Data Management Plans: Implications for Automated Analyses
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 comp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2023-01-01
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| Series: | Data Science Journal |
| Subjects: | |
| Online Access: | https://datascience.codata.org/articles/1473 |
| _version_ | 1811173004053315584 |
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| author | Ngoc-Minh Pham Heather Moulaison-Sandy Bradley Wade Bishop Hannah Gunderman |
| author_facet | Ngoc-Minh Pham Heather Moulaison-Sandy Bradley Wade Bishop Hannah Gunderman |
| author_sort | Ngoc-Minh Pham |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T17:40:48Z |
| format | Article |
| id | doaj.art-28b74c822c264038822a33809338fa55 |
| institution | Directory Open Access Journal |
| issn | 1683-1470 |
| language | English |
| last_indexed | 2024-04-10T17:40:48Z |
| publishDate | 2023-01-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Data Science Journal |
| spelling | doaj.art-28b74c822c264038822a33809338fa552023-02-03T14:01:03ZengUbiquity PressData Science Journal1683-14702023-01-0122110.5334/dsj-2023-002870Data Management Plans: Implications for Automated AnalysesNgoc-Minh Pham0Heather Moulaison-Sandy1Bradley Wade Bishop2Hannah Gunderman3University of MissouriUniversity of MissouriUniversity of TennesseeCarnegie Mellon UniversityData 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.https://datascience.codata.org/articles/1473data management plans (dmps)automated approachestext-miningdmp evaluation |
| spellingShingle | Ngoc-Minh Pham Heather Moulaison-Sandy Bradley Wade Bishop Hannah Gunderman Data Management Plans: Implications for Automated Analyses Data Science Journal data management plans (dmps) automated approaches text-mining dmp evaluation |
| title | Data Management Plans: Implications for Automated Analyses |
| title_full | Data Management Plans: Implications for Automated Analyses |
| title_fullStr | Data Management Plans: Implications for Automated Analyses |
| title_full_unstemmed | Data Management Plans: Implications for Automated Analyses |
| title_short | Data Management Plans: Implications for Automated Analyses |
| title_sort | data management plans implications for automated analyses |
| topic | data management plans (dmps) automated approaches text-mining dmp evaluation |
| url | https://datascience.codata.org/articles/1473 |
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