B

Active Data Management Plans as a Planning Tool

A data management plan (DMP) is a formal document that outlines data types and formats, dissemination and sharing plans, roles and responsibilities, and preservation plans for data generated by a project. Various federal research funders, including the National Science Foundation (NSF) and various institutes of the National Institutes of Health (NIH) require grantees to propose a DMP, but specific requirements are not uniform among the agencies. Quality and utility of DMPs are an issue, with the perception that DMPs are an “annoying administrative exercise” (Simms et al., 2017). The typical DMP is a text document written as a verbose narrative.

Various guidance documents and templates for DMP production evolved (e.g., the National Network of Libraries of Medicine [NNLM]¹), and a first generation of tools to facilitate the production of DMPs was created, and are widely available. Some examples of tools include DMPTool (California Digital Libraries),² the DMPOnline (Digital Curation Centre—UK),³ and the Interdisciplinary Earth Data Alliance (IEDA) DMP Tool.⁴ Generally, those tools guided the creation of Microsoft Word or PDF documents and incorporated templates for use with various funders. More recently, a second generation of those tools was created, with the goal of making DMPs “machine actionable” (i.e., written so that computer programs can parse the content cleanly and take action based on the information in such machine-actionable DMPs [maDMPs]). Newer DMP tools incorporate richer information, such as a list of acceptable repositories, and better guidance, with richer prefillable information.

While the creation of a DMP guides the researcher in formulating a data management process, the role of the DMP in the evaluation of grant proposals and in post-award evaluations is less clear. Most grant agencies require a DMP but do not explicitly score DMPs or integrate them formally into scoring a proposal under consideration for funding. For instance, NIH scoring guidelines⁵ make no reference to DMPs, although data-sharing plans are a required element of proposals (in fact, the word “data” does not appear in the scoring guide). The NSF-wide

___________________

¹ The NNLM website providing a collection of data management guides is https://nnlm.gov/data/data-management-plan, accessed January 14, 2020.

² The website for DMPTool is https://dmptool.org/, accessed January 14, 2020.

³ The website for DMPOnline is https://dmponline.dcc.ac.uk/, accessed January 14, 2020.

⁴ The website for the IEDA DMP is https://www.iedadata.org/dmp/, accessed January 14, 2020.

⁵ The website describing the NIH scoring guidance is https://grants.nih.gov/grants/policy/review/rev_prep/scoring.htm, accessed January 14, 2020.

Proposal and Award Policies and Procedures Guide⁶ notes a requirement for (two-page) DMPs but only as a supplementary material, leaving the evaluation thereof in the realm of individual divisions and program managers. The IEDA, acting as a repository for observational geoscience data, provides researchers with the ability to generate a Data Compliance Report⁷ based on their NSF award number. The resulting output can be used “to demonstrate that your data are registered with IEDA systems and you are compliant with NSF Data Policies,” but it is not clear how much weight that carries in post-award evaluation or in subsequent proposals. The NSF Directorate for Biological Sciences notes a requirement for inclusion in annual and final reports, as well as part of “Results of prior NSF Support” in subsequent proposals, but, again, it is not clear what weight these are given during the evaluation process.⁸ The Canadian “Tri-Agency Statement of Principles on Digital Data Management”⁹ highlights researcher and institution responsibilities only with respect to the development of and compliance with DMPs, and the Canadian Institutes of Health Research’s Peer Review Manual¹⁰ does not include DMPs in its scoring criteria.

Halbert (2013) and Keralis and colleagues (2013) identify lack of consistency across funding agencies as a barrier for a consistent response by researchers and data librarians to data management challenges (see also Williams et al., 2017). The development of the second generation of DMP tools may be seen as a direct result of these findings and the attempt to construct a “meta-DMP” that provides consistent guidance regardless of the underlying agency reporting requirement.

maDMPs, also referred to as dynamic DMPs (Simms and Jones, 2017; Simms et al., 2017) or data management records (Morgan and Janke, 2017), may be useful for forecasting of costs of data preservation. They are specifically proposed as a more formal (machine-readable) document, allowing for data exchange across various entities, in particular across the entire data life cycle. Integration with funders, as well as institutional and community capacity planning, are specifically identified (Simms et al., 2017). maDMPs are evolving, and a standard has not yet emerged, although several use cases and implementations (Morgan and Janke, 2017) exist. At the time of writing, working groups at the Research Data Alliance¹¹ and FORCE11¹² (Chodacki et al., 2016) are working on use cases from a variety of disciplines and coordinating on standards. In particular, maDMPs target metadata such as quantity and type of data, regardless of storage location, allowing for an assessment of time-varying cost of storing such data. They strongly encourage use of persistant identifiers for people, institutions, and assets, so that maDMPs are globally intelligible (Bakos et al., 2018).

REFERENCES

___________________

⁶ The NSF Proposal and Award Policies and Procedures Guide can be found at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg&WT.z_pims_id=0, accessed January 14, 2020.

⁷ The IEDA data compliance reporting tool can be found at http://app.iedadata.org/dcr/report.php, accessed January 14, 2020.

⁸ The website for the NSF Directorate for Biological Sciences updated information about DMP is https://www.nsf.gov/bio/pubs/BIODMP061511.pdf, accessed January 14, 2020.

⁹ The Government of Canada website with the 2015 Tri-Agency Statement of Principles on Digital Data Management is http://science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html, accessed January 14, 2020.

¹⁰ The Canadian Institutes for Health Research Peer Review Manual can be found at https://cihr-irsc.gc.ca/e/49564.html, accessed January 14, 2020.

¹¹ The website for the Data Research Alliance is https://www.rd-alliance.org/, accessed January 14, 2020.

¹² The website for FORCE11 is https://www.force11.org/, accessed January 14, 2020.

Simms, S., and S. Jones. 2017. Next-generation data management plans: Global, machine-actionable, FAIR. International Journal of Digital Curation 12(1):36. https://doi.org/10.2218/ijdc.v12i1.513.

Simms, S., S. Jones, D. Mietchen, and T. Miksa. 2017. Machine-actionable data management plans (maDMPs). Research Ideas and Outcomes 3:e13086.

Williams, M., J. Bagwell, and M.N. Zozus. 2017. Data management plans: The missing perspective. Journal of Biomedical Informatics 71(July):130-142.