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Descrição: Conjunto de recursos para apoiar o utilizador ao longo de todo o ciclo de vida da gestão de dados de investigação.

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Why a research data management toolkit?

Today, we create research data in greater volumes than ever before. Therefore, we must ensure that it is safely managed, stored, shared and preserved to act as an evidence base for findings and also so that it can be a valuable resource for future research use.

This toolkit is designed to help the research community discover information on all aspects of research data management - this is currently spread all over the internet and often difficult to reach. It draws together advice and guidance and provides links to best practice in the area.

The research data lifecycle

The RDM toolkit is themed according to the various stages in the research data lifecycle.

Wheel image showing how the four key stages are linked. These include:
Data creation and deposit

Plan and design

Collect and capture

Managing active data

Collect and capture

Collaborate and analyse

Data respositories and archives

Manage, store and preserve

Share and publish

Data catalogues and registries

Share and publish

Discover, reuse and cite

Plan and design

Research data management planning underpins any project. It helps you ensure that data is shared at the right time, with the right people, and after seeking appropriate permissions. It also helps you organise, store and preserve data in a FAIR (Findable, Accessible, Interoperable and Reusable) way.

You should consider research data at the project start, before beginning any practical work. Many research funders now require research data management planning at the grant application stage. As you begin planning, also consider relevant policies, services, and requirements in your organisation. These might not always align with the practices highlighted in this toolkit or in the literature but can override them. For example, your organisation may have certain processes or systems in place that you have to use. A good data management plan reflects the organisational context and leverages it to minimise costs.

This toolkit will help you design your approach to research data management by discussing the main issues in the field and by signposting relevant resources. You should also seek support from research data management specialists within your organisation, who can provide you with tailored advice.

We have prepared a checklist to help you ensure you have a solid RDM plan.

Download the RDM checklist (docx).

Introduction to research data management

Research data management is an important practice for institutions as well as researchers. In essence, managing research data means keeping it well-organised and documented, so that it is easy to share with other researchers and the public. This is an oversimplification, but this toolkit aims to ease your way into the more complex aspects of research data management.

These documents can help you get a sense of the field as a whole and, where they include a bibliography, they might be a useful springboard to get your head round this field.

For the UK context, we recommend reading the Concordat on Open Research Data (pdf) and the report Science as an open enterprise. For an overview of how open data is progressing globally, see the State of Open Data report.

Data management planning

A data management plan is a formal statement describing how you will manage and document data in a research project, and how you will ensure long-term preservation. Researchers should consider data management planning for very good reasons, as this practice:

Improves efficiency in research

Ensures information is protected

Allows results to be checked by others

Improves exposure via sharing

Allows you to comply with funders' policies

Data management plans are increasingly required by research funders. Principal investigators are often expected to submit one at the research proposal stage and maintain it after obtaining funding. The Digital Curation Centre provides examples of data management plans for the most common research funders in the UK.

Data management plans should be clear and concise, proportionate, focused, and structured. You should not approach data management planning rigidly, as projects have different durations, output types, team structures, responsibilities, and more.

Getting help in preparing a research data management plan

The UK-based DMPOnline (and its US counterpart DMPTool) provides a step-by-step wizard to build a data management plan tailored to your needs and compliant with your funder's requirements. Not everyone in a project will be familiar with all questions in the template, so it is important to have access to project partners and local specialists when needed.

For a checklist of topics to keep in mind when preparing your plan, we recommend the Australian National Data Service's guide to data management plans (pdf) .

Policy compliance

When carrying out research, you should ensure you comply with relevant funders' policies. However, you should not forget your organisation might have policies, too.

The transition to open science started with efforts to support open access to research publications, but the agenda has now expanded to embrace open data. Research funders are starting to require that the results of publicly-funded research and the underlying data be made available along with the publications they accompany.

Policies on research data are sometimes dealt with at national level, although research funders often play a role. Funders normally have widely different policies, based on their remit and the type of data created by researchers in the field. In the UK, RCUK has set out clear expectations that higher education institutions must take responsibility for RDM, preservation and sharing.

Researcher data is not limited to STEM disciplines - your research will likely generate data and, if you receive public funding, you may well be subject to a data management policy.

What do funders want?

The Digital Curation Centre and the University of Bristol curate a list of funders' requirements, and the latter provides pdf guides on funders' requirements. For more detailed information on policies, you can refer to the following pages:

In addition, remember to check your organisation's internal data management policy. This will be different from funders' policies, but largely overlapping. You should be aware of what your organisation expects, too - get in touch with your local research data management team (this is likely part of library services or information services) if you have questions about the relationship between organisational and funder policies.

FAIR principles in research data management

FAIR principles ensure that research outputs are findable, accessible, interoperable and reusable. In practice, they enable higher visibility and reuse potential for data.

A research data management plan must consider who will be able to reuse the data, under what conditions, and how. If circumstances change during a project, you can review the data management plan and update it accordingly.

A key concept to improve your data sharing practices is that data should be FAIR:

Findable: your data should include metadata and a persistent identifier, to make it discoverable.

Accessible: data and metadata should be retrievable through a free and open communications protocol. Metadata should always be available, even if data is not.

Interoperable: metadata should use controlled vocabularies, be machine-readable and include references to other metadata. Data should use open formats whenever possible.

Reusable: metadata should conform to standards for greatest reusability. It should be clear to humans and machines alike. Data should also come with a clear and accessible licence to regulate reuse.

Accessibility does not imply that data should always be open, as access might be constrained due to legitimate concerns around privacy, national security, or commercial interests. In such cases, you should explain why the data is not open and what the conditions for access are.

Responsibilities for making data FAIR are shared between researchers and infrastructure providers (eg online repositories).

What are the benefits of FAIR data?

Making data FAIR benefits all the stakeholders in the research data landscape. You will be able to increase the visibility of your research and the number of citations.

In addition, you will make the research reproducible and aligned with international standards. Researchers and universities can leverage FAIR data to build new partnerships and answer new research questions. The impact of research data can be maximised when data is FAIR. This follows from it being easily and clearly accessible, both technically and legally.

EUDAT developed a useful checklist to help you understand whether your research data management plan complies with the FAIR principles, and how you can improve it.

Costing

Performing research data management is an investment of both time and money. Therefore, costing it appropriately in a research data management plan is essential.

A difficult question is how to account for costs. This is often required in data management plans and should be kept in mind when applying for funding. There are two main approaches to costing:

Calculating the cost of all data-related activities, including generation, curation, sharing, access, and preservation

Costing only the activities needed to preserve data and make it shareable. Note that this approach would exclude anything related to the research itself (eg generating data).

The first approach would return a larger figure and document the full costs from data creation to sharing, while the latter would only consider the activities required to manage, document, organise, store, and share data.

The second approach is perhaps a more precise measure of research data management costs, but disentangling research data management activities from research is a complex exercise.

Costing methodologies

Even within these approaches, there are different methodologies in use. In particular, we highlight bottom-up costing and top-down costing (also known as Activity Based Costing (ABC) and Traditional costing, respectively).

The bottom-up approach adopts an activity-based costings framework, which breaks the broad services into detailed activities (for example ingest, access, acquisition, disposal) and identifies the various labour and capital costs associated with each of them. See the CCSDS's reference model for an open archival information system (pdf)

The top-down approach identifies broad research data management services (for example data management planning, data cataloguing and registry services) and the various labour and capital costs associated with them. In both cases, assumptions about data volumes are used to scale up and derive a total cost at the institutional level.

While more accurate costings result from the bottom-up method, it is more resource-intensive and demanding with regard to data collection and monitoring requirements. The cost data for the top-down approach can often be drawn from existing cost data collected by institutions.

The UKRI Common Principles state that "it is appropriate to use public funds to support the management and sharing of publicly-funded research data". You should check with your funder(s) whether these are going to be met as part of the research grant. The University of Bristol has a helpful list of funder policies.

Getting help to cost your data management activities

To cost research data management you can list all relevant activities and estimate the time and resources each activity needs. The UKDS released a costing tool with a checklist of activities and clear recommendations on expected costs. When costing research data management you should check what your organisation already offers. For example, some research performing organisations have systems in place to store and backup data, which can decrease the cost of research data management, even significantly.

Costing research data management in research performing organisations

Costing is a pressing matter at a higher level, too. Higher education institutions and online repositories deal with data curation and preservation on behalf of researchers and data users. These costs are difficult to analyse because cost models often do not apply to different organisations. To bridge this gap, the Curations Costs Exchange platform was set up to understand and compare the costs of curation and preservation.

Finally, keep in mind that not all organisations are funding digital preservation at the moment. If your organisation wishes to commence such activities, the Digital Preservation Coalition prepared a business case toolkit. In addition, CESSDA released a cost-benefit advocacy toolkit, which outlines how different factors can affect the costs of curation and digital preservation activities.

Research data in arts, humanities and social sciences

The idea that data management does not apply to arts, humanities and social sciences is a little old-fashioned. Researchers in these disciplines frequently collect and analyse both quantitative and qualitative data that must be safely captured, catalogued, stored, shared and protected.

Your research data

In these fields, the concepts of "data" and "evidence" are very nuanced. However, virtually all information you record during your research counts as such. Here are some common (yet not exhaustive) examples of data captured in the arts, humanities and social sciences:

As a researcher in these disciplines, you will often collect evidence empirically and you may be more likely than other disciplines to collect personally identifiable information with direct identifiers. Make sure you consider both the ethical and the legal requirements that apply to your data when planning for its collection, sharing and long-term preservation.

You can consult the guidance provided by the ICPSR (pdf).

Digital humanities and social sciences

Digital humanities and digital social sciences have risen as interdisciplinary research fields that use information sciences as a research tool in the traditional humanities and social sciences domains. Digital humanities and social sciences use technology to address some research questions more effectively and on a larger scale. This includes two approaches:

'Big data' digital humanities and social sciences analyse large and interconnected datasets using cutting-edge data processing and interpretation tools and methods.

'Small data' digital humanities and social sciences use digital tools to perform the analysis and interpretation of smaller and simpler datasets.

If you are planning to embrace digital humanities and social sciences, these articles will help you understand how these methods can contribute to your research and to find examples of existing research projects:

Collect and capture

Once a research data management plan is in place and a project has started, attention moves to data collection.

You need to implement the original plan rigorously and make sure that you follow good practice in the field. Methods and equipment for data collection are research-specific and will be detailed in the research proposal. This section discusses how you can organise and document research data and where it should be kept:

Use the right system to store research data, along with an appropriate backup solution. Your organisation may be able to help with this, but complex projects might need a custom approach

Document the data you collect by adding metadata. Metadata is information about your data and is one of the most important aspects of research data management. Accurate and well-structured metadata enables others to find your work, contextualise it, reuse it, and cite it

Think about file formats, naming and folder structure. File formats will affect future compatibility and data reuse. Use a file naming convention and an appropriate folder structure to help locate information

Active data storage and backup

Active data is data that is added as a research project develops. Knowing where to store it, including physical copies, digitised documents, datasets and multimedia files, is important. It might well prevent the failure of your project, as good data storage practices protect you from data loss and enable effective collaborations.

Before making any plans about active data storage, you should check whether your organisation has a preferred approach or has solutions already in place such as networked drives or institution-provided cloud storage. This could reduce costs and help you comply with local research data policies.

Tips for safe data storage

Media fails! You will experience a failure in your lifetime. Whether or not it is catastrophic depends upon steps you take. To store digital information, you can choose CDs/DVDs, hard drives, SSDs, the cloud and other media. Each storage solution presents risks, often related to the long-term reliability of the device, the medium used and, for some projects that span a number of years, the longevity of the software required to access and use the data. Best practice suggests that you should:

Use at least two types of storage media

Replace them every two to five years

Carry out integrity checks based on the principle of fixity

In some cases, you might wish to store laboratory notes throughout a project. The state-of-the-art approach to this is to use electronic research notebooks, which allow you to make notes but also to share, search, protect, and back them up.

There is a growing trend towards using cloud-based dropbox style storage products for storing active data. Whilst undoubtedly convenient, there are some potential drawbacks that need to be considered. For instance, although some products allow a degree of 'roll-back' to previous versions, many don't. If something is deleted from such a storage space, recovery may be difficult, if not impossible. Similarly, overwriting a file may prevent you from recovering a previous version. Most often, cloud-based storage solutions are not a substitute for a correctly configured and instigated backup procedure.

This might seem like a lot of work for just deciding where to store your everyday work, but data loss horror stories are a sobering reminder of the consequences of taking data storage and backup lightly!

Why back up research data?

The best way to keep your research data safe is to consistently back it up. Backups can protect your data from hardware failures, thefts, software faults and more. You will need to think about:

What to backup

What format to use

How many copies to keep

How long to keep them for

How frequently you back-up

How often you take a snapshot of the data to preserve an "instance in time"

It is also important to assign responsibilities for data storage and backup: always make sure that someone in the team knows it is their job to check that data exists and is safeguarded.

Perhaps most important of all is to test your backup procedures to make sure the data is actually being backed up and that it is recoverable should a disaster happen. Your organisation might have a strategy in place to manage backups, so check with relevant IT specialists. Alternatively, there are tools that can help you back up research data more efficiently via automated workflows. Note that backup should not be confused with data preservation!

Backing up personally identifiable information

When backing up your research, you need to think about how to protect personally identifiable information (such as racial or ethnic origin, political opinions, religious beliefs). Try to create as few copies of personal data as possible, which reduces security concerns and makes it easier to destroy the backed-up data when the project ends.

The storage location of personally identifiable information is equally important. It is also paramount that you consider the security of such information, who might gain access, how they might gain access and the consequences if this should happen.

Find tips on security and how to keep data safe.

Metadata - documenting research data

Metadata is a description of your research data. It helps others interpret and reuse your findings by providing information and insights into your work and methodology.

When you have a conversation, context drives understanding. The same goes for data: data without context is either unclear or unintelligible, as you won't know how it came to exist, where, and why.

The context of your data is called metadata, which means "data describing other data". When you provide metadata along with your research, other people can reuse it. Your data becomes more easily discoverable and the potential for citations increases. Note that good metadata can also help its original creator should they wish to return to it in the future.

Ideally, you should prepare metadata at study level and at data level (for the stored datasets). The former should include information on:

The aims of your project

The methods used to collect data

The contents of your data

The folder structure and file naming conventions

The data processing techniques used

The modifications made to the initial data throughout the project

Data validation and other quality assurance processes

Roles and responsibilities within the project

Details on identifiers, licensing, and sensitive information.

Metadata at the data level is a completely different story, as disciplinary differences and different standards exist (more on this in the section below).

Creating metadata for your research results

In practice, creating metadata means filling in some fields with information about your work. However, finding out which fields you should use and when can be a daunting task. The two options to create metadata for your work are using templates or using standards.

Templates are most often simplified versions of standards and you will just have to look at what other people have done and follow their example.

Standards are more complicated and require technical understanding, but maximise reach and discoverability. If you wish to use a metadata standard, this list of standards developed by the Research Data Alliance can help you pick one that is appropriate to your field. Another good starting point is the FAIRsharing list of standards. If in doubt, you can use generic schemas such as Dublin Core.

As a side note, mainstream software for quantitative and qualitative analyses support the addition of metadata. This includes, SPSS, MS Access, or MS Excel for quantitative studies or NVivo for qualitative ones.

File management and formats

File management includes folder structures and naming conventions, plus the choice of appropriate formats.

Creating a folder hierarchy

With regards to folder structures, a good way to go about organising your research data is to create a hierarchy of folders. As an example, you could use a folder for each project, with two subfolders:

'Data', to store your research data, including images, databases, media, etc.

'Documentation', to store all relevant project documents, including, eg methodology and consent forms

The more complex the project, the more detailed your folder structure can be. If you are working alone or in a small team, you might wish to use just a handful of subfolders.

However, if you work on a large project you could create a range of subfolders to suit the team's needs. As an example, under 'Data', you might create a subfolder for each data type, such as 'Databases', 'Images', and 'Sounds'. Similarly, under 'Documentation' you might create a subfolder for each category of document, such as 'Methodology', 'Consent forms', and 'Information sheets'.

Naming your files to support collaboration

When it comes to file naming, we recommend using simple but meaningful names. Best practice includes the following:

Capital letters should be used to delimit words in the place of spaces or underscores

File names and paths should avoid unnecessary repetition and redundancy

Numbers should always include at least two digits (ie 01 to 09 instead of 1 to 9)

Naming will follow a convention chosen by you and other project members. For instance, you may decide that 'InterviewTra07JD20180214' means 'Interview Transcript 7, written by John Doe, on 14/02/2018'. You will need to describe such conventions as part of your study's metadata.

Choosing the right file format

Finally, choosing appropriate file formats is key to ensuring data is reusable, as some formats become obsolete in time and may make your research inaccessible. You can use whatever software or format is convenient during your research, but when sharing the data you should follow best practice and ensure future reusability of your work. The US Library of Congress maintains a recommended formats statement, which we invite you to consult. This lists a series of file formats in order of preference by output type, including recommended metadata fields.

When saving research data for sharing, you might need to convert your working files. If you do so, always ensure that the conversion was successful and that no errors appear (eg missing values, wrong characters, text formatting, resolution, etc.).

Collaborate and analyse

Research is often conducted in teams, and these can operate nationally or even internationally. Establishing (and documenting) appropriate data management processes before project start is key. This includes setting up a data management resources library, defining roles and responsibilities, and tracking data carefully. In collaborative projects, file sharing takes a front seat and complicates how you deal with active data storage, too.

Assigning roles and responsibilities appropriately during a project is also important. This includes, eg putting someone in charge of a dataset and its maintenance. They will have to document the methods of analysis and the conventions used, so as to enable collaborators to reuse the data smoothly.

We believe that this lifecycle step makes our journey through research data management clearer. You can now start seeing how good data management planning leads to successful collaborations and analysis: the more you think about research data management initially, the smoother your pathway to impact will be.

Collaborative research

If you work in a team, we recommend you read this page carefully. Here, we highlight solutions and considerations specific to teamwork, which differ from non-collaborative projects. Broadly speaking, there are two possibilities when collaborating in a project:

Your team is located within your organisation

Your team includes external collaborators (eg researchers at other universities, commercial partners, etc)

Approaches to data sharing in collaborative projects

The first option above is simpler, as the organisation can provide a research data management infrastructure. For dispersed teams, the project leader(s) will have to decide whether to use:

A single participating organisation's data management infrastructure, or

A custom shared data management infrastructure

The benefit of using an existing infrastructure, including methods and standards, is that you will be able to use existing documentation to describe the approach used. However, some project members might not be familiar with your choice or prefer a different one.

Custom shared spaces include popular services such as Dropbox, Google Drive, OneDrive or Box and advanced options such FTP servers, virtual research environments, content management systems (eg SharePoint), and more. Note that some institutions offer accounts with the above cloud service providers. If available, we recommend you use these rather than a personal account with the same providers, as institutional subscriptions would typically include more advanced functionality and more storage space.

Online repositories are viable as well, although they tend to be better for long-term storage. When using custom solutions, remember to document your approach to research data management. While an organisation would most often have guidelines, a team spanning several ones or even different countries will need shared guidance that is easy to consult.

Platforms and tools to support collaborative projects include:

Security in collaborative projects

When choosing collaborative platforms, you should think carefully about data security. If security is a concern (eg if you collect personally identifiable information, we recommend using encrypted storage areas as opposed to cloud-based services like Dropbox and Google Drive. Should you need to set up encrypted storage areas, we recommend you get in touch with local IT specialists to see what is available in your organisation.

You should also remember that the system used for day-to-day file storage must be different from the one used for backups: the former can be online and in the cloud, while the latter should preferably use offline media.

In addition, international collaborators might be subject to different policy and regulatory requirements. Therefore, you should ensure that data security considerations take into account all the appropriate policy landscapes and that personally identifiable information is protected. Particularly, you should be careful when transferring data beyond national boundaries (pdf) .

File versioning

Ensure that all team members are up to speed with file versioning too. Cloud-based systems commonly include file versioning in their paid-for editions, but this can also be done manually by team members. There is no right or wrong approach here, as long as one approach is used consistently and is well documented.

As an example, you could add a version number at the end of a filename, such as "InterviewTra07JD20180214v2" - meaning "Interview Transcript 7, written by John Doe, on 14/02/2018, version 2".

Visualisations

A picture (or chart) is worth a thousand words. They help researchers disseminate their findings more effectively and to a wider audience, although careful thought is needed to design them.

Creating visualisations used to be a niche activity, but has gained importance as a tool to increase dissemination and impact. In simple terms, a visualisation is a chart or figure that represents information graphically. They're a researcher's best friend as they help communicate data succinctly and convey ideas effectively.

When using visualisation to communicate research, your goal is to strengthen and simplify the main message of your research for your audience. You can pilot your ideas with colleagues, as they will spot issues and you will be able to address them. Thinking about colours, formatting and presentation is essential - design professionals can help.

Visualisations as an investigation tool

Visualisations are also helpful to understand data, as they allow you to discover patterns that aren't immediately evident. In this case, you would be the audience for the visualisation, so no design considerations are needed. You can go into as much detail as required, and plot the most obscure relationships you might think of.

The main challenge is when you wish to both understand data and to communicate it. Think, for example, of a team meeting where you need to show data to receive feedback or guidance. You will have to consider how to represent enough detail to allow people to comment on your work but also little enough to make your point clearly.

Approaches to creating visualisations

Creating powerful visualisations can be difficult. In a simple scenario, you might create a static visualisation for, eg a slideshow or a poster. You can also use interactive visualisations, where users can play with the data, especially as a digital resource embedded in a webpage. Interactive visualisations are more complex but tools such as tableau public and Google Charts can be helpful. Remember to ensure that data is not stored in unsafe locations and that sensitive information is not exposed when using online tools.

In conclusion, you should keep in mind that data visualisation is an art and a science at the same time. While creativity is important, clarity is, too. If you are missing either the technical or graphical skills required, get in touch with support staff in your organisation.

Manage, store and preserve

The efforts mentioned in the previous lifecycle steps aren't one-off exercises. Every decision you make before starting a project has clear repercussions on its evolution. The storage location chosen affects file management and formats and this will affect preservation. As you carry out your research, you need to routinely think about data management, storage, and preservation - although the first two are a priority earlier on in the process.

Throughout the life of a project, you might realise that your approach to research data management has become outdated or unfit for purpose. If so, you can make changes to the original data management plan. However, you should keep in mind that any decision will affect all subsequent steps so these should be revisited too.

Here, we discuss how you can protect research data during a project. This includes technical considerations (ie security, preservation) and legal ones (ie data protection regulation). We also shed light on the topic of managing software created within a research project. Although this is often not regarded as data, it actually is. Researchers sometimes treat software as a means to an end, yet it is an essential part of the research process and should be documented and shared. In addition, note that the availability of the computer code behind a study (if any) is an enabler of research reproducibility.

Many of the issues discussed in this section are relevant for both active data and archival datasets. The former is data that is added as a research project develops, while the latter are the final outputs underpinning your work.

Preservation

Data preservation aims to keep both content (data) and context (metadata) safe for future reuse. Preservation is made significantly easier when considered in your data management planning process.

What is preservation? Putting your data on a spare USB stick is not preservation. Backup is not preservation. Putting the data in a repository is not preservation.

Digital preservation includes a series of activities aimed to ensure continued access to digital materials. This practice is defined very broadly and deals with all the actions needed to maintain access to data regardless of media failure or technological/organisational change. Preservation applies to all data, including "born-digital" material and the products of digitisation processes (as defined by the Digital Preservation Coalition).

Why preserve?

In short, credibility, reproducibility and mandate.

Funders have begun to mandate that the data underpinning published research that they have funded is made freely available for other researchers to use (note that this is not the same as 'open data' - some data covered by this mandate may only be made available under strictly controlled conditions). The mandate is often further interpreted as 'keep available and usable'. There is little value in keeping data if it cannot be used. Note that some institutions also require data to be retained for fixed periods of time. Check with yours to find out whether this is the case.
Preservation systems are designed with future usability in mind.
They make multiple copies of preserved data, automate checks to make sure data has not been changed or damaged and convert data in old formats to newer ones so it can be opened with new technology. These systems are also sometimes designed to make metadata discoverable to other researchers and users. Where appropriate, preservation systems may also provide emulation services.

Preserving the data and/or computer code underpinning research also provides a degree of protection for the reputation of a researcher and/or institution. This practice allows others to reproduce and validate results - in some cases, many years after the original project.

Keeping content and context safe

This practice is important because both content and context need protection (and research funders know it). When data and/or computer code are preserved with appropriate metadata, content and context are safely stored and can be reused in the future. If either is lost, data immediately becomes at risk. Sometimes, just a change in file formats will be enough to make your research unusable. This is why thinking about file management and formats is so important! The above, however, does not mean that you should preserve everything you produce. As part of a project, you should select data for preservation and delete what does not need sharing. (See Digital Curation Centre's guide Five steps to decide what data to keep (pdf))

Digitally endangered species

The Digital Preservation Coalition maintains a list (Bit List) of digitally endangered species, including the types of data the community believes are at risk. Among these, unpublished research outputs are classified as critically endangered - which is a call to action for researchers to better engage with data management. In the Bit List, we recommend you also look at the aggravating conditions which may make data loss more likely - in the case of unpublished research outputs, these include, among others, single copies of a file, dependence on devices and dependence on obsolete formats or processes.

PhD data is also considered at risk because there is no widespread approach to its preservation. Some higher education institutions mandate PhD archiving in their regulations, which we encourage as good practice.

Software and hardware preservation

Some data is produced by bespoke hardware and/or software, often producing bespoke file formats. In some instances, the same bespoke system may be needed to read and analyse the data in the future. If this is the case, and there is no alternative, it may be necessary to preserve the software and even the hardware (or an emulation of the hardware) in order to keep the data usable. This is not a trivial task and considerable effort is being put into making better software in the first place and preserving what has already been used.

Preservation for research data managers and IT specialists

Preservation often relies on the IT infrastructure of research performing organisations. Tool registries are available to choose an appropriate approach to this. Well-known systems include:

Should you need to pick a preservation solution, compare them carefully to ensure your choice is appropriate for your objectives.

These systems are generally designed to undertake a number of standard tasks to keep the data safe including:

Identifying the file type and, if necessary, converting the file to what has been defined in local policy as the 'Standard' for that file type ('Normalise')

Creating an archival copy (or copies) and a display copy of the data

Creating a checksum for the archival copy and regularly performing checks to make sure there is no deterioration and the data is an authentic copy of the originally-submitted record (fixity checks to check for bit-rot)

Performing file migrations to alternative file types as old formats become obsolete and documenting the characteristics of this migration so that its impact can be understood

Exposing the data to external display systems where appropriate

Exposing the metadata to external systems where appropriate

Security

You must ensure that only authorised people can access, copy or edit your research data. Doing so should mean that data will be safe from unauthorised use and personally identifiable information will be seen only by authorised people. Remember that you are legally responsible for protecting personally identifiable information, when present - in these cases, security becomes a requirement rather than a choice.

When does security matter?

Data security includes a series of practical measures to protect yourself and the safety and privacy of data subjects (if any are involved) in worst case scenarios. Think, for example, of the following:

Someone could accidentally delete your research data

Commercially-confidential data could be leaked outside of your organisation

Hackers could get hold of your data

Personally identifiable information could be exposed

Many other similar scenarios exist and we are sure you wish to avoid them. In fairness, it is usually not too complex to set up security measures for a research project.

Data security in practice

We highly recommend the following practices when you manage research data:

Digital access control: only trusted colleagues should have 'write' access to important files and directories. All operating systems and most software let you control access levels and permissions for each user

System level precautions: systems used to store and manage data need to be kept secure through a combination of regular patching and security audit. In this day and age, it is unusual for systems not to be connected to a network: these connections must be kept secure through the appropriate use of firewalls and access control technology. For particularly sensitive information, you could consider data safe havens, secure file transfer networks such as Safe Share or even storing the data on a totally isolated machine

Physical access control: you should ensure that only trusted people can enter the building/area where you hold data

Passwords: you should have a strong password controlling access to your computer and any device used in your research. Software to manage passwords may help where you have many of them. CESSDA assembled a few suggestions on password security

Encrypted storage and files: it is best practice to encrypt your hard drive, which can be done natively on Windows and Apple systems. You can also decide to encrypt single files, which we recommend when using personal data. When encrypting a file, make sure you never lose the encryption key! As an example, mainstream software such as the Microsoft Office suite allows you to encrypt files with a password when saving

Secure file erasing: when you dispose of sensitive data, you should make sure it is fully removed from your hard drive (pressing the delete key to send it to the bin is insufficient). You can achieve this by physically destroying storage devices (often too extreme), or by using software tools. Data stored in the cloud poses another set of problems - it can be difficult to satisfy yourself that data has been completely destroyed

Data protection

Data protection deals with all information that allows you to identify a person. There are specific rules to follow and serious fines in the case of non-compliance.

Personally identifiable information, personal data, or sensitive data all refer to data that allow you to identify a person. Data protection law recognises the protection of natural persons in relation to the processing of personal data as a fundamental right. When you carry out research involving people, informed consent and protecting your participants are crucial.

The ethical review process

The research process is hardwired to achieve the above. Before you even start carrying out the research, an ethical review process is usually required - this is meant to help you think about the ethical issues involved in your work. Most research performing organisations have their own research ethics committee to review your planned research. Note that compliance with data protection law is not always monitored during the ethical review process. In these cases, you should ensure you follow the appropriate local approach to ensure your planned research is compliant.

When ethical review is not mandated, CESSDA recommend that you still perform a self-assessment. This will help you appraise your approach and ensure it is safe and scientifically robust.

Collecting data from human participants

After obtaining ethical approval, you can start your research. Collecting data from human participants is subject to their informed consent and the safest approach to this is seeking written consent via a form. This should explain what participation entails, how results will be disseminated and the impact of the project on participants. You must ensure that participants know that they have full freedom of choice. This information should be stored and linked to the published research and data for possible future review. Obviously, by its very nature, consent information is personally identifiable information and should not be available openly. When a consent form is not a viable option, there are several other approaches to obtaining informed consent, as outlined by the UK Data Service.

Your organisation and you are responsible for protecting participants' data at every stage of the research: collection, processing and storage.

Data reuse

One of the tensions between data protection and data sharing relates to the reuse of personally identifiable information post-project. In some cases, researchers may plan work that is radically different from that for which consent was originally given. If possible, any informed consent should make provision for future research.

Guidance on data protection

For more detailed information on data protection, see the self-assessment toolkit by The Information Commissioner's Office.

Protecting personally identifiable information

Personally identifiable information should be protected. This refers to any information that can be used to identify an individual. Anyone processing such information must follow strict legal guidelines. Managing and sharing research data concerning individuals must therefore be understood in this legal context and appropriate safeguards must be put in place.

There is stronger legal protection for data revealing an individual's:

Racial or ethnic origin

Political opinions

Religious or philosophical beliefs

Trade union membership

Genetic data

Biometric data

Health

Sex life or sexual orientation

Types of personally identifiable information

There are different types of personally identifiable information:

Direct identifiers: information that, on its own, allows you to identify an individual. This includes names, email addresses including one's name, fingerprints, facial photos, etc. This information presents a high risk.

Strong indirect identifiers: information that allows you to identify an individual through minimal effort. This includes postal addresses, telephone numbers, email addresses not including one's name, URL of personal pages, etc. This information presents a moderate risk.

Indirect identifiers: information that allows you to identify an individual when linked with other available information. This includes background information on people, such as age, location, gender and job title. This information presents a low risk.

You should address indirect identifiers as thoroughly as you would the direct ones.

Anonymising research data

A key approach to protecting personally identifiable information in research data is anonymisation, ie the irreversible removal and deletion of personal identifiers. As a starting point, you should know whether you are performing quantitative or qualitative research. In the former case, anonymisation is slightly simpler, because sometimes all you have to do is delete direct identifiers.

If your dataset is more complex (eg it contains free text), you will have to be more thorough and resort to ad-hoc anonymisation techniques. If you are undertaking qualitative research, anonymisation is far more complicated. It requires a much higher extent of personal judgement and you should follow the best practices highlighted by the UK Data Service.

When anonymising data, it is good practice to consider how and why your data could be linked to other datasets and to take steps to prevent such linking from being possible.

Protection solutions

There are software solutions available for secure bioscience research collaboration. One such solution is DataSHIELD which enables the remote and non-disclosive analysis of personally identifiable information.

Encryption is an approach favoured by some for data security purposes. However, this is not without its own problems, particularly in relation to real-time access and storage/recovery of encryption keys. Key Escrow - the storage of a key by a trusted third party - is a potential solution to key loss, but is not yet widely adopted in the research community and has drawbacks of its own.

Software

Computer code (or software) is a series of instructions written in human-readable language, usually stored in a text file. In the DPC's list of digitally endangered species, research software is classified as critically endangered. It is easy to understand why: software is often considered as a means to an end rather than as part of the research. Yet software too can be a research output and, as such, it should be managed, curated and preserved.

What computer code should be saved?

Not all code written within a project is worthy of preservation, so keep in mind what software is worth keeping or sharing with other researchers. A few questions you can ask yourself in this respect include:

Is there motivation for preserving the software?

Do the predicted benefit(s) exceed the predicted cost(s)?

Are the necessary capability and capacity available?

If your computer code is on a GitHub public repository, the Software Heritage archive takes care of saving it automatically. On their website, you can check if they have already saved your work.

Dealing with computer code during a research project

When planning and conducting research using software, consider the following:

Treat computer code like any other output of your research. It should be part of your research data management plan or have a tailored management plan.

Prepare documentation and metadata for your computer code. This includes not only inline comments, but also dependencies and their versions. Documentation should be clear enough for others to replicate the features of your code.

Share your computer code like you would any other research output. You can share it via a repository (eg GitHub) or in a journal. We highlight the Journal of Open Research Software (JORS, Open Access) by the Software Sustainability Institute, but also their list of suitable alternatives by field. Alternatively, the Code Ocean platform provides researchers with a way to share and run the code they publish along with data and articles.

Computer code should have a URL or a DOI (digital object identifier). Always include these when citing the code, including information on the version you used. Note that code deposited on Code Ocean receives a DOI. Similarly, it is possible to obtain a DOI for computer code on GitHub using Zenodo.

Apply a suitable licence. Computer code is slightly different from research data, so appropriate licences exist.

Since software is a complex field, many of its features fall outside the scope of this toolkit. However, in 2018, the Software Sustainability Institute developed a set of complementary guides covering the main aspects of depositing software into digital repositories.

No matter your field, we recommend that you share your research data. There are many ways you can achieve this. You can share data informally during a project (eg using emails or file sharing services); or you can share data formally at project end or at certain milestones via:

Data repositories

Data journals

Supplementary material to publications.

Sharing and publishing data means that your peers will be able to discover your work more easily. Your data can be citable just like a publication, thanks to persistent identifiers. Moreover, you will enable others to replicate and validate your findings.

If you followed the previous steps carefully, sharing data will be easy. When depositing your data, you will need tidy files, in the right format and with appropriate documentation and metadata. If you haven't done this in a structured way, odds are that gathering this information will be burdensome.

This is the stage where you have to consider what licence you want to apply to your data. This is essential, as picking the wrong licence might lead to your work being unusable.

In some cases you will not be allowed to share research data because of pre-existing terms and conditions or other legal obligations (eg concerns about national security, or commercial interests). If this is the case, this lifecycle step may not apply to your research.

It should be noted that publishing data is a complex undertaking, as it involves disciplinary norms, technical infrastructure, training and more - the Open Data Institute (ODI) released a report providing useful insights and recommendations for data publishers.

Intellectual property and copyright

Whenever you create something, you automatically receive certain intellectual property rights. Therefore, to enable others to reuse your work, you have to choose an appropriate licence.

Whenever you create something, you automatically receive certain intellectual property rights. This applies to data as well as paintings, books, or music. Intellectual property is what gives the owner control over how their work is used, modified, shared, or licensed.

Copyright is an intellectual property right that is assigned to an author when creating content. It effectively makes research outputs (eg tables, reports, computer code) count as literary works from a legal viewpoint and it has a pivotal role in the sharing and reuse of research data.

Key information on copyright

Here are a few things you should know about copyright:

The creator(s) of research data automatically get copyright when the work exists in a recorded form

When you collaborate with others, copyright is shared between the authors or their organisations

If you create work during employment, your employer is legally the copyright holder. Some employers such as universities sometimes give copyright back to authors, but you should check whether this is your case. If you are a research student (eg a PhD student), your situation might differ - you should refer to your contract to find out which rules apply

If you deal with recorded or transcribed interviews you hold the copyright on these. Yet interviewees are considered as authors of their recorded words. If you wish to share these, you should seek a copyright transfer from the interviewees

To transfer copyright, you need a written document called an assignment

The concept of fair dealing normally allows you to reproduce data for non-commercial teaching or research purposes. However, be careful about your actions as there is no statutory definition of fair dealing and each case is evaluated qualitatively. Note that this is a national UK law and alternatives exist in other countries (eg the fair use doctrine in the United States)

Databases can benefit from two forms of protection: copyright deals with the content of the database, while database right deals with its structure. Note that database right only applies under certain conditions

When you wish to share your research data, you need to consider all of the above. You need to make sure you identify the rights holder(s) and get permission when relevant.

Building on other people's work

When your work builds on other datasets, you have to ensure all rights holders have agreed to:

The reproduction of their original work

The sharing of their work in a modified form (eg when you merge different datasets to create a new and larger one).

At times, third-party copyright infringements prevent the publication of research outputs, such as electronic theses. Therefore, do not overlook the importance of copyright, as the future visibility of your work might be affected.

Licensing your work to enable reuse

To specify how you wish people to reuse your work, you have to license it. By choosing a licence, you can effectively tell others a number of things:

Whether you require attribution

What type of licence they should apply to work building upon your data

Whether work resulting from a transformation of your data or building upon it can be shared

Whether commercial use is permitted.

Note that, to enable digital preservation, a copyright declaration permitting format shifting is normally required.

Licensing

Licences are essential to enable data reuse. You need a licence to clearly state what others can do with your work, whether they must cite it, and how they can share derivative work.

Bespoke and standard licences

The two main types of licence are bespoke and standard licences. We suggest you avoid bespoke licences, as they are difficult to create, although we recognise you might need them in some cases. Standard licences are usually available when depositing the data in a repository - often you will be asked to pick an option among:

Each of these has sub-options, which might be confusing if you have never heard of them. We recommend that you stick to public domain licences as far as possible. These allow the greatest reusability and place no restrictions upon the way you use data. If you are concerned about attribution, it is possible to add an optional suggestion that re-users state the origin of the data.

Getting help to choose a licence

These sites can help you to make an informed decision:

The three layers of licences

When depositing data, you should consider three layers of licences:

The legal code (ie the law): this is the traditional version of a licence, written with legal jargon and used in courts of law

A human-readable licence: this is a version of the legal terms that laymen will understand. As a researcher, a human-readable licence will clarify what can and cannot be done with the data

A machine-readable licence: this is the least common version of licence but a very important one. A machine-readable licence tells computers what the key freedoms and obligations are with respect to the data. Automated software, search engines and customised algorithms all benefit from machine-readable licences

When applying a licence to your data, these three layers should be in place. In some cases, it is more practical to focus on one of them and then address the others at a later stage. If possible, we recommend you avoid this, but it is certainly more desirable to have at least one of these layers in place rather than none at all.

Note that Creative Commons licences always consider all three layers mentioned above.

Identifiers

Identifiers are a key component in scholarly communications. They allow you to cite other people's work, be cited and document the impact of your work . Their full name is persistent identifiers (PIDs), as they ensure that an object is discoverable even if the original web address goes offline or changes location.

With the growing importance of the impact agenda, identifiers support the evaluation of researchers' work. You can use them to provide evidence in impact tracking platforms, such as ResearchFish, KOLOLA, or Impact Tracker.

Digital object identifiers

In the case of research data, the use of digital object identifiers (DOIs) is widespread. The International DOI Foundation manages the DOI system and provides detailed documentation on it. Many applications of the DOI system exist, including:

Crossref to manage citations in scholarly publications

DataCite to help you locate, identify and cite research data.

The main aim of DOIs is to encourage sharing and citation. They also enable transparency and reproducibility: if a link breaks, the data is lost and research cannot be verified. As of early 2018, over 148 million DOI names have been assigned to date and over 5 billion DOI resolutions are performed every year.

In some cases, you cannot openly share your data, but you can still create a DOI. Think, for example, of cases where a researcher has to apply for ethical approval before using the data. Also, you are probably familiar with academic articles: nowadays, they all have a DOI but this doesn't mean they are freely available.

To learn how to use DOIs in research, read linking your data to a publication.

Permalinks

Permalinks are another form of persistent identifiers. They are URLs meant to remain unchanged and are often used in blogging platforms. In research, permalinks are important when you wish to cite a webpage, which may move or go offline at any point. To address this, you may wish to create a permanent record of the pages you cite using services such as perma.cc. This service allows readers to reach a copy of the page looking exactly as it did when a researcher cited it.

Other identifiers in research

In the field of research more generally, there are other identifiers, such as ORCID . This is a persistent digital identifier defining you as a researcher and including all sorts of information on your work. Through ORCID, you can link your publications, datasets, grants and more, showcasing your experience.

Linking your data to a publication

To link your data to a publication effectively you usually need a Digital Object Identifier (DOI). Once you have deposited the data, you will be able to get one and add a data access statement to your publication. Such a statement should explain where and how data can be obtained and, if not, why. Higher education institutions often share examples of data access statements, so check your local website or perform a web search to find out more.

Getting a DOI

Linking data to a publication is relatively simple. The key is obtaining a DOI, and you could do so by:

Depositing data in disciplinary repositories

Depositing data in third-party or generalist repositories

Sharing data along with a journal publication

Depositing data in institutional repositories

Publishing data in a data journal.

Choosing the right option to share data

It is usually wise to go for a disciplinary repository, if any are available in your field. This gives you visibility among your peers and allows you to spot similar work that might be relevant. Otherwise, institutional or generalist repositories are a good alternative to deposit research data. Institutional repositories are most often used for articles, theses and dissertations, but they are increasingly accepting other research outputs including data. You can find appropriate options from:

Alternatively, find other options on where to deposit data.

You might find that research data is best shared alongside (one of) its accompanying publication(s). In other cases, this is mandated by academic journals. Either way, the best approach to achieve this is to deposit the data in a repository and then include its DOI in the article (eg in the form of a citation or in a formal data availability statement), so as to establish a clear link between the publication and any supporting information.

You can also share your data, accompanied by a data article, on data journals. The article would discuss the data collection and processing methods used, particularly in the case of large datasets. This is the most burdensome option, as the data would go through peer-review (while in the other options you are free to deposit what you wish). However, trust in your work will be higher, and other researchers may feel more confident when reusing the data you shared.

Finally, you could share research data on your project webpage, which we do not recommend as it would not have a DOI but just a "regular" URL. If you wish to pursue this, we suggest that you deposit the research data in a repository and only share a DOI on the project website. The risk of broken links on a dated website may be detrimental to your reputation and should be avoided.

Where should I deposit my data?

Choosing where to deposit research data is a crucial matter. It highly affects its impact, reach and audience. Making the right choice leads to increased citations and data reuse.

The million-dollar question for many researchers is where to deposit their data. This matter troubles STEM and humanities researchers alike: your choice of repository may mean a large and far-reaching impact or little to no reuse.

In linking your data to a publication, we discuss a few options to deposit data. Here, we provide further guidance on this topic.

Pathways to deposit research data

Generally speaking, we highlight three pathways to share your data:

Using an online repository: this option involves depositing your results in a database, which can be disciplinary, institutional, or generalist. We recommend the use of disciplinary repositories for the highest visibility among your peers. Institutional and generalist repositories are suitable alternatives, as long as they provide a DOI

Sharing data along with a publication: journals often give you the chance to share data when submitting an article. This might limit the visibility of your data (as it will be a "subset" of the article) but it will stress its links with the results. For more information on this pathway, you should refer to the policy of your journal of choice or to the guidance by the Australian National Data Service

Preparing a data article for a data journal: if you have carried out extensive work on the data, you might wish or need to explain it. A data article gives you the chance to describe tools, methods and processes, to fully characterise your data. A data article accompanies a dataset and goes through peer review. This increases its credibility but is an extra burden for the data depositor

Choosing the right repository solution

If you decide to go down the repository route, your institutional library will be able to advise on local provision, while the following websites may help in identifying discipline-specific and generalist options:

re3data: this website gathers details for repositories, which you can filter by subject and country

FAIRsharing: this website gathers details for repositories, which you can filter by subject, domain and taxonomy

SpringerNature Scientific Data's recommendations: these include options sourced from re3data and FAIRsharing. SpringerNature appraised these repositories to ensure they meet their requirements for data access, preservation and stability

Research Pipeline: a privately-maintained list of repositories, this includes 140 disciplinary databases. Note that this page is updated less often than the above, so we recommend you use it as a last resort

Generalist repositories: there exist a few alternatives for these, with different affiliations and governance. You can have a look at Zenodo, Figshare, Dryad and Harvard Dataverse. Figshare is a commercial solution, while Zenodo and Harvard Dataverse are public ones

Other considerations on data deposit

If you wish to deposit software, the Software Sustainability Institute compiled a list of suitable journals split by discipline.

In all the above cases, do remember that you need appropriate metadata. When choosing where to deposit your work, you should also make sure that you make a note of the metadata standards and fields required. The Research Data Alliance compiled a list of disciplinary metadata standards. These have all been appraised and represent current practice in the fields considered.

Discover, reuse and cite

There are two sides to research data management: you may be managing the process, or just enjoying its benefits as a consumer. This step focuses on your role as a data user and should help you understand why research data management is important.

As a data user, you can find suitable data by visiting disciplinary, generalist or institutional repositories. These normally allow you to apply filters and may offer the option to download the data. Alternatively, you might find data by following links in an article. In some cases, you may have to contact the author and/or seek permission to use a certain dataset (eg ethical approval). If this is the case, you will have to account for extra time in your project plan. Note that all of the above is enabled by good research data management practices, including documentation, metadata and the use of digital object identifiers.

When reusing someone else's data, you need to be methodical, as there will be (or should be) terms and conditions attached to it. A solid approach is to:

Make sure the licence applied by the author(s) is suitable for your purposes

Respect the authors' intellectual property

Cite the data, if required by the licence (although we recommend always citing your sources as a good practice)

Share any new data you might generate using the original authors' work in accordance with the licence they chose

You might become a data re-user to verify someone else's results. This is always permitted once you get access to a dataset and you should get in touch with authors should replication be impossible.

Data citation and metrics

Data should be cited just like academic publications. There is evidence showing how this practice yields additional citations, plus it acknowledges researchers' efforts and allows increased transparency.

When it comes to citations, we normally think about publications. Yet citing data is becoming more common - and why shouldn't it be? Citing data comes with a range of advantages:

Evidence shows that, if a publication cites data, it will be cited more (see SPARC Europe - The Open Data Citation Advantage (pdf))

Citing data acknowledges researchers' efforts

Linking publications to their underlying data allows transparency and reproducibility in research

Links to data deposited online can be included in CVs and online profiles (eg ORCID)

Deposited data contributes to impact evaluation instead of gathering dust in researchers' desk drawers.

Equipment can be cited, too. This makes research more easily replicable, as a specific tool in an organisation can be referenced (eg via a permalink) and reused by other investigators.

Enabling data citation through research data management

Citing data is possible only when the authors deposit it in a repository and include metadata. You need their names, the publication date, a title and a DOI for a basic citation. However, there are other metadata fields that it is sometimes helpful to include, such as the data version and the resource type. Just like citing literature, you can choose between different citation styles, such as APA, Chicago, MLA and Oxford. The Digital Curation Centre collected some overarching guidance that works in most cases.

If you wish your data to be cited, we also recommend the following:

Deposit the data in a trustworthy repository

Use permissive licences

Always cite your data sources

The role of metrics in research

Research data metrics (or indicators) can help you demonstrate the impact of your data sharing efforts and drive reward mechanisms. Clarivate Analytics maintains a data citation index, while altmetrics are a less formal approach to analysing the way data is perceived. Altmetrics include the number of views or downloads, social media interactions, or recommendations, and are starting to appear in several websites, such as figshare and Springer Nature's Scientific Data.

There is a wealth of other services that allow you to measure the impact of data and its citation based on metrics. However, they are all at a relatively early stage of development and there is no agreement upon the measures used to estimate real impact.

You should be mindful that some metrics might just be a measure of the level of attention received by data. Traditional citation counts are the most highly regarded, yet uncertainty plagues the field. We recommend that you cite data as often as appropriate, to push debate forward and drive attention on these pressing matters.

Training and resources for research data management

This toolkit includes a number of resources on research data management. However, due to its broad scope, the toolkit is not structured as an online course.

If that is what you are looking for, we recommend you have a look at the list below. Alternatively, you can check out the further reading gathered in the pages of this toolkit.

Courses

Guides

Research data management videos

Infographics

Books

Data Management for Researchers by Kristin A. Briney (2015)

Delivering Research Data Management Services by Graham Pryor, Sarah Jones and Angus Whyte (2013)

Exploring Research Data Management by Andrew Cox and Eddy Verbaan (2018)

Managing Research Data by Graham Pryor (2012)

The Data Librarian's Handbook by Robin Rice and John Southall (2016)

Journals

Other material

Events

Stay up to date with RDM

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This toolkit aims to support you through the entire lifecycle of research data management (RDM). It explains what you should consider and signposts resources from a wide range of websites and organisations.

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NOVO!

Guia | Genérico | EN

Descrição: Guia (em vídeo) para bibliotecários para apoiar os investigadores na identificação de revistas confiáveis e evitar revistas predatórias.

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This video is a guide for librarians that support researchers in identifying trusted journals and avoiding predatory journals.

Supporting researchers in identifying trusted journals and publishers for their research.

The presenter is Katherine Stephan the Research Engagement Librarian at Liverpool John Moores University. Katherine is also the library community representative on the Think. Check. Submit. committee.

Katherine draws on her experience to explain how she uses the principles of Think. Check. Submit to identify trusted publishers when helping researchers. She reminds us that there is no single list to check, and so the due diligence process can help researchers avoid pitfalls.

Three common scenarios

Katherine talks us through three of the most frequent problems researchers present:

I have never heard of this journal.

How do I know if a publisher is predatory or problematic?

Does this journal have a cost?

Katherine's advice also covers what to do if a researcher realises that they have submitted an article to a predatory journal.

Notes:

You can download the transcript of the recording.

In the video, Katherine mentions several journal finders:

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Guide for librarians video * Think. Check. Submit.

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This video is a guide for librarians that support researchers in identifying trusted journals and avoiding predatory journals.

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NOVO!

Genérico | EN

Descrição: 5 módulos, que apresentam as definições, ferramentas e recursos para partilhar os resultados de investigação de forma mais aberta:

Ethos of Open Science

Open Tools and Resources

Open Data

Open Code

Open Results

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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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FAIRsFAIR together with EOSC Synergy have launched an online Data Steward Training course developed using the materials from the Data Steward Instructor Training Workshops run throughout 2020 and 2021.

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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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O Essencial da Gestão de Dados de Investigação

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O Essencial da Gestão de Dados de Investigação

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Genérico | PT
Descrição: Foco nos principais componentes para uma compreensão ampla e funcional dos processos associados à gestão de dados ao longo do ciclo de vida da investigação.
Duração: 5h/semana (6 semanas)

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Sobre o curso

O desenvolvimento e disponibilização de um MOOC sobre gestão de dados de investigação integra-se na execução da estratégia nacional de informação e formação para a Ciência Aberta, que procura responder a várias necessidades de capacitação existentes, particularmente no que respeita à gestão de dados de investigação.

O curso "O Essencial da GDI" foca-se nos principais componentes para uma compreensão ampla e funcional dos processos associados à gestão de dados ao longo do ciclo de vida da investigação: i) o que são dados de investigação; ii) planos de gestão de dados; iii) documentar e organizar dados; iv) licenciamento, proteção e propriedade dos dados; v) Partilhar e depositar dados em repositórios; vi) requisitos dos financiadores para a partilha de dados e políticas dos editores para a disponibilização de dados.

Destinatários

O curso dirige-se em primeiro lugar aos investigadores e alunos de doutoramento, procurando facultar os conhecimentos e competências básicas que permitam começar desde logo a gerir os dados que são produzidos no âmbito da sua investigação. Mas o curso será também útil a outros profissionais que exercem atividades de suporte à gestão de dados de investigação.

Formadores

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O essencial da gestão de dados de investigação

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O desenvolvimento e disponibilização de um MOOC sobre gestão de dados de investigação integra-se na execução da estratégia nacional de informação e formação para a Ciência Aberta, que procura responder a várias necessidades de capacitação existentes, particularmente no que respeita à gestão de dados de investigação.

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Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle

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Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle

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Guia | Ciências Sociais | EN
Descrição: Guia sobre boas práticas para a preparação dos dados para depósito e arquivo.

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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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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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Genérico | EN
Descrição: Este curso explica a diferença entre dados abertos e dados FAIR.

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FOSTER FACILITATE OPEN SCIENCE TRAINING FOR EUROPEAN RESEARCH

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This course explains the difference between open data and FAIR data Upon completion of this course, you will be able to:

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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 | Genérico | EN
Descrição: Guia sobre a gestão de dados de investigação abertos.

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If you aren't sure whether you have any research data, it's important to know that data exists in many different formats: textual, numerical, databases, geospatial, images, audio-visual recordings, data generated by machines or instruments, etc. Research data may also include non-digital materials or 'sources', and some non-digital data can be digiti z ed.

In cases where data cannot be easily digitized in a way that maintains its usefulness for you and others, you can still 'share' the data by creating an extensive metadata record describing the object, where it is store d , and how to access it. Depositing this metadata record openly in a repository will allow others to find it. You can cite the metadata record in any associated articles (and vice-versa) to establish links between the published work and the dataset.

Where published work genuinely has no data associated with it, it's best to indicate this clearly in a data availability statement . This confirms the absence of data to readers, rather than the a bsence of data sharing.

The practice of data sharing varies considerably from one discipline to another, and this includes how often it is done and how much support there is. However, the key benefits remain the same: reproducibility, c redit, and potential reuse.

Open Research Europe endorses the FAIR Data Principles a nd these can be applied to your research data regardless of discipline. There are numerous generalist repositories that accept a wide range of data types in a wide range of formats . You can find guidance on selecting a repository . Across disciplines , data sharing mandates are becoming more common at national, funder, and organizational levels, so it is important to understand these before you start conducting your research project so you can plan how bes t to meet the requirements .

the handling of research data during and after the end of the project ;

what data will be collected, processed and/or generated ;

which methodology & standards will be applied ;

whether data will be shared/made open access and

how data will be curated & preserved (including after the end of the projec t

Using a data management plan will help make it clear who has the rights to share data, as well as how and when.

Open Research Europe recognizes that openly sharing data may not always be feasible. Exceptions are permitted according to the relevant policy of Horizon 2020. These policies c onsider the obligation to protect results, confidentiality obligation, security obligations, the obligation to protect personal data, if providing open access would jeopardize the achievement of the main objec tive of the H2020 project from which the research data derives, and other legitimate constraints. For more information, please see the Data Guidelines .

Following on from the previous point, if your data is too sensitive to share, you should consider sharing your . You can openly publish a description of your data (known as a 'metadata record'). This helps others to discover your data and provides essential information about how the data can be accessed and cited.

For example, you could post a "data codebook" or "data dictionary" in a repository that describes the variables used in your dataset. In this document, you can cite the article in which it appears in order to connect the data description to the paper. Similarly, you can cite the metadata record in your article as part of a data availability statement, which should also include the conditions under which your data can be accessed.

It is important that your data is accompanied by sufficient contextual information to allow others to fully understand your dataset. A data dictionary is a separate file where each variable is defined, including units and ranges, and often includes other useful information for inter preting the dataset. By helping others better understand your data, a data dictionary supports the reuse and reproducibility, and helps to avoid misinterpretation.

Good documentation is key to both stopping and identifying inappropriate use. It is important that your data is supported by rich metadata that describes both the purpose of the dataset and any restraints. Where sensitive data is involved, a data use agreement makes clear the terms under which the data can be used.

With this in mind, sharing your research data is likely to have a positive impact and greater reach than you might think. Research data is consumed by a variety of stakeholders beyon d researchers, including policymakers and educators. Sharing your data may also help to reduce duplication of work while promoting integrative analysis.

Some researchers feel apprehensive t o share data as they think others may take the data and claim priority of the results (sometime known as 'scooping') . However, there is no evidence to support this claim. Instead, data sharing establishes and confirms ownership of your data via authorship. Sharing your data openly increases the opportunity for others to credit your work - where your data is reused by another party, you will receive recognition through a formal data citation, and you may even find new collaborations as a result.

It is true to say that not sharing your data does not hurt your research career, but there are huge benefits to doing so such as increasing your chances of citations and the potential to introduce new collaborations . So, sharing your data now may be valuable to you career in time. In addition, the European Commission believes that data sharing will help to facilitate more evidence-based policies, better solutions to societal challenges such as climate change, and innovation in science, healthcare and business practices.

Another worry is often that sharing data now may impact on your ability to publish later on. However, Open Research Europe agrees with the majority of publishers that the novelty of a research article is not undermin e d by data sharing. This means that sharing data early-on should have no impact on your ability to publish a research article later. In addition, there is evidence to suggest that publicati ons associated with a shared dataset have a citation advantage.

Data that is generated f rom projects that focus on innovation in a commercial and /or industrial setting can also be shared on O p en Research Europe. However, in some circumstances it is realiz ed that due to the nature of th is research it may not be possible as there is an obligation to protect the confidentiality and security of certain results . In this instance, an extensive metadata record describing the research, where it is stored, and how to access can be deposited openly in a repository and cited in the vailability statement .

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ORE - Guide to Open Data - F1000 Research

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If you aren't sure whether you have any research data, it's important to know that data exists in many different formats: textual, numerical, databases, geospatial, images, audio-visual recordings, data generated by machines or instruments, etc. Research data may also include non-digital materials or 'sources', and some non-digital data can be digiti z ed.

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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

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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.

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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

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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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Genérico | 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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Having trouble managing the biological data of your institution, laboratory, project or thesis?

Scientific data production is increasing at an exponential rate. Good data management practices and compliance with FAIR data principles are key to addressing this challenge.

This has been recognized by science funders and publishers who are now enforcing open science policies and adherence to the FAIR principles.

Ready for BioData Management? is a capacity building program in data management for the life sciences to empower researchers and institutions in managing their data more effectively and efficiently.
BioData.pt is currently offering training courses on the following topics:
Courses on "day-to-day data management" and "FAIR plant data with MIAPPE" are under preparation.

For more information, contact us.

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Having trouble managing the biological data of your institution, laboratory, project or thesis? Scientific data production is increasing at an exponential rate. Good data management practices and compliance with FAIR data principles are key to addressing this challenge. This has been recognized by science funders and publishers who are now enforcing open science policies and adherence to the FAIR principles.

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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 FACILITATE OPEN SCIENCE TRAINING FOR EUROPEAN RESEARCH

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The FOSTER taxonomy defines Open Science as the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society.
Sounds good but what does Open Science (OS) mean in a practical sense? These courses answer some of the most common questions you might have about putting open science into practice. Each course takes about 1-2 hours to work through and you'll receive a badge upon completion. The courses include practical tips on getting started with OS as well as providing information on discipline specific tools and resources you can use. There is no specified order through the courses - just explore topics that you want to learn more about at your own pace.

Acknowledgements

These modules have been developed reusing openly available content produced by a range of content providers including DataOne, Research Data Netherlands, Open Data Institute, European Data Portal, Digital Curation Centre, UK Data Service, CESSDA, DARIAH, ELIXIR, Software Sustainability Institute, FOSTER and many others actively developing open educational resources relating to Open Science.

The modules are presented in a similar style to that employed by the Open Data Institute (ODI) and the European Data Portal in the hopes that this will enable our content to augment the body of Open Science related materials already produced and make their collective reuse more seamless. To this end, we have also made use of the Adapt authoring tool also used by the ODI and European Data Portal.

We have employed a variation of the case study approach developed by the European Commission's Open Science Monitor to help illustrate useful tools and initiatives from disciplinary perspectives.

Images used in the modules are under CC0 license unless otherwise stated.

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The FOSTER taxonomy defines Open Science as the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society. Sounds good but what does Open Science (OS) mean in a practical sense? These courses answer some of the most common questions you might have about putting open science into practice.

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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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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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Genérico | EN
Descrição: Este curso explica a diferença entre dados abertos e dados FAIR.

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FOSTER FACILITATE OPEN SCIENCE TRAINING FOR EUROPEAN RESEARCH

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This course explains the difference between open data and FAIR data Upon completion of this course, you will be able to:

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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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Genérico | EN

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This introductory course will help you to know the concepts of Open Science and RRI, key principles and the implications for practicing research.

Upon completion of this course, you wi...

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This introductory course will help you to know the concepts of Open Science and RRI, key principles and the implications for practicing research. Upon completion of this course, you wi...

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Ciências Sociais | EN
Descrição: Coleção de recursos de formação para os vários intervenientes na gestão dos dados em Ciências Sociais. Disponível também o guia Data Management Expert Guide.

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It is part of CESSDA's mission to facilitate teaching and learning in the social sciences. CESSDA has a Training Working Group that is responsible for supporting continuous learning and training of its Service Provider staff and the social science user community. The Training Group maintains the information on this training website.

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Manage, improve and open up your research and data

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Manage, improve and open up your research and data

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Genérico | EN
Descrição: Análise das tendências emergentes e as melhores práticas na gestão de dados, avaliação da qualidade e questões dos direitos de propriedade intelectual.

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Manage, Improve and Open up your Research and Data

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This module will look at emerging trends and best practice in data management, quality assessment and IPR issues We will look at policies regarding data management and their implementation, particularly in the framework of a Research Infrastructure By the end of this module, you should be able to: Understand and

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Research Data Management Librarian Academy (RDMLA)

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Research Data Management Librarian Academy (RDMLA)

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Genérico | EN
Descrição: Este curso explora os componentes de gestão de dados de investigação (RDM). O RDMLA é composto por 11 unidades que podem ser tomadas individualmente, ou como um programa completo.

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Research Data Management Librarian Academy (RDMLA)

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Genérico | EN
Descrição: este módulo fornece cinco lições para assegurar que os dados de um investigador são devidamente geridos, publicados e pesquisáveis.

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This module provides five lessons to ensure that a researcher's data is properly managed and published to ensure it enables reproducible research.

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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