Data Management Plans: Implications for Automated Analyses

Ngoc-Minh Pham; Heather Moulaison-Sandy; Bradley Wade Bishop; Hannah Gunderman

doi:10.5334/dsj-2023-002

Data Management Plans: Implications for Automated Analyses

Authors

Ngoc-Minh Pham University of Missouri https://orcid.org/0000-0002-9776-3981
Heather Moulaison-Sandy University of Missouri https://orcid.org/0000-0001-7783-7069
Bradley Wade Bishop University of Tennessee https://orcid.org/0000-0002-5022-2707
Hannah Gunderman Carnegie Mellon University https://orcid.org/0000-0002-7710-7055

DOI:

https://doi.org/10.5334/dsj-2023-002

Keywords:

Data management plans (DMPs), Automated approaches, Text-mining, DMP evaluation

Abstract

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2). This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

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Published

2023-01-25

Issue

Vol. 22 (2023)

Section

Research Papers

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