De-Mystifying Data Management Requirements

This week the DMPTool Partners are pleased to offer a guest post from the authors of a recent article on data management plan requirements in Issues in Science and Technology Librarianship. In the post below, Dianne Dietrich (Cornell), Trisha Adamus (Syracuse), Alison Miner (Syracuse), and Gail Steinhart (Cornell), offer a summary and bit of perspective on their recent article, and what this means to the community of interest around data management.  Let us know what you think.  – Andrew Sallans

 

Contributed by Dianne Dietrich (Cornell), Trisha Adamus (Syracuse), Alison Miner (Syracuse), and Gail Steinhart (Cornell)

Those of us who have run information sessions on the NSF Data Management Plan requirement (http://www.nsf.gov/eng/general/dmp.jsp) have probably heard the participants express some level of anxiety about its implications. Perhaps you’re familiar with comments like these: Will I be required to make all of my data available on the web in perpetuity? Where will I put all of this data? Who will pay for storage after the grant is finished? (http://data.research.cornell.edu/nsf-data-management-plan-faq) The answers to these questions aren’t always straightforward, especially since the NSF requirements are relatively general.

The NSF requirement isn’t the only one researchers face, however: many federal funding agencies have data management requirements for PIs. What does this landscape look like? What can we learn from examining a range of data policies? In investigating over two dozen funder policies, we observed that many were quite general, like the NSF-wide policy, but there were others that were more specific about aspects such as data publication options. The more specific policies tended to come from units within agencies (http://science.nasa.gov/earth-science/earth-science-data/data-information-policy/), and this makes sense: a closer connection to a discipline provides the opportunity to provide more concrete examples of accepted metadata standards, for instance. Strong policies can provide researchers with the tools to share data in ways that make sense for them and their research and more vague policies might seem limiting. For instance, we noted that few policies provided a thorough description of embargoes, an option that might alleviate some concerns about sharing research data openly.

Of course, this landscape is continually evolving. Several of the policies we looked at had been around for a number of years and had been revised one or more times. One way to approach a consultation on writing a data management plan is to tell researchers to start by describing their current data management practices, look to see how what they’re already doing aligns with their funder’s data policy, and then plan to fill the gaps. The more information funders receive about what actual needs are, the better they will be able to understand the gaps in this area, and the better positioned they will be to adapt policies to the needs of their research communities. We hope that our survey helps both communities as data management policies evolve.

De-Mystifying the Data Management Requirements of Research Funders
by Dianne Dietrich, Cornell University, Trisha Adamus, Syracuse University, Alison Miner, Syracuse University, and Gail Steinhart, Cornell University

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