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Ciência Aberta e Dados FAIR: Princípios, Práticas e Políticas

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Ciência Aberta e Dados FAIR: Princípios, Práticas e Políticas

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NOVO!

Mooc | Genérico | PT

Descrição: Fundamentos da curadoria de dados e da Ciência Aberta, aplicando os Princípios FAIR e explorando o uso da Inteligência Artificial para transformar a prática científica.

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Ciência Aberta e Dados FAIR: Princípios, Práticas e Políticas

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Desvenda os segredos da curadoria de dados! Explora a Ciência Aberta, aprende a gerir dados com os Princípios FAIR e utiliza a Inteligência Artificial para transformar a forma como fazes ciência.

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Genérico | EN
Descrição: Curso introdutório para profissionais que desempenham funções de suporte à gestão de dados de investigação

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Guia | Genérico | EN
Descrição: Saiba como elaborar um Plano de Gestão de Dados, em todo o seu cuclo de vida.

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Introduction

This section of the guide provides an overview of the basic concepts, procedures, and vocabulary involved in data management and the development of data management plans (DMP). It can be used as both an introduction to the topic as well as a reference source for those developing a data management plan

Why write a data management plan?

The goal of a data management plan (DMP) is to demonstrate that you have an organized, effective, and well thought-out system to manage your data throughout the research process.

This means going beyond taking care of the data during the collection and analysis phases; your DMP must also address your plans for the data after the research is complete.

In fact, the primary focus of most federal data management plans is on data sharing and data preservation and not on the details of data management during the research. This is because you cannot share or preserve data unless it has been well managed during the research phase.

Before you start:

If you are writing a DMP as part of a grant proposal, read the requirements and keep them on-hand.

Keep in mind that most grant proposals limit your data management plan to just two pages which means that you can't provide too much detail. Grant proposal DMPs need to written as a concise summary to stay within this limit.

Write Your DMP with DMPTool

About this guide

This guide was developed as a resource to introduce scholars and researchers of all experience levels to the basic concepts of data management and to serve as a tool to help researchers write data management plans that accompany grant proposals.

ISU Librarian Megan O'Donnell authored most of the content on this guide and is responsible for periodically updating the content. If you have a question, suggestion, or would like to report an error, please email Megan.

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Library Guides: Data Management Plan (DMP) Guide: Write a DMP

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This section of the guide provides an overview of the basic concepts, procedures, and vocabulary involved in data management and the development of data management plans (DMP).

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DCC Because good research needs good data

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DCC Because good research needs good data

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Genérico | EN
Descrição: Apoio e consultadoria a gestores de dados. Inclui formação e aconselhamento.

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DCC | Because good research needs good data

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Introducing User Acceptance Testing for your newly customised DMPonline pages The benefit of subscribing to our enhanced package is that you get to customise your pages. In our recent blog post, we have laid out how we revisited and improved the process.

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Genérico | EN
Descrição: Conjunto de recursos sobre os princípios FAIR, que vão desde documentos, workshops, inclui também um "RDM Starter Kit".

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This page is dedicated to resources that you might find useful in your FAIR endeavours. We have collected our GO FAIR materials as well as interesting papers & publications, tools and more for your information and use:

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This page is dedicated to resources that you might find useful in your FAIR endeavours. We have collected our GO FAIR materials as well as interesting papers & publications, tools and more for your information and use: GO FAIR Materials... Continue reading →

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Genérico | EN
Descrição: Plataforma de recursos e sofware em Ciência Aberta dos vários campos de investigação.

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Making science more open and even more reliable, efficient and relevant to society.

Utrecht University ambition

Utrecht University wants to promote open science as part of its promise to make science more open and even more reliable, efficient and relevant to society. In order to follow up on the ambition formulated in the strategic plan (pdf), the Rector Magnificus commissioned a task force, supported by a university library work group, to draft an Open Science Programme for the period 2018-2021.

Utrecht University aims to be at the forefront of open science. Therefore, the Executive Board has launched the Utrecht University Open Science Programme. A programme that aims to stimulate and facilitate researchers to put open science into practice. Read the most recent version (pdf).

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Making science more open and even more reliable, efficient and relevant to society. Utrecht University wants to promote open science as part of its promise to make science more open and even more reliable, efficient and relevant to society.

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Genérico | EN
Descrição: Agregador de recursos de formação, bases de dados, informação e política de dados

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Across the research disciplines there are thousands of standards and several thousand databases, designed to assist the virtuous data cycle, from collection to annotation, through preservation and publication to subsequent sharing and reuse. As consumers of these standards and databases, it is often difficult to know which resources are the most relevant for your specific domain and needs. As producers, you want to be sure your standard or database is findable by prospective users, and recommended in data policies by funders, journals and other organisations.

With our growing and interlinked content, functionalities and endorsements, FAIRsharing is the most comprehensive informative and educational resource of standards, databases and data policies and is here to help you!

The FAIRsharing team works with and for the community to map the landscape of community-developed standards and to define the indicators necessary to monitor their:

development, evolution and integration;

implementation and use in databases; and

adoption in data policies by funders, journals and other organisations.

Whether you are a researcher, standard/database developer, funder, journal editor, librarian or data manager, FAIRsharing can help you understand which standards are mature and appropriate to your use case. By mapping the relationships between standards and the databases that implement them, or the policies that recommend them, FAIRsharing enables you to make an informed decision as to which standard or database to use or endorse.

FAIRsharing is a web-based, searchable portal of three interlinked registries, containing both in-house and crowd-sourced manually curated descriptions of standards , databases and data policies , combined with an integrated view across all three types of resource . By registering your resource on FAIRsharing, you not only gain credit for your work, but you increase its visibility outside of your direct domain, so reducing the potential for unnecessary reinvention and proliferation of standards and databases.

However, if you still cannot find what you are looking for, please email us with your question and we will add it to this page.

THE BASICS OF STANDARDS

HOW FAIRSHARING HELPS YOU

6. Looking for guidance on standards, databases and policies

HOW TO USE THE FAIRSHARING PORTAL

8. Indicators and descriptors to help you selecting resources

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Across the research disciplines there are thousands of standards and several thousand databases, designed to assist the virtuous data cycle, from collection to annotation, through preservation and publication to subsequent sharing and reuse. As consumers of these standards and databases, it is often difficult to know which resources are the most relevant for your specific domain and needs.

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Genérico | EN
Descrição: Curso introdutório dirigido àqueles que apoiam os investigadores no armazenamento, gestão, arquivo e partilha dos seus dados de investigação.

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About this course: Essentials for Data Support course is an introductory course designed for professionals entering the field of research data management (RDM) support. This hybrid course equips participants with knowledge about tools, best practices, and support mechanisms for effectively assisting researchers in managing their data.

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Genérico | EN
Descrição: Neste curso aprenderá como avaliar a aplicação dos princípios FAIR nos dados de investigação utilizando ferramentas e recursos de livre acesso.

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FOSTER FACILITATE OPEN SCIENCE TRAINING FOR EUROPEAN RESEARCH

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In this course you'll learn how to go about assessing the FAIRness of research data using freely accessible tools and resources. This course will:

introduce you to the key terms and explain what they mean in a practical sense

tell you how data management planning can help to make data FAIR from the very start of research projects

show you how you can use freely available tools to help assess the FAIRness of data

Start the Free Course

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In this course you'll learn how to go about assessing the FAIRness of research data using freely accessible tools and resources.

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Data formats for preservation. What you need to know when creating a DMP

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Data formats for preservation. What you need to know when creating a DMP

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Guia | Genérico | EN
Descrição: Guia para investigadores sobre formatos de dados para preservação.

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Guides for Researchers - Data formats for preservation: What you need to know when creating a DMP by Paula Christina Marinho Moura and Venkat Shanmugasundaram

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How to deal with sensitive data: Learn how to preserve your sensitive data safely

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How to deal with sensitive data: Learn how to preserve your sensitive data safely

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Guia | Genérico | EN
Descrição: Guia sobre a gestão e preservação de dados sensíveis.

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Guides for Researchers

How to deal with sensitive data

Learn how to preserve your sensitive data safely

What is Sensitive data

What is the best way of managing access to sensitive data? This is not a straightforward question as it involves ethical, legal and technical issues to be tackled. This guide will help you on your way to preserve your sensitive data safely. It explains the different types of sensitive data, how to prepare them for storage and the possible cost involved in the process.

What is Sensitive data

Sensitive data is data that must be protected against unwanted disclosure. Access to sensitive data should be safeguarded. Protection of sensitive data may be required for legal or ethical reasons, for issues pertaining to personal privacy, or for proprietary considerations.

Examples of sensitive data are:

Personal data: identifiers such as names or identification numbers, physical, physiological, genetic, mental, economic, cultural or social characteristics, it also includes location data from GPS or mobile phones

Confidential data: trade secrets, investigations,data protected by intellectual property rights Security: passwords, financial information, national safety, military information...

Combination of different datasets that can be combined into sensitive or personal data

Biological data: endangered (plant or animal) species, where their survival is dependent on the protection of their location data (biodiversity community)

Personal and sensitive metadata

When handling and dealing with sensitive data, keep in mind that special attention should be given to collecting, processing, handling and storing data throughout the research process. In particular research data that contains personal data with which a living person can, directly or indirectly, be identified has to be handled with care. This concerns both textual data and image and sound data. Examples of direct data are someone's name and address, but it could also be a photo or an interview. An indirect fact is, for example, someone's employer. For personal data fully informed consent should be given for collecting, processing and storing data.

How to prepare sensitive data for storage and sharing?

When storing sensitive data, the first concern is to find a good security strategy for your type of data.

Sensitive data can still meet the requirements of the FAIR Data Principles (findability, accessibility, interoperability, and reusability) and be processed in a way that the needed protection is guaranteed also in the future.
Anonymization

Anonymization irreversibly destroys any way of identifying the data subject. Personal data that has been rendered anonymous in such a way that the individual is not or no longer identifiable is no longer considered personal data. For data to be truly anonymised, the anonymisation must be irreversible.

OpenAIRE provides researchers with a tool to anonymise data: Amnesia. The guide for which you can find here.

Pseudonymization
Pseudonymization substitutes the identity of the data subject in such a way that additional information is required to re-identify the data subject. The pseudonym allows tracking back of data to its origins, which distinguishes pseudonymization from anonymization, where all person-related data that could allow backtracking has been purged. Pseudonymised data are still legally considered as sensitive data because the data can be linked back to a person, but it's considered as a secure approach since personal identifiers are stored somewhere else.
Encryption
Encryption is a very generic term and there are many ways to encrypt data. The key to a good encryption strategy is using strong encryption and proper key management. Encrypt sensitive data before it is shared. Encryption will make your data totally unintelligible to those who may try to access it which might reduce re-usability.

In case none of these options are available for your dataset, data should not be made open and be archived under a closed license in a Trustworthy Repository. You can however publish a description (i.e.public metadata) of your data without making the data itself openly accessible, which enables you to place conditions around access to the data.

There are other approaches to sharing sensitive data too:

Information professional: is someone who collects, records, organises, stores, preserves, retrieves, and disseminates printed or digital information.

Data access committees: A committee that reviews and authorizes applications for data access and use.

Safe havens: provide access to data and services to enable research while protecting the confidentiality of the data.

Institutional data archives/vault: safe, private, store of data that is only accessible by the data creator or their representative.

Storing sensitive data

To make sure your data is archived securely you should look for a Trustworthy Repository.
If possible choose a certified repository that matches the needs of your data regarding file formats, access and licensing. Look for a repository that provides a persistent and globally unique identifier, guidance on data citation and clear information on costs (if any). re3data and FAIRsharing can give you information on what data repositories provide in terms of information, access, licensing, persistent identifiers, policy and certificates and standards.

In case you are archiving sensitive data you should always go for a restricted license or closed access ( How do I license my research data?). You should also keep in mind that it is not always necessary to keep all your data. What you need to store will likely depend on criteria such as their uniqueness, long-term value, reuse potential and the necessary to validate results in publications.

When archiving your research data keep in mind to describe it with metadata so it can be findable for other researchers. Make sure the repository you've selected supports the metadata standards you are using for your data. You can find further information on metadata for research data here: What is metadata.

Remember that it can also be useful to store consent forms to the data, with restricted or closed access.

Costs

The cost related to open access to research data are eligible as part of the Horizon 2020 grant - refer to your Grant Agreement conditions.

For further information, see our guide on cost in research data management.

More information

Pages Tools Guides
Factsheets

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Guides for Researchers - How to deal with sensitive data: Learn how to preserve your sensitive data safely by Emilie Hermans and Olivia Kaiser

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Guia | Genérico | EN
Descrição: Guia para investigadores sobre a gestão de dados não digitais.

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For researchers - How to deal with non-digital data: The benefits of digitising data by Judit Fazekas-Paragh and Venkat Shanmugasundaram

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Guia | Genérico | EN
Descrição: Guia sobre a aplicação dos princípios FAIR aos dados de investigação.

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European Open Science Infrastructure, for open scholarly and scientific communication

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Guia | Genérico | EN
Descrição: Apresentação e descrição dos princípios FAIR.

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In 2016, the ' FAIR Guiding Principles for scientific data management and stewardship' were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and R euse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

A practical "how to" guidance to go FAIR can be found in the Three-point FAIRification Framework.

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata (defined by R1 below) F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource
Accessible
Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.
A1. (Meta)data are retrievable by their identifier using a standardised communications protocol A1.1 The protocol is open, free, and universally implementable A1.2 The protocol allows for an authentication and authorisation procedure, where necessary A2. Metadata are accessible, even when the data are no longer available
Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
R1. (Meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (Meta)data are released with a clear and accessible data usage license R1.2. (Meta)data are associated with detailed provenance R1.3. (Meta)data meet domain-relevant community standards
The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).

Download this overview of FAIR Principles as a pdf-file.

For a downloadable format of the FAIR Principles, visit the FAIR-nanopubs page on GitHub.

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In 2016, the 'FAIR Guiding Principles for scientific data management and stewardship' were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of... Continue reading →

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Guia | Ciências Sociais | EN
Descrição: Guia para auxiliar os investigadores na aplicação dos princípios FAIR aos seus dados de investigação.

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This guide is written for social science researchers who are in an early stage of practising research data management. With this guide, CESSDA wants to contribute to professionalism in data management and increase the value of research data.
This video is also available on Zenodo. Please cite it as: CESSDA Training Team. (2020). What is the CESSDA Data Management Expert Guide & how can it help you?. Zenodo. http://doi.org/10.5281/zenodo.4569323 Overview
If you follow the guide, you will travel through the research data lifecycle from planning, organising, documenting, processing, storing and protecting your data to sharing and publishing them. Taking the whole roundtrip will take you approximately 15 hours, however you can also hop on and off at any time.

During your travels, you will come across the following recurring topics represented by images:

As the data management plan (DMP) is an important tool to structure the research data management of your project, it plays a central role in this guide. Each chapter ends with a section with questions that are generally to be answered in a DMP. In the chapter's paragraphs you will be presented with the information you need to answer the proposed questions.

We have designed a list of DMP-questions especially for this Data Management Expert Guide. You can view and download the checklist as pdf (CESSDA, 2018a) or editable form (CESSDA, 2018b), and keep them as a reference while you are studying the contents of this guide.

If you see this image, you have landed in a spot where you will learn about European diversity in data management. E.g., think of diversity in:

Funder requirements in making your Data Management Plan

Data protection law

Informed consent

Ethical review

The requirements of local data repositories

In each chapter, you will find our expert holding up a hand to give you some tips. These expert tips aim to provide you with food for thought and practical how-to information.

To give you in-depth tips on how to deal with your own specific type of data, chapters may contain examples of how to handle quantitative or qualitative data.

The tour operator on the left showcases expertise in quantitative data management, while the one on the right hand side illustrates qualitative data management.
Seven chapters
The following seven chapters are currently available. Click on an icon to visit the appropriate chapter.

Get started
To get started with the first tour, simply click on the button below.

Project details

The Data Management Expert Guide has been created for CESSDA ERIC by a number of its service providers' experts at: ADP, AUSSDA, CSDA, DANS, FORS, FSD, GESIS, NSD, SND, So.Da.Net and UKDS and is illustrated and edited by Verbeeldingskr8. The authors are mentioned by their names in the overview of the relevant chapter(s). DANS led the creation of this expert guide.

Having a background in learning and knowledge sharing, it was a great pleasure to co-ordinate the creation of this Data Management Expert Guide.

Ellen Leenarts (DANS) | Project leader

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Data Management Expert Guide - CESSDA TRAINING

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This guide is written for social science researchers who are in an early stage of practising research data management. With this guide, CESSDA wants to contribute to professionalism in data management and increase the value of research data. This video is also available on Zenodo. Please cite it as: CESSDA Training Team.

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Manual | Genérico | EN
Descrição: Uma história num guia confiável dos princípios FAIR para dados de investigação.
A fake story in a trustworthy guide to the FAIR principles for research data.

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Ciências Sociais e Humanas | EN
Descrição: Plataforma que identifica uma variedade de recursos e de ações de formação em diversas latitudes.

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Building expertise and exploiting the potential within the existing partner network are two key elements in the SSHOC project. The team responsible for "Fostering Communities, Empowering Users & Building Expertise" will create a definitive SSHOC Train the Trainer Toolkit and establish the SSHOC Training Network.

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Ciências da Vida | EN
Descrição: Plataforma com cursos de formação

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Welcome to TeSS: ELIXIR's Training Portal

Browsing, discovering and organising life sciences training resources, aggregated from ELIXIR nodes and 3 rd-party providers.

Events

Discover the latest training events and news from ELIXIR nodes and 3 rd-party providers.

Materials

Browse the catalogue of training materials offered by ELIXIR nodes and 3 rd-party providers.

Workflows

Create training workflows to visualise learning steps and link to resources specific to your training needs.

Providers

Browse training providers to discover training resources they offer and follow links to their materials and courses.

Latest Content in TeSS

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Browsing, discovering and organising life sciences training resources, aggregated from ELIXIR nodes and 3 rd-party providers. Discover the latest training events and news from ELIXIR nodes and 3 rd-party providers. Browse the catalogue of training materials offered by ELIXIR nodes and 3 rd-party providers.

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ORION MOOC for Open Science in the Life Sciences 2.0

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ORION MOOC for Open Science in the Life Sciences 2.0

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Ciências da vida | EN
Descrição: Este MOOC é uma introdução aos princípios de Ciência Aberta em biomedicina, ciências da vida e outros campos de investigação relacionados.
Duração: 12h

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About this course

Sign up to get more

You can start learning at any time. By signing up and enrolling you can track your progress and earn a Statement of Participation upon completion, all for free.
View this course Sign up to get more

Welcome to the ORION MOOC 2.0 for Open Science in the Life Sciences!

What is this course about?

This course is an introduction to Open Science principles in biomedicine, life sciences and other related research fields. It is intended to help scientists to share their research with the world more effectively.

Who is this course for?

This course is for researchers in biomedicine, life sciences and related research fields interested in Open Science practices.

What can you learn?

In the following 6 modules you will get an introduction to a variety of useful tools and research practices, as well as Open Science principles and workflows along the research cycle in biomedicine and life sciences.
Please note this course is now a standalone self-paced course that will not be moderated. In order to gain a completion certificate all the modules and activities must be completed.

Find out more in the Overview section under Course Content...

Gain digital badges

By studying the course you will have the opportunity to gain digital badges - you need to click on the 'Enrol' button to be able to do the quizzes and earn the badge.

After finishing this MOOC you will...

understand the concept and principles of open science

know about platforms and technologies for open research practices

be able to integrate open science practices into your research workflow

be familiar with open science data management practices

know about ethical, legal and technical aspects of sharing research data in biomedicine

be able to apply FAIR principles to your research data

If this is your first visit to this site, you need to register for a free account, then login on this site and click on the Enrol button for this course.

Course content

Below is the course content. You can click on any section here and it will take you through to this section of the course.

If this is your first visit to this site, you need to register for a free account, then login on this site and click on the Enrol button for this course.

2 reviews for this course

We invite you to discuss this subject, but remember this is a public forum.

Please be polite, and avoid your passions turning into contempt for others. We may delete posts that are rude or aggressive; or edit posts containing contact details or links to other websites.

Course reviews

Matias Policani Barrios

Mireia Tomas

If this is your first visit to this site, you need to register for a free account, then login on this site and click on the Enrol button for this course.

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website: orion mooc for open science in the life sciences 2.0

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OLCreate: ORION_MOOC_2.0 ORION MOOC for Open Science in the Life Sciences 2.0

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You can start learning at any time. By signing up and enrolling you can track your progress and earn a Statement of Participation upon completion, all for free. View this course Sign up to get more What is this course about? This course is an introduction to Open Science principles in biomedicine, life sciences and other related research fields.

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Artes e Humanidades| EN

Descrição: Recursos em Gestão de Repositórios de Dados, Prinícpios FAIR e Ciência Aberta, entre outros.

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DARIAH-CAMPUS is a discovery framework and hosting platform for DARIAH learning resources.

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Ciências da Vida | EN
Descrição: Disponibilização de materiais de apoio ao planeamento e gestão de dados de investigação.

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Training has been one of the core priorities of BioData.pt ever since its establishment.

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LEARN Toolkit of Best Practice for Research Data Management

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LEARN Toolkit of Best Practice for Research Data Management

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Guia | Genérico | EN
Descrição: Manual de boas práticas em gestão de dados de investigação

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ELIXIR Research Data Management Kit (RDMkit)

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ELIXIR Research Data Management Kit (RDMkit)

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Guia | Genérico | EN
Descrição: O ELIXIR RDMkit é um guia online contendo boas práticas de gestão de dados aplicáveis a projetos de investigação.

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Find the answers to your Research Data Management questions in Life Sciences.

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Genérico | EN
Descrição: Cursos sobre os seguintes temas:

What is Open Science?

Best practices

Managing and Sharing Research Data

Open Source Software and Workflows

Data Protection and Ethics

Open Licensing

Open Access Publishing

Sharing Preprints

Open Peer Reviews

Open Science and Innovation

Use Open Data in Teaching

Assessing the FAIRness of data

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FOSTER Plus (Fostering the practical implementation of Open Science in Horizon 2020 and beyond) is a 2-year, EU-funded project, carried out by 11 partners across 6 countries. The primary aim is to contribute to a real and lasting shift in the behaviour of European researchers to ensure that Open Science (OS) becomes the norm.

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MANTRA: Research Data Management Training

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MANTRA: Research Data Management Training

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Genérico | EN
Descrição: Contempla unidades de formação sobre: Research data in context; Data management planning; Organising data; File formats & transformation; Documentation, metadata, citation; Storage & security; Protecting sensitive data; FAIR sharing and access;

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MANTRA is an online course designed for researchers or others planning to manage digital data as part of the research process.

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Genérico | EN
Descrição: Neste curso aprenderá como avaliar a aplicação dos princípios FAIR nos dados de investigação utilizando ferramentas e recursos de livre acesso.

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FOSTER FACILITATE OPEN SCIENCE TRAINING FOR EUROPEAN RESEARCH

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In this course you'll learn how to go about assessing the FAIRness of research data using freely accessible tools and resources. This course will:

introduce you to the key terms and explain what they mean in a practical sense

tell you how data management planning can help to make data FAIR from the very start of research projects

show you how you can use freely available tools to help assess the FAIRness of data

Start the Free Course

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In this course you'll learn how to go about assessing the FAIRness of research data using freely accessible tools and resources.

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Ambiente e Ciências da Natureza | EN
Descrição: Cursos vocacionados para as Ciências da Natureza, em e-training. Contém recursos em outras línguas (ex: Italiano).

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The ENVRIplus game focuses on the scientiphic methodology (i.e., the e-learning goal), specifically it is oriented on scientific/ecological contents.

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Australian Research Data Commons :Resources

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Australian Research Data Commons :Resources

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Genérico | EN
Descrição: Plataforma com uma série de webinars destinados a todos os atores que trabalham em gestão de dados.

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Resources for researchers, librarians and eResearch professionals We have a huge collection of resources for researchers, librarians and eResearch professionals. The topics covered by our resources and guides range from...

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Genérico | EN
Descrição: Guia sobre a aplicação dos princípios FAIR na prática, a criação de planos de gestão de dados, e divulgação dos resultados de um projecto de investigação.

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This website will take you on a deep dive into the subject matter of FAIR research data. Over the course of about two hours, it will show you that FAIR is not a time-consuming administrative mantra, but a set of principles that makes your research efficient, transparent and sustainable. Working in line with the FAIR principles to make your data more FAIR will improve your research data management and safeguard your research data for the future.

The aim here is to explore six key FAIRification practices, and show how they apply to your research. You will meet researchers working across Denmark in different research disciplines who share their thoughts and experiences on how they make their research data more FAIR.

Go through the course all in one sitting, or come back to each FAIRification practice as needed, it's up to you.

Get started

What is FAIR?

The FAIR principles stand for making your research data Findable, Accessible, Interoperable and R eusable. Start the course here for an introduction to FAIR and FAIRification practices and ensure you can make full use of this How to FAIR website.

If you have not heard about the FAIR principles before, you may also want to take our 60-minute e-Learning introduction to Research Data Management and FAIR.

Read more

Why make your research data FAIR?

Making your research data FAIR could help to maximise your research impact and help peers and your 'future self' understand your research projects and data. But don't just take our word for it. Hear more from t wo of the authors of the FAIR principles on the advantages of making research data FAIR here.

Read more

How to make your research data more FAIR

This is where this H ow to FAIR website takes off. This section will show you how you can make your research datamore FAIR by taking you through six key FAIRification practices . You will be shown s hort video clips of researchers in Engineering, Humanities, Health Science s , and Social Sciences. Recommendations, standards and examples are given for quantitative, qualitative and sensitive data. These will guide you through the steps you can take before, during and after your own research project to help you produce FAIRer research data in a way that makes sense for you.

Read more

Test your FAIR-ability here

Are you a FAIR- apprentice or a FAIR-master?

Test your knowledge of the FAIR principles here. Three quizzes on qualitative, quantitative and sensitive quantitative data will take you through example research situations, where the best practices for FAIR may clash with what is practical or possible in reality. Choose the quiz that is most relevant for you - or test yourself on all three .

Try the quiz

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This website will take you on a deep dive into the subject matter of FAIR research data. Over the course of about two hours, it will show you that FAIR is not a time-consuming administrative mantra, but a set of principles that makes your research efficient, transparent and sustainable.

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Webinar | Ciências Sociais | EN
Descrição: Recursos e formação de apoio à investigação, gestão de dados e preservação de dados.

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To facilitate teaching in the social sciences and to support continuous learning and training, the CESSDA Training Working Group designs various training materials and resources for finding, managing and preserving data.

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To facilitate teaching in the social sciences and to support continuous learning and training, the CESSDA Training Working Group designs various training materials and resources for finding, managing and preserving data.

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Eurodoc Open Science Ambassador Training

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Eurodoc Open Science Ambassador Training

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Genérico | EN
Descrição: Formar investigadores em práticas chave em Ciência Aberta. O curso consiste em 10 módulos centrados em aspetos da Ciência Aberta

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Open Science: Sharing Your Research with the World

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Open Science: Sharing Your Research with the World

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Genérico | EN

Descrição: Aplicação dos princípios da Ciência Aberta no contexto académico, aprendendo a partilhar os resultados de investigação de forma eficaz e responsável, construindo uma maior visibilidade e impacto.

Realizam várias edições, disponibilizando os recursos mediante registo.

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You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Science principles along with practices for open data management and open data sharing.

Since research increasingly relies on software which is used to model and simulate, and to deal with the ever growing volume of research data, the course will also introduce FAIR software practices.

You'll learn to establish links between publications, data, software and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you'll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?

What kinds of open access publishing are most effective?

How can researchers increase the visibility and impact of their research?

How can the use of social media contribute to the visibility and impact of research?

How can researchers be acknowledged for the research software they write?

You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.
The development of this course is supported by the VRE4EIC project with funding from the European Union's Horizon 2020 Research and Innovation Programme.

More about this course

Describe concepts and objectives of open science

Analyze and discuss benefits and barriers of particular open science cases, including cases from within your own region or discipline

Set up an open data sharing strategy to increase your research visibility

Determine appropriate route to take when publishing an open access article

Additionally, you can choose and learn more about any of these topics:

Increase your research visibility

Set up a research software sharing strategy and make it citable

Expand what you'll learn

Week 1: Introduction to Open Science
Introduction to the Open Science movement. What are the objectives, main concepts, and benefits of Open Science? This includes:

Discussing the traditional subscription-based journal system with regards to Open Access publishing

Practical Open Science benefits for researchers

Policies of important funding organizations

Programs on Open Data and Open Access publishing and

Case studies on successful application of Open Science by researchers from different backgrounds.

Week 2: Research Data Management
Introduction to effective and secure research data management, including:

Learning the disciplinary standards of FAIR data sharing

Evaluating the strengths and weaknesses of different data storage and backup options

Organizing, documenting, and adding metadata to research data to optimize the visibility of your data

Data archiving, access, sharing and re-use with the use of data repositories

Understanding the different copyright licenses designed to deal with open data

Dealing with confidential data, company restrictions, and third-party agreements through case studies

Evaluating a data management plan

Explaining how Open Data can be applied in your field of research

Week 3: Publishing Open Access
Here you will discuss the main differences between the open access and subscription-based publication model in science, and the main misconceptions about open access publishing. In this week we also introduce the creative commons licenses used by open access journals and self-archiving policies. You will examine how you can maximize the accessibility of publications in subscription-based journals, you will present your opinion of the open access publishing model and assess the 'openness' of the main scientific journals in your field of research.
Week 4: Choose topic(s) depending on your interests: Increasing your research visibility
Here you discuss and formulate your communication strategy, describing and choosing your social media channels for reaching certain target groups.
Making your research software FAIR
Research relies ever more on software. Software is used to model and simulate, but it is also almost impossible to deal with the ever growing volume of research data without software. Software is used to read, record, collect and generate data; clean, filter and analyse data; present and visualise data.

This module looks at research software: software that is specific to a research field or research project.

In this module you will learn the role of publicly accessible software version control repositories, the importance of choosing the right software licence, how to make your software citable and tips on how to make it easier for other researchers to reuse your software.

Finally, conclusions of this course will be discussed and we will reflect on what has been taught in the previous weeks.

Expand syllabus

Pursue a Verified Certificate to highlight the knowledge and skills you gain

LICENSE

The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.

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Open Science: Sharing Your Research with the World

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You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Science principles along with practices for open data management and open data sharing.

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Webinars | Genérico | EN

Descrição: O "Center for Open Science" tem como missão aumentar a abertura, integridade, e reprodutibilidade da investigação académica. A promoção destas práticas nas comunidades de financiamento e publicação da investigação acelera o progresso científico.

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Share your videos with friends, family, and the world