maintenance Group

Date/Time: November 3, 12:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: John Wieczorek, VertNet, Rauthiflor LLC, Bariloche, Río Negro, Argentina

Description: Annual open meeting of the Darwin Core Maintenance Group to Introduce Darwin Core, share updates on developments, support usage, and make progress on open issues.

Repeated Session (ITG07-R): November 11, 23:00 UTC ( Convert to your time zone)

ITG08: Collection Descriptions Interest Group

Date/Time: November 18, 14:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Matt Woodburn, Natural History Museum, London, UK | Sharon Grant, Field Museum of Natural History, Chicago, IL, USA

Description: The TDWG Collection Descriptions (CD) Task Group has developed a data standard for describing collections The standard is currently approaching formal review for ratification, and this session will cover:

What it is & Why it is: Background and intro; Problem description; Scope; Nuts and bolts

How you use it: Use cases and example-models; General recipes and how-to's; Showing the process

Now use it!: Tutorial; Create a wikibase record for your institution/collection

ITG09: MIDS Task Group

Date/Time: November 4, 13:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Elspeth Haston, RGBE, Edinburgh, UK | Alex Hardisty, Cardiff, UK

Description: The MIDS Task Group has developed a draft specification. The group is now focused on finalising the specification for MIDS Level 1 (Basic record). A conformance statement template has been developed and is being tested. The meeting will include feedback from test implementations and guidance for more institutions to test implementation.

ITG10: How did it die? Task Group

Date/Time: November 17, 17:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Sophia Ratcliffe, NBN Trust, Nottingham, UK

Description: We will report on the progress that we have made. A proposal has been submitted for the addition of a new Darwin Core term called vitality to indicate whether the organism was alive or dead at the time of collection or observation: https://github.com/tdwg/dwc/issues/363. The proposal is ready for public comment, and we would appreciate any comments and support for the term.

We are preparing a proposal for a new extension for information relating to the cause(s) of death. We would welcome feedback on the proposal and any use cases of datasets with cause of death details that we could map to our proposed controlled vocabulary.

Our Github repository is here: https://github.com/tdwg/how-did-it-die.

ITG11: Views Controlled Vocabularies Task Group

Date/Time: November 11, 21:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Steve Baskauf, Vanderbilt University Heard Libraries, Nashville, TN, USA

Description: This task group is chartered by the Audubon Core Maintenance Group and is tasked with developing controlled vocabularies for the ac:subjectPart and ac:subjectOrientation terms. It has neared completion of its work, and at this session we will describe the proposed controlled vocabularies and how they may be used.

ITG12: Audubon Core Maintenance Group

Date/Time: November 10, 21:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Steve Baskauf, Vanderbilt University Heard Libraries, Nashville, TN, USA

Description: This group maintains the Audubon Core vocabulary. Over the past year, we completed sound-related term proposals, worked extensively on a proposal for defining regions of interest (ROIs), and worked on example guides for still images and sound. At this session, we'll review these accomplishments and answer questions about them, as well as solicit suggestions for activities during the coming year.

ITG13: Observation and Specimen Records, what's new

Date/Time: November 17, 15:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Quentin Groom, Meise Botanical Garden, Meise, Belgium

Description: The observation and specimens are at the heart of what interests many in the Biodiversity Informatics community. At this meeting we aim to give you an overview of what is happening within the related task groups, what progress they are making, and how you might get involved. The current task groups of the Interest Group are Humboldt Core, Material Sample and "How did it die?". Some of which will hold their own, more specific, meetings. However, there is lots going on and this meeting will aim to show the links and overlaps between the task groups.

ITG14: Humboldt Extension to Darwin Core Task Group

Date/Time: November 17, 13:00 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Yanina Sica, Yale University, Hamden, CT, USA | Paula Zermoglio , VertNet, Bariloche, Argentina

Description: In 2021 the TDWG Humboldt Core Task Group was established to review how to best integrate the terms proposed in the Humboldt Core original publication (Guralnick et al. 2018) with existing standards and implementation schemas. Since the different types of inventories can be thought of as Events with different nesting levels (events within events, e.g., plots within sites), and after an initial mapping to existing Darwin Core terms, it was deemed appropriate to start from a Darwin Core Event Core and build an extension to include Humboldt Core terms. The task group members currently meet every other Wednesday to revise all original Humboldt Core terms, reformulating definitions, comments, and examples, and discarding or adding new terms where needed. We are also gathering real datasets to test the use of the extension once an initial list of revised terms is ready, before undergoing a public review period as established by the TDWG process. At this session we will explain the process we followed, present the preliminary list of terms that would compose the extension, and invite all of you to join in an open discussion.

ITG15: Biodiversity Services & Clients IG meeting

Date/Time: November 11, 17:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Falko Glöckler, Museum für Naturkunde (MfN) Berlin, Berlin, Germany; Anton Güntsch, Botanic Garden and Botanical Museum Berlin, Berlin, Germany

Description: Based on the three sessions coping with APIs during the TDWG conference 2021 we would like to discuss the outcome and identify requirements for the community of both users and developers of API endpoints, their accessibility and documentation. We will discuss the future of the Biodiversity Catalogue (https://www.biodiversitycatalogue.org/) and other opportunities to register web services in the domain of biodiversity, ecology and earth sciences. Outcome of the meeting can be a work plan for the upcoming months, ideas for new or additional Task Groups that cope with feasible pilots to support the community regarding web services and APIs.

ITG16: Plant Phenology Standards Task Group

Date/Time: November 3, 17:30 UTC ( Convert to your time zone)

Session Type: Interest/Task Group meeting

Organizers: Jenn Yost, California Polytechnic State University, San Luis Obispo, CA, USA | Katie Pearson, California Polytechnic State University, San Luis Obispo, CA, USA

Description: In this session, we will discuss a standard vocabulary for a phenology DwC extension using vascular plants as our primary case study.

By participating in TDWG 2021, you agree to abide by our Code of Conduct.

Please send questions to WrkSessCoord@tdwg.org

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TDWG will host working sessions of the Interest and Task Groups (IG/TG) each Wednesday and Thursday in November. These sessions will be hosted in Zoom by TDWG and be free but require registration. Working sessions will include introductions, overviews of IG/TG use cases, solutions, collaboration resources, and current work practices.

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iDigTRIO Biology Career Conference and Fair

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iDigTRIO Biology Career Conference and Fair

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We are excited to announce that the iDigTRIO Biology Career Conference and Fair will be taking place throughout the week of February 21st, 2022. The iDigTRIO conference is aimed at collegiate and pre-collegiate students associated with TRIO programs around the United States. The mission of the iDigTRIO Conference is to provide opportunities to explore careers and graduate programs in the biological sciences to first generation, limited income, and underrepresented students (Black, Latinx, Indigenous, other students of color, and students with disabilities). Through intentional programming and networking, we seek to create future leaders in science and build a sense of community for their academic, personal, and professional journeys.

iDigTRIO 2022 will be a hybrid experience. There will be in-person career shadowing and mentoring February 21-23, as well as an in-person resource fair and reception on Wednesday February 23rd followed by a virtual 3-day conference February 24-26, 2022.  Virtual mentoring opportunities will be made available after the conference. This event is completely free, but we do not have travel or lodging support currently.

IMPORTANT: Register by November 1 st to receive the in-person shadowing/mentoring sign-up form!

Important Links

Conference Website

In-person experiences registration

Virtual conference registration

If you have any questions please reach out to Molly Phillips mphillips@flmnh.ufl.edu

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2022 Conference Details

We are excited to announce that the iDigTRIO Biology Career Conference and Fair will be taking place throughout the week of February 21st, 2022. The iDigTRIO conference is aimed at collegiate and pre-collegiate students associated with TRIO programs around the United States. The mission of the iDigTRIO Conference is to provide opportunities to explore careers and graduate programs in the biological sciences to first generation, limited income, and underrepresented students (Black, Latinx, Indigenous, other students of color, and students with disabilities). Through intentional programming and networking, we seek to create future leaders in science and build a sense of community for their academic, personal, and professional journeys.

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iDigTRIO 2022 will be a hybrid experience. As of now we are planning for in-person career shadowing and mentoring February 21-23, as well as an in-person resource fair and reception on Wednesday February 23rd followed by a virtual 3-day conference February 24-26, 2022. Virtual mentoring opportunities will be made available after the conference. This event is completely free, but we do not have travel or lodging support currently.

In-person experiences registration (R egister by November, 1st to receive the mentoring and shadowing opportunities form!)

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Virtual conference registration

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Download a preliminary agenda to get a sneak peak into the 2022 program.

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Check out our 2022 FAQ page for more info or contact us with any questions.

Read some testimonials about previous conferences.

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2022 Conference Details We are excited to announce that the iDigTRIO Biology Career Conference and Fair will be taking place throughout the week of February 21st, 2022. The iDigTRIO conference is aimed at collegiate and pre-collegiate students associated with TRIO programs around the United States.

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Voyages of scientific exploration 1770 - 1899
This map displays historical maritime voyages which collected biodiversity specimens. These specimens are now deposited in institutions around the world, this map shows where and when they were collected.

https://github.com/benscott/voyager

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[27 Oct 2021] An online workshop series on how the international scientific community can study biodiversity in the changing landscape of international policy.  Registration is now open for the session on “Applied Ecology and Infectious Disease.” Learn more at io.aibs.org/Nagoya

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How does sharing genetic sequence data impact biodiversity science and conservation?

Topic: Applied Ecology and Infectious Disease

Date: October 27, 2021

Time: 1:00 - 3:00 PM EST or 5:00 - 7:00 PM UTC (this program will be recorded)

Location: Online via Zoom

Digital Sequence Information (DSI), such as genetic sequence data, play a critical role in understanding and addressing infectious diseases. DSI about pathogens provides key information, such as mutation rate, provenance, and migration trajectories, which can all enable disease management in a variety of systems. Furthermore, exemplary research on zika, lyme disease, malaria, and SARS viruses have relied on international partnerships. Join us for this workshop to recognize and document the value of DSI for infectious disease research, and learn together how to navigate the Nagoya Protocol and national policies to conduct upstanding biodiversity science to benefit the planet.
Speakers:

Jocelyn Colella, University of Kansas Biodiversity Institute, USA

Marcelo Weksler, Museu Nacional, Universidade Federal do Rio de Janeiro, Brazil

Gerardo Suzan, Universidad Nacional Autonoma de Mexico, Mexico

Luciana Silvestri, Instituto de Ciencias Humanas, Sociales y Ambientales (INCIHUSA), Argentina.

Hosted by:

American Society of Mammalogists

Natural Science Collections Alliance

Society for the Preservation of Natural History Collections

This workshop series is supported by the National Science Foundation under Award No. 2136950. Learn more at io.aibs.org/Nagoya

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International Workshop Series: How does sharing genetic sequence data impact biodiversity science and conservation?

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This workshop on 'applied ecology and infectious disease' is part of the international series being organized by the American Institute of Biological Sciences and the USA Nagoya Protocol Action Group to explore how the international scientific community can study biodiversity in the changing landscape of international policy.

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The UNESCO Information For All Programme (IFAP) Working Group on Information Accessibility (WGIA), is hosting it's second online one-day conference on 28 September 2021. The theme of the conference is Inclusive AI with topics addressing information accessibility.

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Annually, 28 September
In observance of the International Day for Universal Access to Information (IDUAI)

Artificial Intelligence
for Information Accessibility

The UNESCO Information For All Programme (IFAP) Working Group on Information Accessibility (WGIA), is hosting it's second online one-day conference on 28 September 2021. This event will be hosted in collaboration with the Kule Institute for Advanced Studies (KIAS) and AI for Society (AI4S), both at University of Alberta, Canada, the International Centre for Information Ethics (ICIE), Future Africa at the University of Pretoria, South Africa, the Centre for New Economic Diplomacy (CNED) in ORF, India and the Broadcasting Commission of Jamaica. It is being organised under the auspices of the UNESCO Cluster Office for the Caribbean, Kingston, Jamaica and the UNESCO Regional Office for Southern Africa, Harare, Zimbabwe.

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The theme of the conference is Inclusive AI with topics addressing information accessibility. The conference is being organised to commemorate the International Day for Universal Access to Information (IDUAI) 2021 under the global theme, "What We Know about Our Right to Know" and in the context of the Covid 19 pandemic, "The Right to Know- Building Back Better with Access to Information."

International Day for Universal Access to Information focussed on the right to information in times of crisis and on the advantages of having constitutional, statutory and/or policy guarantees for public access to information to save lives, build trust and help the formulation of sustainable policies through and beyond the COVID-19 crisis.

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The aim of this specific event is on promoting, but also understanding the barriers to, inclusive artificial intelligence. AI can be very beneficial to society but if abused it can also be very harmful. The theme therefore raises a range of issues, including the relationship between Artificial Intelligence (AI) and Law, AI and Ethics, media and our right to know, creativity and innovation. It is necessary to understand how AI can be made inclusive, thereby enabling the widest cross-section of society.

This event provides a platform for open discourse involving participants from academia, civil society, private sector and government.

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For any questions, please contact the Organising Committee at: ai4iaconference@gmail.com

The AI4IA conference is an on-demand conference,

with live interactive sessions with the speakers on 28 September 2021.

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On-demand viewing of the conference line-up will be available

from 00:00 GMT (+0) from 26 September until 28 September 2021.

A live program will run on 28 September 2021.

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Please click on the image below, or here to view the on-demand program

Please click on the image below, or here to view the live program

(Please note programs are subject to change)

The conference is hosted on Gather.town. If you have never used Gather.town before, please review the user guide here. We look forward to seeing everyone!

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Annually, 28 September In observance of the International Day for Universal Access to Information (IDUAI) Artificial Intelligence for Information Accessibility The UNESCO Information For All Programme (IFAP) Working Group on Information Accessibility (WGIA), is hosting it's second online one-day conference on 28 September 2021.

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GBIF Integrated Publishing Toolkit (IPT) User Manual 

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GBIF Integrated Publishing Toolkit (IPT) User Manual 

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The GBIF Integrated Publishing Toolkit (IPT) is a freely available open source web application that makes it easy to share biodiversity-related information.

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The GBIF Integrated Publishing Toolkit (IPT) is a freely available open source web application that makes it easy to share four types of biodiversity-related information:

An IPT instance as well as the data and metadata registered through the IPT are connected to the GBIF Registry, are indexed for consultation via the GBIF network and portal, and are made accessible for public use. The IPT can also be configured with a DataCite account in order to assign DOIs to datasets transforming it into a data repository. More information about the GBIF IPT can be found at the IPT website.

Founded and funded by governments in 2001, The Global Biodiversity Information Facility (GBIF) is the world's largest multi-lateral initiative for enabling free access to biodiversity data via the Internet. GBIF's diverse Participants include primarily countries and international organizations. GBIF also has formal partnerships with relevant international treaty bodies. GBIF's mission is to promote and enable free and open access to biodiversity data worldwide via the Internet to underpin science, conservation and sustainable development. More information about GBIF can be found at GBIF.org.

Several factors have provided motivation for GBIF to lead the development of the IPT:

limitations of previous publishing tools (DiGIR, TAPIR, BioCASe) to easily publish and transfer large datasets;

the need to reduce the load on both the publisher's server, and GBIF's server during indexing. The reason being that indexing from DiGIR, TAPIR, or BioCASe caused heavy loads due to repeated HTTP request-response interactions.

the need to speed up the process of indexing biodiversity occurrence datasets;

the need to offer additional benefits and services to the data publishers to encourage data publication;

the lack of appropriate tools to publish certain types of biodiversity data, such as names checklists and data set metadata.

To understand how the IPT works, try watching this concise 25 minute live demo showing how a dataset gets properly published and registered through GBIF.org.

How to publish biodiversity data through GBIF.org using the IPT

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GBIF Integrated Publishing Toolkit (IPT) User Manual

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The GBIF Integrated Publishing Toolkit (IPT) is a freely available open source web application that makes it easy to share four types of biodiversity-related information: An IPT instance as well as the data and metadata registered through the IPT are connected to the GBIF Registry, are indexed for consultation via the GBIF network and portal, and are made accessible for public use.

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[4-6 October 2021] Three days of actionable tips and strategy advice on the digital side of collections

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If you're struggling to keep up with the ever changing nature of the digital side of collections, this event is for you.
Over three days you'll hear about the latest digital platforms, strategies and tools, and get the guidance you need to move forward with confidence.

Imagine having the knowledge you need to do your best work, learning from museums around the world and hearing first hand how they are approaching digital collections.

From digitisation to collection management, contemporary collecting to taking collections beyond our walls on mobile, what's next for digital museum collections.

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If you're struggling to keep up with the ever changing nature of the digital side of collections, this event is for you. Over three days you'll hear about the latest digital platforms, strategies and tools, and get the guidance you need to move forward with confidence.

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Webinar Announcement:Funding Opportunities for Scientific Collections at NSF

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Webinar Announcement:Funding Opportunities for Scientific Collections at NSF

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[4 October 2021] Join the Natural Science Collections Alliance (NSC Alliance), the American Institute of Biological Sciences (AIBS), the Biodiversity Collections Network (BCoN), the Society for the Preservation of Natural History Collections (SPNHC), and the United States Culture Collection Network (USCCN) for a webinar with program directors from the National Science Foundation (NSF) about structural changes to collections-related funding opportunities at the agency.

Date: October 4, 2021
Time: 2:00-3:00 PM EST (this program will be recorded)
Location: Online via Zoom

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Join us for a webinar with program directors from the National Science Foundation (NSF) about structural changes to collections-related funding opportunities at the agency.

Date: October 4, 2021
Time: 2:00-3:00 PM EST (this program will be recorded)
Location: Online via Zoom

Event Partners:
Natural Science Collections Alliance
American Institute of Biological Sciences
Biodiversity Collections Network
Society for the Preservation of Natural History Collections
United States Culture Collection Network

Moderator:
Jyotsna Pandey, AIBS, NSC Alliance, and BCoN

Speakers from NSF:
Reed Beaman, Program Director, Division of Biological Infrastructure
Peter McCartney, Program Director, Division of Biological Infrastructure
Roland Roberts, Program Director, Division of Biological Infrastructure

Discussion:
The National Science Foundation plays an important role in protecting, supporting, and expanding access to our nation's scientific collections, which include preserved specimens in natural history museums and herbaria; fossilized specimens; living collections in zoos, aquaria, and botanical gardens; tissues, DNA and stock collections; and media collections. NSF supports research that uses existing collections as well as studies that gather new specimens. Importantly, it also provides critical support for national biological research infrastructure that houses biodiversity collections as well as digitization initiatives through the Advancing Digitization of Biodiversity Collections (ADBC), Thematic Collections Networks (TCN), and Partner to Existing Networks (PEN) programs.

The collections community and stakeholders, including collection and data managers, curators, Sponsored Research Officers (SROs), and other administrators are invited to join us for a virtual learning session that will explore recent structural changes to NSF programs that support collections and how these might impact the community. There will also be a discussion of new opportunities that currently exist or may be available in the near future. The program will start with a presentation from NSF program directors responsible for collections-related programs at the agency followed by an opportunity for the community to ask questions.

Webinar: Funding Opportunities for Scientific Collections at NSF

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Webinar: Funding Opportunities for Scientific Collections at NSF

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Join us for a webinar with program directors from the National Science Foundation (NSF) about structural changes to collections-related funding opportunities at the agency.

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The CARE Principles for Indigenous Data Governance

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The CARE Principles for Indigenous Data Governance

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Introduction

As the world engages with open data, big data, data reuse, and open science, data have increasingly become a global resource used for wielding power, making decisions, spurring innovation and discovery, and commercialization (Independent Expert Advisory Group 2014, The Economist 2017). The effects of this shift are amplified by the adoption of artificial intelligence technologies and the increasing convergence of biological and digital worlds facilitated through data sharing platforms. Historically plagued by data inequities and data exploitation, Indigenous Peoples have raised concerns about the need to integrate Indigenous knowledges and approaches into data practices and policies as both the volume and opportunities for secondary use of data increase (Jackson et al. 2019; Kukutai & Cormack 2019; Carroll et al. 2019; Garrison et al. 2019; Rainie et al. 2019; Kukutai & Taylor 2016b). The articulation of Indigenous Peoples' rights and interests in data about their peoples, communities, cultures, and territories is part of reclaiming control of data, data ecosystems, data science, and data narratives in the context of open data and open science. Control, coupled with a focus on collective benefit and equity, repositions Indigenous Peoples, nations, and communities from being subjects of data that perpetuate unequal power distributions to self-determining users of data for development and wellbeing. Harnessing data for governance and acting in the governance of data, shifts Indigenous Peoples from invisibility within data ecosystems to vibrant contributors to data policies, practices, ethics, and innovation.

Leveraging Indigenous Rights for Indigenous Data Futures

Background

Global Indigenous presence spans over 90 countries and includes over 370 million Indigenous persons living out more than 5,000 distinct cultures (UN 2009). Indigenous Peoples have continuity with their pre-colonial societies and seek to use their own social, political, and economic systems to preserve, develop, and transmit to future generations their cultures, knowledges, and relationships with their territories and resources (Martinez Cobo 1982). While often limited by settler-colonial oppression and definition, Indigenous Peoples retain their rights to self-determine their forms of collective governance and rules of membership.

In the contemporary world, Indigenous Peoples and nations, people, and communities have become actors across global societies. Indigenous Peoples and their nations are the rightsholders as affirmed by the United Nations and nation-state legal recognition of Indigenous sovereignty (Echo-Hawk 2013; UN 2007; Anaya 2004). Indigenous Peoples as sovereign political collectives often participate in supra- and inter-tribal organizations such as the Oceti Sakowin Oyate and the Iwi Chairs' Forum, and have local or regional sub-systems such as Tohono O'Odham districts in the US and the Federation of Victorian Traditional Owner Corporations in Australia. Indigenous people often live as citizens of nations (Indigenous) within nations (the nation state, e.g., the United States). Indigenous people also participate in other Indigenous stakeholder groups that do not hold inherent sovereignty, including daily life within Indigenous communities (groups of individuals) within their nations or mainstream society and interaction with Indigenous organizations such as health care entities, non-profits, and businesses.

Ongoing processes of colonization of Indigenous Peoples and globalization of Western ideas, values, and lifestyles have resulted in epistemicide, the suppression and co-optation of Indigenous knowledges and data systems (Carroll, Rodriguez-Lonebear & Martinez 2019; Kukutai & Taylor 2016a; Kukutai & Walter 2015; Smith 2012). These processes have limited the ability of Indigenous Peoples to recover, develop, and sustain their knowledges, an ability that is central to Indigenous Peoples' capacity to realize their human rights and fulfil their responsibilities (Wilson, 2004; Ratima 2001).

Alongside Indigenous aspirations for self-determination, social justice movements have promoted the importance of equity within society and become more deliberate about addressing the negative effects of unconscious bias and structural racism. The pervasiveness of bias across data ecosystems (digital infrastructures, analytics, and applications), within sub-systems (regions/platforms), as well as institutions and communities indicate the importance of addressing inequities at multiple levels. Indigenous people recognize and already incorporate this multi-scale complexity in their knowledge systems and activities; however, this may not be the case for other communities of practice, or knowledge systems.

The Emergence of Indigenous Data Sovereignty

Since the 1970s, there has been a resurgence in discourse around Indigenous knowledges, identities, and rights, culminating in the 2007 United Nations Declaration on the Rights of Indigenous Peoples [UNDRIP] (UN 2007). UNDRIP reaffirms Indigenous Peoples' rights to self-determination as political entities and honors the principle of Indigenous control over Indigenous data (Tsosie 2019; Davis 2016). The rights articulated in UNDRIP (especially Article 31) also reflect discourse around Indigenous Cultural and Intellectual Property Rights (ICIP) and Indigenous research ethics (Mātaatua Declaration 1993; Julayinbul Statement 1993; Janke 1998, 2004; Drugge 2016; Tsosie 1997, 2019; Anderson 2009, 2015). UNDRIP reflects a broad approach to Indigenous data that is not restricted by mainstream conceptions of knowledge and intellectual property (Posey & Dutfield, 1996).

Indigenous Peoples' data include data generated by Indigenous Peoples, as well as by governments and other institutions, on and about Indigenous Peoples and territories. As well as information about Indigenous communities and the individuals, Indigenous and non-Indigenous, that live within (Carroll, Rodriguez-Lonebear & Martinez 2019; Rainie et al. 2019; MnW 2018; Nickerson 2017; TMR 2016). Indigenous Peoples' data comprise (1) information and knowledge about the environment, lands, skies, resources, and non-humans with which they have relations; (2) information about Indigenous persons such as administrative, census, health, social, commercial, and corporate and, (3) information and knowledge about Indigenous Peoples as collectives, including traditional and cultural information, oral histories, ancestral and clan knowledge, cultural sites, and stories, belongings.

The advancement of Indigenous self-determination and the reclamation of Indigenous identity and knowledges over the past 50 years has led to the emergence of Indigenous Data Sovereignty, an assertion of the rights and interests of Indigenous Peoples in relation to data about them, their territories, and their ways of life (Carroll, Rodriguez-Lonebear & Martinez 2019; Rainie et al. 2017; Kukutai & Taylor 2016a; Snipp 2016). Indigenous Data Sovereignty has become an increasingly relevant topic as big data, open data, open science, and data reuse become an integral part of research and institutional practices (Carroll, Rodriguez-Lonebear & Martinez 2019; Lovett et al. 2019; Rainie et al. 2019; Kukutai & Taylor 2016a; Taylor & Kukutai 2015). Given that most Indigenous data are held by non-Indigenous governments, institutions, and agencies, increasing Indigenous Peoples' participation in data governance activities is central to realizing Indigenous Data Sovereignty (Carroll, Rodriguez-Lonebear & Martinez 2019; Rainie et al. 2019; Kukutai & Taylor 2016b; Rodriguez-Lonebear 2016; Smith 2016; Snipp 2016). Indigenous data governance includes both the stewardship and the processes necessary to implement Indigenous control over Indigenous data (collection, storage, analysis, use, reuse) (Carroll, Rodriguez-Lonebear & Martinez 2019; Rainie et al. 2019; Walter 2018; Rodriguez-Lonebear 2016; Smith 2016). The development of conceptual frameworks to inform processes for stewardship and control of Indigenous data has been ongoing in areas such as science, research methods, and research governance (Cajete, 2000; Wilson, 2008; Kovach, 2009; McGregor, Restoule & Johnston, 2018). There is a need to apply insights generated from these frameworks within data ecosystems to help address the challenge of producing data that reflect Indigenous Peoples' interests and governance needs.

The Care Principles

Rationale

Mainstream values related to research and data are often inconsistent with Indigenous cultures and collective rights. For instance, data historically belonged to the researcher in mainstream settings, a notion that has shifted under open data and open science movements where publicly funded research requires data sharing. Meanwhile, Indigenous worldviews have typically centered 'people' and 'purpose' through governance processes that emphasize collective ownership and control of data (Carroll, Rodriguez-Lonebear & Martinez 2019; Kukutai & Taylor 2016b; Smith 2016; Snipp 2016). Indigenous Peoples have been working on ways to authenticate Indigenous forms of knowledge and to fortify their rights to govern research and resulting data (Garrison et al. 2019; Hudson et al. 2016; Smith 2012).

Drawing on the First Nations principles of OCAP® that created data standards for Ownership, Control, Access, and Possession in Canada in the 1990s (FNIGC 2019), Indigenous Data Sovereignty networks in Aotearoa New Zealand, Australia, and the United States, as well as Indigenous scholars, leaders, and allies, determined that there was an urgent need to develop global principles for the governance of Indigenous data. Underlying this need were the primary goals of (1) fostering Indigenous self-determination by enhancing Indigenous use of data for Indigenous pursuits and (2) honoring the 'FAIR Guiding Principles for scientific data management and stewardship' (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016), while ensuring data sharing on Indigenous terms.

The 'CARE Principles for Indigenous Data Governance' (Collective Benefit, Authority to Control, Responsibility, and Ethics) (RDA IG 2019) empower Indigenous Peoples by shifting the focus from regulated consultation to value-based relationships that position data approaches within Indigenous cultures and knowledge systems to the benefit of Indigenous Peoples (Castellano 2004; Anderson et al. 2003). This shift ultimately promotes equitable participation in processes of data reuse, which will result in more equitable outcomes.

Development

Academics and practitioners in the International Indigenous Data Sovereignty Interest Group within the Research Data Alliance (RDA 2017) acknowledged the tension between protecting Indigenous rights and interests in data and supporting open data. With this challenge in mind, a workshop was held in Gaborone, Botswana, on Indigenous Data Sovereignty as part of International Data Week in 2018. This Indigenous-led workshop convened Indigenous and allied academics and practitioners to draft principles for Indigenous data governance. Workshop organizers prepared an analysis of principles from frameworks issued by Indigenous Data Sovereignty and governance networks, as well as by mainstream sources, to inform dialogue (Table ).

Participants concluded that existing frameworks were oriented toward data, people, and purpose (see Figure ). While both Indigenous and mainstream principles identified data-centric principles (such as those named in the FAIR Principles), the Indigenous frameworks emphasized people- and purpose-oriented principles.

Workshop participants felt that the mainstream frameworks adequately covered data-centric principles and focused their attention on the 'people' and 'purpose' foci of the Indigenous principles to create the CARE Principles. Once the four primary CARE Principles of Collective benefit, Authority to control, Responsibility, and Ethics were identified, participants broke into small groups to outline and draft the language for both primary and sub-principles. The resulting draft was shared among nation-state based network members, Indigenous leaders, Indigenous scholars, and allies for input (edits, revisions, comments, feedback) via electronic media, presentations, and engagements.

The CARE Principles are designed to complement the FAIR Principles and guide the inclusion of Indigenous Peoples in data processes that strengthen Indigenous control for improved discovery, access, use, reuse, and attribution in contemporary data landscapes.

Exposition

The CARE Principles address important considerations for modern data ecosystems and across data lifecycles that support both innovation and Indigenous self-determination (Figure ). The Principles describe high-level actions applicable within research, government, and institutional data settings. The goal is for data stewards and other users of Indigenous data to implement CARE and FAIR Principles in tandem.

While each is conceptually distinct, the CARE Principles are related to each other. The Principles define rights, interests, and concepts to be employed in facilitating Indigenous control in data governance and reuse. An enacted mechanism can address a single Principle or multiple simultaneously. However, data governance must address the full set of Principles to demonstrate a CARE Full process.

The CARE Principles and Their Supporting Concepts

Indigenous data must facilitate collective benefit for Indigenous Peoples to achieve inclusive development and innovation, improve governance and citizen engagement, and realize equitable outcomes. Benefits accrue when data ecosystems are designed and function to support (1) Indigenous nation and community use and reuse of data; (2) use of data for policy decisions and evaluation of services; and (3) creation and use of data that reflect community values.

UNDRIP affirms Indigenous Peoples' rights and interests in their data. Recognition of these rights bolsters Indigenous Peoples' authority to control and govern such data, further affirming the need for 'data for governance.' Indigenous Peoples must have access to data that support Indigenous governance and self-determination. Indigenous Peoples must be the ones to determine data governance protocols, while being actively involved in stewardship decisions for Indigenous data that are held by other entities.

When working with Indigenous data, there is a responsibility to nurture respectful relationships with Indigenous Peoples from whom the data originate. Aspects of the relationship include investing in capacity development, increasing community data capabilities, and embedding data within Indigenous languages and cultures. Pursuing these goals fulfills the ultimate responsibility of supporting Indigenous data that advances Indigenous Peoples' self-determination and collective benefit.

Indigenous Peoples' rights and wellbeing should be the focus across data ecosystems and throughout data lifecycles in order to minimize harm, maximize benefits, promote justice, and allow for future use. Paramount to ethics in data practices is representation and participation of Indigenous Peoples, who must be the ones to assess benefits, harms, and potential future uses based on community values and ethics.

The CARE Principles in Action

The usefulness of high-level Indigenous data governance principles relies on both Indigenous communities and research/data communities to understand operative concepts and to apply them preemptively across data ecosystems and lifecycles. Creating opportunities for non-Indigenous understanding and for Indigenous leadership in data processes will contribute to the development of data and data systems that can lead to Indigenous innovation and development.

Early Adopters of the CARE Principles

Defining principles is an important step toward exercising Indigenous Data Sovereignty and addressing longstanding concerns about data governance. Some organizations are adopting the CARE Principles through relationships, policies, and mechanisms. Examples include the Research Data Alliance, the Smithsonian Institution, and the Open Data Charter.

Research Data Alliance (www.rd-alliance.org), in research environments

In 2016, the Research Data Alliance (RDA) hosted a workshop on Indigenous Data Sovereignty. Recognizing the importance of enhancing Indigenous rights and interests in data and potential implications for mainstream data policy, the RDA supported the creation of the International Indigenous Data Sovereignty Interest Group (IG). The IG convenes existing Indigenous Data Sovereignty networks with other Indigenous and allied researchers, which has resulted in broader global connections, a deeper focus on research data, and the CARE Principles (Lovett et al. 2019; Rainie et al. 2019; RDA IG 2019; RDA 2017). In further support of the IG and the CARE Principles, the RDA formally affiliated with the Global Indigenous Data Alliance (GIDA), which hosts the Principles. The affiliation affirms the importance of both CARE and GIDA, and represents an effort to promote implementation of the Principles throughout the RDA community (RDA 2019).

The Smithsonian Institution (www.si.edu), in collections environments

In 2012, the American Museum of Natural History at the Smithsonian Institution in Washington, DC released an Access and Benefit Sharing Policy on Genetic Resources in response to the Nagoya Protocol (2010) addressing Indigenous expectations around benefit sharing, prior informed consent, and mutually agreed terms for collections that it stewards (UN 2011; SI 2012). Subsequently, the US government released the new Open Access to Research Policy in 2013 as an important step in providing unprecedented access to research and data (Stebbins 2013). However, this policy (and others like it) treats all data in the same way: without attention to the nature, the content, or the conditions under which the data was created in the first place (Creative Commons 2019, Wikimedia 2019).

In December 2019, Smithsonian Directive 609 was released (SI 2019). With specific focus on digital asset access and use, it sets forth Smithsonian policy for access and use of Smithsonian digital assets ahead of the expected release of the institutional Open Access Statement, in February 2020. The SD 609 is important, because it helps identify specific categories of material that might need more attention and care in their management. One of these includes sensitive content, which is:

defined in different ways by members of individual communities, nations, tribes, ethnic groups, and religious denominations, but may include materials that relate to traditional knowledge and practices. Such materials may: a) be considered the private domain of specific individuals, clans, cults or societies; b) require an appropriate level of knowledge to view and understand; c) threaten the privacy and well-being of a community when exposed or disclosed to outsiders; and/or d) give offense if inappropriately used or displayed, or when appropriated or exploited for commercial purposes. SD 609 Restriction Code: B1. (SI 2019)

This identification offers an important opportunity to further explore the incorporation of the CARE Principles into the Smithsonian's imminent Open Access policy. Recognizing complexity within Smithsonian digital assets and accompanying metadata, the CARE Principles would add an important layer of relational and temporal governance for Indigenous collections within an open access logic. Additionally, there are further opportunities for the CARE Principles to be directly extended into collection management practices. For example, work is about to begin to develop a unique set of 'Collection CARE Notices' for Indigenous collections at the Smithsonian and then into other US institutional contexts. Extending the current Cultural Institution Notices developed by Local Contexts (LC 2018), these new Notices will reflect inherent relationships of care, responsibility and governance in collection stewardship within the institution. As they are developed, these Notices will integrate the CARE Principles directly, embedding these into the Smithsonian's cataloguing and digital infrastructure for Indigenous collections. By providing item- and collection-specific information, the Collection CARE Notices will function as a direct mechanism to assist in management and decision-making consistent with CARE Principles.

Open Data Charter (opendatacharter.net), in government environments

After release of the CARE Principles, the Open Data Charter (ODC) Implementation Working Group hosted a conversation on the Principles (Stone and Calderon 2019). Discussion focused on implementation mechanisms as well as resonance among ODC, FAIR, and CARE Principles (Figure ). While ODC Principles reflect all four FAIR Principles, they directly address only the CARE Principle of 'Collective benefit.' The ODC Principle of 'Open by default' does not directly map to the FAIR or CARE Principles. During a review of ODC Principles in 2018, open data community members hotly debated 'Open by default,' showing a broad range of interpretations ranging from construing 'Open by default' as a key tenet of open data to considering the principle as tough to define and operationalize (Stone and Calderon 2019). The CARE Principles can help refine and specify what 'Open by default' means by (1) ensuring roles for Indigenous Peoples and other marginalized communities in open data decision-making and (2) using mechanisms to protect access to data as indicated by these communities to minimize harms and maximize benefits.

While implementation has commenced in these examples and elsewhere, further research is needed (1) to identify mechanisms that support implementation of the CARE Principles; (2) to create tools, policies, and practices that enact the Principles; (3) to explore the application of the Principles in different contexts, such as research repositories, knowledge co-production, institutional policies and practices, and, retrospectively, in already existing databases and systems; and (4) to operationalize the Principles in tandem with the FAIR Principles.

Conclusion

Being CARE Full is a prerequisite for equitable data and data practices. The CARE Principles draw from, integrate, and build on the work of mainstream stakeholders focused on data for reuse (e.g., FAIR Principles) and the efforts of Indigenous-led networks and coalitions focused on Indigenous data governance and research control. While centering data in the FAIR Principles complements other efforts to inform responsibilities for data producers and repositories, the CARE Principles extend that work to actions that align with the 'people' and 'purpose' for which data exist and are used. As the mainstream community of data stakeholders advances standards and practices to facilitate data reuse and research reproducibility, the CARE Principles enhance that work by reversing historical power imbalances. The CARE Principles address historical inequities by creating value from Indigenous data in ways that are grounded in Indigenous worldviews and by realizing opportunities for Indigenous Peoples within the knowledge economy.

In addition, increasingly robust management and stewardship frameworks for Indigenous digital resources to the benefit of Indigenous Peoples have broader implications for other marginalized populations, highlighting the importance of local and group control for the quality and reproducibility of research and data (Rainie et al. 2019). Furthermore, the CARE Principles address issues of relevance for many populations (e.g., privacy, future use, reuse, stewardship), to the extent that the Principles provide a normative foundation for setting standards, crafting policy, and developing research agendas in relation to data acquired about populations as collectives. Those populations might be minority groups, distinctive communities, or other collectives wanting to maintain high levels of trust and accountability in the use of data about their communities.

The implementation of the CARE Principles in tandem with the FAIR Principles will result in data that reflect the realities of Indigenous Peoples, be useful for Indigenous purposes, and remain under Indigenous control, while promoting knowledge discovery and innovation. The CARE Principles are a guide for data producers, stewards, and publishers to affirm Indigenous rights to self-determination through CARE Full data practices that will ultimately address complex issues related to privacy, future use, and collective interests, and increase the value of data for reuse. Just as the FAIR Principles are a precursor to good data management and stewardship practices, the CARE Principles encourage data users to be FAIR and CARE.

Acknowledgements

The original workshop "Indigenous Data Sovereignty Principles for the Governance of Indigenous Data," was organized by Stephanie Russo Carroll and Maui Hudson, in collaboration with the Research Data Alliance (RDA) International Indigenous Data Sovereignty Interest Group at the International Data Week held Thursday, 8 November 2018, in Gaborone, Botswana. The Principles described in this manuscript represent voluntary contributions and participation of the authors at, and/or subsequent to, this workshop and from the wider US Indigenous Data Sovereignty Network, Te Mana Raraunga Māori Data Sovereignty Network, and Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective communities. We acknowledge the Indigenous Peoples of Botswana, on whose land the Principles emerged, as well as Indigenous Peoples worldwide. We are grateful to the RDA for the workshop location and to the RDA US for travel support for some attendees. This paper was supported by the RDA Europe 4.0 project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777388. Thank you to the following individuals for their comments, edits, and suggestions: Randy Akee, Leah Ballantyne, Donna Cormack, Dominique David-Chavez, Bhiamie Eckford-Williamson, Nanibaa' Garrison, Sharon Hausam, Lydia Jennings, Tahu Kukutai, Kelsey Leonard, Christina Ore, Qunmigu (Kacey Hopson), Andrew Sporle, Michele Suina, Maggie Walter, the Alaska Native Policy Center at the First Alaskans Institute, and the attendees at the "The International Law, United Nations Declaration of the Rights of Indigenous Peoples and Indigenous Data Sovereignty" Workshop at the Oñati International Institute of the Sociology of Law. We also acknowledge Andrew Martinez's contributions to designing the tables and figures.

Competing Interests

M.P. is Editor-in-Chief of the Data Science Journal, but played no role in the review and editing of this paper. The authors have no other competing interests to declare.

Author Contributions

S.R.C. was the primary author of the manuscript, and coordinated and participated extensively in the drafting and editing of the CARE Principles. S.R.C., I.G., and D.R.L received partial support from the Morris K. Udall and Stewart L. Udall Foundation. As senior author, M.H. was significantly involved in the drafting of the CARE Principles and this manuscript text. I.G. was significantly involved in the drafting of the manuscript. J.A. contributed a case that illustrates the emerging implementation of the CARE Principles. All other authors are listed alphabetically, and contributed to the manuscript by their participation in the initial workshop and/or by editing or commenting on the manuscript text.

References

1. Drugge, A-L. 2016. Ethics in Indigenous Research: Past Experiences - Future Challenges. Umea, Sweden: Vaartoe Centre for Sami Research, Umea University.

2. Julayinbul Statement in Indigenous Intellectual Property Rights. 1993. The Julayinbul Conference on Intellectual and Cultural Property. Australia: Jingarrba.

3. Research Data Alliance International Indigenous Data Sovereignty Interest Group. September 2019. CARE Principles for Indigenous Data Governance. The Global Indigenous Data Alliance. GIDA-global.org. Available at gida-global.org/care [Last accessed 17 January 2020].

4. Taylor, J and Kukutai, T. 2015. Report to the Academy of the Social Sciences in Australia on the Workshop Data Sovereignty for Indigenous Peoples: Current Practice and Future Needs. 9-10 July 2015. University House, Australian National University. Available at https://socialsciences.org.au/publications/data-sovereignty-for-indigenous-peoples-current-practice-and-future-needs/ [Last accessed 17 January 2020]. DOI: https://doi.org/10.22459/CAEPR38.11.2016.01

5. United State Indigenous Data Sovereignty Network. 2020. The US Indigenous Data Sovereignty Network Indigenous Data Governance Principles.

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As the world engages with open data, big data, data reuse, and open science, data have increasingly become a global resource used for wielding power, making decisions, spurring innovation and discovery, and commercialization (Independent Expert Advisory Group 2014 , The Economist 2017 ).

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Developing Community Standards with TDWG

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Developing Community Standards with TDWG

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[16 Sep 2021] Do you know how community standards develop and evolve? Join TDWG 's third webinar on 16 September 13:00 or 23:00 UTC to learn more! Pls register at https://t.co/GhdXMo3VcW

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Webinar: How Community Standards Develop and Evolve
Date / Time: 16 September 2021 Presenters: Steve Baskauf and Stan Blum Abstract:
TDWGs core function is the development and maintenance of biodiversity information standards, usually carried out in interest and task groups. The process of how these groups are established and how standards are reviewed and ratified is given in the TDWG Process (also known as By-laws; 2006). The most recent enhancement to these processes is the Vocabulary Maintenance Standard (2017), which creates more efficient pathways for making incremental changes to TDWG vocabularies. In TDWG's third Webinar we will explain standards development and maintenance, including how task groups get organized to make changes, with particular emphasis on the essential role of and opportunities for users to make these standards more effective as vehicles for sharing biodiversity information. We will use the latest Darwin Core Public Review and other current examples to better understand this theoretical process, follow its implementation and thus encourage the whole community to contribute.
Moderators: Mareike Petersen (13:00 UTC) / Paula Zermoglio (23:00 UTC)Platform: Zoom; registration required.

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Webinar: How Community Standards Develop and Evolve

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Date / Time: 16 September 2021 Presenters: Steve Baskauf and Stan Blum Abstract: TDWGs core function is the development and maintenance of biodiversity information standards, usually carried out in interest and task groups. The process of how these groups are established and how standards are reviewed and ratified is given in the TDWG Process (also known as By-laws; 2006).

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WeDigBio 2021: Chicago! Registration Open

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WeDigBio 2021: Chicago! Registration Open

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Scientists from the Field Museum and around the world need your help!

To register for this virtual event, please click here.

Join us in transcribing scientific label data from our collections and scientific field notebooks using online and computer tools. WeDigBio will be held online daily from Thursday, October 14th–Sunday, October 17th.

Thank you everyone for considering participating in this event!

The WeDigBio Team
Matt, Yarency, Heaven, Ayesha, and Tom

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2022 Unconference on Open Scholarship Practices in Education Research

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2022 Unconference on Open Scholarship Practices in Education Research

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The 2022 unconference will feature participant-led sessions analyzing the current state of open scholarship practice and interactive hackathons seeking solutions to identified problems. Participants will assess barriers to adoption of open scholarship practices unique to the education community and brainstorm strategies for promoting greater awareness.

The 2022 Unconference will be hosted in Charlottesville, Virginia on Feb 24-25, 2022 with virtual participation facilitated for general and relevant unconference sessions.

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Attending the Conference

The 2022 unconference will be at the Omni Charlottesville Hotel. A room block has been secured at the adjacent Residence Inn by Marriott Charlottesville Downtown Hotel at $126/night plus taxes and fees. You will be emailed a unique booking link to complete your registration or you can call 1-888-236-2427 for assistance. Limited rooms are available in the room block. Please book as soon as possible to ensure you are able to secure a room at the group rate. Hotel reservations

Breakfast and lunch will be provided to all in person conference attendees.

The Charlottesville Albemarle Airport is 10 miles from the Omni Charlottesville Hotel. Directions here.

Limited travel stipends of up to $500 are available if funding is a barrier to in-person attendance at the unconference. Please submit a request for travel support here. Preference for awarding travel support will be directed to colleagues who do not have access to alternative travel funding and early career researchers. To be considered, please register for the unconference and submit your travel support request as soon as possible. Travel support determinations will be announced by October to allow participants time to make travel arrangements. If we receive more requests than we can accommodate, awards will be issued on a lottery basis. We will strive to provide support to as many attendees as possible.

A decision about a vaccine mandate for in-person attendees will be made closer to the event. The hotel cancellation policy permits a no penalty cancellation until 48 hours in advance of the event.

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Virtual Unconference on Open Scholarship Practices in Education Research

# data[].attributes.attachment_link.description
The 2022 unconference will be at the Omni Charlottesville Hotel. A room block has been secured at the adjacent Residence Inn by Marriott Charlottesville Downtown Hotel at $126/night plus taxes and fees. You will be emailed a unique booking link to complete your registration or you can call 1-888-236-2427 for assistance.

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AltLabels ( Q108311046)

If you're viewing an item for which there's no label in your current language this will insert the 3 most common labels across other languages below the input box. A simple click on one of them will approve & submit it for the current user language.

This web application finds works with a given "author name string" value in Wikidata, groups them based on coauthors and other attributes, and allows you to map those "author name string" values to an author item via a QuickStatements upload, or directly via the Wikidata API for users who authenticate using OAuth. See the wiki page for more information.

This script adds automatically all labels with the help of the interwiki links and adds descriptions from an editable list. To use it, click on Automatic addition in Tools.

Check site links ( Q108311130)

When editing a Wikipedia article, the script adds an action link (near search box) to remove the sitelinks which are already available on Wikidata.

Adds citations metadata (i.e., what other items an item cites) support to the reference management software Zotero, using cites work (P2860) information from Wikidata, and enabling users to easily contribute missing data.

Draggable sitelinks ( Q108311160)

Drag sitelinks onto claims to make "imported from" references.

DragNDrop ( Q108311175)

Add statements and references from Wikidata or Wikipedia by dragging and dropping them.

DuplicateReferences ( Q97500668)

Adds a link to copy references and add them to other statements on the same item.

Find redirects for aliases ( Q97500659)

Adds a "Find redirects" button in a submenu next to the "Watch" button that, when clicked, displays a list of Wikipedia redirects and allows the user to select some of them to add as aliases.

Label Collector ( Q108311207)

A script to semi-automatically import labels, descriptions and aliases for multiple languages based on the articles in Wikipedia and other Wikimedia projects. Suggests descriptions by extracting definitions from article introductions, aliases based on what's bolded in the article introduction, and labels from the article title, and provides a mask to compare existing values with the suggestions made and the article introductions, and to review and edit them all at once. When finished, optionally the next item with no label or description can be loaded automatically, allowing fast processing of many items. See this page for a more detailed documentation.

LabelLister ( Q87070602)

This script shows a list of all labels in all languages that are supported in the specific item. And edit label without having to change your main language.

Adds a "Merge it with ..." button in a submenu next to the "Watch" button. When clicked, it allows to merge items and automatically turn the merged item into a redirect.

Adds a move tool to the sitelink edit toolbar to move one or all sitelinks to another item.

Move claims

Adds a "move" button to statements, for moving or copying them to another item, lexeme or property.

Move claims 2

Like "Move claims" above, but can also move claims to a different property of the same item.

Open source data wrangling software which can be used to align datasets with Wikidata, with advanced matching on names and properties. It can then be used to import these datasets in Wikidata. See the tutorials for that.

SlurpInterwiki

This script imports automatically all interwiki-links from one Wikipedia article. The gadget then can be launched by clicking on the "Import interwiki" link in the toolbox. After that a dialog appears where a language for import can be selected.

The Brown Tool

Adds a movable box to the right side of the page. There are some predefined property-items (man, woman, some occupations), which gets activated by clicking on them. But the main feature is that the user can input property-item pairs for repeated use. First, there is a flexible storage system, which uses cookies. After the start there is only one row for a property (p) and one for a value (q). By clicking p+ and q+ you can add as much rows as you need. If you write in a property, an item, or a short text (label) this will be saved in the cookies. That means, after checking an property-item pair, which transfers the inputs to the Wikidata datasource, you can go to the next item and what you typed into the storage system is still there and can be used again. The newest version of this tool has further an input for item descriptions. The second option is a file in the Wikidata userspace. Each user has a separate wikipage in its own userspace where they could write in some property-item pairs, which uses the tool to fill an additional select. The address is User:your username/data. Take as example User:Goldzahn/data. In this way data is stored until it is deleted, while the data stored in cookies last only as long as the browser is not closed.

UseAsRef

Allows you to add references to statements using external IDs as sources. Detailed instruction in User:Bargioni/UseAsRef

WikidataComplete

This tool adds a UI element in Wikidata that proposes statements to be inserted into Wikidata. The statements are currently extracted from Wikipedia by a ML algorithm. The statements that are approved are automatically inserted into Wikidata under your name. The following link briefly describes the tool.

An extension for Mozilla Firefox, that displays Data from Wikidata on various websites and also allows extraction of data from these websites.

Wikidata Game

http://magnusmanske.de/wordpress/?p=203

Wikidata Game is a complicated website with many activities. Here, a machine finds potential actions and has buttons to accept or deny. Then the machine edits Wikidata automatically under your name. However, you are responsible for these edits. A WiDaR account is needed to access the Game.

Wikidata Missing Pictures (offline)

Traveling with a smartphone but no Internet (or expensive Internet)? Load the GPX or KML file in your offline maps application, and go take pictures of Wikidata items that do not have an image (P18) yet. Like Wiki ShootMe! but offline.

Wikidata useful

Adds a box of quick "add statement" links to the right side of the page. Entity and phylogeny types, countries (location and citizenship). Also, tries to load and display a blurb from en or de Wikipedia.

Wiki ShootMe!

Find places nearby (by coordinates, or Q, or article) that have items on Wikidata but no images yet. If you are a traveler, insert your location to find places near you that have items on wikipedia, but no photos. Once you find a location near you, you can take photos and upload them to wikipedia to help expand the article. Manual.

Zotero (import)

Has a Wikidata script that can import metadata about works from Wikidata, and another that exports data as commands for QuickStatements. See Wikidata:Zotero

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AltLabels ( Q108311046) If you're viewing an item for which there's no label in your current language this will insert the 3 most common labels across other languages below the input box. A simple click on one of them will approve & submit it for the current user language.

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The Confidence Information Ontology: a step towards a standard for asserting confidence in annotations

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The Confidence Information Ontology: a step towards a standard for asserting confidence in annotations

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https://github.com/BgeeDB/confidence-information-ontology

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Abstract

Biocuration has become a cornerstone for analyses in biology, and to meet needs, the amount of annotations has considerably grown in recent years. However, the reliability of these annotations varies; it has thus become necessary to be able to assess the confidence in annotations. Although several resources already provide confidence information about the annotations that they produce, a standard way of providing such information has yet to be defined. This lack of standardization undermines the propagation of knowledge across resources, as well as the credibility of results from high-throughput analyses. Seeded at a workshop during the Biocuration 2012 conference, a working group has been created to address this problem. We present here the elements that were identified as essential for assessing confidence in annotations, as well as a draft ontology-the Confidence Information Ontology-to illustrate how the problems identified could be addressed. We hope that this effort will provide a home for discussing this major issue among the biocuration community.
Tracker URL: Ontology URL: https://raw.githubusercontent.com/BgeeDB/confidence-information-ontology/master/src/ontology/cio-simple.obo https://github.com/BgeeDB/confidence-information-ontology
Introduction

Curation in biology has become essential for capturing information from publications or results from experiments, and for making these data available through public repositories. Whether to allow efficient data retrieval (e.g. functional annotations of single genes or gene products), or to make sense of the overwhelming amount of data produced by current technologies [e.g. gene ontology (GO) enrichment analyses on large datasets, or protein-protein interaction network analyses], this curation work provides us with standardized datasets that are essential for downstream analyses ( 1 , 2 ). However, the curated data itself can often be difficult to assess, because it arises from different types of experiments and analyses, each with varied outputs at different levels of quality.

As the volume of biological data has grown, so has the amount of annotations available ( 3 ). This growth in turn creates a pressing need to assess the confidence in these annotations, to allow users to decide whether to use large sets of annotations with possible high rates of false positives, or more restricted sets of annotations of expected higher quality.

The type of evidence used to support the assignment of an annotation is often used as a proxy for judging its quality, in large part owing to the extensive use of the evidence ontology (ECO) ( 4 ). The ECO allows curators to provide information about the type of method used to support an annotation, for example experimental or computational. Due to the lack of a confidence evaluation system, the evidence terms have often been used as a proxy to evaluate the quality of certain data. However, evidence terms are not sufficient to infer confidence, and a same evidence term can be used to support annotations based on experiments of very different quality.

For example, 'microarray evidence' (ECO:0000058) may report results from a high quality experiment with several biological replicates, or from a single low quality experiment. Or a 'protein BLAST evidence' (ECO:0000208) could correspond to a weak similarity over part of the protein, or 99% identity over the whole length of the protein. Another example is the use of annotations automatically assigned by computational methods, without curator supervision, tagged with the related evidence term (ECO:0000501 or GO evidence code IEA); they have often been considered the least reliable, whereas after evaluation, these annotations appeared to be as reliable as curated non-experimental annotations ( 1 ).

Although evidence sources and quality of annotations are intertwined, they are nevertheless two different concepts, and users would be better served if they were captured separately.

Several groups have implemented methods for addressing the problem of heterogeneous quality of annotations that are derived from the same source, and for estimating the confidence in the annotations they provide. For instance, the ChEMBL team has defined a confidence score ranging from 1 to 9, assessing both the quality of protein targets, and of the curation process ( 5 ); the Bgee team has been using a controlled vocabulary to assess confidence in homology relations between species-specific anatomical structures, ranging from 'uncertain' to 'well-established', depending on the agreement level found in literature ( 6 ); neXtProt ( 7 ) classifies data and annotations with 'gold', 'silver' and 'bronze' qualifiers to represent data quality; and UniProtKB/Swiss-Prot ( 8 ) provides an annotation score ranging from 1 to 5 at the level of the protein entry, which documents both the quantity of annotations and their provenance.

Several resources also provide distinct datasets of different qualities. For example, UniProtKB/Swiss-Prot distinguishes 'unreviewed' from 'reviewed' entries, the latter consisting of manually curated records providing a critical review of experimental data from literature, as well as curator-evaluated computational analysis; such a distinction is also used by the Catalytic Site Atlas ( 9 ), and MACiE ( 10 ); similarly, the NCBI provides RefSeq ( 11 ), a subset of better annotated sequences.

These different datasets represent an attempt to address the problem of annotation confidence assessment, and have proven their usefulness. For example, because the high-quality datasets are expected to have a lower false positive rate, they often serve as templates for transitively assigned annotations, e.g. to define protein names for bacterial genome annotations ( 12 ).

As useful as they are, the above efforts lack standardization. Although the precise means of assessing confidence is highly dependent on the data type ( 13 ), it should be possible to define a standard way to encode and provide this information. To that aim, a workshop was organized during the Biocuration 2012 Conference ( 14 ), followed by discussions though a wiki and an un-conference at Biocuration 2014. The work described here is a community effort, resulting from discussions between several groups who wished to capture statements of confidence information about annotation assertions in a more systematic manner, i.e. statements of confidence information about assertions.

This article presents the consensual principles that arose from these discussions, as well as a draft ontology used as a proof of principle: the Confidence Information Ontology (CIO). Since the CIO is still in relatively early stages of development, we anticipate that there might be significant changes to the terms, as the community begins to explore the use of CIO. With this publication, we hope to expand the set of active CIO developers and users, so as to build an optimal ontology for this domain. We then provide suggestions of implementation, to highlight how the use of such an ontology could address the problem of standardizing confidence information about annotations.

Relationship between evidence, quality and confidence

Annotations are created on the basis of available evidence lines. In this work, evidence is considered to be any scientific evidence obtained from laboratory experiments, computational methods, or manual curation, as described in the ECO. Confidence in a particular assertion can then be defined, based on the set of related evidence lines.

Although confidence and quality are related concepts, they are nevertheless distinct. Quality refers to the value of the source or of the annotation, whereas confidence refers to the level of certainty that an assertion is correct. For instance, several evidence lines of low quality, produced by methods known to be noisy (e.g. yeast two-hybrid), could yield an assertion of high confidence, because they are repeatedly confirmed (e.g. with different reporter genes); or, an annotation could be of poor quality because of missing information, but capture a high confidence assertion.

In this manuscript, we aim at defining how to provide global confidence about annotations, and not at defining criteria to assess quality of evidence.

Standardizing confidence information

To convey confidence information in a standard way, an obvious solution is to use an ontology, to provide standard terms for assessing confidence, in the same way the ECO provides standard terms to annotate evidence types. Although it is neither feasible nor desirable for different annotation groups to use a single definition of quality metrics, it should be feasible and desirable for each group to map their quality assessments to terms from a common ontology.

In order to demonstrate how such an ontology could address the need for a standard method to provide confidence information, we present here a new ontology that we have developed: the CIO ( Figure 1 ). This ontology was created to address the main points identified to provide clear and meaningful information about confidence in annotations. Here, we present the rationale behind the design of this ontology, followed by suggestions for implementing its use, which also highlight the issues it addresses. The ontology implementation is described in the supplementary material .

Asserting confidence in single evidence

Parameters for determining confidence in annotations are highly heterogeneous among groups working on different data types. Yet it is possible to summarize the confidence information using a basic rating system. Each group working on a specific data type could define clear parameters to assign these levels of confidence, and several groups have already implemented such a rating system (see Introduction).

Three CIO terms corresponding to basic levels of confidence have been defined in the ontology: 'low confidence from single evidence, medium confidence from single evidence, and high confidence from single evidence' ( Figure 2 ). These confidence statements might summarize different quantitative measures, e.g. numeric scores, minimum information requirements or standards for experimental procedures. Computational annotations could also be given a confidence score, based, e.g. on an E-value or P -value threshold, or a percentage of identity.

An additional term, 'rejected', allows to tag assertions that were retracted, for instance, following paper retraction, author misinterpretation or curator misinterpretation. This term is used to circumvent the fact that, when results are retracted, associated annotations are often deleted. Consequently, end-users might not be aware that a result was annotated, and then shown to be incorrect. Annotating an assertion with this confidence term would allow to keep this information available, and keep track of the invalidation. Note that this is different from negative annotations, used to negate annotation interpretation. It is also different from conflicting multiple evidence lines, where each single evidence has not been directly invalidated. Rejection is a stronger assertion about an annotation source than conflict, capturing that this evidence should no longer be used.

These basic confidence statements should be used for annotating each individual evidence. While it might seem an added burden to annotate an assertion with yet another ontology term, we believe that curators already informally assess evidence sources they use. Thus in many cases a basic rating system could be applied with little added effort (see Discussion).

Although some evidence types can have numerical estimates of confidence (e.g. BLAST score), others can only be judged qualitatively (e.g. non-traceable author statement in GO). Confidence information should always be examined in the light of the evidence source used. For this reason, we believe that annotations using the CIO should always be provided along with terms from the ECO. We also believe that for those evidence sources that allow numerical estimates, annotation providers should always publish the parameters used to assign a confidence statement.

We are hopeful that, in the long run, agreement on these parameters might be reached by different groups working in the same field. For example, it might be possible to define a certain level of sequence identity that would be a good predictor for a particular class of GO terms; or, certain experimental setup that would provide more trustworthy protein-protein interaction data. It is the standardization of annotation assignment in ontologies such as ECO and CIO that facilitates this.

Global confidence from aggregation of multiple evidence lines

The strongest point of agreement among all workshop participants was that assertions supported by multiple evidence lines are more reliable than assertions supported by a single line of evidence. This means that a global confidence in an assertion can arise from taking all available evidence into account. This led us to create two branches in the ontology: a 'confidence statement from single evidence' branch' and a 'confidence statement from multiple evidence lines' branch ( Figure 1 ). The former term is the parent of the basic single-evidence terms described above ( Figure 2 ).

The best practice would be that for assertions supported by multiple evidence lines, each individual evidence would still be assigned a term from the 'confidence statement from single evidence' branch. The overall confidence in the assertion, assessed from all available evidence lines, would then be summarized using a term from the 'confidence statement from multiple evidence lines' branch.

Global confidence from multiple experimental or computational types

Another consensual point was that different, non redundant, experimental or computational methods provide a stronger support for an annotation. For this reason, we created the terms 'confidence statement from multiple evidence lines of same type' and 'confidence statement from multiple evidence lines of multiple types' ( Figure 1 ), where evidence type corresponds to any evidence term in ECO.

An example of the stronger support provided by evidence lines of multiple types can be found in the annotations of homology between anatomical entities, provided by the Bgee team (see https://github.com/BgeeDB/anatomical-similarity-annotations/wiki/Similarity-annotations ). For instance, an assertion states that the urinary bladder is homologous among Tetrapoda . Some evidence lines provided ( 15 , 16 ) are from the type 'phylogenetic distribution evidence' (as the structure is present in various Tetrapoda species). These individual statements are of medium confidence, and if we were to integrate these two lines of evidence of the same type, the confidence would be unchanged. However, the assertion is also supported by an evidence of type 'developmental similarity evidence' ( 16 ), which allows to corroborate the assertion, using a different evidence type, and to grant the overall annotation high confidence.

A similar procedure is applied by curators of the International Molecular Exchange (IMEx) consortium in the field of protein interactions ( 17 ), with the aim of capturing all of the experimental details available.

Such a summary annotation, taking all evidence lines into account, could be automatically produced, with rules specifying how individual confidence values should contribute to the overall confidence (see section Suggestions of implementation for an example). It would also be possible to decide to not formally assess confidence for each individual evidence, but to manually annotate only the overall assertion, which would represent an advantage for curation teams with limited resources.

Reconciling congruent and conflicting evidence lines

Finally, the information provided by multiple evidence lines needs to be reconciled. When all evidence lines supporting an assertion are congruent, the assertion should be trusted with higher confidence than when some evidence lines are conflicting. Congruent assertions could be, for instance, annotations between the same gene product and GO term, based on different evidence sources. Conflicting assertions are annotations yielding opposite interpretation, for instance, a positive annotation between a gene product and a GO term, and a negative annotation between the same gene product and GO term, using the 'NOT' qualifier; or incompatible GO terms assigned to the same gene product, for instance the GO term 'DNA replication' from the 'Biological Process' branch, and the GO term 'cytoplasm' from the 'Cellular Component' branch; in any case, it is the resources implementing the CIO that should define what a conflict is, for the type of data that they annotate.

When evidence lines are contradictory, we can distinguish 'weak' contradictions (e.g. a single low-confidence evidence contradicting several high-confidence evidence lines), from 'strong' contradictions (e.g. several high-quality evidence lines, all contradictory).

For these reasons, the 'same type' and 'multiple types' terms each have two subclasses, e.g. when evidence lines are of the same type: 'confidence statement from congruent evidence lines of same type' and 'confidence statement from conflicting evidence lines of same type'. Such a 'conflicting evidence lines' term also has two subclasses, e.g.: 'confidence statement from strongly conflicting evidence lines of same type' and 'confidence statement from weakly conflicting evidence lines of same type' ( Figure 3 ).

The term 'confidence statement from congruent evidence lines of same type' has three subclasses, based on the overall confidence obtained from the associated evidence lines ( Figure 3 ). For instance, if an annotation is supported by two evidence lines, a 'high confidence assertion' and a 'medium confidence assertion', the best confidence is 'high confidence assertion', and the term used for the overall annotation could then be: 'confidence statement from congruent evidence lines of same type, overall confidence "high"'. Or, if an assertion is repeatedly confirmed by many low-confidence individual evidence lines, the overall confidence obtained could still be high. It is the responsibility of the annotation teams to define and communicate the parameters, relevant to their field, which are used to produce overall confidence.

The term 'confidence statement from weakly conflicting evidence lines of same type' has similar subclasses. Indeed, when using weakly contradicting evidence lines, the implication is that the overall assertion is believed to be true. It is then possible to provide an overall confidence from the set of available evidence lines. The following three subclasses were thus created: 'confidence statement from weakly conflicting evidence lines of same type, overall confidence "high" ' ; 'confidence statement from weakly conflicting evidence lines of same type, overall confidence "medium"'; 'confidence statement from weakly conflicting evidence lines of same type, overall confidence "low"'.

Such subclasses were not deemed relevant for the term 'confidence statement from strongly conflicting evidence lines of same type'. In that case, the supported assertion is believed to need further validation, as evidence lines yield different conclusions of similar confidence. Assertions tagged with this term should be targeted in priority when integrating new findings, as they are more likely to evolve, following the development of new methods or technologies. The individual single-evidence confidence annotations associated to strongly conflicting evidence lines would be in that case useful to define directions for further analyses.

Note that the rationales are the same when using terms from the branch 'confidence statement from multiple evidence lines of multiple types'. Also, assertions annotated with the term 'rejected' should not be considered when aggregating multiple evidence lines.

Example of a potential workflow using GO annotations

An example of use would be to extend the GO annotation conventions ( http://geneontology.org/page/go-annotation-conventions ). Please note that this is an illustrative example, and that the implementation of such conventions is pending further discussion within the GO consortium. We provide this example only to present how the CIO could be used. For an example of an actual implementation, see next section.

The current recommendations of the GO consortium are that each annotation must include a gene product identifier, a GO identifier, and an evidence term identifier. If multiple sources support an annotation, then multiple annotations with identical GO identifiers and reference identifiers, but different evidence terms, may be made.

A first possible modification could be to additionally annotate each of these individual annotations with a term from the branch 'confidence statement from single evidence'; this would require defining clear parameters to assign confidence for each evidence type.

For instance, in the GO annotation file gene_association.goa_uniprot_noiea (retrieved as of 3 February 2015, see http://geneontology.org/page/download-annotations ), the product of the Candida glabrata gene ERG9 (UniProtKB ID Q9HGZ6) is associated to the GO term GO:0051996 'squalene synthase activity', with two annotations:

One of them is supported by the evidence term ECO:0000200 'sequence alignment evidence' (GO evidence code ISA). It was produced based on a publication ( 18 ) providing an alignment of the deduced amino acid sequence of this protein to Erg9p sequences in five other species. The alignment was performed on a predicted open reading frame of 443 amino acids, and exhibited from 33.4% identity in Arabidopsis thaliana , to 71.3% identity in Saccharomyces cerevisiae , and identified three conserved predicted kinase motifs. This protein sequence is thus highly conserved on a significant length, even in distant species, with relevant motifs found; this annotation could be assigned the term CIO:0000003 'high confidence from single evidence'. Of note, the GO consortium does not define numerical cutoffs for the extent or percentage identity of sequence similarity (see http://geneontology.org/page/isa-inferred-sequence-alignment ). These cutoffs should vary, depending on, e.g. the organism studied, or the protein function captured. Again, each annotation team should define and communicate clear parameters to assign confidence.

The other annotation is supported by the evidence term ECO:0000015 'mutant phenotype evidence' (GO evidence code IMP), and was produced on the basis of the same publication. The authors generated C. glab r at a strains where ERG9 was under the control of the tetracycline-regulatable promoter. The correct replacement of the endogenous ERG9 promoter was verified by PCR, and the ERG9-controllable strains exhibited a severe growth defect in medium with DOX; the growth defect caused by DOX was suppressed by the addition of serum containing exogenous cholesterol. Overall, as this experiment was carefully designed, and provided a clear result, this annotation could also be assigned the term CIO:0000003 'high confidence from single evidence'.

Another potential modification could be to generate an additional summary line, when several annotations are available in support of an assertion, summarizing all of them; this additional line could be assigned a term from the 'confidence statement from multiple evidence lines' branch. This approach would have several advantages, and notably would provide a clear overview to end-users about the status of an association.

In the example described above, as the evidence lines provided are of different types, and are all of high confidence, the summary annotation could be assigned the term CIO:0000012 'confidence statement from congruent evidence lines of multiple types, overall confidence high'. This represents the highest level of confidence from the CIO. Of interest, it is possible to automatically determine whether two evidence lines are of a same or of different types, using the structure of the ECO.

To retrieve the evidence terms and references used to produce the additional summary line, either the corresponding single-evidence annotation lines (notably with the same gene product identifier and GO identifier) could be used if provided, or the multiple evidence terms and references could be integrated in the summary line as a list (as it is already possible for, e.g. columns 6 and 11 of the GO Annotation File, albeit for a different purpose, see http://geneontology.org/page/go-annotation-file-gaf-format-20 ). The summary line and the summary confidence term could also be automatically produced from the set of individual assertions, as it is already the case in the Bgee project (see next section).

In the GO annotation file format, it is also possible to provide negative assertions, using the NOT qualifier (column 4, see http://geneontology.org/page/go-annotation-file-gaf-format-20 ). Such negative assertions can be in conflict with other, positive, assertions, a conflict that could be captured in the summary line, with the use of terms from the branches 'confidence statement from conflicting evidence lines of same type' and 'confidence statement from conflicting evidence lines of multiple types'. This would allow a simple and clear overview of the status of a particular assertion.

Example of implemented workflow to annotate anatomical homology

An example of use can be found in the annotation of homology between anatomical entities, provided by the Bgee team (see https://github.com/BgeeDB/anatomical-similarity-annotations/wiki/Similarity-annotations ). The format of this annotation file is inspired from the GO annotation file format (see http://www.geneontology.org/GO.format.gaf-2_0.shtml ), and the procedure to provide supporting information is inspired from the guide to GO evidence codes (see http://www.geneontology.org/GO.evidence.shtml ); additionally, the procedure follows the guidelines described here.

For example, to annotate the homology of the autopod among Vertebrata , there exist two alternative hypotheses: one considering that the autopod was a novel feature of Tetrapoda , another one considering that it appeared earlier, during Vertebrata evolution ( 19 ). These hypotheses notably allow to produce two annotations, positive and negative, about this homology originating in Vertebrata . Each of these assertions is manually annotated in Bgee, notably with evidence and confidence terms. Because these assertions represent general accepted knowledge in the field, but were captured only through the term ECO:0000033 'traceable author statement', they were each assigned a medium confidence (annotations retrieved as of February 3 2015).

An overall assertion, taking into account each line of evidence, is then automatically produced, summarizing whether the evidence lines are conflicting or congruent, and using the ECO to decide whether they are of a same or different types ( Table 1 ). As the two evidence lines are of a same confidence, the overall assertion is assigned a 'strongly conflicting' confidence term.

Finally, the terms from the CIO that are the most informative and likely to be used are described in Table 2 .

Discussion

The aim of this work is to show how confidence in annotations can be provided in a standard way; it is not to impose one specific practice in assessing confidence. One purpose of the draft CIO described in this manuscript is to invite feedback and comments from the community. Whatever solution is eventually adopted, the problem of assessing confidence in annotations must be addressed. We hope that this project will provide a home for discussing this major issue (ideally through its associated tracker, available at https://github.com/BgeeDB/confidence-information-ontology ), as well as a practical solution for those who wish to rapidly implement it. Once the principle and design of the CIO are approved by the community, the formalization of this draft ontology could then be improved, by properly defining the semantics of the terms created, using, e.g. the Ontology for Biomedical Investigations ( 20 ), or the Information Artifact Ontology ( https://code.google.com/p/information-artifact-ontology/ ).

The main practical additional task described here is annotating single-evidence assertions with a confidence statement using a basic rating system, a solution akin to what is already adopted by several resources (see Introduction). Summary confidence annotations could then be automatically generated and assigned a confidence term from the 'confidence statement from multiple evidence lines' branch, as it is the case, e.g. in the Bgee homology annotations, as long as individual assertions are provided with confidence information and ECO terms. Alternatively, annotation teams with limited resources could choose to provide annotations only at the global summary level. However, the latter solution has the disadvantage of masking the evaluation of confidence at the level of each evidence, which limits the transparency of annotations.

Even when it is impossible to provide confidence in each statement, whether because of lack of manpower or because of methodological limitations (e.g. in case of many electronic annotation methods), it is still very relevant to record whether one evidence line or several supported an assertion, whether they were of the same type or not, and whether they were contradictory or consistent. Such an approach can also be used to integrate the CIO with legacy annotations, by imposing an arbitrary confidence statement to all single evidence lines (e.g. medium confidence from single evidence), and then automatically generating terms from the multiple evidence lines branch, thus providing confidence information at least according to the multiplicity and consistency of evidence lines.

Assessing the quality of the data being captured is one of the most difficult, yet essential, tasks of biocurators. Until now, this has not been done systematically, or even explicitly. There are several reasons for this: first, all papers go through a process of peer-review, and published data is usually assumed to be trustworthy. Second, due to the scale of the task, the resources available are not sufficient to capture the data from all published papers; a selection must be done as to which papers provide the most relevant information for the users of the resource being developed. As curated databases' usage increases, biocurators have an editorial role that effectively filters published articles into biological databases. A careful selection of the data is thus essential.

This opens the question of defining what makes a 'high confidence' evidence. Many biocurators are accustomed to estimating the confidence in evidence sources that they use, yet it can be difficult to transform such subjective estimates into standardized levels. This issue is akin to inter-curator agreement for GO annotations ( 21 ), that despite being highly consistent, highlights the inevitable subjectivity of the process of assigning an ontology term to an assertion.

Indeed, biocuration is a translation problem: the language of biologists must be translated into a structured vocabulary suitable for computational analyses. Ideally, it would be done without losing any of the original meaning, but that is hardly possible. An important aspect which is often missing from annotations is their biological context. For example, a protein may be found in the nucleus in one article by some immunocytochemistry approach, but may be known to have a function that is more consistent with a mitochondrial localization. Ideally at some point, one should be able to integrate all information and try to reconstruct the biological meaning of the annotations. Having a confidence in the different annotations that describe a proteins' role will certainly help to resolve some of the apparent discrepancies in the annotations (and in the literature).

One important feature we are proposing is to systematically provide a summary annotation when several evidence sources are available. We believe that the use of a summary annotation would be of great benefit, by allowing to have a clear overview of an assertion, taking into account all evidence lines. This can often be difficult when many sources are available, especially when they are contradictory.

Another advantage of the guidelines proposed here is the ability of maintaining erroneous assertions, for informing users about retracted results, while being able to discard them to produce summary interpretations. Indeed, while resources providing a global overview of annotations about an entry, such as UniProtKB/Swiss-Prot, can remove erroneous information, and provide comments to warn about incorrect information, this is hardly possible for resources presenting data based on individual assertions. For instance, for GO annotations, while the presence of the NOT qualifier allows to track conflicting information, it is not sufficient since information from dubious publications remains. Moreover, when an annotation is removed, e.g. following a paper retraction, no trace of this annotation is maintained. A user coming across the original article, unaware of the retraction, might conclude that the publication has just not yet been annotated.

An example of this issue was described by Poux et al . ( 22 ), who showed how erroneous statements about the SIRT5 protein, based on incomplete in vitro studies, are repeatedly published, still today. The approach proposed here would allow to identify assertions that have later been shown to be based on misleading conclusions, owing to the use of the 'rejected' term. Users would then be aware of the retraction, while retracted results would not impact summary annotations, presenting the correct interpretation. Also, as summary annotations can be generated automatically (as long as individual assertions are provided with confidence information and ECO terms), the reevaluation of an incorrect statement could be easily propagated to the summary annotation.

An aspect that is not addressed by the guidelines provided here is the different levels at which confidence in assertions can be estimated: at the level of the experimental procedure; at the level of the author interpretation, as authors might have selected results not allowing an unbiased interpretation; and at the level of the annotator interpretation. For now, the basic terms from the 'confidence statement from single evidence' branch should be used to take into account these different layers all together. The CIO could be expanded if that turned out to be a need of the community. Possible solutions could be to capture the confidence at these different levels independently, or to modify the branch 'confidence statement from single evidence' for this purpose. We hope that the current work will promote discussions towards this aim, possibly through the associated tracker.

This issue is related to the definition of the provenance of an annotation. Data provenance aims at documenting origin of data, but also annotation steps or task workflow (see http://www.w3.org/TR/prov-overview/ ). However, in the current state, documenting the full provenance of annotations might be overwhelming for most curation teams. The CIO is designed to be easily used in daily curation work, and to be as close as possible to the rating systems already adopted by several resources. Capturing provenance of an annotation in accordance with W3C and other standards (e.g. linked data frameworks) should be a long term goal; our current work represents a first step towards such structured capture.

We believe that the CIO, as well as the guidelines presented here, will allow end-users to better evaluate the pertinence of curated data. This is expected to enhance data dissemination across resources, as well as analyses based on curated data, thanks to the improved possibilities of filtering data, and of evaluating their trustworthiness.

Conclusion

With the growth of annotations available, it has become essential to assess confidence in these annotations. This article is an attempt at defining guidelines for standardizing the exchange of confidence information, and at showing the feasibility of this approach.

We propose three basic principles: (i) while it is difficult to standardize parameters to define confidence in annotations across resources, it is possible to use a common ontology language to provide this information; (ii) in the same way that the GO guidelines recommend to provide annotations at the level of each individual evidence, a confidence statement might also be assigned to each single evidence, using a basic rating system; (iii) when several evidence lines are available relative to an assertion, it is desirable to provide a global summary assertion, taking all evidence into account.

We created the CIO in order to illustrate these principles. We hope it might be a trigger for the biocuration community to address this need for standardizing confidence information. Whether this ontology undergoes major changes in the near future, following feedback from the community, or whether it is used 'as is' by several resources, we hope that annotation confidence will be increasingly available in biocuration efforts.

Acknowledgements

We thank Amos Bairoch for insightful comments provided in preparation of the workshop held at the Biocuration 2012 conference.

Funding

G.L.H. is funded by NSF DBI-1356193. S.P. is funded by the Swiss Federal Government through the state Secretariat for Education, Research and Innovation (SERI), and by the National Institute of Health (NIH) grant 1 U41 HG006104. F.B.B., M.R.R., and A.N. are funded by the Swiss Federal Government through the state Secretariat for Education, Research and Innovation (SERI), the Swiss National Science Foundation (grant number 31003A_153341), SystemsX.ch (project AgingX), and Etat de Vaud. S.O. is funded by the BBSRC MIDAS grant (BB/L024179/1) and the European Commission grant Affinomics (FP7-241481). M.C.C. and M.G. are funded by National Institutes of Health (R01 GM089636). Funding for open access charge: Etat de Vaud funding to MRR.

Conflict of interest . None declared.

References

Author notes

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

# data[].attributes.attachment_link.title
Confidence Information Ontology: a step towards a standard for asserting confidence in annotations

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Biocuration has become a cornerstone for analyses in biology, and to meet needs, the amount of annotations has considerably grown in recent years. However, the reliability of these annotations varies; it has thus become necessary to be able to assess the confidence in annotations.

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HERITAGE CONNECTOR: TRANSFORMING TEXT INTO DATA TO EXTRACT MEANING AND MAKE CONNECTIONS

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HERITAGE CONNECTOR: TRANSFORMING TEXT INTO DATA TO EXTRACT MEANING AND MAKE CONNECTIONS

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https://onlinelibrary.wiley.com/doi/10.1002/ail2.23

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CONTEXT

As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant.

Furthermore, whilst recent years have seen a growth in the publication online of scholarly research related to heritage collections, this material remains hard to discover as it is rarely linked to from related collection records.

In this project, we will apply a battery of digital techniques to connect similar, identical and related items within and across collections and other publications. The project will explore a range of data analysis approaches that will analyse catalogues, published material and knowledge graphs (primarily Wikidata), and build links at scale between these that can then be used for new forms of research and access.

Aim

The project's research questions are:

How can existing digital tools and methods be used to build relationships at scale between poorly and inconsistently catalogued digitised collection objects and other content sources?

Which software tools and approaches, in combination, provide the most effective approaches?

How might the relationships built using these techniques complement and amplify the benefits of the use of persistent identifiers?

What gaps and biases emerge when these relationships are created, and which hitherto unexpected connections are made?

How might confidence in these relationships impact on their usefulness in research and discovery? Can 'degrees of confidence' become a useful concept in searching across collections as it is in statistics?

Is such an approach scalable to larger volumes of content and different types of collections?

Where is the best use of human input in supporting such an approach? What expertise and skills are required for this input?

How might these new automatically established relationships help museums to gain greater understanding of how their collections are being and could be used and interpreted?

What benefits arise from using such tools to link to scholarship such as theses and journal article abstracts/keywords? What new forms of research are afforded?

What ethical issues are raised by such an approach?

What are the most effective methods for organising interdisciplinary and cross-sectoral research projects concerned with digital cultural heritage, and how can these methods usefully be shared across the independent research organisations (IRO)?

Outcomes

Heritage Connector seeks to provide researchers with more targeted and relevant information than ever before, and generate new opportunities for research projects.

The project will deliver insights into cataloguing practices that have the potential to increase the depth of materials categorised, expand access to museum records, identify new digital catalogue formats, and cross-reference materials so that more thorough and systematic data analysis can be undertaken.

Outputs

To conduct a review of relevant literature and digital tools.

Document events, findings and work in progress through a project blog.

To construct in open source software a 'Heritage Connector' capable of holding a dense web of links between object records and knowledge graphs such as Wikidata for evaluation and experimentation.

To apply a series of digital tools / computational methods to create speculative identifications between different records within the test dataset. Among the computation methods to be trialled are:

Named entity recognition to determine which items in the catalogue text map to proper names, such as people or places.

Named entity linking to recognise and disambiguate named entities to relevant knowledge bases.

Machine learning to construct classifiers and cluster similar and related content.

A range of existing reconciliation tools and services to establish relationships and associated confidence levels for links.

To work on successively larger and varied datasets as the project proceeds in three stages, starting with Science Museum Group Collection records, then adding those of the V&A.

At every stage to hold focused workshops for groups relevant to each of the project's phase, including a hackathon towards the end of the project.

To write a report to the AHRC on our findings.

To write two articles for peer reviewed journals.

Related links
Zotero Library Project blog GitHub repository YouTube channel
Project team

Kalyan Dutia, Research Developer

Rhiannon Lewis, Project Coordinator and Doctoral research student, School of Advanced Study, University of London

John Stack, Digital Director, Science Museum Group

Jamie Unwin, Technical Architect, Science Museum Group

Jane Winters, Professor of Digital Humanities & Pro-Dean for Libraries, School of Advanced Study, University of London

Angela Wolff, Full Stack Developer, V&A

Project partners

In partnership with the School of Advanced Study, University of London and the V&A.

Heritage Connector is funded by the Arts and Humanities Research Council's Towards a National Collection: Opening UK Heritage to the World fund.

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Heritage Connector: Transforming text into data to extract meaning and make connections

# data[].attributes.attachment_link.description
As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant.

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IUCN status shows on species pages on the website now. See example.

https://www.gbif.org/species/2442647 (This is something that Andrew worked with Informatics to get up on the Species pages).

Also, this tool was just featured in July.

https://www.gbif.org/data-use/2hb9pd02gOKZaCpgRVhw8I/

Olly Griffin developed EasyOpen Red List. https://git-og.github.io/EasyOpenRedList/

Here is a CITES Data Use article that was featured in July. https://www.gbif.org/data-use/IWFUJcctBBTvN8E2wF0kM/

# data[].attributes.body
IUCN status shows on species pages on the website now. See example here:

https://www.gbif.org/species/2442647.

Also, this tool:

https://www.gbif.org/data-use/2hb9pd02gOKZaCpgRVhw8I/

Olly Griffen developed EasyOpen Red List. https://git-og.github.io/EasyOpenRedList/

As for CITES, here is a Data Use article. https://www.gbif.org/data-use/IWFUJcctBBTvN8E2wF0kM/

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The Digital Object (DO) Architecture (also known as the DO Architecture or simply the DOA) is a logical extension of the Internet architecture that addresses the need to support information management more generally than just conveying information in digital form from one location in the Internet to another.

# data[].attributes.attachment_link.readable_text
A digital object (DO) is a sequence of bits, or a set of sequences of bits, incorporating a work or portion of a work or other information in which a party has rights or interests, or in which there is value, each of the sequences being structured in a way that is interpretable by one or more of the computational facilities, and having as an essential element an associated unique persistent identifier.

For all practical purposes, the concept of a digital object is substantially similar to the notion of "digital entity" as defined in ITU-T Recommendation X.1255 that was based largely on the Digital Object Architecture. The ITU-T Recommendation is available in other languages. An "entity", in that recommendation, is defined as anything that can be separately and uniquely identified. It also describes a "digital entity" (DE) as an entity that is represented as, or converted to, a machine-independent data structure consisting of one or more elements that may be parsed by different information systems.

# data[].attributes.attachment_link.description
A digital object (DO) is a sequence of bits, or a set of sequences of bits, incorporating a work or portion of a work or other information in which a party has rights or interests, or in which there is value, each of the sequences being structured in a way that is interpretable by one or more of the computational facilities, and having as an essential element an associated unique persistent identifier.

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Biodiversity Digitization: Celebrating a decade of progress

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Biodiversity Digitization: Celebrating a decade of progress

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This full two-day virtual event will be co-sponsored by iDigBio, Smithsonian's National Museum of Natural History, and GBIF and will highlight the collective successes of mobilization and digitization efforts in the US and abroad.

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Welcome! You are invited to join a webinar: Biodiversity Digitization: Celebrating a decade of progress. After registering, you will receive a confirmation email about joining the webinar.

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This full two-day virtual event will be co-sponsored by iDigBio, Smithsonian's National Museum of Natural History, and GBIF and will highlight the collective successes of mobilization and digitization efforts in the US and abroad.

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PDA Event: Enhancing DEIA in Digital Collections Symposium

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PDA Event: Enhancing DEIA in Digital Collections Symposium

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[5 August 2021 - 8:30am to 12:30pm] The Minnesota Digital Library is hosting an online symposium on Enhancing Diversity, Equity, Inclusion, and Accessibility (DEIA) in Digital Collections on Thursday, August 5th.

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The Minnesota Digital Library is hosting an online symposium on Enhancing Diversity, Equity, Inclusion, and Accessibility (DEIA) in Digital Collections on Thursday, August 5th. The event will take place from 8:30 am to 12:30 pm Central, and our keynote is Mark Puente, the Associate Professor and Associate Dean for Organizational Development, Diversity, and Inclusion at the Purdue University Libraries and School of Information Studies. He will be followed by a select panel of speakers from around the country, including Melanie Adams, Director, Anacostia Community Museum; Stephanie Johnson-Cunningham, Co-Founder, Museum Hue; Dr. Adriene Lim, Dean of Libraries, University of Maryland-College Park; Kristi Park, Executive Director, Texas Digital Library; and Dr. Anne Phibbs, Founder and President of Strategic Diversity Initiatives. The event will close with an opportunity to participate in small group discussions to share information and develop potential strategies for action to enhance diversity, equity, inclusion, and accessibility in your organization's collection. This symposium is free and open to the public.
Register for this event.
Sponsored by Minitex

# data[].attributes.attachment_link.title
PDA Event: Enhancing DEIA in Digital Collections Symposium

# data[].attributes.attachment_link.description
The Minnesota Digital Library is hosting an online symposium on Enhancing Diversity, Equity, Inclusion, and Accessibility (DEIA) in Digital Collections on Thursday, August 5th. The event will take place from 8:30 am to 12:30 pm Central, and our keynote is Mark Puente, the Associate Professor and Associate Dean for Organizational Development, Diversity, and Inclusion at the Purdue University Libraries and School of Information Studies.

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Data sharing practices and data availability upon request differ across scientific disciplines

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Data sharing practices and data availability upon request differ across scientific disciplines

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Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility.

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Introduction

Technological advances and accumulation of case studies have led many research fields into the era of 'big data' - the possibility to integrate data from various sources for secondary analysis, e.g. meta-studies and meta-analyses. Nearly half of the researchers commonly use data generated by other scientists. Data sharing is a scientific norm and an important part of research ethics in all disciplines, also increasingly endorsed by publishers, funders and the scientific community. Despite decades of argumentation, much of the published data is still essentially unavailable for integration into secondary data analysis and evaluation of reproducibility, a proxy for reliability. Furthermore, the deposited data may also be incomplete, sometimes intentionally, e.g. in cases these exhibit mismatching sample codes or lack information about important metadata such as sex and age of studied organisms in biological and social sciences.

Although the vast majority of researchers prefer data sharing, scientists tend to be concerned about losing their priority in future publishing and potential commercial use of their work without their consent or participation. Researchers working on human subjects may be bound by legal agreements not to reveal sensitive data. Across research fields, papers indicating available data are cited on average 25% more. In research using microarrays, papers with access to raw data accumulate on average 69% more citations compared with other articles. Unfortunately, higher citation rate has not motivated many researchers enough to release their data, although referees and funding agencies account for bibliometrics when evaluating researchers and their proposals. Multiple case studies have revealed high variation in data availability in different journals and disciplines, ranging from 9 to 76%. Data requests to authors are successful in 27-59% of cases, whereas the request is ignored in 14-41% cases based on previous research. To promote access to data, many journals have implemented mandatory data availability statements and require data storage in supplementary materials or specific databases. Because of poor enforcement, this has not always guaranteed access to published data because of broken links, the lack of metadata or the authors' lack of willingness to share upon request.

This study aims to map and evaluate cross-disciplinary differences in data sharing, authors' concerns and reasons for denying access to data, and whether these decisions are reflected in article citations (Fig. 1). We selected the scholarly articles published in journals Nature and Science because of their multidisciplinary contents, stringent data availability policies outlined in authors' instructions, and high-impact conclusions derived from the data of exceptional size, accuracy and/or novelty. We hypothesised that in spite of overall improvement in data sharing culture, the actual data availability and reasons for declining the requests to share data depend on scientific disciplines because of field-specific 'traditions', 'sensitivity' of data, or their economic potential. Our broader goal is to improve data sharing principles and policies among authors, academic publishers and research foundations.

Results

Initial and final data availability

We evaluated the availability of most critical data in 875 articles across nine scientific disciplines (Table S1) published in Nature and Science over two 10-year intervals (2000-2009 and 2010-2019) and, in case these data were not available for access, we contacted the authors. The initial (pre-contacting) full and at least partial data availability averaged at 54.2% (range across disciplines, 33.0-82.8%) and 71.8% (40.4-100.0%), respectively. Stepwise logistic regression models revealed that initial data availability differed by research field, type of data, journal and publishing period (no vs. full availability: n = 721; Somers' D = 0.676; R ² model = 0.476; P < 0.001). According to the best model (Table S2), the data were less readily available in materials for energy and catalysis (W = 68.0; β = −1.52 ± 0.19; P < 0.001), psychology (W = 55.6; β = −1.11 ± 0.15; P < 0.001), optics and photonics (W = 18.8; β = −0.59 ± 0.14; P < 0.001) and forestry (W = 9.8; β = −0.52 ± 0.19; P = 0.002) compared with other disciplines, especially humanities (Fig. 2). Data availability was relatively lower in the period of 2000-2009 (W = 82.5; β = −0.57 ± 0.10; P < 0.001) and when the most important data were in the form of a dataset (relative to image/video and model; W = 41.5; β = −1.23 ± 0.19; P < 0.001; Fig. 3). Relatively less data were available for Nature (W = 32.7; β = −0.57 ± 0.19; P < 0.001), with striking several-fold differences in optics and photonics (Fig. 2).

Types of critical data (n = 875). ( a) Distribution of data types among disciplines (blue, dataset; purple, image; black, model); ( b) Partial (light shades) and full (dark shades) data availability among disciplines depending on the type of critical data (DS, dataset; Img, image; Mod, model) before contacting the author(s).

Upon contacting the authors of 310 papers, the overall data availability was improved by 35.0%. Full and at least partial availability averaged 69.5% (range across disciplines, 57.0-87.9%) and 83.2% (64.9-100.0%), respectively (Fig. 4), after 60 days since contacting, a reasonable time frame. The final data availability (after contacting the authors) was best predicted by scientific discipline, data type and time lapse since publishing (no vs. full availability: n = 580; D = 0.659; R ² model = 0.336; P adj < 0.001; Table S2) but with no major changes in the ranking of disciplines or data types compared with the initial data availability (Fig. 4). It took a median of 15 days to receive data from the authors (Fig. 5), with a minimum time of 13 minutes. Four authors sent their data after the 60-days period since the initial request (max. 107 days). The rate of receiving data was unrelated to any studied parameter.

Authors' responses to data requests

The data were obtained from the authors in 39.4% of data requests on average, with a range of 27.9-56.1% among research fields. The likelihood of receiving data, the request being declined or ignored depended mostly on the time period and field of research. According to the best model (n = 310; D = 0.300; R ² model = 0.106; P adj < 0.001; Table S2), the data were obtained slightly less frequently for the earlier time period (29.4% vs. 56.0%; W = 20.4; β = 0.56 ± 0.12; P adj < 0.001). Receiving data upon request tended to be lowest in the field of forestry (W = 3.6; β = −0.31 ± 0.16; P adj = 0.177), especially when compared with microbiology (Fig. 2).

Declining the data request averaged 19.4% and it differed most strongly among the research fields. The best model (n = 310; D = 0.508; R ² model = 0.221; P adj < 0.001) revealed that the data were not made available upon request most likely in the fields of social sciences (W = 24.3; β = −1.09 ± 0.22; P adj < 0.001), psychology (W = 20.0; β = −0.73 ± 0.20; P adj < 0.001) and humanities (W = 5.0; β = −0.67 ± 0.30; P adj = 0.078) compared with natural sciences (Fig. 2). Furthermore, the data request was more likely to be declined when the data complexity was high (W = 9.8; β = −0.59 ± 0.19; P adj = 0.005), the paper was not open access in ISI Web of Science (W = 4.0; β = −0.37 ± 0.18; P adj = 0.132) and published in Science rather than Nature (W = 4.6; β = −0.35 ± 0.16 P adj  = 0.096), although these two latter figures are non-significant when accounting for multiple testing.

We received no response to 41.3% of our data requests, including two biweekly reminders. Responding to the data request differed most strongly among scientific disciplines and time periods (Fig. 6). Altogether 28.9% and 49.0% of requests were ignored by the authors of earlier (2000-2009) and later (2010 to 2019) papers, respectively. According to the best model (n = 310; D = 0.429; R ² model = 0.200; P adj < 0.001; Table S2), articles from the earlier time period (W = 9.3; β = 0.41 ± 0.13; P adj = 0.007) and the fields of forestry (W = 13.4; β = −0.57 ± 0.16; P adj < 0.001) and ecology (W = 7.0; β = −0.53 ± 0.20; P adj = 0.024) had the greatest likelihood of no response, whereas social scientists (W = 7.7; β = 0.87 ± 0.31; P adj  = 0.016) answered most frequently.

Authors' response to data request (n = 199) depending on discipline (blue, declined; orange, ignored; purple, obtained). Bars indicate 95% CI of Sison and Glaz. Letters above bars indicate statistically significant difference groups in frequency of data availability by each category based on Tukey post-hoc test and Bonferroni correction.

In general, there was no residual effect of time since publication when the publication period was included in the best model. Within the 2010-2019 period, we specifically tested whether the authors publishing in 2019 and 2018 were less likely to share their data because of potential conflicting publishing interests. This hypothesis was not supported and a non-significant reverse trend was observed as the proportion of data obtained from the authors increased from 44% in 2010-2017 to 63% in 2018-2019. Accounting for time since publishing across the entire survey period, the data availability upon request decayed at a rate 5.9% year −1 based on an exponential model. This estimate was marginally higher than the 3.5% annual loss of publicly available data (Fig. 7). The number of articles was insufficient to test differences in data decay rates among disciplines.

Authors' concerns and reasons for declining data sharing

Upon contacting the authors, we recorded and categorised their concerns and requests related to data sharing (n = 188 authors) and their reasons for decline (n = 65). Altogether 22.9% of authors were concerned about certain aspects of our request (Fig. 8). Authors of non-open access publications (W = 4.6; β = 0.49 ± 0.23; P adj = 0.064) and the field of humanities (W = 9.7; β = 1.11 ± 0.36; P adj = 0.004) expressed any types of concerns or requests relatively more often (Table S2). In particular, researchers in the fields of humanities (W = 15.2; β = 1.36 ± 0.35; P adj < 0.001), materials for energy and catalysis (W = 6.4; β = 0.65 ± 0.26; P adj = 0.022) and ecology (W = 5.6; β = 0.81 ± 0.34; P adj  = 0.036) were more concerned about the study's specific purpose than researchers on average.

Frequency distribution of authors' ( a) Concerns and requests (n = 199) and ( b) reasons for declining data sharing (n = 67). White bars indicate answers where no concerns or reasons were specified.

Data sharing was declined by 33.0% of the 188 established contacts. When we specifically inquired about the reasons, the lack of time to search for data (29.2%), loss of data (27.7%) and privacy or legal concerns (23.1%) were most commonly indicated by the authors (Fig. 8), whereas no specific answer was provided by 10.8% of authors. According to the best binomial models (Table S2), social scientists indicated data loss more commonly than other researchers (W = 10.9; β = 1.04 ± 0.32; P adj = 0.003) and psychologists pointed most commonly to legal or privacy issues (W = 4.9; β = 0.85 ± 0.38; P adj = 0.078). Data decline due to legal issues became increasingly important in more recent publications (days since 01.01.2000: W = 7.2; β = 0.07 ± 0.03; P adj = 0.035). The lack of time to search tended to be more common for older studies (W = 4.0; β = −0.73 ± 0.37; P adj  = 0.135).

Data storage options and citations

The ways how the data were released differed greatly among disciplines (Fig. 9), with most common storage options being the supplementary materials on the publisher's website (62.2% of articles), various data archives (22.3%) and upon request from corresponding authors (19.7%). Although 29.8% articles declared depositing data in multiple sources, no source was indicated for 35.0% of articles. Declaring data availability upon request (n = 172) ranged from 1.0% in psychology to 52.0% in forestry, with greater frequency in earlier (days back since 31.09.2019: W = 15.0; β = 0.016 ± 0.004; P < 0.001) studies and articles by non-North American corresponding authors (by primary affiliation; W = 5.6; β = 0.23 ± 0.10; P = 0.018). With a few exceptions (three datasets only commercially available, one removed during final acceptance and one homepage corrupt), all data were successfully located for other indicated data sources, but only 42.3% of data could be obtained from the authors upon request in practice. This rate is comparable to articles with no such statement (38.3%; Chi-square test: P = 0.501).

The number of citations to articles ranged from 0.0 to 692.9 per year (median, 23.1). In contrast to the hypothesis that articles with available data accumulate more citations, general linear modelling revealed no significant effect of initial or final data availability on annual citations. The model demonstrated that the average number of yearly citations was explained by research discipline (F 8,855 = 11.2; R ² = 0.105; P < 0.001), data type (F 2,855 = 7.0; R ² = 0.016; P < 0.001), open access status (F 1,855 = 4.5; R ² = 0.005; P = 0.034) and the interaction term between open access and discipline (F 8,855 = 2.94; R ² = 0.027; P = 0.003). Post-hoc tests indicated that articles with a dataset as a critical data source were cited on average 6% more than those with an image or model, and open access articles attracted 9% more citations than regular articles. Because of high variability in citation counts, it was not possible to test the interaction terms with scientific discipline in the current dataset. We speculate that the articles in Nature and Science are heavily cited on the basis of their key findings and interpretations that may mask the few extra citations raising from re-use of the data.

Discussion

Our study uniquely points to differences among scientific disciplines in data availability as published along with the article and upon request from the authors. We demonstrate that in several disciplines such as forestry, materials for energy and catalysis and psychology, critical data are still unavailable for re-analysis or meta-analysis for more than half of the papers published in Nature and Science in the last decade. These overall figures roughly match those reported for other journals in various research fields, but exceed the lowest reported values of around 10% available data. Fortunately, data availability tends to improve, albeit slowly, in nearly all disciplines (Figs. 3, 7), which confirms recent implications from psychological and ecological journals. Furthermore, the reverse trend we observed in microbiology corroborates the declining metagenomics sequence data availability. Typically, such large DNA sequence data sets are used to publish tens of articles over many years by the teams producing these data; hence releasing both raw data and datasets may jeopardise their expectations of priority publishing. The weak discipline-specific differences among Nature and Science (Fig. 2) may be related to how certain subject editors implemented and enforced stringent data sharing policies.

After rigorous attempts to contact the authors, data availability increased by one third on average across disciplines, with full and at least partial availability reaching 70% and 83%, respectively. These figures are in the top end of studies conducted thus far and indicate the relatively superior overall data availability in Science and Nature compared with other journals. However, the relative rates of data retrieval upon request, decline sharing data and ignoring the requests were on par with studies covering other journals and specific research fields. Across 20 years, we identified the overall loss of data at an estimated rate of 3.5% and 5.9% for initially available data and data effectively available upon request, respectively. This rate of data decay is much less than 17% year −1 previously reported in plant and animal sciences based on a comparable approach.

While the majority of data are eventually available, it is alarming that less than a half of the data clearly stated to be available upon request could be effectively obtained from the authors. Although there may be objective reasons such as force majeure, these results suggest that many authors declaring data availability upon contacting may have abused the publishers' or funders' policy that allows statements of data availability upon request as the only means of data sharing. We find that this infringes research ethics and disables fair competition among research groups. Researchers hiding their own data may be in a power position compared with fair players in situations of big data analysis, when they can access all data (including their own), while others have more limited opportunities. Data sharing is also important for securing a possibility to re-analyse and re-interpret unexpected results and detect scientific misconduct. More rigorous control of data release would prevent manuscripts with serious issues in sampling design or analytical procedures from being prepared, reviewed and eventually accepted for publication.

Our study uniquely recorded the authors' concerns and specific requests when negotiating data sharing. Concerns and hesitations about data sharing are understandable because of potential drawbacks and misunderstandings related to data interpretation and priority of publishing that may outweigh the benefits of recognition and passive participation in broader meta-studies. Nearly one quarter of researchers expressed various concerns or had specific requests depending on the discipline, especially about the specific objectives of our study. Previous studies with questionnaires about hypothetical data sharing unrelated to actual data sharing reveal that financial interests, priority of additional publishing and fear of challenging the interpretations after data re-analysis constitute the authors' major concerns. Another study indicated that two thirds of researchers sharing biomedical data expected to be invited as co-authors upon use of their data although this does not fulfil the authorship criteria. At least partly related to these issues, the reasons for declining data sharing differed among disciplines: while social scientists usually referred to the loss of data, psychologists most commonly pointed out ethical/legal issues. Recently published data were, however, more commonly declined due to ethical/legal issues, which indicates rising concerns about data protection and potential misuse. Although we offered a possibility to share anonymised data sets, such trimmed data sets were never obtained from the authors, suggesting that ethical issues were not the only reason for data decline. Because research fields strongly differed in the frequency of no response to data requests, most unanswered requests can be considered declines that avoid official replies, which may harm the authors' reputation.

Because we did not sample randomly across journals, our interpretations are limited to the journals Nature and Science. Our study across disciplines did not account for the particular academic editor, which may have partly contributed to the differences among research fields and journals. Not all combinations of disciplines, journals and time periods received the intended 25 replicate articles because of the poor representation of certain research fields in the 2000-2009 period. This may have reduced our ability to detect statistically significant differences among the disciplines. We also obtained estimates for the final data availability for seven out of nine disciplines. Although we excluded the remaining two disciplines from comparisons of initial and final data availability, it may have slightly altered the overall estimates. The process of screening the potentially relevant articles chronologically backwards resulted in overrepresentation of more recent articles in certain relatively popular disciplines, which may have biased comparisons across disciplines. However, the paucity of residual year effect and year x discipline interaction in overall models and residual time effect in separate analyses within research fields indicate a minimal bias (Figure S1).

We recorded the concerns and requests of authors that had issues with initial data sharing. Therefore, these responses may be relatively more sceptic than the opinions of the majority of the scientific community publishing in these journals. It is likely that the authors who did not respond may have concerns and reasons for declining similar to those who refused data sharing.

Our experience shows that receiving data typically required long email exchanges with the authors, contacting other referred authors or sending a reminder. Obtaining data took on average 15 days, representing a substantial effort to both parties. This could have been easily avoided by releasing data upon article acceptance. On the other hand, we received tips for analysis, caution against potential pitfalls and the authors' informed consent upon contacting. According to our experience, more than two thirds of the authors need to be contacted for retrieving important metadata, variance estimates or specifying methods for meta-analyses. Thus, contacting the authors may be commonly required to fill gaps in the data, but such extra specifications are easier to provide compared with searching and converting old datasets into a universally understandable format.

Due to various concerns and tedious data re-formatting and uploading, the authors should be better motivated for data sharing. Data formatting and releasing certainly benefits from clear instructions and support from funders, institutions and publishers. In certain cases, public recognition such as badges of open data for articles following the best data sharing practices and increasing numbers of citations may promote data release by an order of magnitude. Citable data papers are certainly another way forward, because these provide access to a well-organised dataset and add to the authors' publication record. Encouraging enlisting published data sets with download and citation metrics in grant and job applications alongside with other bibliometric indicators should promote data sharing. Relating released data in publicly available research accounts such as ORCID, ResearcherID and Google Scholar would benefit both authors, other researchers and evaluators. To account for many authors' fear of data theft and to prioritise the publishing options of data owners, setting a reasonable embargo period for third-party publishing may be needed in specific cases such as immediate data release following data generation and dissertations.

All funders, research institutions, researchers, editors and publishers should collectively contribute to turn data sharing into a win-win situation for all parties and the scientific endeavour in general. Funding agencies may have a key role here due to the lack of conflicting interests and a possibility of exclusive allocation to depositing and publishing huge data files. Funders have efficient enforcing mechanisms during reports periods, with an option to refuse extensions or approving forthcoming grant applications. We advocate that funders should include published data sets, if relevant, as an evaluation criterion besides other bibliometric information. Research institutions may follow the same principles when issuing institutional grants and employing research staff. Institutions should also insist their employees on following open data policies.

Academic publishers also have a major role in shaping data sharing policies. Although deposition and maintenance of data incur extra costs to commercial publishers, they should promote data deposition in their servers or public repositories. An option is to hire specific data editors for evaluating data availability in supplementary materials or online repositories and refusing final publishing before the data are fully available in a relevant format. For efficient handling, clear instructions and a machine-readable data availability statement option (with a QR code or link to the data) should be provided. In non-open access journals, the data should be accessible free of charge or at reduced price to unsubscribed users. Creating specific data journals or 'data paper' formats may promote publishing and sharing data that would otherwise pile up in the drawer because of disappointing results or the lack of time for preparing a regular article. The leading scientometrics platforms Clarivate Analytics, Google Scholar and Scopus should index data journals equally with regular journals to motivate researchers publishing their data. There should be a possibility of article withdrawal by the publisher, if the data availability statements are incorrect or the data have been removed post-acceptance. Much of the workload should stay on the editors who are paid by the supporting association, institution or publisher in most cases. The editors should grant the referees access to these data during the reviewing process, requesting them a second opinion about data availability and reasons for declining to do so. Similar stringent data sharing policies are increasingly implemented by various journals.

In conclusion, data availability in top scientific journals differs strongly by discipline, but it is improving in most research fields. As our study exemplifies, the 'data availability upon request' model is insufficient to ensure access to datasets and other critical materials. Considering the overall data availability patterns, authors' concerns and reasons for declining data sharing, we advocate that (a) data releasing costs ought to be covered by funders; (b) shared data and the associated bibliometric records should be included in the evaluation of job and grant applications; and (c) data sharing enforcement should be led by both funding agencies and academic publishers.

Materials and Methods

Data collection

To assess differences in data availability in different research disciplines, we focused our study on Nature and Science, two high-impact, general-interest journals that practise relatively stringent data availability policies. Because of major changes in the public attitude and journals' policies about data sharing, our survey was focused on two study periods, 2000-2009 and 2010-2019. We selected nine scientific disciplines as defined by the Springer Nature publishing group - biomaterials and biotechnology, ecology, forestry, humanities, materials for energy and catalysis, microbiology, optics and photonics, psychology and social sciences (see Table S1 for details) - for analysis based on their coverage in Nature and Science journals and data-driven research. These nine disciplines were selected based on the competence of our team and the objective to cover as different research fields as possible including natural sciences, social sciences and humanities. The articles were searched by discipline, keywords and/or manual browsing as follows. For Nature, our search was refined as https://www.nature.com/search?order=date_desc&journal=nature&article_type=research&subject=microbiology&date_range=2010-2019 (italicised parts varied). For Science, the corresponding search string was the following: https://search.sciencemag.org/?searchTerm=microbiology&order=newest&limit=textFields&pageSize=10&startDate=2010-01-01&endDate=2019-08-31&articleTypes=Research%20and%20reviews&source=sciencemag%7CScience. In both journals, the articles were retrieved by browsing search results chronologically backwards since September 2019 or September 2009 until reaching 25 articles matching the criteria. When the number of suitable articles was insufficient, we searched by using additional discipline-specific keywords in the title and browsed all issues manually when necessary. In some research fields, 25 articles could not be found for all journal and time period combinations and therefore, data availability was evaluated for 875 articles in total (Table S1). In each article, we identified a specific analysis or result that was critical for the main conclusion of that study based on both the authors' emphasis and our subjective assessment. We determined whether the underlying data of these critical results - datasets, images (including videos), models (including scripts, programs and analytical procedures) or physical items - are available in the main text, supplementary materials or other indicated sources such as specific data repositories, authors' homepages, museums, or upon request to the corresponding author (Figure S2). When available, we downloaded these data, checked for relevant metadata, identifiers and other components, and evaluated whether it is theoretically possible to repeat these specific analyses and include these materials in a field-specific metastudy. For example, in the case of a dataset, we evaluated the data table for the presence of relevant metadata and sample codes necessary to perform the analysis; for any statistical procedure, the authors must have used such a data table in their original work. We considered the data to be too raw if these either required a large amount of work (other than common data transformations) to generate the data table or model, or we had doubts whether the same data table can be reproduced with the methods described. Raw high-throughput sequencing data are typical examples of incomplete datasets, because these usually lack necessary metadata and require a thorough bioinformatics analysis, with the output depending on software and selected options. For further examples, certain optical raw images or videos make no sense without expert filtering, and computer scripts are of limited use without thorough instructions.

If these critical data were unavailable or only partly available (i.e., missing some integral metadata, instructions or explanations), we contacted the first corresponding author or a relevant author referred in relation to access to the specific item, requesting the data for a meta-study by using a pre-defined format and an institutional email address (Item S1). In the email, we carefully specified the materials required to produce a particular figure or table to avoid confusion and upsetting the authors with a messy request. We indicated that the data are intended for a metastudy in a related topic to test the authors' willingness to share the data for actual use, not just their intention to share for no reasonable purpose. We similarly evaluated the received data for integrity and requested further information, if necessary, to meet the standards. We also recorded the responses of corresponding authors to data requests, including any specific requests or concerns and reasons for declining (Item S1).

The authors were mostly contacted early in the week and two reminders were sent ca. 14 and 28 days later if necessary (Item S1). The reminders were also addressed to other corresponding authors if relevant. If emails were returned with an error message, we contacted other corresponding authors or used an updated email address found from the internet or newer publications. We considered 60 days from sending the first email a reasonable time period for the authors to locate and send the requested data.

For each article, we recorded the details of publishing (date printed, journal, discipline), corresponding authors (number, country of first affiliation, acquaintance to the contact author) and data (availability, type, ways of access). Data complexity was evaluated based on the authors' relative amount of extra work to polish the raw data (e.g. low-complexity data include raw DNA sequence data, raw images, artefacts; high-complexity data include bioinformatics-treated molecular data sets, noise-removed images, models and scripts). As of 23.03.2020, we recorded the open access status and number of citations for each article using searches in the ISI Web of Science ( https://apps.webofknowledge.com/). The citation count was expressed as citations per year, discounting the first 90 days with initially less citations.

Data analysis

The principal aim of this study was to determine the relative importance of scientific discipline and time period on data availability and authors' concerns in response to data sharing requests, by accounting for multiple potentially important covariates (Fig. 1). The response variables, i.e. initial and final data availability (none, partly or fully available), author's responses (ignored, data shared or declined), concerns and reasons for decline, exhibit multinomial distribution and were hence transformed to dummy variables. Similarly, the multi-level explanatory variables (discipline, topic overlap, countries and continents of corresponding authors, data type and complexity) were transformed to dummies, whereas continuous variables (linear time, number of citations, time to obtain data, number of corresponding authors) were square root- or logarithm-transformed where appropriate. All analyses were performed in STATISTICA 12 (StatSoft Inc., Tulsa, OK, USA).

Data analysis of the dummy-transformed multinomial and binomial variables was performed using stepwise logistic regression model selection with a binomial link function using corrected Akaike information criterion (AICc) as a selection criterion, and Somers' D statistic and model determination coefficients (R ²) as measures of overall goodness of fit. Determination coefficients and Wald's W statistic were used to estimate the relative importance of explanatory variables. We calculated 95% confidence intervals for multiple proportions using the R package multinomialCI ( https://rdrr.io/cran/MultinomialCI/). Increasing false discovery rates related to multiple comparisons were accounted for by using Bonferroni correction of P-values (expressed as P adj) where appropriate.

Models with continuous response variables (proportion of available data, annual citations, time to receive data) were tested using general linear models in two steps. First, the model selection included only dummy and continuous explanatory variables. Multilevel categorical predictors corresponding to significant dummies as well as significant continuous variables were included in the final model selection as based on forward selection. To check for potential biases related to the article selection procedure in both periods, we tested the effect of discipline, period and year and all their interaction terms on initial data availability by retaining all variables in the model (Figure S1). Differences in these factor levels were tested using Tukey post-hoc tests for unequal sample size, which accounts for multiple testing issues.

Data availability

The entire dataset is available as in a spreadsheet format in plutoF data repository.

Code availability

No specific code was generated for analysis of these data.

Acknowledgements

We thank all authors who released their data along with their article or responded to our data request. Although some of the obtained datasets are used in a series of meta-analyses or released by us upon agreement, we apologise to the authors who spent a significant amount of time to provide the data, which we cannot use for secondary analyses. We thank A. Kahru, T. Soomere, Ü. Niinemets and J. Allik for their constructive comments on an earlier version of the manuscript.

Author information

Affiliations

1. Estonian Young Academy of Sciences, Kohtu 6, 10130, Tallinn, Estonia

Leho Tedersoo, Rainer Küngas, Ester Oras, Kajar Köster, Helen Eenmaa, Äli Leijen, Marju Raju, Anastasiya Astapova, Heli Lukner, Karin Kogermann & Tuul Sepp

2. Mycology and Microbiology Center, University of Tartu, Ravila 14a, 50411, Tartu, Estonia

Leho Tedersoo

3. Institute of Chemistry, University of Tartu, Ravila 14a, 50411, Tartu, Estonia

Ester Oras

4. Institute of History and Archaeology, University of Tartu, Jakobi 2, 51005, Tartu, Estonia

Ester Oras

5. Department of Forest Sciences, University of Helsinki, PO Box 27 (Latokartanonkaari 7), Helsinki, FI-00014, Finland

Kajar Köster

6. School of Law, University of Tartu, Näituse 20, 50409, Tartu, Estonia

Helen Eenmaa

7. Institute of Education, University of Tartu, Salme 1a, 50103, Tartu, Estonia

Äli Leijen & Margus Pedaste

8. Department of Musicology, Music Pedagogy and Cultural Management, Estonian Academy of Music and Theatre, Tatari 13, 10116, Tallinn, Estonia

Marju Raju

9. Institute for Cultural Research and Fine Arts, University of Tartu, Ülikooli 16, 51003, Tartu, Estonia

Anastasiya Astapova

10. Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411, Tartu, Estonia

Heli Lukner

11. Institute of Pharmacy, University of Tartu, Nooruse 1, 50411, Tartu, Estonia

Karin Kogermann

12. Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, 51003, Tartu, Estonia

Tuul Sepp

Contributions

All authors contributed to study design, work with literature and writing. L.T. analysed data and led writing.

Corresponding author

Correspondence to Leho Tedersoo.

Ethics declarations

Competing interests

The authors declare no competing interests.

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Tedersoo, L., Küngas, R., Oras, E. et al. Data sharing practices and data availability upon request differ across scientific disciplines. Sci Data 8, 192 (2021). https://doi.org/10.1038/s41597-021-00981-0
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Data sharing practices and data availability upon request differ across scientific disciplines - Scientific Data

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Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors' concerns, requests and reasons for declining data sharing.

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Phase 2 of #DES consultation extended to 17 August

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Phase 2 of #DES consultation extended to 17 August

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Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration Phase 2.

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Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration Phase 2.

Start Here
This is the home page for the 2nd phase of the virtual consultation: Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration . GBIF, under the umbrella of the alliance for biodiversity knowledge, is leading the consultation as part of the European Commission-funded SYNTHESYS+ project. See here for Phase 1.

Please bookmark this page to follow the consultation.

On Tuesday, 15th June two virtual introductory sessions will take place to accommodate different time zones. Tentative Agenda**
Session 1 registration Session 2 registration About the consultation Read the discussion on Phase 1 topics 1-5.
Information resources (More on individual category pages)

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Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration Phase 2. Start Here This is the home page for the 2nd phase of the virtual consultation: Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration .

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RRIDs: A Way to Track Samples Through the Scientific Literature

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RRIDs: A Way to Track Samples Through the Scientific Literature

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[10 Aug] If you are interested in persistent identifiers, if you like reagents, but do not like how they are usually described in your friends’ papers, or you are someone within or outside the biomedical field and could learn about the implementation of persistent identifiers in scholarly communication then you should attend!

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There is an urgent need for better integrating physical objects into the digital research data ecosystem, both in a global and in an interdisciplinary context. This webinar series is aimed to help facilitate cross-domain exchange and convergence on key issues related to the digital representation of physical samples and collections.

The second webinar in this series will outline how RRIDs (Research Resource ID's), persistent identifiers used to track key biological resources, i.e., "physical samples" for the biomedical field, successfully drove improvements in resource identification. It will highlight lessons learned, and how any research field could learn from the challenges that the RRID project faced in implementing RRID's. Particularly the successful adoption of RRIDs by publishers and the research community is of interest to anyone interested in persistent identifiers.

The project that developed RRIDs started in 2012 and in 2014 launched the first RRID identifiers. Today, RRIDs are used in thousands of research papers, are included in 1000+ Journal instructions and guidelines, and have made a significant impact across the scientific literature.

In this webinar, we will share information about the RRID project including the things that worked well and the things that we might have done better. We will also discuss the data showing the impact of RRIDs across the scientific literature and how and when RRIDs should be used.

What types of samples are included in the RRID project?

What types of samples are not included in the RRID project?

Data, pipette tips, salts, and other "stuff" that mostly works as it is supposed to. RRIDs track the tricky bits in biology that do not necessarily work the same way from lab to lab, and end up being the cause of many reproducibility issues.

Attendees will learn about why RRIDs were created, when RRIDs should be used in a manuscript, and how to obtain and use an RRID. Computational scientists can learn how to obtain data on resource usage, as this is now becoming an interesting dataset in and of itself (see RRIDs in Hypothes.is).

If you are interested in persistent identifiers, if you like reagents, but do not like how they are usually described in your friends' papers, or you are someone within or outside the biomedical field and could learn about the implementation of persistent identifiers in scholarly communication then you should attend!

Presenter

# data[].attributes.attachment_link.title
RRIDs: A Way to Track Samples Through the Scientific Literature

# data[].attributes.attachment_link.description
The second webinar in this series will outline how RRIDs (Research Resource ID's), persistent identifiers used to track key biological resources, i.e., "physical samples" for the biomedical field, successfully drove improvements in resource identification. It will highlight lessons learned, and how any research field could learn from the challenges that the RRID project faced in implementing RRID's.

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Ecotaxonomy is an open cloud platform developed to integrate ecological research with taxonomic knowledge and expertise.

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Species are the main actors in environmental science and conservation politics. The knowledge of species diversity and composition is the foundation for studies on ecosystem functioning, community ecology ect.. But what makes a species so valuable? Apart from the aesthetic aspect, it is the ecosystem role that this species plays. On the global scale, only little is known on ecosystem roles of different species. This is especially true for tropical regions, where species are numerous and at the same time biological diversity remains undescribed. Ecological roles of species to an extent can be inferred from their morphology, allowing to extrapolate existing knowledge to unexplored regions and taxa. However, in most groups, the link between morphology and ecological roles is still not described. To fill this gap, we are developing a hub where people in different countries can together work on this task.

The Ecotaxonomy is a repository for identification characters and ecological traits of species and morphospecies. Our taxonomic backbone is based on the Global Biodiversity Information Facility (GBIF), which is being further attached with flexible system of morphological and ecological parameters, pictures and literature. As an output we are compiling publicly available catalogue on our web site. In collaboration with specialists worldwide we use the data on morphological identification characters to develop easy-in- use pictorial identification keys for different taxa.

We further developing universal morphological grids for different taxonomic groups to integrate existing biological knowledge in morphospecies-based parataxonomy. The Ecotaxonomy is a morphospecies repository that provides standardized definitions and linked to conventional taxonomic system. We believe that developing standardized system for morphospecies is mandatory to improve inter-study comparability and is a particularly important first step in ecological research in unexplored regions.

The Ecotaxonomy is a tool for data management in ecological projects. Whenever web connection is available, all members of the research team around the world is up-to- date with the project activities. A single common taxonomic and environmental backbone is used for the project to avoid simple typing mistakes or redundancy. With this, consistent and conclusive datasets can be produced to investigate ecological questions. Project relevant data of study sites and environmental parameter are linked directly to taxonomic backbone, and species morphological and ecological traits. By this link, species or supraspecific taxa collected during the project connect geographical and environmental data to morphological characters and ecological traits. New taxa and traits added to the database during the project with time become publicly available and can be used by any other project.

The Ecotaxonomy database was developed in the framework of the EFFortTS project to manage the undescribed species and connect several people working on the same dataset. Initially, database was used for soil mites (Oribatida) from Jambi province, Indonesia. Successful experience with this group inspired us to further develop a universal system that can be used for various projects and groups of organisms. Identification key that based on the morphology of species is created by continuous work and can be improved anytime. Existing data can be easily uploaded to the database but primary way is to enter data during the ongoing research, which requires minimum additional efforts and moreover - can greatly facilitate the research process.

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Ecotaxonomy database | traits and species

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Species are the main actors in environmental science and conservation politics. The knowledge of species diversity and composition is the foundation for studies on ecosystem functioning, community ecology ect.. But what makes a species so valuable? Apart from the aesthetic aspect, it is the ecosystem role that this species plays.

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How Science Moved Beyond Peer Review During The Pandemic

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How Science Moved Beyond Peer Review During The Pandemic

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And what scientists learned they still needed it for.

By Maggie Koerth

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When papers from China began flooding the websites bioRxiv and medRxiv in the first months of 2020, it was a strange and notable change. Founded as places where scientists could post drafts of research papers before those papers went through a traditional peer review, these sites had never really advertised much in China or gotten many submissions from scientists in that country before, said Richard Sever, the co-founder of bioRxiv and medRxiv. The sudden shift turned out to be a preview of the pandemic to come. "We got a wave of submissions from China and then a wave of submissions from Italy. And I remember being with a colleague, looking at submission numbers, and the chart was so eerily familiar," Sever said. "It looks just like the progression of pandemic caseloads."

"Preprint," or "prepress," servers have been around for decades, but during the COVID-19 pandemic, they took on a new notoriety and level of importance. Two of these sites alone, bioRxiv and medRxiv, hosted 25 percent of all COVID-19-related scientific research published during the pandemic's first 10 months - more than 10,000 papers. In contrast, only 78 preprints were uploaded to bioRxiv during the entire 2015-16 Zika epidemic. (The medRxiv site didn't exist yet.) COVID-19 proved to scientists that preprint servers are crucial resources in a crisis. At the same time, though, experts said the pandemic also made the shortcomings of scientific publishing clear. Preprints share a lot of the same limitations as peer-reviewed research, especially when it comes to how the media and the public use research once it's out in the world for all to see.

Websites that post scientific research before it has been peer-reviewed and published in a scientific journal have been active since at least 1991, when the classic arXiv site, originally used mainly by physicists, went live. Even before that, scientists have shared drafts and notes among themselves through personal correspondence ever since "science" as a field became a thing. Those lines of communication exist parallel to the traditional process of peer review, which puts gatekeepers between research and publication. First, a paper has to be accepted by the editors of a scientific journal. Then, it goes to a (usually anonymous) panel of other scientists for critique. Their notes will lead to edits, which usually lead to more experiments, and then (hopefully) eventual publication.

But the peer-review process does not happen quickly. The editors are sorting through thousands of dense submissions, and the reviewers are volunteers reading in their free time. On average, it takes about six months for a life-sciences research paper to go from acceptance to publication. "During the outbreak we maintained the same peer-review and editing process, just on an accelerated schedule ... but I don't think that this is a sustainable model," said Dr. Eric Rubin, editor in chief of the New England Journal of Medicine.

A year without karaoke | FiveThirtyEight

He and other scientists said the issue of speed was particularly important for clinical research during the pandemic because doctors needed the most up-to-date information to make life-saving decisions for critically ill patients. For example, the large trial of the steroid dexamethasone - which first demonstrated that this cheap, widely available drug could reduce the likelihood of death in COVID-19 patients - was originally posted as a preprint. It would show up, traditionally published, in the New England Journal of Medicine eight months later, but the preprint made it possible to get that knowledge into doctors' hands faster. "I had one MD who contacted me, and he said, 'You know, there are probably people who are alive today who would have been dead if not for preprints,'" Sever said.

Successes like that have contributed to making preprints more widely accepted among biological and medical scientists. It also helps that research in preprints isn't a lot different statistically from how those same papers eventually end up being published in a peer-reviewed scientific journal. A 2020 study of pre-COVID-19 bioscience that compared preprint papers and their later, peer-reviewed versions, for example, found that the biggest differences were in details like how clearly the title reflected the conclusions or how easy it was to find relevant information in the article.

But that should be understood as a critique of the peer-review process rather than a glowing endorsement of preprint paper accuracy, said Alice Fleerackers, a graduate student and researcher at the Scholarly Communications Lab, a joint project of the Simon Fraser University and University of Ottawa. "There's really a perception that peer review is a trustworthy quality-control mechanism," Fleerackers said. But research hasn't been able to support that idea. All the jokes and paranoia about COVID-19 being spread by 5G cellphone technology, for instance, trace their origin to a paper published in - and later retracted by - a scientific journal that claims to peer-review its content.

"We've seen with lots of the misinformation and many of the retractions that came out during COVID ... 'high-quality' - and I'm using air quotes here - research in peer-reviewed publications can be just as flawed as research in preprint," Fleerackers said.

The real trouble with preprints - which is, funnily enough, also the real trouble with peer-reviewed research - is how those studies are promoted and written about on social media and by the press, experts told me.

"Prior to the pandemic, I had fewer concerns about poor-quality science being preprinted and then widely disseminated," said Maia Majumder, a computational epidemiologist at Harvard Medical School. "But now, everyday people are reading them too - and the media is covering them at a rate that far outpaces pre-2020. This means that when bad science is preprinted and happens to be sensational enough to get traction, it can quickly shape the discourse around a given topic."

One of the top-tweeted and top-reported-on preprints in the first 10 months of the pandemic was a study that tried to estimate the share of the population in Santa Clara County, California, who had already been infected with COVID-19. The results, which found evidence for a higher rate of infection in the general population than anyone had previously calculated, was used to support the idea that COVID-19 would cause few deaths and that no lockdowns or distancing measures would be necessary. It was heavily critiqued and estimates had to be revised downward before BuzzFeed News revealed the research had been funded in part by an airline magnate who was critical of COVID-19-related social restrictions.

It's unclear how much of the study's coverage in the media was actually reporting the findings as opposed to the criticism and controversy it caused, but Fleerackers's research suggests digital media outlets aren't routinely providing readers with the context they need to understand the limitations of preprint studies. In a study published in January, Fleerackers found that, about half the time, digital publications made no mention that a COVID-19 preprint study being discussed even was a preprint.

Reporting on preprints without explaining their limitations risks misleading the public the same way that reporting on peer-reviewed research as inherently accurate does, Fleerackers said. Though this reality was certainly known before the pandemic, the importance of preprint servers over the past year and a half has really driven the lesson home. Servers like bioRxiv and medRxiv have added prominent disclaimers to their pages. University press offices have begun to institute new rules that control the way preprint articles are promoted to the media. There's even a new journal specifically dedicated to reviewing hyped COVID-19 preprints - and debunking them, if necessary.

But the pandemic has also made clear how much the information in a preprint slips out of scientists' control the moment it is posted online. "I hope they're developing an awareness," said Fleerackers, "that if you put something in public it is fair game for journalists, and for the public ... and for conspiracy theorists."

'I buy that' lotteries are a 'smart nudge' to improve vaccination rates: Nate Silver

How White House economists are thinking about COVID-19 relief | FiveThirtyEight

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How Science Moved Beyond Peer Review During The Pandemic

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When papers from China began flooding the websites bioRxiv and medRxiv in the first months of 2020, it was a strange and notable change. Founded as places where scientists could post drafts of research papers before those papers went through a traditional peer review, these sites had never really advertised much in China or gotten many submissions from scientists in that country before, said Richard Sever, the co-founder of bioRxiv and medRxiv.

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Games for Change Virtual Festival 2021

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Games for Change Virtual Festival 2021 - Jul 12 | Hopin

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Games for Change Virtual Festival 2021 - Jul 12 | Hopin

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Get tickets to Games for Change Virtual Festival 2021, taking place 07/12/2021 to 07/15/2021. Hopin is your source for engaging events and experiences.

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Publishing DNA-derived data through biodiversity data platforms.

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Publishing DNA-derived data through biodiversity data platforms.

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Atlas of Living Australia (ALA)

Amplicon Sequence Variant (ASV)

Application Programming Interface (API)

Set of protocols and tools for interaction and data transmission between different computer applications.

Barcode Index Numbers (BINs)

Barcode of Life Data System (BOLD)

BOLD is the reference database maintained by the Centre for Biodiversity Genomics in Guelph on behalf of the International Barcode of Life Consortium ( IBOL). It hosts data on barcode reference specimens and sequences for eukaryote species, particularly COI for animals, and maintains the Barcode Index Number ( BIN; Ratnasingham & Hebert 2013) system, identifiers for OTUs of approximately species rank, based on clusters of closely similar sequences.

Biodiversity data platform

Clustering

In taxonomic classification, the process of grouping organisms together according to some similarity criterion. See Operational Taxonomic Unit.

Community (bulk) DNA

DNA from bulk samples (e.g. plankton samples or Malaise trap samples consisting of several individuals from many species). For the purpose of this guide, bulk sample DNA is included in the eDNA concept.

Darwin Core Archive (DwC-A)

Compressed (ZIP) file format for exchange of biodiversity data compiled in accordance with the Darwin Core (DwC) standard. Essentially a self-contained set of interconnected CSV files and an XML document describing included files and data columns, and their mutual relationships.

Darwin Core (DwC) standard

Standard for sharing and publishing biodiversity data, originating from the Biodiversity Information Standards (TDWG) community. In principle, a set of terms used for describing different entities of biodiversity observations, such as sampling events, occurrences and taxa. Current Darwin Core terms are described in the Quick Reference Guide.

Data vocabulary

Set of preferred terms or concepts with specific, well-defined meanings and interrelationships, facilitating data exchange and reuse.

ddPCR (droplet digital Polymerase Chain Reaction)

Denoising

In metabarcoding, method for separation of true biological sequences (see ASVs) from spurious sequence variants caused by PCR amplification and sequencing error.

Digital Object Identifier (DOI)

Long-lasting reference used to uniquely identify (and locate) digital information objects, such as a biodiversity data set or a scientific publication.

DNA barcoding and metabarcoding (amplicon sequencing)

Use of short, standardized DNA fragments to identify individual organisms via sequencing. Metabarcoding combines barcoding with high-throughput DNA sequencing, using universal primers to amplify and sequence large groups of organisms in eDNA samples.

DNA marker

A DNA fragment used as a marker of some property (e.g., taxonomic affiliation). May, but does not have to, be a gene or a part of a gene.

DNA metabarcoding database

Database containing DNA sequences (DNA barcodes) from previously recovered or studied organisms. The reference sequences were ideally generated from individuals of described, well-studied species-with the type specimen serving as the ideal-or higher taxonomic level (e.g., genus, family), but may also stem from eDNA sequencing efforts. It is wise not to trust "reference sequences" blindly.

DNA probe

European Bioinformatics Institute (EMBL-EBI)

Intergovernmental organization for bioinformatics research and services, part of the European Molecular Biology Laboratory (EMBL), providing eg. (raw) sequence reads and assembly data via the European Nucleotide Archive (ENA).

Environmental DNA (eDNA)

DNA from an environmental sample, e.g. soil, water, air or host organism. An often used definition is that environmental DNA is the genetic material (DNA) obtained from environmental samples without any obvious evidence of biological source material ( Thomsen and Willerslev 2015).

European Nucleotide Archive (ENA)

FASTQ

Text-based standard for storing molecular sequences and associated quality measures deriving from High-throughput sequencing (HTS). For each sequence position, single ASCII-characters are used to represent base call (identified nucleotide) and score, respectively.

Global Biodiversity Information Facility (GBIF)

International network and research infrastructure, mainly focused on mobilizing and providing open access to global biodiversity data.

Global Genome Biodiversity Network (GGBN)

International network of institutions concerned with efficient sharing and usage of genomic biodiversity samples and associated metadata, e.g. promoting the Darwin Core-compatible GGBN Data Standard.

Global Positioning System (GPS)

Satellite navigation system operated by the United States Space Force.

High-throughput sequencing (HTS)

Different technologies for massively parallel sequencing, producing millions of DNA sequence reads from library preparations of genetic material, rather than targeting single amplicons as in traditional Sanger sequencing. Also called Next Generation Sequencing (NGS).

Ingestion

Process of importing data from heterogeneous sources, such as local databases, text files or spreadsheets, to a common destination system, such as an online biodiversity data platform, for storage and further analysis. Typically includes steps of extraction, transformation (cleaning) and loading (ETL).

Indexing

Organization of information in accordance with a specific schema or structure, making data easier to access and present.

International Nucleotide Sequence Database Collaboration (INSDC)

Metagenomics

PCR-free sequencing of random genomic fragments in a mixed sample.

Minimum Information about any (x) Sequence (MIxS) standard

Family of standards (checklists) for sequence metadata, developed by the Genomic Standards Consortium (GSC).

molecular Operational Taxonomic Unit (mOTU)

See Operational Taxonomic Unit (OTU).

National Center for Biotechnology Information (NCBI)

Division of United States National Library of Medicine (NLM) housing important bioinformatics resources, such as the GenBank database of DNA sequences, and the Sequence Read Archive (SRA) of high throughput sequencing data.

Next Generation Sequencing (NGS)

See High-throughput sequencing (HTS).

Occurrence

An existence of an Organism (sensu http://rs.tdwg.org/dwc/terms/Organism) at a particular place at a particular time.

Operational Taxonomic Unit (OTU)

Cluster of organisms based on similarity in specific DNA marker sequence(s), used for taxonomic classification. Includes, for example, Species Hypothesis in UNITE, and Barcode Index Numbers in the Barcode of Life Data System (BOLD). Amplicon Sequence Variants (ASVs) may be considered analogous to zero radius OTUs (zOTUs).

Polymerase Chain Reaction (PCR)

Technique for fast amplification and detection of specific fragments of target DNA (or RNA) sequences. Amplified regions are determined by the pair of PCR primers used in the reaction.

Pipeline

In bioinformatics, a set of algorithms or tools applied in a predefined workflow to process e.g. High-throughput sequencing (HTS) data.

Primers (PCR primers)

Short, synthetic, single-stranded DNA fragments that bind to a selected region of target DNA (marker) to initiate replication during PCR. A pair of primers is necessary for the polymerase enzyme to amplify the selected marker.

qPCR (quantitative Polymerase Chain Reaction)

Sample

Material (water, soil, gut content, etc) obtained for analysis.

Sequence alignment

Bioinformatic process of comparing and arranging two or more molecular (DNA, RNA or protein) sequences to detect similarities caused by e.g. evolutionary relatedness.

Species Hypothesis (SH)

Species-level Operational Taxonomic Unit (OTU) as defined in the UNITE database and sequence management environment, for Fungi.

Specimen

An individual animal, plant, fungus, etc. used as an example of its species or type for scientific study or display.

Sequence Read Archive (SRA)

Public repository of high throughput ( NGS) sequencing data, with instances operated by the National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EMBL-EBI), and the DNA Data Bank of Japan (DDBJ). Includes both raw (non-denoised) sequencing output and sequence alignments. One of three components of the European Nucleotide Archive (ENA), and previously known as the Short Read Archive.

Target-capture sequencing

Sequencing of DNA fragments isolated with hybridization probes.

UNITE

UNITE is a web-based sequence management environment centred on the eukaryotic nuclear ribosomal ITS region. All public sequences are clustered into species hypotheses (SHs), which are assigned unique DOIs. An SH-matching service outputs various elements of information, including what species are present in eDNA samples, whether these species are potentially undescribed new species, other studies in which they were recovered, whether the species are alien to a region, and whether they are threatened. The DOIs are connected to the taxonomic backbone of the PlutoF platform and GBIF, such that they are accompanied by a taxon name where available. The data used in UNITE are hosted and managed in PlutoF. Data are represented through a range of standards, primarily Darwin Core, MIxS, and DMP Common Standard; partial support is available for EML, MCL, and GGBN. PlutoF exports data primarily through the CSV and FASTA formats. PlutoF can also be used to publish data in GBIF (using the DwC format) and to prepare GenBank submission files. It is furthermore possible to download species lists from your data and download your project as a JSON document with project data in hierarchically structured.

Zero radius otu (zOTU)

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Anders F. Andersson, Andrew Bissett, Anders G. Finstad, Frode Fossøy, Marie Grosjean, Michael Hope, Thomas S. Jeppesen, Urmas Kõljalg, Daniel Lundin, R. Henrik Nilsson, Maria Prager, Cecilie Svenningsen, Dmitry Schigel

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Publishing DNA-derived data through biodiversity data platforms

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Atlas of Living Australia (ALA) Amplicon Sequence Variant (ASV) Application Programming Interface (API) Set of protocols and tools for interaction and data transmission between different computer applications. Barcode Index Numbers (BINs) Barcode of Life Data System (BOLD) BOLD is the reference database maintained by the Centre for Biodiversity Genomics in Guelph on behalf of the International Barcode of Life Consortium ( IBOL).

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[Jul 2021] Registration is now open. Dates: October 18-22

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As in 2020, our annual conference must again be virtual. While biomedical scientists have made great progress against the SARS-COV-2 pandemic, the distribution of vaccines worldwide is very uneven. Moreover, travel budgets at most organizations have been eliminated. The Fall of 2021 is therefore still too soon to return to an in-person annual conference. As we did last year, TDWG 2021 will be composed of webinar-style symposia, presentations, panel discussions, and virtual posters. We are again separating working sessions from the virtual conference, but in response to feedback from last year's conference, we will hold working sessions after the main conference. The main conference will be held over the week of October 18-22, and working sessions will be scheduled for the following month. The theme for this year's meeting will be:
Connecting the world of biodiversity data: standards uniting people, processes, and tools
The 2021 meeting will be hosted "virtually" by the University of Florida, Gainesville and its Florida Museum of Natural History, with Dr. Pam Soltis, Director of the University of Florida Biodiversity Institute, and Research PI for iDigBio, leading the hosting effort. Among the lessons learned from last year's conference is that virtual events still require a lot of work, so this year we will be using the University of Florida Conference Services group to help organize and manage TDWG 2021. We will have a modest registration fee for this virtual event (discounted for students and participants from lower middle-income countries). Abstracts for all contributions (oral presentations and virtual posters) will be published in our open access journal, Biodiversity Information Science and Standards. Unlike last year, publication costs will not be passed to authors, but defrayed by registration fees and conference sponsorships.

In the virtual conference week, we expect to have 3 or 4 sessions per day, with each session lasting 90-120 minutes, and with at least a 30 minute break between sessions. The time zone differences across our global community makes it unavoidable that some people will be have to participate in some sessions well outside their normal working hours. Still others might find that a highly relevant session has been scheduled in the middle of their night. To mitigate these scheduling difficulties, we will post the recorded presentations as quickly as we can.

The call for organized session proposals (symposia, panel discussions, etc.) was distributed 18 March.

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As in 2020, our annual conference must again be virtual. While biomedical scientists have made great progress against the SARS-COV-2 pandemic, the distribution of vaccines worldwide is very uneven. Moreover, travel budgets at most organizations have been eliminated. The Fall of 2021 is therefore still too soon to return to an in-person annual conference.

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[Jun 2021] Recorded sessions are here: https://gnm2021.gbif.org/sessions

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Background

Global Nodes Meetings are an opportunity for all GBIF nodes to come together to learn from each other, discuss ideas, develop best practices, share their concerns, define recommendations and set priorities.

Due to the ongoing COVID-19 pandemic, the Nodes Steering Group (NSG) decided to hold the first-ever virtual global nodes meeting this year. The idea was presented to the nodes in a community webinar in February.

The agenda for this event was developed following a consultation with all nodes on ideas for the virtual nodes meetings in 2021.

Audience

The meeting targets the nodes community (Node managers and Nodes staff), with invited observers who wish to learn more about GBIF and the work of the nodes. Participation in the meeting will require pre-registration for each session.

Participation must follow GBIF's code of conduct.

Recordings of the sessions will be publicly available from this website and can be shared with the broader community.

Meeting format

The meeting will combine:

Pre-recorded presentations that will be available from this website ahead of sessions for participants to watch at a time that suits their location

Online discussion forums using the GBIF community forum, allowing participants to comment on the presentations and raise questions for discussion

Live sessions focusing on interactions between nodes, and with the Secretariat.

All activities will take place in the week 28 June-2 July 2021. The registration process allows you to register for specific sessions individually. All sessions will be recorded and the videos will be available from the website for viewing afterwards.

Optional preparatory activity - Introduction to GBIF training

GBIF has developed a new self-paced training module, entitled 'Introduction to GBIF', which we plan to use across all our training courses in future.

We invite nodes to review the contents of this first edition and let us know your feedback in the GBIF Community forum. This training will be especially relevant for any new nodes wishing to gain familiarity with GBIF's work ahead of the global nodes meeting.

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Global Nodes Meetings are an opportunity for all GBIF nodes to come together to learn from each other, discuss ideas, develop best practices, share their concerns, define recommendations and set priorities. Due to the ongoing COVID-19 pandemic, the Nodes Steering Group (NSG) decided to hold the first-ever virtual global nodes meeting this year.

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Training the Next Generation of Indigenous Data Scientists

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Training the Next Generation of Indigenous Data Scientists

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[Jun 2021] A new workshop explores the right of Indigenous people to govern the collection, ownership and use of their biological and cultural data.
https://indigidata.nativebio.org/info/

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A new workshop explores the right of Indigenous people to govern the collection, ownership and use of their biological and cultural data.

Krystal Tsosie is a genetics researcher at Vanderbilt University and a member of the Navajo Nation. Credit... Tomás Karmelo Amaya for The New York Times

When Krystal Tsosie introduces her genomics students to the concept of biocommercialism - the extraction of biological resources from Indigenous communities without benefit - she always uses the same example: the Human Genome Diversity Project.

The researchers who conceived of the project in the 1990s aimed to collect samples from human populations around the world, with particular emphasis on what they deemed " vanishing " Indigenous populations. "A lot of that information is now publicly available to advance the course of science," said Ms. Tsosie, a genetics researcher at Vanderbilt University and a member of the Navajo Nation. "But who accesses these data sets?"

Ms. Tsosie, answering her own question, cited as examples Ancestry and 23andMe, two companies that commercialize and profit from Indigenous genomic data sourced without consent from people in Central and South America. In 2018, 23andMe sold access to its database of digital sequence information to GlaxoSmithKline for $300 million. In 2020, 23andMe licensed a drug compound it developed from its trove of genetic information.

Accordingly, Ms. Tsosie helped to organize IndigiData, a four-day remote workshop that took place for the first time in June. The workshop's core goal was to introduce data science skills to undergraduates and graduates, and the definition of data was expansive, from the genetic sequences of soil microbiomes to traditional worldviews.

"If you can't view oral history as data, as something that can be parsed and archived and used to predict things, then you're missing out on a whole data set," said Keolu Fox, a Native Hawaiian geneticist at the University of California, San Diego, who presented at the workshop.

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Ms. Tsosie helped to organize IndigiData, a four-day remote workshop that took place for the first time in June. Credit... Tomás Karmelo Amaya for The New York Times

The workshop centered on Indigenous data sovereignty, the idea that nations have the right to govern the collection, ownership and application of their own data. The movement pushes back on a long history of how researchers have taken Native data without permission, often stigmatizing the communities who participated or disregarding their customs surrounding the dead. In one infamous example, an Arizona State University researcher studying the high rates of diabetes in the Havasupai Tribe, who live near the Grand Canyon, gave other researchers access to the samples without the tribe's consent. When the Havasupai learned of this, they went to court, won their samples back and banished the university from their borders.

"Why are we over here spitting in tubes, giving our genomes away when we know that type of information can be used to make pharmaceutical drugs?" Dr. Fox asked. "Why not position ourselves so we're in control of a treasure chest of data?"

Ms. Tsosie, one of the leaders of the conference, started her career in cancer biology. But she realized early on that any success she might have developing a cancer therapy drug might never reach her own community. Ms. Tsosie's father worked for the Phoenix Indian Medical Center in Arizona for 42 years, and she remembered how difficult it was for her tribal community to access specialty services.

"What am I doing in cancer biology?" Ms. Tsosie remembered thinking. She switched her academic focus and is now a graduate student in genomics and health disparities.

In 2012, Ms. Tsosie met Joseph M. Yracheta, who is of the P'urhepecha and Raramuri peoples, through the Summer Internship for Indigenous Peoples in Genomics, a workshop that trains researchers in genetic science. They started talking about data ethics, and a few years later Matt Anderson, a microbiologist at Ohio State University and a descendant of the Eastern Band of Cherokee Indians, joined the conversation. The organizers recognized that there were limited resources to train Indigenous people how to think about and interpret their data.

In January, with funding from the Amgen Foundation and the National Science Foundation, the workshop began to take shape. The participants hail from Indigenous communities across the country and internationally, and have wide-ranging research interests, such as archaeology and pollinators. "What ties us together is colonialism," Dr. Fox said, and laughed.

The theme of the inaugural conference was environmental microbiomes, which the organizers felt would resonate with participants. An individual's microbiomes - the communities of microorganisms that live inside and on a person - is deeply intertwined with the surroundings; for instance, the composition of the gut microbiome can be altered by diet as well as air pollution.

In recent years, the "vanishing" rhetoric of the Human Genome Diversity Project has shifted to refer to the "vanishing" microbiome of traditional communities, Dr. Anderson said. "Except instead of people, they're talking about the microbes associated with people," Dr. Anderson said. One 2018 article in the journal Science emphasized the need to collect samples from "traditional peoples in developing countries" in order "to capture and preserve the human microbiota while it still exists."

Ms. Tsosie started her career in cancer biology but is now a graduate student in genomics and health disparities. Credit... Tomás Karmelo Amaya for The New York Times

Mr. Yracheta, who is the managing director of the Native BioData Consortium - the first biobank in the U.S. led by Indigenous scientists and tribal members - believes the microbiome will be one of the next targeted data sets that Western scientists may seek from Indigenous communities. In Tanzania, the Hadza people have been studied extensively for the " richness and biodiversity " of their gut microbiota.

"Native DNA is so sought after that people are looking for proxy data, and one of the big proxy data is the microbiome" Mr. Yracheta said. "If you're a Native person, you have to consider all these variables if you want to protect your people and your culture."

In a presentation at the conference, Joslynn Lee, a member of the Navajo, Laguna Pueblo and Acoma Pueblo Nations and a biochemist at Fort Lewis College in Durango, Colo., spoke about her experience tracking the changes in microbial communities in rivers that experienced a mine wastewater spill in Silverton, Colo. Dr. Lee also offered practical tips on how to plan a microbiome analysis, from collecting a sample to processing it.

In a data-science career panel, Rebecca Pollet, a biochemist and a member of the Cherokee Nation, noted how many mainstream pharmaceutical drugs were developed based on the traditional knowledge and plant medicine of Native people. The anti-malarial drug quinine, for example, was developed from the bark of a species of Cinchona trees, which the Quechua people historically used as medicine. Dr. Pollet, who studies the effects of pharmaceutical drugs and traditional food on the gut microbiome, asked: "How do we honor that traditional knowledge and make up for what's been covered up?"

One participant, the Lakota elder Les Ducheneaux, added that he believed that medicine derived from traditional knowledge wrongly removed the prayers and rituals that would traditionally accompany the treatment, rendering the medicine less effective. "You constantly have to weigh the scientific part of medicine with the cultural and spiritual part of what you're doing," he said.

Over the course of the IndigiData conference, participants also discussed ways to take charge of their own data to serve their communities.

Mason Grimshaw, a data scientist and a board member of Indigenous in A.I., talked about his research with language data on the International Wakashan A.I. Consortium. The consortium, led by an engineer, Michael Running Wolf, is developing an automatic speech recognition A.I. for Wakashan languages, a family of endangered languages spoken among several First Nations communities. The researchers believe automatic speech recognition models can preserve fluency in Wakashan languages and revitalize their use by future generations.

Mason Grimshaw is a data scientist and a board member of Indigenous in A.I., in Rapid City, S.D. Credit... Dawnee LeBeau for The New York Times

Typical language models, such as Apple's voice-controlled Siri, often try to predict the next word, or set of words, based on the start of a sentence or a prompt. But such models might falter under the cultural nuances of many Indigenous languages, Mr. Grimshaw noted. "The Wakashan folks have certain stories you would only tell in certain kinds of weather or at certain times of day," he said, by way of example.

Additionally, many Indigenous languages are polysynthetic; they do not have fixed vocabularies but rely instead on the recombinations of small building blocks of words. A polysynthetic language like Lakota technically allows there to be infinite words, Mr. Grimshaw said. Indigenous languages often have much less recorded language data to analyze, such as audio files of speakers in conversation, than more common languages do.

Mr. Grimshaw sees these complications not as a problem but as a puzzle to be unscrambled. When asked about his wildest data dreams by a participant at the conference, Mr. Grimshaw smiled. "I want a Lakota version of Siri," he said.

IndigiData has funding for the next four years, and the organizers hope that the conference next year will be held in person at the Native BioData Consortium on the Cheyenne River Sioux Reservation. That location, Dr. Anderson noted, is a one-day drive from 13 tribal colleges.

Dr. Fox hopes the conference will train the next generation of Indigenous data scientists not just to protect their data but to be empowered by its possibilities.

"I'm not saying that I like capitalism," he said. "But data is power, and that's the way for us to revitalize our communities."

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Training the Next Generation of Indigenous Data Scientists

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A new workshop explores the right of Indigenous people to govern the collection, ownership and use of their biological and cultural data. When Krystal Tsosie introduces her genomics students to the concept of biocommercialism - the extraction of biological resources from Indigenous communities without benefit - she always uses the same example: the Human Genome Diversity Project.

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A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data paper

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A streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data paper

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GigaScience, Volume 10, Issue 5, May 2021, giab034, https://doi.org/10.1093/gigascience/giab034

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Abstract

Introduction

Independently from the recent advances in omics technologies and data generation, however, the published omics biodiversity data and their accompanying, standardized metadata are still neither harmonized nor interoperable [ 6]. Existing infrastructures in omics data science focus on the sequence or molecular data generated from omics studies. Since 1988, the databases of the International Nucleotide Sequence Database Collaboration (INSDC) [ 7-9] have provided a trusted archive for these data. In parallel, major infrastructures to handle higher-order biodiversity data (e.g., occurrences linked to taxa, specimen records) have emerged and include the Global Biodiversity Information Facility (GBIF) [ 10], the Integrated Digitized Biocollections (iDigBio) [ 11], the Distributed System of Scientific Collections (DiSSCo) [ 12], the Ocean Biogeographic Information System (OBIS) [ 13], the Global Genome Biodiversity Network (GGBN) [ 14], DataONE [ 15], and others. Some of these infrastructures support data repositories that follow community-accepted metadata standards. GBIF uses the Ecological Metadata Language (EML) standard for describing ecological datasets in XML files [ 16], whereas biodiversity data are recorded by following the Darwin Core Standard (DwC) [ 17, 18]. Likewise, the GGBN have developed their own GGBN Data Standard, which interoperates with DwC and the Access to Biological Collections Data (ABCD) schema for primary biodiversity data [ 19-21]. The INSDC cooperates with community standards initiatives such as the Genomic Standards Consortium (GSC) to implement their Minimum Information about any (x) Sequence (MIxS) checklists for genomic, metagenomic, and environmental metadata descriptors, and with the Global Microbial Identifier (GMI) group for pathogen sequence metadata [ 22, 23]. MIxS consists of 3 checklists, each containing several packages for the description of various environments from which genomic material could be sampled [ 23]. Other international data repositories such as EBI EMBL's ArrayExpress [ 24] and the BioSamples [ 25] database implement standards such as Minimum Information about a high-throughput nucleotide SEQuencing Experiment (MINSEQE) and Minimum Information About a Microarray Experiment (MIAME) and various MIxS environmental checklists [ 26]. Databases such as the Genomic Observatories Metadatabase (GeOMe) [ 27] offer integrative solutions for the data management of genomic, geographical, and ecological metadata by providing mechanisms to create standard-compliant templates tailored to specific use cases and linking metadata to dataset records via stable identifiers [ 28].

A more comprehensive approach towards omics metadata mobilization is undertaken by the ISA Commons community [ 29], which uses the "extensible, cross-domain format" ISA-Tab for organising metadata [ 30]. This format focuses on 3 major components of any scientific research: "Investigation," "Study," and "Assay" to help structure the underlying study and assay-specific metadata records [ 30]. Software for creating and validating ISA-Tab files has also been developed as part of the ISA Tools framework [ 30]. This framework aims to complement existing omics standards to improve the description of research outputs in the field of omics. Several omics data repositories, such as EMBL-EBL's Metabolights [ 31] and GigaScience's GigaDB [ 32], have utilized the ISA model and serializations to integrate experimental metadata.

There are different ways that scientists can publish their data in a FAIR (Findable, Accessible, Interoperable, and Reusable) [ 33, 34] manner; however, all can be attributed to 2 main routes: (i) data publishing through international trusted data repositories, such as INSDC [ 7] and GBIF [ 10]; and (ii) scholarly data publishing in the form of data papers or as data underpinning a research article [ 3, 5-38]. While the first route focuses on data aggregation, standardization, and reuse, the second one augments the quality and reusability of data and metadata through peer review and data auditing in the scholarly publishing process. Scholarly data publishing provides an opportunity to enhance the original metadata in the data paper narrative and to link it to the original dataset via stable identifiers, thus improving the reproducibility and findability of the data [ 39]. Furthermore, it creates a citable scientific record, enabling the crediting and acknowledgement of the data creators and researchers. Academic publishing involves dissemination of research through additional channels, such as journal distribution networks, and creates increased opportunities for open science collaboration [ 39].

While standards and infrastructures are crucial to the advancement of data sharing and reuse within the field of omics, we argue that incentivizing authors to publish their data in the form of peer-reviewed journal articles (data papers) creates the driving force towards a truly FAIR data world (Fig. 1).

The aim of the present article is to conceptualize the omics data paper, to create a specific article template for it, and to describe a prototype workflow for automated import of genomic metadata into an omics data paper manuscript. This workflow also accommodates the peer review and publication processes associated with the manuscript.

Methods

Approach

We took the following steps to approach the goal of establishing an omics data paper template and workflow:

1. Identify the high-level needs of the omics communities to better describe their datasets

2. Review existing standards, infrastructures [ 42], and datasets, as well as the existing data paper formats for describing (gen)omic data [ 43-45].

3. Synthesize the technical solutions and incorporate further functional needs to create the structure of the new type of data paper.

We created a dedicated data paper template, defining article sections and subsections to map the article narrative to metadata associated with the dataset(s) described in an omics data paper.

Workflow for extracting relevant metadata from ENA XML files

In addition, the R Shiny app can be run without installation as an interactive web app [ 53] deployed in an RStudio cloud environment [ 54] and hosted on a Shinyapps.io server [ 47, 48]. The code behind the interactive web app is openly available on Github [ 55] under the Apache 2.0 license.

Integration of metadata extraction workflow with the ARPHA Writing Tool

After testing the metadata extraction and import workflow in the R shiny app, it was realized as a production-grade workflow integrated via Web service with the Pensoft ARPHA Writing Tool (AWT) [ 38]. The AWT is a web platform for collaborative authoring, reviewing, and publishing of manuscripts, developed and used by Pensoft in their publication process. It supports the creation of manuscripts by manual entry of text into different templates corresponding to separate article types, such as "Research Article," "Software Description," "Data Paper," and so forth. In addition, there are existing implemented workflows for import of metadata from files or web resources into the templates of some article types (e.g., "Data Paper" or "FSKX [Food Safety Knowledge] Paper") [ 38, 40]. After acceptance, the data papers consisting of peer-reviewed, corrected, and extended metadata descriptors are published under the Creative Commons CC-BY 4.0 license, which allows free and unlimited distribution and reuse given that the original source is credited. The datasets described in the data paper are made available under a license determined by the authors, which is specified in а dedicated "Usage rights" section of the data paper. Thus, we address 1 of the key aspects of the FAIR principles (R1.1) specifying that "(meta)data are released with a clear and accessible data usage license" to achieve data reusability [ 33].

Similarly, we established a new publication type, "OMICS Data Paper," and a manuscript template for it (see Additional File 1), following the proposed data paper structure. Essential sections of the omics data paper template were made mandatory in AWT such as the "Methods" section and the "Data resources" section. This means that the system requires the authors to fill them in before they can submit the manuscript for review.

We then replicated the genomic metadata import workflow from the R Shiny app inside the ARPHA Writing Tool. The workflow was designed to automatically populate some of the fields from the omics data paper manuscript template. It must be noted that not all fields from the template would be automatically filled in with metadata records by the workflow because ENA metadata records only cover a limited amount of information. For instance, sections such as "Environmental profile" and "Societal value" would not be populated and the users would have to manually fill them in with information if they wish to keep these sections in the data paper.

An important component of the design and implementation of the omics data papers is the BioSamples Supplementary Table. ENA metadata records that contain links to associated BioSamples metadata (MIxS checklists) [ 23, 25] are retrieved by the automatic import workflow and are transformed into a narrow format table, which will be attached to the manuscript as a comma-separated value (CSV) file. We restrict editing of Supplementary Tables imported from BioSamples to prevent metadata loss and tampering. Authors can only change the MIxS checklists related to their manuscript if they re-upload them to the long-term, trusted source repository: BioSamples. Synchronization with BioSamples from the manuscript in the ARPHA Writing Tool is enabled through a button labelled "Re-import from BioSamples."

Findings

Structure of the OMICS data paper

The omics data paper describes datasets generated in omics research. The described dataset is at the core of the data paper, but the methodology required to obtain it is just as valuable as the data itself. To guide authors in the authoring process and to better inform the readers about the contents of the proposed data paper, we designed a detailed manuscript template. Table 1 outlines each section and associated subsections of the template to be used either for manual population in the AWT or to match the metadata records extracted by the workflow to populate certain sections and subsections of the template. Many data paper sections do not have ENA metadata fields associated with them, and the authors are encouraged to fill in their contents in the data paper manuscript, as well as to update the original ENA record accordingly if possible.

The template focuses on the value of the data, the methods used to generate it, and the qualitative and quantitative characteristics of the dataset. We have included a section to describe the biological entities that are the focus of the research: "Biodiversity profile." In addition to filing in the relevant subsections of this section, authors can attach a supplementary EML file or an Appendix table [ 57] to describe the different dimensions of the research target. Such Appendix tables can be used to record and link taxonomic, genomic, ecology, image, and other types of data using community-agreed vocabularies and ontologies. A spreadsheet template and instructions have been published as part of the Author's Guidelines of the Biodiversity Data Journal [ 58].

Genomics metadata extraction workflow

Omics data papers can be created via 2 separate routes: (i) manually, by filling in all sections from the omics data paper template relevant to the research experiment inside the ARPHA Writing Tool; and (ii) semi-automatically, by using the genomics metadata extraction workflow with ENA metadata records and later manually enhancing the extracted metadata by filling in missing information inside the ARPHA Writing Tool. Here we outline the second route.

Metadata describing the datasets were used to facilitate creation and authoring of the data paper manuscript. By following ENA's metadata model [ 42], including its links to the ArrayExpress [ 24] and BioSamples [ 25] databases, we designed a workflow that orchestrates the extraction of relevant metadata from the various ENA XML files (Fig. 2). The Study XML and the Project XML are the starting points in the proposed workflow because they integrate all other types of data and metadata available in ENA for a given scientific study. Each metadata object in the ENA metadata model is associated with a unique identifier, which can be used to retrieve its corresponding XML file via the ENA API [ 42].

As outlined in our proposed workflow (Fig. 2), the Study or Project accession number is used to obtain an XML file that contains the accession numbers for all associated Experiment and Sample metadata objects, and in some cases ArrayExpress and BioSamples metadata objects.

Another database within EMBL-EBI's infrastructure is BioSamples, a database that "stores and supplies descriptions and metadata about biological samples" [ 25]. Metadata descriptors in BioSamples records follow the MIxS standard [ 26]. Depending on the type of sample, submission to BioSamples requires different MIxS checklists to be filled in, after which they are publicly available in the form of XML files [ 25]. Unique identifiers link Sample XMLs from ENA with their associated BioSamples XML records. Thus, we are able to extract BioSamples information for any samples from a given ENA Study or Project. BioSamples records are imported into a table that is attached to the manuscript as a Supplementary CSV file. This supplementary table is a mandatory component of a manuscript, when accompanying BioSamples records are available, and cannot be removed by the authors. In cases when the authors spot a mistake in their submitted metadata, they are encouraged to change it within the BioSamples database. Upload of stand-alone BioSamples MIxS checklists is not permitted in the ARPHA Writing Tool, so that authors perform their corrections in the original metadata repository. After that, they can automatically retrieve them from BioSamples and import them into the manuscript with the click of a button. Thus, we promote the reuse and interoperability of MIxS-compliant metadata sourced from BioSamples.

R shiny app-deployment and reproducibility

The template and workflow were first prototyped in an R shiny app [ 53], the code for which is open source and available on Github under Apache 2.0 license [ 55, 54], as outlined in the Methodology section of this article. The R shiny app is a web application emulating the functionality of the metadata import workflow in the ARPHA Writing Tool. The application runs in a virtual R environment [ 47, 54] and is deployed and hosted on the web via Shinyapps.io [ 48], configured to allow up to 50 concurrent connections.

The interface of the application features a text field for input of ENA Study or Project ID and a "Convert" button controlling the import of metadata and conversion to manuscript. Three buttons to download the outputs appear after the Convert button is clicked. The generated manuscript narrative, along with a data frame containing the BioSamples MIxS checklist, is visualized in the R shiny app interface. The narrative can be downloaded as an HTML file by clicking the "Download HTML" button, whereas the BioSamples checklist can be downloaded as a CSV file by clicking the "Download Supplementary Material" button. This CSV file is identical to the one generated as a supplementary file by the ARPHA Writing Tool.

Despite being tailored to the Pensoft omics data paper template, JATS XML files generated via the R shiny app can be used by other publishers or individuals to generate their own omics data paper manuscripts. Together with ENA's documentation about programmatic access to its resources [ 46], the codebase enables reproducibility of our workflow and creates the potential for it to be deployed by other journals or publishers.

Discussion

The data and metadata publishing landscape

The concept of data papers is not new; in fact, they have been in existence for >2 decades already. One of the first journals to implement this concept was Ecological Society of America's Ecological Archives [ 61, 62]. In 2011, Chavan and Penev envisioned metadata as a resource for authoring data papers for primary biodiversity data and identified a lack of clear guidelines and good practices for authoring metadata (the "how") and the incentives for authors to do so (the "why") [ 39]. They proposed data papers as a "mechanism to incentivise data publishing in biodiversity science" and introduced them to the biodiversity community through Pensoft's journals. To further simplify data paper authoring, Pensoft pioneered an integrated workflow for automatic metadata-to-manuscript conversion of primary biodiversity datasets published through GBIF's Integrated Publishing Toolkit (IPT) [ 37-63].

This streamlined metadata conversion workflow was first introduced in several of Pensoft's biodiversity journals and then in journals by other publishers, such as Nature's Scientific Data, PLoS One, BMC Ecology, and many others [ 64]. Since 2011, nearly 300 data papers have been published in Pensoft's journals and there is a steady uptake of this type of publication not only among Pensoft's journals but among journals of other publishers too [ 65]. Data papers are no longer an abstract idea but have already been practically implemented in multiple journals in different disciplines.

Since 2011, Pensoft has developed other integrative ways to streamline metadata authoring and data paper publication by integrating different workflows into its collaborative online authoring tool, AWT, and associated Biodiversity Data Journal [ 66]. For instance, metadata files following the GBIF EML profile used in the IPT can be directly converted and imported into manuscripts in AWT at the click of a button, then edited in the tool and submitted to the Biodiversity Data Journal [ 63, 67]. This workflow closely resembles the workflow described in this article but it is focused on ecological data. The EML workflow accepts a single specimen record identifier and imports information about that record from several infrastructures (GBIF, Barcode of Life Data Systems [BOLD], iDigBio, or PlutoF) into manuscripts [ 67]. It also enables conversion of an EML-formatted file into a biodiversity data paper [ 67], a functionality not covered by the present workflow, which only performs API requests.

Generation of extended metadata descriptors has been the focus of other tools, such as the Metadata Shiny Automated Resources and Knowledge (MetaShARK) [ 68] and Datascriptor [ 69], which is still under development. MetaShARK aims to facilitate assembly of ecology metadata by providing a user-friendly workflow for metadata packaging [ 68]. Unlike the workflow described here, it is more focused on primary metadata generation than metadata sharing and reuse [ 68]. Our workflow uses already generated metadata and provides a template for their extension to create an extended metadata description converted to narrative. Datascriptor is more closely related to our workflow because it aims to transform metadata, generated by following community standards, into a data article [ 69]. To do so, the developers have envisioned the generation of a JATS XML [ 69], which is what we have implemented in our R shiny app demonstrating the workflow for import of metadata into omics data paper manuscript.

Data papers for the field of omics: rationale and benefits

Authoring omics data papers, despite being aided by the automated workflow, requires additional effort and time largely because ENA records do not contain all metadata needed to assemble a thorough genomics study description. As the quality, breadth, and depth of metadata records in the sourced repositories improves, that additional effort will decrease. The prototype workflow merely demonstrates the possibility of interoperable metadata sharing and integration inside the publishing process. Here we outline some of the benefits that make the process of creating such manuscripts worthwhile, as well as how data interoperability contributes to the FAIR data and metadata publishing landscape.

Prior to peer review of submitted omics data paper manuscripts, all underlying datasets go through mandatory data auditing, cleaning, and quality checks to ensure that they meet the journal standards for publication [ 71, 72]. This is done by a data auditor, whose role is to technically evaluate the submitted datasets for compliance to a data quality checklist [ 71] and to provide authors with a detailed report, including recommendations for improving the dataset. Only after the authors change the dataset according to the recommendations can it be approved for peer review. Peer reviewers review not only the narrative but also the datasets according to the Data Review Guidelines [ 73]. The introduction of data scientists into the publishing process ensures to an extent that submitted data and metadata are FAIR and consistent [ 72]. This double checking-first of the datasets by the data auditors and then of the whole manuscript by the reviewers-is a meticulous approach to enhancing the quality of datasets and to our knowledge has not been adopted by any other publisher so far.

Metadata that follow community-accepted standards are vital for data-driven discoveries because they provide the necessary context to characterize the dataset that they describe. Omics data papers not only improve the quality of the metadata but also constitute an enhanced metadata record themselves.

The improved visibility of the omics datasets can open new possibilities for reuse of the datasets. For instance, in pharmaceutical science, old compounds are commonly researched as part of the development of new drugs because they could harbour unexplored biological activities [ 77]. By giving further visibility to omic datasets through their publication in an omics data paper, indexed in journal-focused databases and search engines, and by enhancing metadata through publication, we stimulate scientific research and data-driven discovery.

Publishing data papers at early stages of the research process can provide an important benefit for authors: the opportunity to get the first scientific record for their effort in assembling a dataset and obtain feedback from the research community. It is well known that many authors are hesitant to publish datasets that they have not yet analysed or used for supporting any research findings for fear of someone else using the data and getting "scooped." By publishing a data paper, the authors are guaranteed that the described data can be reused in accordance with the Open Science principles, following all community-accepted ethical norms for citation, priority, and generating new knowledge through joint publications based on shared data.

Science crediting further incentivizes researchers to publish omics data papers because their work impact can be measured in a way familiar to authors of traditional research papers, adding to their researcher impact metrics. In addition, the data managers and scientists who generate the data are not always among the authors of traditional research articles, which focus on the data analysis and outcomes. Thus, data paper publishing can be a way for all actors involved in the process of gathering, curating, and managing the data-be they early-stage researchers, technicians, or data scientists-to obtain credit for their valuable work.

Limitations and future outlook

The automated workflow prototype for importing omics metadata into data paper manuscripts currently works only with ENA metadata records. While INSDC metadata are exchanged across all 3 databases in the consortium (ENA, GenBank, and DDBJ) [ 7], it would be beneficial if users could import metadata from any of the 3 data repositories via their associated identifier. The reason for the current limitation is the requirement for additional integrations, produced by the variation of APIs and the differing metadata schemas even between ENA, GenBank, and DDBJ repositories, which hold identical data and are synchronized. We decided to integrate the prototype workflow with ENA as the first showcase of this novel method of creation of data paper manuscripts because of the more straightforward links between ENA, BioSamples, and ArrayExpress compared to GenBank or DDBJ.

Integrations between existing infrastructures and data-driven initiatives are key to the FAIRness of data and metadata. The streamlined workflow for import of metadata from ENA, ArrayExpress, and BioSample is another step in this direction. However, to make metadata truly FAIR, there should be a 2-way link between the original data and metadata repository (e.g., ENA) and the enhanced metadata record (e.g., the omics data paper).

Conclusions

The new omics data paper, implemented in Pensoft's publishing process, provides a mechanism for incentivizing omics data sharing and reuse through scholarly publishing. In addition, the workflow for import of metadata into manuscripts encourages and incentivizes authors to enhance data quality and completeness. The workflow also demonstrates the importance of linking data from different infrastructures using stable identifiers and thus sets an example for future integrations with other metadata and data repositories.

Availability of Supporting Source Code and Requirements

* Project name: Omics Data Paper Generator

* Project home page: https://github.com/pensoft/omicsdatapaper

* Operating system(s): Platform independent

* Programming language: R

* Other requirements: R version 4.0.0 (2020-04-24) (Arbor Day), R Studio

* License: Apache 2.0

* biotoolsID: biotools:omics-data-paper-shinyapp-golem

Data Availability

There are no data set(s) supporting the results of this article. The availability of all code used in this research is listed in the section Availability of Supporting Source Code and Requirements. A snapshot of the code is also available in the GigaScience GigaDB repository [ 80].

Additional Files

Additional File 1. HTML file demonstrating the structure of the omics data paper template in the ARPHA Writing Tool.

Abbreviations

ABCD: Access to Biological Collections Data; API: Application Programming Interface; AWT: ARPHA Writing Tool; BOLD: Barcode of Life Data Systems; CSV: comma-separated value; DiSSCo: Distributed System of Scientific Collections; DwC: Darwin Core Standard; ENA: European Nucleotide Archive; FAIR: Findable, Accessible, Interoperable, and Reusable; FSK-M: Food Safety Knowledge Markup Language; GBIF: Global Biodiversity Information Facility; GeOMe: Genomic Observatories Metadatabase; GGBN: Global Genome Biodiversity Network; GMI: Global Microbial Identifier; GSC: Genomics Standards Consortium; iDigBio: Integrated Digitized Biocollections; INSDC: International Nucleotide Sequence Database Collaboration; IPT: Integrated Publishing Toolkit; JATS: Journal Article Tag Suite; LTER: Long Term Ecological Research Network; MetaShARK: Metadata Shiny Automated Resources and Knowledge; MIAME: Minimum Information About a Microarray Experiment; MINSEQE: Minimum Information about a high-throughput nucleotide SEQuencing Experiment; MIxS: Minimum Information about any (x) Sequence; OBIS: Ocean Biogeographic Information System.

Competing Interests

M.D. is a PhD student at Pensoft through the EU-funded project IGNITE, and one of her key tasks during the PhD project is to develop methods for publication, dissemination, and reuse of biodiversity-related genomics data. T.G., G.Z., S.D., and L.P. are employed by Pensoft, and L.P. also holds a professorship position in the Bulgarian Academy of Sciences. Pensoft's Biodiversity Data Journal has implemented the genomics data paper workflow in its routine editorial practices. The authors declare that they have no other competing interests.

Funding

This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement IGNITE (No. 764840) and from Pensoft Publishers.

Authors' Contributions

M.D. performed the background research, designed the template and workflow, developed the workflow, R Shiny app and installable R package, wrote the original manuscript and reviewed the manuscript.R.M. designed the template and workflow, wrote the original manuscript and reviewed the manuscript.P.L.B. designed the template and workflow, wrote the original manuscript and reviewed the manuscript.T.G. supervised the implementation of the workflow into Pensoft's ARPHA Writing Tool.G.Z. implemented the workflow into Pensoft's ARPHA Writing Tool.S.D. implemented the workflow into Pensoft's ARPHA Writing Tool.V.S. wrote the original manuscript and reviewed the manuscript.L.P. developed the idea and conceptual framework, designed the template and workflow, supervised the implementation of the workflow into Pensoft's ARPHA Writing Tool, wrote the original manuscript and reviewed the manuscript.

References

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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streamlined workflow for conversion, peer review, and publication of genomics metadata as omics data papers

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Independently from the recent advances in omics technologies and data generation, however, the published omics biodiversity data and their accompanying, standardized metadata are still neither harmonized nor interoperable [ 6]. Existing infrastructures in omics data science focus on the sequence or molecular data generated from omics studies.

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Tim Berners-Lee
Date: 2006-07-27, last change: $Date: 2009/06/18 18:24:33 $
Status: personal view only. Editing status: imperfect but published.

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Many research and evaluation projects in the few years of the Semantic Web technologies produced ontologies, and significant data stores, but the data, if available at all, is buried in a zip archive somewhere, rather than being accessible on the web as linked data.

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Great resource for looking up people related to collections!

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Link natural history specimens to the world's collectors.

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A gentle introduction to Apache Kafka. Mitch Seymour

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Let's install Kafka in your forest!

 # clone the repository git clone \ https://github.com/round-robin-books/gently-kafka.git # start kafka docker-compose up

Please take a minute to provide feedback.
There's a bonus drawing at the end of the survey :)

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Let's install Kafka in your forest! # clone the repository git clone \ https://github.com/round-robin-books/gently-kafka.git # start kafka docker-compose up Please take a minute to provide feedback. There's a bonus drawing at the end of the survey :)

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The difference between data backup and data archiving, and why it matters to you

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The difference between data backup and data archiving, and why it matters to you

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Data is your organisation’s most valuable asset – it’s paramount that you protect it. May 2021

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Data is your organisation's most valuable asset - it's paramount that you protect it.

If you are faced with large amounts of precious data which you will need to access in the future, it is imperative that you understand the importance in the use of a digital archive and not relying solely on a data backup.

Throughout this blog, I'll explain the high-level differences between an archive and a backup, discuss the importance of treating data differently, share a real-life example of data loss, explore the options for creating a digital archive and share the questions you can ask solution providers to ensure that they meet your archiving needs.

Storage backup vs Digital archiving

When organisations create a long-term strategy for the archival of data, mistakes tend to occur when an individual involved in the process does not fully comprehend the differences between a backup and a digital archive. Assumptions that a backup will match the functionality of an archive are false and could result in data loss.

Fundamentally, a data backup is where a snapshot of your data is taken and stored elsewhere so that, if required, you are able to recover this data from a certain point in time (PIT). Typically, this recovery method is used in disaster situations, such as an incident of data deletion or corruption.

In the diagram below we've outlined an example of how a backup can work for an organisation. Typically, daily backups are kept for a certain amount of days, which are then replaced by weekly backups, which are then replaced by monthly backups. Businesses usually keep a number of daily and weekly backups at one time which are replaced on a continual basis.

As you can see, when a disaster strikes data can be lost in a backup system therefore it is important to think about your recovery point objectives and recovery time objectives - we'll cover this in more detail in the next section of the blog. Organisations must also complete ongoing checks and tests of their backups to ensure that they are not exposing themselves to ransomware - a malware that employs encryption to hold a victim's information at ransom.

A data archive is an ongoing, managed environment that focuses on the preservation of your data. It goes beyond creating a copy and instead, focuses on the accessibility and re-use of your data - long into the future.

Relying on a backup for the purposes of archiving your data for a long period of time does not protect against data loss, corruption or from formats becoming obsolete. This is before taking into account the data being stored in such a way that it can be easily searched and retrieved if required. Examples of how this is managed within an archive can include leveraging metadata to improve searchability and maintaining preservation copies of files so they can be used and read long into the future.

Leaving your data in a live system long-term with backups running on a continual basis can lead to a lot of data being stored, opening you up to risks of data loss, excessive costs or data deletion.

The build-up of this data can also cause issues around data portability, a concept where users have their data stored in silos that are incompatible with one another thus subjecting them to vendor lock-in, once again opening your business to further costs.

Treating data differently

Another area that can cause real issue is when individuals involved in designing your archiving and backup strategy treat all data the same.

When it comes to your data there will be varying levels of retention and required accessibility, for example you may have files that have a 25-year retention, such as the eTMF in the life sciences industry, while other documents such as financial statements may require a 7-year retention. I'd also like to highlight at this stage that not all data may need to be managed in an archive, some documents such as marketing materials, you may be comfortable leaving within your live site.

When deciding on the best strategy for your organisation's data, you'll need to think about your Recovery Point Objective (RPO) and Recovery Time Objective (RTO). RPO defines the maximum allowable amount of lost data measured in time from a failure occurrence to the last valid backup. RTO is the downtime and refers to how long it takes to restore from the incident until normal operations are available to users.

Selecting a good backup provider is important. Selecting a substandard provider or one without the right safeguards in place can have a detrimental effect on your business. Remember that a backup tends to be operational while an archive focuses on long-term retention. Your archive and backup should work hand in hand.

To build the most effective strategy, you should ask yourself:

What is the purpose of my data?

Does it need to be maintained in an operational state, in a 'live' production environment, or does it need to be archived to preserve the data, years into the future?

What are my recovery point objectives (RPO) and recovery time objectives (RTO)?

What are my legal or regulatory requirements for retaining some/all of my data?

Once you have determined which data you require long-term access to, you should move this into an archive. That way you ensure the data's preservation, rather than depending on a backup which can't always guarantee the files' accessibility long-term.

The real effects of data loss

Typically, backup technologies have a fairly high reliability rate for recovery but when you solely rely on an unverified or substandard backup provider and something goes wrong, it can have detrimental effects on a business. These could vary from reputational damage, fines or the requirement to repeat previous business activities to retrieve the data that has been lost.

Last year we released a blog called Whoops where's my data? in which we shared a few high-profile examples of where data had been lost by the BBC, Pixar and NASA. More stories of data loss continue to emerge with some specifically highlighting the failings of backups and demonstrating why these are not always enough to protect your data.
Memorial University, Canada
In 2016, the staff at the Queen Elizabeth II library, Memorial University were undertaking routine maintenance which required power to the building to be cut and switched to a backup system. During this process, the backup system failed, and more than 70 terabytes of data were lost during this event.

Luckily for the university, physical documents and objects still existed but they had to begin the process of digitising these collections again, which can be very expensive and time-consuming. As you can see in this example, the backup failed to protect the libraries' data.

Incorporating digital preservation strategies into your organisation and combining the creation of a long-term archive with a backup for your data can assist in alleviating the risk of data loss.

What are my options for building a digital archive?

Now that we have outlined the difference between a backup and an archive and looked at an example of data loss, you may be starting to think about your next steps and options for creating a data archive.

For all industries, when it comes to creating a data archive you have two options: building the archive in-house or working with a third-party solution provider.

(We recently wrote a blog which delves into this topic for drug sponsors and relates specifically to the archiving and preservation of the eTMF.)

When looking at new technology, IT engineers sometimes debate whether to build their solution in-house. The main benefit of this is the opportunity to own and build the solution around your organisation's specific needs, offering more flexibility, however, this takes significant time, effort and skills because of the specialist nature of corresponding workflows, systems and integrations. The Total Cost of Ownership (TCO) of on-premise solutions, especially over time, is significant.

Furthermore, organisations risk having periods where they don't have the requisite resources to sustain this approach, for example because of loss of staff or fluctuations in funding. Even large and well-founded institutions such as national libraries or international research facilities are increasingly choosing to opt for cloud hosted solutions.

To assist in the buy vs build decision, ask yourself: does your IT team have the capability to manage and access years and years of data, long into the future?

Choosing to outsource your solution could reduce that burden for them.

Questions to ask solution providers

Making the decision on whether to build your archiving solution in-house or work with a third-party isn't always easy, but if you find yourself in this predicament, we'd suggest reaching out to third parties initially so you can understand the capabilities of their solution and compare this with your internal capabilities.

Outlined below are a list of questions and talking points we'd suggest you use when speaking to third parties so that you can better understand their solution and their business, and how this will align with your company's data archiving requirements.

What do you use to backup your system?

What protections do you have in place to stop data being tampered with?

How can you meet industry regulations?

Does your solution enforce best practice approaches for good data management, such as FAIR and ALCOA+?

Are you ISO 9001 and ISO 27001 certified?

Will you store my data in multiple geographic locations?

Do you have an exit strategy?

At Arkivum we are recognised internationally for our expertise in the archiving and digital preservation of valuable data and digitised assets in large volumes and multiple formats. If you'd like to learn more about how we have worked with other organisations to deliver a digital archive for their business contact us today.

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The difference between data backup and data archiving, and why it matters to you - Arkivum

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Data is your organisation's most valuable asset - it's paramount that you protect it. If you are faced with large amounts of precious data which you will need to access in the future, it is imperative that you understand the importance in the use of a digital archive and not relying solely on a data backup.

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The AIMS Computerization Project at Field Museum. Jan 1977

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Field Museum of Natural History bulletin.

[Chicago, Ill.] :The Museum,[1972]-©1990.

University Library, University of Illinois Urbana Champaign

University of Illinois Urbana-Champaign

Copyright Status:
In copyright. Digitized with permission of the Field Museum of Chicago. Contact dcc@library.uiuc.edu for information.Contact dcc@library.uiuc.edu for information.

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Field Museum of Natural History bulletin. [Chicago, Ill.] :The Museum,[1972]-©1990. University Library, University of Illinois Urbana Champaign University of Illinois Urbana-Champaign Copyright Status:In copyright. Digitized with permission of the Field Museum of Chicago. Contact dcc@library.uiuc.edu for information.Contact dcc@library.uiuc.edu for information.

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[15-16th June] An unprecedented scientific effort to create a digital twin of the entire surface of the Earth and everything on it before it's too late.
Chapter I : The Amazon
June 15th - 16th 2021

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An unprecedented scientific effort to create a digital twin of the entire surface of the Earth and everything on it before it's too late.

The Earth Archive is an emerging conservation initiative that is revolutionizing the fight against the climate crisis. By scanning the planet's land surface with very high resolution lidar, the Earth Archive will create a true three-dimensional digital twin of our world - an open source, digital record of the Earth that will reflect the landscape exactly as it was at the time of scanning. With this endeavor, the Earth Archive is positioned to provide geospatial data that will serve as the baseline for understanding and exploring our world.

While our scope is the entire planet, we've tasked ourselves with first scanning areas that are not only most susceptible to change, but also deep in value for understanding our past. The 2021 inaugural Earth Archive Congress is centered on our initial campaign to scan the entire Amazon Basin. The Amazon rainforest plays a monumental role in the Earth's climate, has an incredibly rich Indigenous history, and boasts a remarkable level of ecological diversity - but is vanishing before our eyes. With the ability to digitally preserve landscapes at any moment in time, very high resolution lidar can enhance archaeological, anthropological, and conservation studies and provide needed information to help advance sustainable development, as well as provide us with more groundbreaking revelations of the Amazon's astounding past.

The Congress will connect researchers, students, NGOs, stakeholders, indigenous groups, governmental officials, and corporate interests to become partners in this groundbreaking effort. Expanding the global Earth Archive community in this way encourages all levels of society to collaborate on a project that could prove fundamental to a solution for climate change. Join us for this hybrid event to become part of this unprecedented initiative.

​

- Christopher Fisher PhD.

Archaeologist, Director of The Earth Archive

Professor of Anthropology, CSU

Stay informed

Join our mailing list to get the latest news about The Earth Archive

The Earth Archive will use your submission and/or event registration to provide you with information about The Earth Archive project and upcoming Earth Archive and mapping events. We occasionally share this information with our event partners, who may use your contact information to provide you with information about their company and/or products.

© 2019 Connectmii Delaware Inc.

The Earth Achieve advisory group comprises global leaders in technology, business, academic research, non-governmental organizations and the publics sectors. Our advisors are committed to solving the most challenging economic, environmental and social problems through innovative technologies such as LiDAR mapping and data science. They have a bias for action and collaboration with a commitment to be bold and have an impact on our most challenging global issues.

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Geospatial Conference | The Earth Archive Virtual Congress

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An unprecedented scientific effort to create a digital twin of the entire surface of the Earth and everything on it before it's too late. The Earth Archive is an emerging conservation initiative that is revolutionizing the fight against the climate crisis.

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All sorts of defintions for all sorts of science-y, computer-y things...

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This is the glossary of terms for the EGI Federation.

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[Jun 2021] 7 June 2021: 7CT An online panel discussion by Villa Aurora & Thomas Mann House and Museum für Naturkunde - Leibniz Institute for Evolution and Biodiversity Research. | in English

https://mediapool.expo-ip.com/stand/43

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Digitization Training Opportunity (Applications close June 07, 2021)

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Digitization Training Opportunity (Applications close June 07, 2021)

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iDigBio is offering a 4 day course titled “Introduction to Biodiversity Specimen Digitization” on July 12-15. Details are below. If you have anyone on your team who might benefit from the training, I encourage you to encourage them to apply! Space is limited and applications are due by June 7th.

We are excited to introduce the first professional development opportunity offered by the iDigBio Digitization Academy: Introduction to Biodiversity Specimen Digitization.

This free, online course is focused on introducing the creation of digital data about biodiversity specimens to those who are just beginning this activity.

Applications are due by 8 a.m. ET on Monday, June 7. Should you have any questions, please don't hesitate to contact Michael Denslow at michael.denslow@gmail.com. This course will be taught by Michael Denslow, Erica Krimmel, and Austin Mast.

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We are excited to introduce the first professional development opportunity offered by the iDigBio Digitization Academy: Introduction to Biodiversity Specimen Digitization.

This free, online course is focused on introducing the creation of digital data about biodiversity specimens to those who are just beginning this activity.

The aims of the course are to empower participants with the knowledge and skills to (1) identify and assess relevant facets of information or potential information about specimens, (2) identify and implement common digitization protocols and best practices related to databasing, digital imaging, and georeferencing, (3) identify downstream uses that are relevant to digitization decisionmaking, (4) explain basic database design and the value of Globally Unique Identifiers, (5) identify the major specimen data management system options and the major differences among them, and (6) design a digitization project, including quality control and a data management plan that includes data sharing.

This course is targeted at those already associated with a biodiversity collection, such as student technicians, collections management professionals, or curators. The course will be relevant to a diversity of collection types. Participants do not need to have prior knowledge of biodiversity informatics or specialized software.

The course will occur from July 12-15 (Monday-Thursday) between 11 a.m. and 5 p.m. ET (exact times yet to be determined). You can expect to spend three hours per day in synchronous meetings and as much as two additional hours of preparation time per day outside of class. So this is about a 20-hour time commitment. We expect to cap the course at 20 participants and will make admission decisions based on the relevance of the training to the future of the applicant's organization and a desire to engage a diversity of perspectives. The course will be delivered in English. Those interested in participating from outside the US are welcome to apply.

You may apply to participate in this course at: https://forms.gle/YsP8awfKBAzksBSk9. Applications are due by 8 a.m. ET on Monday, June 7. Should you have any questions, please don't hesitate to contact Michael Denslow at michael.denslow@gmail.com. This course will be taught by Michael Denslow, Erica Krimmel, and Austin Mast.

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We are excited to introduce the first professional development opportunity offered by the iDigBio Digitization Academy: Introduction to Biodiversity Specimen Digitization. This free, online course is focused on introducing the creation of digital data about biodiversity specimens to those who are just beginning this activity.

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[Jun 2021] Be a part of #AAM2021!  June 7-9. Join thousands of colleagues across the country as we dive back into over 80 sessions, dozens of networking opportunities, and more! Plus, get immediate access to all session recordings from our kick-off day.

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Registration is Open for #AAM2019
May 19-22, 2019 - New Orleans
Register Now!

The American Alliance of Museums represents more than 35,000 museum professionals and volunteers, institutions, and corporate partners serving the museum field. Learn more.

© Copyright | American Alliance of Museums | All Rights Reserved

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Register Now! The American Alliance of Museums represents more than 35,000 museum professionals and volunteers, institutions, and corporate partners serving the museum field. Learn more. © Copyright | American Alliance of Museums | All Rights Reserved

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Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

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Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

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Cool paper with nice tips and workflows for GenBank metadata related to pathogen sequence data.

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Abstract

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings. Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Background

The One Health perspective, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, so researchers can easily share what they have learned and allow others to build upon their findings. Two developments have made a great difference in our ability to support these requirements. First, the advent of whole genome sequencing (WGS) made it possible to establish genomic DNA as a standard data type and increase the resolution possible between isolates, dramatically changing how surveillance data for human pathogens could be stored, shared, and analyzed [ 1]. Second, storing and sharing genomic pathogen data and surveillance analyses as "open data" [ 2] has enabled a truly open vision for all global pathogen surveillance, as shown by the success of the open foodborne pathogen surveillance model in the United States [ 2, 3, 4] and in partnering countries, such as the United Kingdom [ 5], Australia [ 6, 7], Mexico [ 8, 9], and Canada [ 10, 11]. Newer open surveillance efforts for health care acquired illness (HAI) [ 10, 11, 12] and viral diseases [ 13, 14] are on a similar trajectory for success. An additional benefit of submitting pathogen genomes to public databases in real-time is earlier collaboration around emerging threats, such the COVID-19 pandemic [ 15] or MCR-1/colistin resistance [ 16].

While this wide array of public data represents the admirable work of many research teams and their particular areas of interest, it also demonstrates how enthusiastic adoption of technologies can pose challenges for the very database standardization necessary to make these systems useful. As more and more data are collected, differences in methods for data description, analysis, storage, and access could eventually silo our efforts, even within the same pathogen surveillance community. Yet the One Health vision demands that we create systems that can integrate knowledge across species, sources, contributors, and analyses. One of the best ways to honor the hard work and ingenuity which developed these resources would be to ensure such silos do not develop. Instead, we could build upon the data analysis standards recommended by PHA4GE [ 17] and from existing Best Practices, such as those we describe below. Together we can establish common methods for reliably storing, retrieving, and genomic data for pathogen surveillance.

Many researchers now host their genomic data and primary analyses publicly at the United States National Center for Biotechnology Information (NCBI), which provides a web-accessible view of their databases and supports seamless collaborations across agencies and political borders. Once a day, NCBI uses the International Nucleotide Sequence Database (INSDC) to synchronize with two other important nucleotide databases, European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) and the DNA Databank of Japan (DDBJ) [ 18] resulting in a truly international database of nucleotide data.

As of March 2020, the genomes of thirty-two important pathogens (31 microbes and one yeast) under active surveillance by public health laboratories and hospitals are now stored at NCBI and are easily available through NCBI Pathogen Detection (NCBI-PD). These genomes, their associated metadata, and automated analysis results can all be accessed through the NCBI-PD browser at https://www.ncbi.nlm.nih.gov/projects/pathogens. First released in 2016, the NCBI PD browser provided the first public analysis portal for bacterial genomic surveillance data in the world [ 19]. This centralized resource has facilitated collaborations across US agencies, academic partners, non-PulseNet public health laboratories, and international contributors all of which submit and utilize the analysis results for making routine public health decisions. Each day, the NCBI-PD integrates its archived clusters with newly submitted genomes, then computes updated phylogenies for clusters of closely related genomes, which can provide insights about past or ongoing disease outbreaks. These results are available both to the contributing public health labs and to the general public. In addition to phylogenetic clustering, NCBI-PD now screens every bacterial genome for genes associated with antibiotic resistance (AMR) [ 20], stress response, and virulence, which allows surveillance of specific genes in circulating pathogens by groups such as the National Antimicrobial Resistance Monitoring System (NARMS).

Since its inception in 2016, the NCBI-PD database and surveillance platform has grown in size and importance to public health. Having this central, public resource focused the community around a common set of tools and standards, rather than spending resources creating new tools for each individual lab. Collaborative efforts to improve NCBI-PD have gone beyond the original group of foodborne pathogens to offer real-time clustering of many other pathogens, including Mycobacterium tuberculosis, several HIA, and one yeast, Candida auris. As the value of having a shared, public resource for genomic surveillance data became obvious, other groups began developing new tools and platforms that utilized NCBI-PD as a common underlying database (Fig. 1). Demand grew for older resources such as BioNumerics (Applied Maths, Sint-Martens-Latem, Belgium), used by the PulseNet community [ 21], to allow users to submit their WGS data to NCBI, as well as the capability to download NCBI data into BioNumerics to perform local, customized analyses. As a result of building interoperable systems, researchers can now use public data from the INSDC in conjunction with private data from individual or industry labs, using platforms such as Integrated Rapid Infectious Disease Analysis (IRIDA) [ 22], INNUENDO [ 23], PathogenWatch [ 24] NextStrain [ 13], IDseq [ 25], and CGE Evergreen [ 26]. And finally, for FDA-specific missions, open source tools to support genomic epidemiology (GalaxyTrakr) [ 27], AMR surveillance (Resistome Tracker) [ 28], and risk assessment (GenomeGraphR) [ 29] have been created.

INSDC hub showing how genomic data in public databases get analyzed by many different software platforms, for different purposes. Included in this figure are most genomic epidemiology-related open source analysis platforms available in March of 2020, and one private software tool, BioNumerics. BioNumerics is also the only platform with submission capability

Connecting data across species and locations is an essential part of One Health. When a physician, veterinarian, or public health official identifies a case of bacterial or viral illness, they must be able to determine how that datapoint may fit with reports from around the world; that depends on whether the genomic data from the likely pathogen can be analyzed, archived, and made accessible to others. However, accessibility is not simply being able to download genomic sequences economically. It also means storing that sequence with standardized attributes (metadata) that allow important environmental and chain of custody connections to be made. By ensuring that central data sources, such as INSDC and NCBI, are stocked with sequence data and associated metadata submitted using standardized attributes and standard templates, scientists can promote interoperability across multiple platforms and analysis types. These actions can allow a true One Heath resource to emerge: pathogens submitted by different stakeholders from different sources (human clinicals, animals, food, and environmental sources) can all be combined and analyzed for different purposes across different analytic platforms.

Over the last 8 years, laboratories in the FDA's pioneering GenomeTrakr network [ 2] have collaborated to build the underlying open-access archive of genomes collected from non-human sources, totaling ~100 K isolates as of July 2020. Sources of these routine surveillance isolates include food (domestic and imported), food production facilities, farms, watershed sampling, and animals from farm, veterinary, and wildlife sources. The resulting public data (genomes and associated metadata), hosted within the NCBI Pathogen Detection, are comprehensive enough to bridge multiple needs (defining outbreaks, identifying their sources, tracking AMR, and primary research), and yet custom NCBI tools and 3rd party analysis platforms meet the unique needs of specific users. As founders with extensive experience and in-depth knowledge of this massive data collection effort, we are pleased to share our Best Practice guidelines here.

Main text

Purpose of this document

Our goal for this best practices document is to provide an easy, direct path for any laboratory in the world to participate in a global pathogen surveillance effort. Increasing the number labs able to contribute to NCBI not only democratizes the ability of laboratories to connect their data with others around the world, but also increases the likelihood that the database will capture the range of real-world pathogen diversity. The majority of genomes now available in the NCBI-PD were submitted by two stakeholder groups: 1) national-level teams surveilling foodborne pathogens, including GenomeTrakr [ 2], PulseNet [ 4, 21], NARMS [ 30], and Public Health England [ 31], among others; and 2) teams surveilling HAI, primarily Brigham and Women's Hospital [ 10] and Public Health England [ 31]. Prior to 2019, most of these submissions were brokered through the large networks, such as GenomeTrakr or PulseNet, rather than from individual laboratories collecting the primary data. With the release of this guidance we are removing GenomeTrakr from the role of being a data broker for our laboratories. In addition, we are taking a step further in making this document broad enough to be relevant for anyone (academics, industry, non-governmental organizations, non-US ministries of health, etc.) interested in starting or contributing to an existing genomic epidemiology effort to have the tools and guidance readily available. The analytical framework has already been established for the 32 pathogens (31 bacteria and one yeast) listed on the NCBI-PD homepage, however communities can request new pathogens be added by submitting a request to NCBI.

Thus, the remainder of this document will describe the NCBI community standards for data collection and provide guidelines for 1) establishing new surveillance projects at NCBI, 2) assessing the quality of your sequence data, 3) submitting raw sequence data and associated isolate metadata to NCBI, and 4) instructions for curating your data and cluster results within specified databases at NCBI. Although the protocols included here were initially developed by the GenomeTrakr team for submitting bacterial, foodborne pathogen isolates collected from non-human sources, these guidelines are written for any laboratory that has the following items in place:

Pure-culture isolates of pathogens (or the ability to amplify a target organism's entire genome from a swab or sample),

WGS data of these isolates from Illumina-based sequencing platforms (Miseq, NextSeq, HiSeq, iSeq) that you are willing to share publicly (non-Illumina data can be submitted as assemblies to GenBank),

Data submission protocols tailored for the NCBI database, although Best Practices apply for INSDC partners (EMBL-EBI and DDBJ) or other databases such as GISAID [ 27],

QC Data that meets or exceeds standard QC thresholds (see Table 1),

Minimum standard metadata ready for submission (see Table 2),

Submission should include contact information so that agencies or individuals can request additional information, metadata, and/or the isolate, and,

Process identified to curate your submitted genomes and metadata, keeping them updated, responding to requests, and/or correcting your submissions.

Table 1 Quality control threshold guidelines for enterica pathogens collected for GenomeTrakr

Table 2 The minimum set of metadata fields recommended by GenomeTrakr for BioSample submission of bacterial pathogens. Consult the "Populating the NCBI Pathogen metadata template protocol" [32] for expanded, up-to-date guidance

The importance of standardized metadata

Ensuring that your laboratory can provide the minimum set of metadata should be done as the project is getting started, BEFORE any sequencing starts or submissions are prepared. In order for pathogen surveillance to be successful, we need standard metadata for each pure culture isolate, especially when there are numerous independent laboratories collaborating in the effort. As an example, the minimum metadata fields for GenomeTrakr are as follows:

laboratory name holding the isolate,

unique strain ID,

isolation type (human source vs. non-human source),

collection date (year minimum),

isolation source (e.g. type of food, animal, or description of environment),

and location (Country and state if in US).

It is equally important to ensure that each piece of metadata gets submitted to the correct metadata attribute (or field) within the package (Table 2), or the information will not get labeled properly and therefore will not be available to those interpreting the results. The INSDC, in collaboration with the Global Microbial Identifier (GMI) ( https://www.globalmicrobialidentifier.org), recommends using the Pathogen metadata template for pathogen surveillance submissions: (NCBI: https://submit.ncbi.nlm.nih.gov/biosample/template/?package=Pathogen.combined.1.0&action=definition and EMBL-EBI: https://www.ebi.ac.uk/ena/submit/pathogen-data). Following the GenomeTrakr metadata guidelines described in the "Submitting metadata" section will enable your data to be Findable, Accessible, Interoperable, and Reusable, also known as meeting FAIR standards [ 33]. This will also allow your laboratory's submissions to get properly analyzed, integrated, and labeled on the resulting tree clusters within the NCBI-PD browser (Fig. 2).

Screen shot of a cluster within the NCBI-PD browser showing harmonized metadata submissions across five different submitting laboratories (PulseNet, GenomeTrakr, Public Health England, Israel Ministry of Health, and CA Food Inspection Agency). URL: https://www.ncbi.nlm.nih.gov/Structure/tree/#!/tree/Salmonella/PDG000000002.1922/PDS000025876.12?treelabel=sra_center,strain,epi_type,collection_date,geo_loc_name,isolation_source

Quality control standards: how to QC data

Quality control (QC) thresholds help ensure the interoperability, accuracy and usefulness of NCBI-PD resources. It is important that contributors only upload material which meets quality control (QC) thresholds for both the metadata and the underlying sequence data. For example, data from an environmental isolate that fails to include descriptive metadata about isolation source, location, or date of collection is of little use in helping inform epidemiologists about potential exposures during an outbreak. Although it is possible to cluster such data, it cannot provide guidance for the investigation. Similarly, if poor quality sequence data is submitted for clinical isolates, those cannot be reliably clustered, resulting in missed opportunities for early detection of an outbreak. Guidance for metadata QC will be given later in this document.

Quality control thresholds recommended for sequence data from bacterial foodborne pathogens, based on current Illumina sequencing technology, are provided in Table 1. We recognize that these thresholds may need to be revised in the future, since continuing improvements to sequencing technologies, as longer read lengths and lower error rates, should lead to lower coverage requirements and better de novo assemblies (thresholds kept current in the "Assessing Sequence Quality in GalaxyTrakr" protocol [ 34]. Data passing these QC thresholds can generally be considered fit for purpose for identifying clusters of isolates involved in foodborne outbreaks, as well as for identifying many antimicrobial resistance and virulence/pathogenicity elements. These levels of quality also support the cgMLST approaches currently used by members of CDC PulseNet.

The values in Table 1 are empirically derived from a large sample of isolates at NCBI-PD that were shotgun sequenced using Illumina technology. For each of the Salmonella enterica, Listeria monocytogenes, Escherichia coli, Shigella sp., and Campylobacter jejuni databases 10,000 random isolates were selected on January 6th, 2020 from each respective NCBI-PD isolate table. On Jan 9th, 2020 1414 of the available Vibrio parahaemolyticus isolates were selected, reflecting its smaller database. A summary of these data (51,414 isolates) shows the range of de novo assembly lengths, defined as the sum of the length of all contigs, for six pathogens relevant for foodborne outbreaks (Fig. 3 and Additional file 1). These results highlight the difficulty in establishing narrow guidelines for foodborne bacteria sequencing metrics as the assembly length can be highly variable as observed in E. coli, or multi-modal as in S. enterica and L. monocytogenes. Increasing coverage does appear to improve the quality of de novo assemblies, as measured by number of contigs, but that the rate of improvement slows once coverage exceeds about 40X (Fig. 4). Importantly, as genomes for different pathogens vary in size and complexity, the coverage needed to obtain a good L. monocytogenes or C. jejuni assembly does appear to be less than for larger, more complex genomes like Shigella and E. coli. GenomeTrakr recommendations for coverage represent a compromise between sequencing cost and data quality (Table 1 and [ 29]). As isolates are typically barcoded and multiplexed, often including isolates from multiple different surveillance efforts on the same sequencing run (e.g. a single MiSeq run could have isolates intended for GenomeTrakr, PulseNet, tuberculosis surveillance, and/or HAI), we strongly recommend performing QC on the entire run, ensuring that sample swaps, misidentifications, and/or contamination can be properly identified.

Density plot showing the distribution of genome lengths for a random sample of isolates with Illumina sequence data available from NCBI Pathogen Detection portal ( n = 10,000 for all species except V . paramaemolyticus where n  = 1414 due to smaller number of samples). Sequences were assembled using SKESA 2.2 and the bars indicate ±3 standard deviations from the mean. Mbp = mega base pairs

Plot of mean coverage (as reported by SKESA v. 2.2) vs number of contigs for a random sample of isolates with Illumina sequence data available from NCBI Pathogen Detection portal (n = 10,000 for all species except V. parahaemolyticus where n = 1414 due to smaller number of samples). The smoothed line was generated using generalized additive smoothing in R. Assembly quality, as measured by a decrease in the number of contigs, generally increases with increasing coverage

If a laboratory and research group does not have bioinformatic capacity, it can be challenging to assess if samples meet relevant QC thresholds. Even for teams that do have bioinformatics resources, it can take significant time to develop the kinds of reports and dashboards that allow quick decisions about whether a sample needs to be re-analyzed or if there are larger issues with a sequencing run. We have removed this hurdle by providing a custom, cloud-based workflow on the Galaxy platform [ 35], called "MicroRunQC", that generates a general QC report appropriate for most microbial pathogens, summarizing sequence quality, coverage, assembly quality, and sequence typing. Laboratorians can upload their FASTQ sequence data to our custom Galaxy instance, called GalaxyTrakr ( https://galaxytrakr.org), and run the MicroRunQC workflow [ 34], producing a QC report in less than an hour for a typical sequencing run of 24-36 isolates. Laboratories should review this QC report to verify that their sequence quality, coverage, and assembly quality thresholds are indeed acceptable for the target organism(s). Sequencing type (ST) results should also be assessed to make sure that the genus/species predictions match the input sample. While this approach will not detect sample swaps between closely related bacterial strains or low levels (< 5%) of contamination, the ST results can identify putative swaps across more divergent strains. Additionally, isolates with multiple ST allele calls (i.e. two or more alleles at one gene) should be further reviewed for evidence of contamination (refer to QC Failures section).

An important feature of the MicroRunQC workflow is that it was built using open-source tools and therefore could be implemented on local bioinformatics systems [ 36]. As with many of the resources and pipelines used within the GenomeTrakr network, the MicroRunQC workflow is not restricted to the common foodborne bacterial pathogens and can be used to track sequencing metrics on a range of pathogens commonly encountered in public health surveillance such as Neisseria, Legionella, Mycobacterium, etc. Extending QC parameters to encompass these and other organisms is an ongoing project, and collaborators are welcome to help establish these.

Identifying anomalies and performing root cause failure analyses

As more species and sources of genomic data are brought into the NCBI-PD, the more essential it becomes that we can all rely on the quality control efforts of participating laboratories. Trust is an essential part of One Health. Ideally all bacterial WGS data meet the QC criteria specified by the coordinating sequence network (e.g. GenomeTrakr requirements listed in Table 1), and when that quality is confirmed, the laboratorians can continue with the next steps of submitting the data to NCBI .

However, when QC failures are detected, laboratorians are faced with a handful of decisions, depending on the type of failure identified. We will provide general guidance to the most common errors flagged in the MicroRunQC report. The most damaging type of errors, for One Health surveillance purposes, are those which relate to proper labeling of the samples. Do we have what we expect, based on the provided metadata? These errors include:

1. 1.
If the genus or species for one or more samples does not match what is predicted in the MLST results

2. 2.
If the genome size is higher or lower than expected - this is potential evidence of sample swap, contamination, or mixed cultures, and

3. 3.
Number of contigs much higher than expected - this is potential evidence of contamination, mixed cultures, or poor quality sequence data.

Root cause investigations into these sorts of errors can be as simple as checking sample sheets to confirm that barcodes/indices were used correctly, and reviewing laboratory notebooks for evidence of sample swaps. However, it can also be helpful to use other bioinformatic tools such as serotype predictions, clustering results, metagenomic analysis, and comparing antimicrobial resistance genotypes to phenotypes if the information is available. If errors remain and the provenance of the WGS data still cannot be established, it may be necessary to re-isolate the bacteria from the original samples, holding off data submission until QC issues have been resolved.

Not all errors rise to the level of questioning the labeling or provenance of a sample. Some QC errors affect the usefulness of the data for downstream applications, and result from low read quality, low coverage, or high number of contigs. Samples exhibiting these problems usually provide enough usable sequence data to verify species; however, the sequence quantity is insufficient for downstream analysis. These types of errors can result from problems with library preparation and potentially loading too little or too much DNA onto the sequencing instrument. Typically, these mistakes can be fixed by either re-sequencing DNA libraries or preparing new libraries from genomic DNA. Although NCBI-PD will accept and process data that is of lower quality, decisions about how and when to correct minor errors may depend upon the resources of the originating laboratory and the uniqueness of the specific isolate.

NCBI data submission overview

Before starting your first data submission it is imperative first establish your submission environment at NCBI. For most pure-culture microbial surveillance projects the BioProject structure will be taxonomically focused (Fig. 5); therefore, a BioProject structure will first be established around each pathogen species of interest. Each species-specific BioProject will contain both the isolate metadata (BioSamples) and the sequence data collected from each isolate (raw sequence at SRA and/or annotated assemblies in GenBank). Accompanying this Best Practices is a custom NCBI submission protocol [ 37] tailored for genomic epidemiology data submission, which represents an expansion of Timme et al.'s submission protocol [ 38].

Overview of the database structure at NCBI showing an example Salmonella umbrella BioProject with three linked laboratory data BioProjects, each with their own BioSamples and associated sequence data

Creating BioProjects

BioProjects are an organizing tool at NCBI [ 39, 40] that pull together different kinds of data submitted across multiple NCBI databases. Each BioProject contains a unique URL, providing a home page with a title, description, links to lab websites, publications, funding resources associated with a particular project, along with links to the deposited data. A basic BioProject holds actual sequence data, assemblies, and their associated metadata. An umbrella BioProject is a way to group two or more basic BioProjects together, which is useful for disease surveillance and for looking across the grouped BioProjects in a single view. If specific umbrella BioProjects are intended for disease surveillance on the NCBI-PD, then the project can be "flagged" reflecting this intention. The result is that any sequence data submitted to a flagged BioProject (data or umbrella BioProject) will automatically get processed through the NCBI-PD pipeline. For example, in the GenomeTrakr network there is one flagged umbrella BioProject for each pathogen species under active surveillance at NCBI-PD (e.g. one for Salmonella, Listeria, etc.). The Salmonella umbrella contains 51 data BioProjects, each associated with a contributing laboratory and each inheriting the umbrella NCBI-PD flag: https://www.ncbi.hlm.nih.gov/bioproject/183844. Individual data BioProjects can also be flagged, enabling flexibility for different types of submitters. Consult the NCBI submission protocol [ 37] for linking to or establishing new umbrella and data bioprojects. If your laboratory is already a member of an established surveillance network you should first check to see if they would like you to link to their existing umbrella BioProject. For reference, the GenomeTrakr umbrella BioProjects are listed and kept current within this protocol.

Submitting metadata

The BioSample database at NCBI is designed to hold the metadata for "samples," or biological source materials [ 39], which can be many different things depending on your research. For microbial pathogen surveillance and GenomeTrakr these materials are the pure-culture bacterial isolates. Before collecting sequence data for your isolates, ensure that you can provide the minimum metadata recommended by your coordinating surveillance body. In Table 2, we provide the GenomeTrakr guidance on how to populate the Pathogen metadata package (see Appendix D for expanded guidance), along with a core set of recommended fields that users should populate with contextual data. Step-by-step instructions for submitting isolate metadata to NCBI are given in the "BioSample creation" section of the NCBI submission protocol [ 37]. If it becomes necessary to update, correct, or retract a Biosample registration, consult the NCBI Data Curation Protocol [ 41].

Submitting sequence data

NCBI comprises separate databases that hold the different types of DNA, RNA, or assembled sequence data [ 42]. For example, the nucleotide database (often called, simply, "GenBank") holds annotated DNA or RNA sequence data, complete or draft bacterial genomes, complete chloroplast or mitochondrial genomes, individual gene sequences, and phylogenetic alignment datasets, such as internal transcribed sequence (ITS) datasets. The Sequence Read Archive (SRA) [ 42, 43] database houses unannotated raw high throughput DNA or RNA sequence reads: for microbial pathogen surveillance these are the Illumina FASTQ file sequences collected from the pure cultured isolates registered in the BioSample database. For each isolate registered in Biosample, there is usually one associated SRA submission (only one SRA submission per isolate is recommended), linked by the run accession (SRR#######). A separate draft or complete genome can be submitted to GenBank under the same BioSample accession, but this is not required. NCBI submits the annotated assemblies they create as part of the Pathogen Detection Pipeline to GenBank. A detailed protocol for submitting raw sequence data to SRA is included in the NCBI Submission protocol.

Data management and curation

Establishing a data submission workflow

If new pathogen species or participating laboratories are added to a surveillance effort after the initial BioProject is established and linked to the Pathogen Detection pipeline, it may be useful to create new BioProjects, especially if the labs have multiple projects they need to track separately. A new BioProject Umbrella should be created if a previously unsurveilled organism is added the surveilled pathogens list, or a new surveillance network has been established. It is also reasonable for countries to request a new Umbrella - e.g., Public Health England uses the Umbrella PRJNA248064 to organize all their submissions. After the BioProjects are established, the routine submission workflow for an individual laboratory would only include submission to two databases, BioSample and SRA.

Responsibilities of contributors

The transition to genomics and open, public systems for pathogen surveillance brings new roles and responsibilities for scientists working in laboratories and public health professionals who use WGS results to resolve outbreaks and identify sources of contamination. Laboratories must build capacity to perform bioinformatic analysis on genomic data, whether locally or via cloud-based tools (e.g. MicroRunQC in GalaxyTrakr as an example), to assess data quality and to support outbreak response and traceback efforts. For their part, epidemiologists also need to gain some understanding of molecular evolution and phylogenetics so they can effectively integrate genomic findings with traditional data sources. In addition, laboratories need to develop internal processes to ensure that data collection is recorded consistently, that it is accurately submitted, and that both metadata and sequence data in those archives are kept current. This last part is crucial to effective surveillance of public data across organizations and for integration of data from different partners into a One Health Framework. The quality of any shared resource depends on the willingness of contributors to maintain the records they submit.

Maintaining current and updated data is an extremely important part of utilizing these data for public health surveillance. Over the course of the sequence data collection (from culturing, through genome sequencing, to internal QC and data submission) it is normal to have a low rate of certain errors, such as sample switches, spelling errors, cut/paste errors, or mis-identified isolates. Although each of the coordinating surveillance network bodies (GenomeTrakr, PulseNet, Vet-LIRN, etc) should work to minimize these errors, each submitting lab must be also diligent about correcting public data as soon as errors are discovered. To facilitate this process, each lab should have a documented curation procedure to ensure data are updated in a timely manner. The task of data curation could be specifically assigned a person or team of people trained for this important task and might involve establishing new, routine communication channels between disparate groups (i.e. sample collection and isolation, sequencing workflow, data analysis, and data submission might all be done by independent teams). Depending on the volume of submissions within a laboratory, the Data Curation workload could become a significant part of a team's effort. We have outlined the step-by-step process in our NCBI Data Curation protocol [ 41].

Conclusion

To enable easy reference, we will keep the protocols and guidelines published with this manuscript current at protocols.io, which will also allow our recommendations to evolve with advances in technology, improvements in metadata interoperability, and expansion of NCBI-PD capabilities. We hope these Best Practices will reduce the high-learning curve experienced by most new submitters to these databases, spurring more laboratories to use the NCBI-PD surveillance tools and participate by submitting sequences and metadata. As more contributors join the effort, these databases will become a richer source of isolates help realize the One Health goal of integrating the understanding of human, animal, and environmental pathogens, along with their sources. A true global surveillance effort fed by hundreds of submitters around the world, all with common standard metadata, quality control, and submission procedures would be groundbreaking for public health, to the advancement of science, and to the overall One Health ideal.

Availability of data and materials

All data analyzed during this study are included in this published article including NCBI accessions listed in Additional file 1. We also published four new protocols, referenced in this manuscript with DOI links to their home at protocols.io ( https://www.protocols.io/groups/genometrakr1/publications), and provided a script to install a local analysis workflow, hosted on GitHub ( https://github.com/estrain/MicroRunQC).

Acknowledgements

We would like to whole-heartedly thank Lili Velez, FDA Scientific Writer, for her editorial contributions. We also thank our Genometrakr collaborators (including CDC, FSIS, NCBI, and David Lipman) for reviewing the manuscript and providing feedback prior to publication.

Funding

This work was supported by the Center for Food Safety and Applied Nutrition at the U.S. Food and Drug Administration and, more specifically, the GenomeTrakr team.

Author information

Affiliations

1. U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, 5001 Campus Drive, College Park, MD, 20740, USA

Ruth E. Timme, Maria Balkey & Marc Allard

2. New York State Department of Health, Wadsworth Center, Albany, New York, USA

William J. Wolfgang & Sai Laxmi Gubbala Venkata

3. Association of Public Health Laboratories, Silver Spring, MD, USA

Robyn Randolph

4. U.S. Food and Drug Administration, Center for Veterinary Medicine, Laurel, MD, USA

Errol Strain

Contributions

RET initiated this project, designed the figures, created the protocols, and wrote the paper. ES lead quality control section of the manuscript, and did the summary analysis of current data for Figs. 3 and 4. MB and SLGV assisted with the protocol development. WJW, RR, MA, and ES were major contributors in writing and editing the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Ruth E. Timme.

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Timme, R.E., Wolfgang, W.J., Balkey, M. et al. Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens. One Health Outlook 2, 20 (2020). https://doi.org/10.1186/s42522-020-00026-3
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Keywords

Genomic epidemiology

GenomeTrakr

Microbial pathogen surveillance

NCBI submission

Whole genome sequencing

QA/QC

One health

# data[].attributes.attachment_link.title
Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

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The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings.

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Join colleagues every other Thursday at noon Eastern in an informal, biweekly discussion on various topics related to digitizing paleontology collections and sharing the digitized data. No advance registration required, and you are welcome to drop in for only part of the hour! Recordings and notes from past discussion sessions can be found linked to the Paleo Digitization Working Group wiki.

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Join colleagues every other Thursday at noon Eastern in an informal, biweekly discussion on various topics related to digitizing paleontology collections and sharing the digitized data. No advance registration required, and you are welcome to drop in for only part of the hour! Recordings and notes from past discussion sessions can be found linked to the Paleo Digitization Working Group wiki.

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Sun, 2020-12-06 18:35 -- ekrimmel Join colleagues every other Thursday at noon Eastern in an informal, biweekly discussion on various topics related to digitizing paleontology collections and sharing the digitized data. No advance registration required, and you are welcome to drop in for only part of the hour!

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Presenter Registration (deadline for registration is June 6th)
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Promotional Flyers (many organismal options to choose from!)
Event Partners/Sponsors
Are you interested in sharing your resources? Educators work hard to develop educational materials that are impactful and help students learn important concepts. Sharing resources expands the impact of your educational materials and provides accessible resources for educators. Please consider sharing your materials at the 2021 DemoCamp. Materials do not have to be perfect! This is your opportunity to workshop your ideas. Share what you have and get great feedback from your fellow educators. If you would like to share your materials online and via a virtual "poster session" learn more about what it means to present in our Presenter Notes.

The Natural History Education DemoCamp concept evolved from the successful model created by from the Ecological Society of America. The 2021 SPNHC DemoCamp will be our second virtual event and our first where we partner across societies in the natural sciences.

Previous Education DemoCamps
SPNHC Annual Meeting 2020

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Natural History Education DemoCamp | The Society for the Preservation of Natural History Collections

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Calling all Educators Looking for Resources (formal and informal, K-12, College) Do you want to discover new ways to connect your students to nature and still meet learning objectives? (Including K-12 standards or Vision & Change Recommendations) The DemoCamp is designed to provide materials and resources to teachers, educators, and faculty looking for easy to adopt educational materials that engage students with the natural world.

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SPNHC and AIC Annual Meeting Collection Tours from around the world every Wednesday in June. 

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SPNHC and AIC Annual Meeting Collection Tours from around the world every Wednesday in June. 

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10 - 10:30 AM - Scientific collection serving Malagasy biodiversity and advancement of research

Association Vahatra and Mention of Zoology and Biodiversity Animal, University of Antananarivo (Madagascar)

Collection room of the Mention of Zoology and Biodiversity Animal, University of Antananarivo is a collection of invertebrates and vertebrates. Currently, this room houses more than 100,000 specimens collected in different parts of Madagascar since 1990, by students and Malagasy researchers and foreigner partners of the Department. Most of these specimens are already cataloged according to international standards with the acronym UADBA (University of Antananarivo, Department of Animal Biology). The museum collection includes the different taxonomic groups of the vertebrate fauna of Madagascar including fish, amphibians, reptiles, birds and mammals. Besides the specimens in liquid, there are also specimens of skin and skeleton. Holotype and Paratype of recently described new species are housed also in this collection room. UADBA collection room also has exhibition specimens in order to present Malagasy biodiversity for education and future generations. One room of necrophagous insects of the genus Dermestes are located in another building of the Department.

Moderator: Laura Eklund

Tour Leader -Voahangy Soarimalala

10:30 - 11:00 AM - The British Geological Survey 'National Geological Repository' - probably the largest collection of UK geoscience samples

National Geological Repository, National Geoscience Data Centre, British Geological Survey (U K)

The UK National Geological Repository is a national and international science facility, and an integral part of the National Geoscience Data Centre and the British Geological Survey. Its unique collections of borehole cores, cuttings, samples, rock, fossil and mineral specimens, and related subsurface information from the UK landmass and continental shelf are used extensively in research, by industry (underpinning a significant part of the UK economy),by the public, and to support university teaching. Parts of the collections predate the origin of the BGS in 1835. Key components include: * The National borehole core and sample collection, which also contains core and samples from onshore, and offshore UKCS hydrocarbon wells, and is used extensively by the industry when researching new prospects. The collection includes over 600km of drillcore from over 22.5k wells and would cost an estimated £200b to re-drill. * The major UK type and stratigraphic fossil collection (250k specimens), plus extensive microfossil (300k slides) and locality based collections (ca. 4m specimens) * The major systematic British rock collection, with over 200k thin sections and 1m samples * The largest UK geoscience records archive, with over 20km of shelving, and including all the original BGS field records and maps, well and borehole logs, site investigations, coal data, seismics, geophysics, geochemistry, etc. Safely storing 16 million specimens weighing around 10,000 tonnes requires sophisticated equipment and a team of curators, conservators and registration staff. illustrates the storage of borehole cores and samples - rock, mineral and fossil specimens are held using more traditional equipment. Most samples are included in online databases and available via APIs and as online GIS datasets - see GeoIndex: https://mapapps2.bgs.ac.uk/geo...

Moderator: Laura Eklund

Tour Leader -Dr Michael Howe
11:00 - 11:30 am - 30.000 years underwater: the Arroyo del Vizcaíno collection
Universidad de la República / SAUCE-P (Uruguay)

The Arroyo del Vizcaíno collection is located in the town of Sauce, Canelones, Uruguay. The collection harbors 30 ka Pleistocene megamammal fossils. The collection is named after the fossiliferous site Arroyo del Vizcaíno, located near the mentioned town, found in the summer of 1997 by local farmers and first excavated by highschool students and teachers. It was not until 2011 when the paleontology team could begin the formal excavation of the site. Since then, the collection has increased the number of fossils to nearly 2000 and after several challenges regarding the conservation and storage establishment, now are placed in an adequate space in the perimeter of the town's high school.

The majority (more than 90%) of the species collected and storage belongs to the giant ground sloth Lestodon armatus, but also a great amount of material of other extinct sloths like Mylodon darwinii and Glossotherium robustum, as well as Glyptodon reticulatus, Doedicurus clavicaudatus, Panochthus tuberculatus, Toxodo extinct sloths like Mylodon darwinii and Glossotherium robustum, as well as Glyptodon reticulatus, Doedicurus clavicaudatus, Panochthus tuberculatus, Toxodon platensis, Notiomastodon platensis, Hippidion sp., Smilodon populator, and indeterminate Cervidae remains are present. Nowadays, the collection is officially affiliated with the Universidad de la República and SAUCE-P (Servicio Académico Universitario y Centro de Estudios Paleontológicos) was created as a formal institution. Here, many activities such as preparation and classification of the fossils, paleontological research and education, and numerous types of outreach activities are performed.

Moderator: Laura Eklund

Tour Leader -Richard Fariña

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AIC & FAIC Learning: 2021 Conference - Week 5

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10 - 10:30 AM - Scientific collection serving Malagasy biodiversity and advancement of research Association Vahatra and Mention of Zoology and Biodiversity Animal, University of Antananarivo (Madagascar) Collection room of the Mention of Zoology and Biodiversity Animal, University of Antananarivo is a collection of invertebrates and vertebrates.

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[Jun 2021] A community resource for entomological collection digitization

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a community resource for entomological collection digitization

Bug flow is a community resource for developing and sharing entomological collection digitization workflows and modules. Plans are to capture the methods and practices used to do this work and incorporate new versioning and metadata practices for workflows developed using these modules. If time allows, include some exemplar digitization workflows that were built from these new modules and incorporating the new metadata information and publishing them in several formats (PDF, DOCX, TXT). To support wider adoption and ease-of-use, the group will endeavor to produce versions in multiple languages.

About BugFlow

See the About BugFlow page to learn about our history and project members.

Get involved with BugFlow!

BugFlow on Slack

We work together, develop ideas, and stay in touch on the BugFlow Slack, we would love to have you join us.

Collaborate on building modules and workflows

We are always looking to grow our team of contributors and value community feedback. Join slack or send an email to any members of BugFlow to get connected. Please leave feedback requests and discussion in our Project Issues tracker.

Have an existing workflow from your institution to deposit? Submit a link to a workflow or create a new workflow from scratch in our Workflow Creator

BugFlow Modules

The primary output of BigFlow is a set of modules that contain outline the tasks and resources needed to create digitization workflows for entomological collections. They are actively being developed within this repository, and are available here:

To see issues and tasks still pending, please see the github repository issues located here: BugFlow Issues

Modules are currently being updated. The following map shows how the modules relate to each other and might fit into a collection-specific set of workflows.

BugFlow is broken into 5 super-modules which are linked below. Each super-module is broken down into constituent submodules that focus on specific tasks related to digitizing particular types of objects contained in entomological collections.

Super-Module 1 - Pre-digitization Curation (Staging) | Super-Module 2 - Project Management | Super-Module 2 - Label Data | Super-Module 4 - Imaging | Super-Module 5 - Value-added

Exemplar workflows

As the modules are developed, examples of workflows containing incorporating these modules will be given here.

If you have a workflow to contribute or deposit into Bugflow, use the corresponding "Workflow Template" here

Entomological Collections Best-Practices

Several members of the BugFlow project have joined with other individuals from the entomological collections community to work on entomology-related Best Practices for the SPNHC wiki, see: SPNHC Best Practices. Contact anyone from BugFlow to get connected with that project.

Legacy Modules

A series of modules were developed through the coordination of iDigBio in 2013. Information about these modules, upon which BugFlow was based, can be found here.

Digitization in context

Understanding why we digitize our collections objects drives the elements of a successful workflow. We digitize our collections for three broad reasons:

our own internal uses of collections management,

intra-community access to collections data without requiring an in-person visit

mobilize and make available collections data to external research, policy, and education initiatives

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Bug flow is a community resource for developing and sharing entomological collection digitization workflows and modules. Plans are to capture the methods and practices used to do this work and incorporate new versioning and metadata practices for workflows developed using these modules.

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[May 2021] Documentation for Tidyverse packages, which include handy tools for cleaning and reshaping data.

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Learn more about the tidyverse package at https://tidyverse.tidyverse.org.

The core tidyverse includes the packages that you're likely to use in everyday data analyses. As of tidyverse 1.3.0, the following packages are included in the core tidyverse:

The tidyverse also includes many other packages with more specialised usage. They are not loaded automatically with library(tidyverse), so you'll need to load each one with its own call to library().

As well as readr, for reading flat files, the tidyverse package installs a number of other packages for reading data:

In addition to tidyr, and dplyr, there are five packages (including stringr and forcats) which are designed to work with specific types of data:

lubridate for dates and date-times.

hms for time-of-day values.

blob for storing blob (binary) data.

dplyr backends

There are also two packages that allow you to interface with different backends using the same dplyr syntax:

dbplyr allows you to use remote database tables by converting converting dplyr code into SQL.

dtplyr provides adata.table backend by automatically translating to the equivalent, but usually much faster, data.table code.

In addition to purrr, which provides very consistent and natural methods for iterating on R objects, there are two additional tidyverse packages that help with general programming challenges:

magrittr provides the pipe, %>% used throughout the tidyverse. It also provide a number of more specialised piping operators (like%$% and %<>%) that can be useful in other places.

glue provides an alternative to paste() that makes it easier to combine data and strings.

Modeling with the tidyverse uses the collection of tidymodels packages, which largely replace the modelr package used in R4DS. These packages provide a comprehensive foundation for creating and using models of all types. Visit theGetting Started guide or, for more detailed examples, go straight to the page.

If you're asking for R help, reporting a bug, or requesting a new feature, you're more likely to succeed if you include a good reproducible example, which is precisely what the reprex package is meant for. You can learn more about reprex, along with other tips on how to help others help you in the help section.

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Installation and use Install all the packages in the tidyverse by running install.packages("tidyverse"). Run library(tidyverse) to load the core tidyverse and make it available in your current R session. Learn more about the tidyverse package at https://tidyverse.tidyverse.org. Core tidyverse The core tidyverse includes the packages that you're likely to use in everyday data analyses.

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OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

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OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Download

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OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data. Download

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[Dec 2020] This is a practical guide for georeferencing. It describes the protocols to determine the shapes of features and how to use them as the basis for georeferencing with the point-radius georeferencing method

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The purpose of this glossary is to describe important concepts in accordance with the intended meaning in this document, the associated Georeferencing Best Practices (Chapman & Wieczorek 2020) and the Georeferencing Calculator Manual (Bloom et al. 2020). These concepts are treated in broader contexts in many other sources. We have adapted the terms presented here from many sources including Wikipedia (as of November, 2019), the ESRI Dictionary (as of November, 2019) ( ESRI n.d.), and various articles within Kemp 2008.

accuracy

"The true value is not known, but only estimated, the accuracy of the measured quantity is also unknown. Therefore, accuracy of coordinate information can only be estimated." ( Geodetic Survey Division 1996, FGDC 1998).

bathymetry

1. The measure of depth of water in oceans, seas and lakes.

2. The shapes of underwater terrains, including underwater topography and sea floor mapping.

boundary

The spatial divide between what is inside a location and what is outside of it.

bounding box

An area defined by the coordinates of two diagonally opposite corners of a polygon, where those two corners define the north-south and east-west extremes of the area contained within.

coordinate format

coordinate precision

coordinate reference system

coordinate system

coordinates

corrected center

The point within a location, or on its boundary, that minimizes the geographic radial of the location. This point is obtained by making the smallest enclosing circle that contains the entire feature, and then taking the center of that circle. If that center does not fall inside the boundaries of the feature, make the smallest enclosing circle that has its center on the boundary of the feature. Note that in the second case, the new circle, and hence the radial, will always be larger than the uncorrected one (see Figure 1).

Darwin Core

A standard for exchanging information about biological diversity (see Darwin Core).

datum

decimal degrees

depth

direction

DMS

easting

elevation

A measurement of the vertical distance of a land or water surface above a vertical datum. On maps, the reference datum is generally some interpretation of mean sea level or the geoid, while in devices using GPS/ GNSS, the reference datum is the ellipsoid of the geodetic datum to which the GPS unit is configured, though the device may make corrections to report the elevation above mean sea level or the geoid. Elevations that are above a reference point should be expressed as positive numbers, while those below should be negative. Compare depth, distance above surface, and altitude.

ellipsoid

A three-dimensional, closed geometric shape, all planar sections of which are ellipses or circles. An ellipsoid has three independent axes. If an ellipsoid is made by rotating an ellipse about one of its axes, then two axes of the ellipsoid are the same, and it is called an ellipsoid of revolution. When used to represent a model of the earth, the ellipsoid is an oblate ellipsoid of revolution made by rotating an ellipse about its minor axis.

entry point

The entry point on the surface of the ocean or lake where a diver enters the water and from which all activities are measured. See illustration in Georeferencing Best Practices (Chapman & Wieczorek 2020).

error

extent

feature

An object of observation, measurement, or reference that can be represented spatially. Often categorized into "feature types" (e.g. mountain, road, populated place, etc.) and given names for specific instances (e.g. "Mount Everest", "Ruta 40", "Istanbul"), which are also sometimes referred to as "named places", "place names" or "toponyms".

gazetteer

geocode

geographic boundary

geographic center

geographic coordinates

geographic radial

The distance from the corrected center of a location to the furthest point on the geographic boundary of that location. The geographical radial is what contributes to calculations of the maximum uncertainty distance using the point-radius georeferencing method. The term geographic radial, as defined here, replaces its equivalent "extent" used in the early versions of this Quick Reference Guide and related documents.

geometry

georeference

georeferencing method

The theory, including a set of rules, general procedures and expected outcomes, meant to produce a specific type of spatial representation of a locality.

georeferencing protocol

The set of specific documented steps that can be applied to produce a spatial representation of a locality, following one or more georeferencing methods.

GPS

GPS (receiver)

grid

A network or array of evenly spaced orthogonal lines used to organize space into partitions. Often these are superimposed on a map and used for reference, such as Universal Transverse Mercator UTM grid.

ground zero

The location on the land surface directly above a radiolocation point in a cave where the magnetic radiation lines are vertical. See illustration in Georeferencing Best Practices (Chapman & Wieczorek 2020).

heading

latitude

The angular distance of a point north or south of the equator.

locality

The verbal representation of a location, also sometimes called locality description.

locality clause

locality type

location

longitude

meridian

northing

offset

path

A route or track between one place and another. In some cases the path may cross itself.

point-radius

A representation of the geographic component of a location as geographic coordinates and a maximum uncertainty distance. The point-radius georeferencing method produces georeferences that include geographic coordinates, a coordinate reference system, and a maximum uncertainty distance that encompasses all of the possible geographic coordinates where a locality might be interpreted to be. This representation encompasses all of the geographical uncertainties within a circle. The point-radius method uses ranges to represent the non-geographic descriptors of the location ( elevation, depth, distance above surface).

precision

1. The closeness of a repeated set of observations of the same quantity to one another - a measure of control over random error.

2. With values, it describes the finest unit of measurement used to express that value (e.g. if a record is reported to the nearest second, the precision is 1/3600 th of a degree; if a decimal degree is reported to two decimal places, the precision is 0.01 of a degree).

prime meridian

radial

shape

synonym of footprint. A representation of the geographic component of a location as a geometry. The result of a shape georeferencing method includes a shape as the geographic component of the georeference, which contains the set of all possible geographic coordinates where a location might be interpreted to be. This representation encompasses all of the geographical uncertainties within the geometry given. The shape method uses ranges to represent the non-geographic descriptors of the location ( elevation, depth, distance above surface).

smallest enclosing circle

A circle with the smallest radius ( radial) that contains all of a given set of points (or a given shape) on a surface (see Smallest-circle problem). This is seldom the same as the geographic center, nor the midpoint between two most distant geographic coordinates of a location.

transect

uncertainty

A measure of the incompleteness of one's knowledge or information about an unknown quantity whose true value could be established if complete knowledge and a perfect measuring device were available ( Cullen & Frey 1999). Georeferencing methods codify how to incorporate uncertainties from a variety of sources (including accuracy and precision) in the interpretation of a location. Compare accuracy, error, bias, precision, and false precision.

Universal Transverse Mercator UTM

A standardized coordinate system based on a metric rectangular grid system and a division of the Earth into sixty 6-degree longitudinal zones. The scope of UTM covers from 84° N to 80° S. (See Universal Transverse Mercator (UTM) Coordinates in Georeferencing Best Practices (Chapman & Wieczorek 2020)).

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Paula F. Zermoglio, Arthur D. Chapman, John R. Wieczorek, María Celeste Luna, David A. Bloom

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The purpose of this glossary is to describe important concepts in accordance with the intended meaning in this document, the associated Georeferencing Best Practices (Chapman & Wieczorek 2020) and the Georeferencing Calculator Manual (Bloom et al. 2020). These concepts are treated in broader contexts in many other sources.

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[Dec 2020] A Location that is poorly georeferenced obscures the information upon which a georeference should be based, potentially making the originally provided information irrecoverable. The resulting georeferences can be misleading to users and lead to errors in research outputs. Thus, an important take-home message is, "To georeference poorly is worse than not to georeference at all."

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Georeferencing Best Practices provides guidelines to the best practices for georeferencing. Though targeted specifically at biological occurrence data, the concepts and methods presented here may be just as useful in other disciplines.

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[May 2020] All sorts of useful documentaion on how to do things...

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Community peer-review may be just a single step in GBIF's digital documentation workflow, but it provides an important opportunity for members of the GBIF community of practice-the intended users and beneficiaries of these documents-to guide the documents' development by offering direct input and feedback. While this process is first and foremost intended to ensure the quality of the documentation, it also serves as a mechanism for fostering community discussion and collaboration.

The process starts from the premise that authors and reviewers are part of the same community. The fact that their identities are not concealed at any point during the process, reviewers and authors should be encouraged toward open, honest and collegial exchanges, with a focus on constructive criticism even where difference of opinion exist. The focus of reviewers should be to help authors to improve their work in ways that benefit the broader biodiversity informatics community. Community members are responsible for ensuring that their actions encourage a "safe, hospitable, and productive environment" that is "professional, respectful and harassment-free for all participating," in adherence with the GBIF Code of Conduct.

Each document's source text is freely and openly available and maintained in a public GitHub repository, or "repo". The use of GitHub enables reviewers and users to raise issues and track their resolution. Reviewers and users can offer comments, suggestions and corrections at any stage of the document's life cycle, making it easier to make corrections to current versions and update future ones while ensuring community access to accurate, well-maintained guidance and information.

Staff from the GBIF Secretariat commits to two operational principles to ensure the transparency and effectiveness of the community review process:

1. Individual contributions by community members will be properly credited and acknowledged

2. Open issues will be resolved in timely fashion, either by the authors or by Secretariat staff, in agreement with the authors

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Community peer-review may be just a single step in GBIF's digital documentation workflow, but it provides an important opportunity for members of the GBIF community of practice-the intended users and beneficiaries of these documents-to guide the documents' development by offering direct input and feedback.

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This is a simple html form to make use of the GBIF name parser. The parser is written in java and based on regular expressions to disect name strings into its components. It does only keep name parts required to reconstruct a full 3-parted name with an optional subgenus, but ignores additional infraspecific parts such as the subspecies given for varieties. Please see our API documentation for details.

You can copy paste a list of names, one per row, or upload a text file with a name per line. Uploaded files have to be encoded as utf8!

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This is a simple html form to make use of the GBIF name parser. The parser is written in java and based on regular expressions to disect name strings into its components. It does only keep name parts required to reconstruct a full 3-parted name with an optional subgenus, but ignores additional infraspecific parts such as the subspecies given for varieties.

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Resolve lists of scientific names against known sources. This service parses incoming names, executes exact or fuzzy matching as required, and displays a confidence score for each match along with its identifier.

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Global Names resolution tools and services

Resolve lists of scientific names against known sources. This service parses incoming names, executes exact or fuzzy matching as required, and displays a confidence score for each match along with its identifier.

Code on GitHub

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Global Names resolution tools and services Resolve lists of scientific names against known sources. This service parses incoming names, executes exact or fuzzy matching as required, and displays a confidence score for each match along with its identifier. Code on GitHub

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GBIF Extended Specimens Phase 2 discussions

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GBIF Extended Specimens Phase 2 discussions

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[15 June thru 15 July 2021]

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Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration Phase 2.

Start Here
This is the home page for the 2nd phase of the virtual consultation: Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration . GBIF, under the umbrella of the alliance for biodiversity knowledge, is leading the consultation as part of the European Commission-funded SYNTHESYS+ project. See here for Phase 1.

Please bookmark this page to follow the consultation.

On Tuesday, 15th June two virtual introductory sessions will take place to accommodate different time zones. Tentative Agenda**
Session 1 registration Session 2 registration About the consultation Read the discussion on Phase 1 topics 1-5.
Information resources (More on individual category pages)

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Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration Phase 2. Start Here This is the home page for the 2nd phase of the virtual consultation: Converging Digital Specimens and Extended Specimens - Towards a global specification for data integration .

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[1 - 30 May 2021 ] Public review open for maintenance of Darwin Core

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Public review open for maintenance of Darwin Core

We are now almost 20 days into the minimum 30-day review period for the 40 changes to Darwin Core proposed on 2021-05-01 (see https://github.com/tdwg/dwc/milestone/14). If the public review of maintenance proposals is new to you, please refer to the Darwin Core Maintenance Frequently Asked Questions).

Of the 40 proposals, one (biome - #38) will certainly be deferred to a Task Group for further development. There is currently consensus on 34 of the proposals, and each of these will be recommended for ratification if no indication of dissent arises through the rest of the review period. The status of two proposals (identificationQualifier - #244, and the nameUsageIDs - #105) is unclear until verification that consensus has been reached. The remaining four proposals (recordedByID - #102, occurrenceStatus - #339 and its controlled vocabulary - #342 are controversial. If no evidence of consensus can be reached by the 30-day minimum review period, the controversial proposals will be deferred for later consideration. If it appears that consensus can be reached on any of the controversial proposals, the review period will be extended for an additional 30 days from the time apparent consensus is established (everyone participating in the discussion expresses their satisfaction with the proposed solution). During any extension, all proposals will remain open for commentary. The current status of proposals to date is shown in the table below.

Thank you on behalf of the Darwin Core Maintenance Group

Summary of proposed changes and current status (2021-05-21)

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Public Review of Darwin Core Maintenance Proposals

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We are now almost 20 days into the minimum 30-day review period for the 40 changes to Darwin Core proposed on 2021-05-01 (see https://github.com/tdwg/dwc/milestone/14). If the public review of maintenance proposals is new to you, please refer to the Darwin Core Maintenance Frequently Asked Questions).

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[18-22 Oct 2021] Connecting the world of biodiversity data: standards uniting people, processes, and tools.

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As in 2020, our annual conference must again be virtual. While biomedical scientists have made great progress against the SARS-COV-2 pandemic, the distribution of vaccines worldwide is very uneven. Moreover, travel budgets at most organizations have been eliminated. The Fall of 2021 is therefore still too soon to return to an in-person annual conference. As we did last year, TDWG 2021 will be composed of webinar-style symposia, presentations, panel discussions, and virtual posters. We are again separating working sessions from the virtual conference, but in response to feedback from last year's conference, we will hold working sessions after the main conference. The main conference will be held over the week of October 18-22, and working sessions will be scheduled for the following month. The theme for this year's meeting will be:
Connecting the world of biodiversity data: standards uniting people, processes, and tools
The 2021 meeting will be hosted "virtually" by the University of Florida, Gainesville and its Florida Museum of Natural History, with Dr. Pam Soltis, Director of the University of Florida Biodiversity Institute, and Research PI for iDigBio, leading the hosting effort. Among the lessons learned from last year's conference is that virtual events still require a lot of work, so this year we will be using the University of Florida Conference Services group to help organize and manage TDWG 2021. We will have a modest registration fee for this virtual event (discounted for students and participants from lower middle-income countries). Abstracts for all contributions (oral presentations and virtual posters) will be published in our open access journal, Biodiversity Information Science and Standards. Unlike last year, publication costs will not be passed to authors, but defrayed by registration fees and conference sponsorships.

In the virtual conference week, we expect to have 3 or 4 sessions per day, with each session lasting 90-120 minutes, and with at least a 30 minute break between sessions. The time zone differences across our global community makes it unavoidable that some people will be have to participate in some sessions well outside their normal working hours. Still others might find that a highly relevant session has been scheduled in the middle of their night. To mitigate these scheduling difficulties, we will post the recorded presentations as quickly as we can.

The call for organized session proposals (symposia, panel discussions, etc.) was distributed 18 March.

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As in 2020, our annual conference must again be virtual. While biomedical scientists have made great progress against the SARS-COV-2 pandemic, the distribution of vaccines worldwide is very uneven. Moreover, travel budgets at most organizations have been eliminated. The Fall of 2021 is therefore still too soon to return to an in-person annual conference.