FAIR tools
TOOLKIT FOR FAIR TRAUMATIC STRESS DATA
Practical tools and guidance to help traumatic stress researchers make their data Findable, Accessible, Interoperable, and Reusable
Project leader: Talya Greene (UK) & Bryce Hruska (US)
Workgroup: Ateka Contractor (US), Chris Hoeboer (Netherlands)
As part of the GCTS FAIR Data Workgroup, an international group of scholars is creating a FAIR data toolkit to help traumatic stress researchers at each stage of the research lifecycle.
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Tools adapted or created by the GCTS FAIR Data Workgroup, tailored to the needs of traumatic stress researchers.
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Tools and resources collected from related fields or research groups outside the GCTS FAIR Data Workgoup, selected for their relevance to trauma researchers. (Scroll down to see table with descriptions and links.)
New to FAIR Data? Start Here!
Do I have to get permission from my participants to share their data? Yes – you should describe how you intend to share data in the consent form that you provide to participants. For example consent form language, see “Sample Consent Language for Data Sharing” under “GCTS FAIR Data Tools.”
Do I have to share all my data? No – you can choose to share as much or as little of your data as you wish. Keep in mind that some variables may make it easy to identify individual participants– especially when variables are combined. You may choose not to share all your variables to protect the identity of your participants. For tips, see the “Checklist for Reducing Re-Identification Risk in Traumatic Stress Research Data” under “GCTS FAIR Data Tools.” Data protection laws and regulations, such as GDPR (General Data Protection Regulation) in Europe and HIPAA (Health Insurance Portability and Accountability Act ) in the United States may prevent you from full data sharing. Check your ethics committee and institution rules about what you are allowed to share. Even if you do not feel you can share your data, there are other options. For example, you can share metadata and codebooks that explain what is in your dataset. You could share aggregated data that present group-level statistics rather than individual responses. You can also share synthetic data (simulated data with similar patterns to your dataset but doesn’t contain any real data from participants). See “Documentation and Metadata,” “Data Deposit Example: Sharing Aggregate Statistics,” and “Synthetic Data” in the table under “External FAIR Data Resources.”
Will I end up getting “scooped” if I share my data? Maybe – but it depends on when you share your data. You can choose when to share your data. People often wait to share data until after they have addressed their primary research questions. Consider preregistering your projects to establish priority for your findings. Still concerned? Right now, researchers gain recognition largely based upon their publication record. However, publications are only the end-product of the research life cycle. By creating high quality data sets that maximize shareability, it will become easier to credit researchers for this work. In fact, it is possible to create and attach digital object identifiers (DOI) to data sets so that researchers can be credited when their data set is used. Many data repositories offer this service for free when researchers deposit their data. See “Information on Choosing a Data Repository” under “GCTS FAIR Data Tools” for repositories that offer this service. You can also attach a license to your data, which can legally oblige users to cite your data if it is used. See “How do I license my data?” in the table under “External FAIR Data Resources.”
What is the benefit to me if I share my data? Sharing your data in a repository that generates and attaches a digital objective identifier (DOI) to your data makes it easier to ensure that your data is appropriately cited when others use it. It is an opportunity to increase your visibility. Sharing data facilitates networking and collaborations. By enabling others to reuse your data, you increase the impact of your research. Your dataset might be valuable in ways you did not originally anticipate, helping researchers address new questions that you hadn’t considered. Many funders and journals now require data sharing. Sharing your data allows for potential new funding and publication opportunities.
Where do I share my data? You should share your data in a reputable repository that has a clear preservation plan. A preservation plan spells out how the repository plans to ensure that your data remain available on the repository in the future. See “Information on How and Where to Share Data” and “Information on Choosing a Data Repository” under “GCTS FAIR Data Tools” for more information on repository selection.
Will it take a long time to get my data ready for sharing? Yes – it can take some time to prepare your data for sharing. Good data sharing practices include thoughtfully naming variables, creating a codebook, and generating other documentation that ensures other people can understand your data. The good news is that the time you take to make your data shareable is time saved in the future if you should ever need to return to your data to re-run analyses. Ideally, you make your decision to share your data even before collecting it. By ‘preparing to share’, you can embed good practices that facilitate data sharing down the line. For more information, see “Variable Naming Tips”, “Codebook Guidelines”, and “Codebook Template” in the table under “External FAIR Data Resources.”
Do I have to make my data open to everyone? No – you might decide that your dataset won’t be fully public. Instead, you can selectively share your data, asking people to submit requests and go through an approval process. This way you can also maintain an overview of who is using your data and how. See “Information on Choosing a Data Repository Table” under “GCTS FAIR Data Tools” for example repositories that offer data restriction.
Is it enough to just share my data set? Are there other materials that I need to include when I share my data? You should include additional materials that will help others understand your data, as well as the research project that generated the dataset. We would encourage you to consider including: A README file – this is a simple document that explains the purpose and structure of the dataset. See the “Guide to writing ‘readme’ style metadata” in the table under “External FAIR Data Resources.” Description of the methodology – this will detail how the data were collected, including measures used, inclusion criteria, recruitment processes, and ethics approvals. Codebook – this clarifies the names and scoring for each variable. See the “Codebook Guidelines”, “Codebook Template”, and “Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set” in the table under “External FAIR Data Resources.” Any relevant publications, presentations, or preprints associated with the dataset. Analytic code – you may choose to share code/scripts that can help researchers replicate your analyses or restructure the data in helpful ways. For tips on how to create code/scripts that will allow others to reproduce your work, see “Best Practices in Writing Reproducible Code” in the table under “External FAIR Data Resources.”
At what point in the research process should I start thinking about sharing my data? At the beginning! When constructing a consent form, it is important to inform your participants about how you will share their data. For sample consent form language, see “Sample Consent Form Language for Data Sharing” under “GCTS FAIR Data Tools” and “Recommended Informed Consent Language for Data Sharing” in the table under “External FAIR Data Resources.”
What do I need to do before sharing my data? Ensure that participants have consented to share their data. For sample consent form language, see “Sample Consent Form Language for Data Sharing” under “GCTS FAIR Data Tools” and “Recommended Informed Consent Language for Data Sharing” in the table under “External FAIR Data Resources.” Assess any risks to privacy and remove any data that would potentially identify your participants. For tips, see the “Checklist for Reducing Re-Identification Risk in Traumatic Stress Research Data” under “GCTS FAIR Data Tools.” Check your data for errors. Create a README file and provide a codebook or data dictionary so that your data can be used. See the “Guide to writing ‘readme’ style metadata,” “Codebook Guidelines,” “Codebook Template,” and “Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set” in the table under “External FAIR Data Resources.” Decide where to share and decide on any limits to access. See “Information on Choosing a Data Repository Table” under “GCTS FAIR Data Tools” for example repositories that offer data restriction. Apply a license that clarifies how the data can be used. See “How do I license my data?” in the table under “External FAIR Data Resources.”
GCTS FAIR Data Tools – created by the GCTS Fair Data Workgroup
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Quick guides on key topics (1-page practical tipsheets)
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Also see VIDEO resources, including Making Your Data Count workshop
External FAIR Data Resources – collected from related fields or research groups outside the GCTS Fair Data Workgroup
Traumatic stress researchers share experiences, challenges, and ideas for sharing and re-using data
With the support of the Trauma Data Institute, we convened a virtual summit in September 2023, bringing together more than 20 researchers who lead integrative data sharing and harmonization projects. The summit generated a wealth of ideas for strengthening this work in the traumatic stress field.
Watch the entire video, or click the list symbol to choose a specific section you're interested in.
VIDEO PRESENTATIONS APPLYING FAIR PRINCIPLES TO TRAUMA RESEARCH
At the 2022 ISTSS meeting in Atlanta GA, USA, members of the FAIR Data Theme delivered presentations that described actionable tips that trauma researchers can take to make their data FAIR and featured trauma intervention projects that demonstrate the implementation of FAIR principles.
Links to these presentations are provided as a resource for trauma researchers.