Data Management Plan Tools: Overview and Evaluation
DOI:
https://doi.org/10.52825/cordi.v1i.338Keywords:
Data stewardship, FAIR data principles, Management Plan, Research Data Management, DMP Tools, Data Management Plan, FAIRAbstract
Data Management Plans (DMPs) are crucial for a structured research data management and often a mandatory part of research proposals. DMP tools support the development of DMPs. Among the variety of tools available, it can be difficult for researchers, data stewards and institutions to choose the one that is most appropriate for their specific needs and context. We evaluated 18 DMP tools according to 31 requirement parameters covering aspects relating to basic functions, DMP contents, technical aspects and user friendliness. The highest total evaluation scores were reached by Data Stewardship Wizard (703.5), DMPTool (615.5) and RDMO NFDI4Ing (549.5). The tools evaluated satisfied between 10 % and 87 % of the requirement parameters. 11 tools cover at least half of the parameters. In terms of correlation among the tools, which indicates to which degree their scores in the different requirement parameters are alike, we found the highest correlation for ezDMP and GFBio DMPT. Regarding the relatedness between the tools, 85 % of the DMP tools were positively and 16 % negatively correlated. Accounting for the recent developments in the area of DMP tools, this study provides an up-to-date evaluation that can support tool developers in identifying potential improvements, and hosting institutions to select a tool suited to their specific needs.
Downloads
References
A. Ball, Review of Data Management Lifecycle Models (version 1.0). Citeseer, 2012.
W. K. Michener, “Ten simple rules for creating a good data management plan,” PLoS computational biology, vol. 11, no. 10, e1004525, 2015. DOI: https://doi.org/10.1371/journal.pcbi.1004525.
European Research Council and Scientific Council, Open research data and data management plans - information for erc grantees, https://erc.europa.eu/sites/default/files/document/file/ERC_info_document-Open_Research_Data_and_Data_Management_Plans.pdf, Accessed: 19.04.2023, 2022.
S. B. Gajbe, A. Tiwari, R. K. Singh, et al., “Evaluation and analysis of data management plan tools: A parametric approach,” Information Processing & Management, vol. 58, no. 3, p. 102 480, 2021. DOI: https://doi.org/10.1016/j.ipm.2020.102480.
T. Miksa, P. Walk, P. Neish, et al., “Application profile for machine-actionable data management plans,” Data Science Journal, vol. 20, no. 1, 2021. DOI: https://doi.org/10.5334/dsj-2021-032.
T. Miksa, S. Oblasser, and A. Rauber, “Automating research data management using machine-actionable data management plans,” ACM Transactions on Management Information Systems (TMIS), vol. 13, no. 2, pp. 1–22, 2021. DOI: https://doi.org/10.1145/3490396.
N.-M. Pham, H. Moulaison-Sandy, B. W. Bishop, and H. Gunderman, “Data management plans: Implications for automated analyses,” Data Science Journal, vol. 22, no. 1, 2023. DOI: https://doi.org/10.5334/dsj-2023-002.
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2023 Carina Becker, Carolin Hundt, Claudia Engelhardt, Johannes Sperling, Moritz Kurzweil, Ralph Müller-Pfefferkorn
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2023-06-30
Published 2023-09-07
Funding data
-
Freistaat Sachsen
Grant numbers 100607005