Dryad is a general purpose repository for data underlying scholarly publications. Each new submission we receive is reviewed by our curation team before the data are archived. Our main priority is to ensure compliance with Dryad’s Terms of Service, but we also strongly believe that curation activities add value to your data publication, since curated data are more likely to be FAIR (findable, accessible, interoperable, and reusable).

Before we register a DOI, a member of our curation team will check each data package to ensure that the data files can be opened, that they appear to contain information associated with a scientific publication, and that metadata for the associated publication are technically correct. We prefer common, non-proprietary file types and thorough documentation, and we may reach out if we are unable to view files as provided.

Our curators are also on the lookout for sensitive information such as personally identifiable human subjects data or protected location information, and for files that contain copyright and license statements that are incompatible with our required CC0 waiver.

To make the data archiving process more straightforward for authors, our curation team has authored sets of guidelines that may be consulted when preparing a data submission for a public repository such as Dryad. We hope these guidelines will help you as you prepare your Dryad data package, and that they will lessen the amount of time from point of submission to registered data DOI!

A series of blog posts will highlight each of the guidelines we’ve created. First up is our best practices for sharing human subjects data in an open access repository, from former Dryad curator Rebecca Kameny.

— Erin Clary, Senior Curator – curator@datadryad.org

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Preparing human subject data for open access

Collecting, cleaning, managing, and analyzing your data is one thing, but what happens when you are ready to share your data with other researchers and the public?

Because our researchers come from fields that run the gamut of academia — from biology, ecology, and medicine, to engineering, agriculture, and sociology — and because almost any field can make use of data from human subjects, we’ve provided guidance for preparing such data for open access. We based our recommendations and requirements on well-respected national and international sources from government institutions, universities, and peer-reviewed publications.

Dryad curators will review data files for compliance with these recommendations, and may make suggestions to authors, however, authors who submit data to Dryad are ultimately responsible for ensuring that their data are properly anonymized and can be shared in a public repository.

In a nutshell, Dryad does not allow any direct identifiers, but we do allow up to three indirect identifiers. Sound simple? It’s not. If the study involves a vulnerable population (such as children or indigenous people), if the number of participants is small, or if the data are sensitive (e.g., HIV status, drug use), three indirect identifiers may be too many. We evaluate each submission on a case-by-case basis.

If you have qualitative data, you’ll want to pay close attention to open-ended text, and may need to replace names with pseudonyms or redact identifiable text.

Quick tips for preparing human subjects data for sharing

• Ensure that there are no direct identifiers.
• Remove any nonessential identifying details.
• Reduce the precision of a variable – e.g., remove day and month from date of birth; use county instead of city; add or subtract a randomly chosen number.
• Aggregate variables that are potentially revealing, such as age.
• Restrict the upper or lower ranges of a continuous variable to hide outliers by collapsing them into a single code.
• Combine variables by merging data from two variables into a summary variable.

It’s also good research practice to provide clear documentation of your data in a README file. Your README should define your variables and allowable values, and can be used to alert users to any changes you made to the original dataset to protect participant identity.

Our guidelines expand upon the tips above, and link to some useful references that will provide further guidance to anyone who would like to share human subjects data safely.