Where’s Your Data and Who Cares

Bradley Wade Bishop

Professor

School of Information Sciences

46^th Annual CCI Research Symposium

Faculty RAP

Where’s Your Data?

• Research data management (RDM) focuses on all stages of the data lifecycle, including data planning, collection, description, access, use, preservation, and reuse of research data.
• Information professionals work with researchers in a variety of settings to provide research data management expertise.
• RDM is essential as funding agencies, publishers and industry increasingly require data management plans or the resulting open data.

Who cares?

• Reuse is necessary due to several factors—data volume, the often, real-time, one-time collection, the associated costs, as well as data’s long-term scientific value across domains beyond its original purpose.
• Reuse involves data that are discoverable, available, and technically compatible with software, code, and other data.
• As many domains move to be more data-intensive, many others in the research enterprise beyond the researchers involved with data creation perform research data management roles.

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Data Management Plan

Implementation for reuse

In perpetuity

Proposal

Data Management Plan (DMP)

• DMP is a structured, formal document describing roles and responsibilities for maintaining and managing data during and after the conclusion of a research project (Bishop & Hank, 2020).
• Formal document
• Outlines what you will do with your data during and after your research
• Ensures your data is safe for the present and the future
• DMPs are intended to document data lineage, and allow portability, transferability, and future use of data.
• With the push for more public-facing scientific research and accountability, many funding agencies (86% of UK Research Councils and 63% of U.S. funding bodies) require DMPs within the initial funding application (Smale et al., 2018).

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Data Management Plans

• DMPs or similar documents by other names (i.e., Data Sharing Plans) have been required by the US National Institutes of Health since 2011 for grants greater than $500,000 and the NSF since 2011 for all projects. Almost all US federal agencies and most private foundations currently implement a DMP requirement.
• DMPs typically address topics such as file management, file types, backup and security, metadata, and sharing and access of data.
• Those involved in DMP review and support of their implementation must also stay up-to-date on these issues, including relevant data standards and processes.

Bishop, B. W., & Hank, C. (2020). Curation, Digital. In Audrey Kobayashi (Ed.), International Encyclopedia of Human Geography, 2e. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-08-102295-5.10531-1

Why Data Management Plans?

• Since 2020, Horizon (EU research and innovation program) required anyone funded to make the research data generated by selected Horizon Europe projects accessible with as few restrictions as possible, while at the same time protecting sensitive data from inappropriate access (Koumoulos, 2019).
• Several academic journals also started requiring researchers to make public the data and digital outputs associated with a publication (The Royal Society, 2017; PLOS, n.d.).
• DMP use has been imposed onto the research community rather than through grassroot efforts of researchers' themselves.
• NIH issued the Data Management and Sharing (DMS) policy (effective January 25, 2023) to promote the sharing of scientific data! Investigators and institutions:
• Plan and budget for the managing and sharing of data
• Submit a DMS plan for review when applying for funding
• Comply with the approved DMS plan

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Components of a General DMP

1. Information about data & data format
2. Metadata content and format
3. Policies for access, sharing and re-use
4. Long-term storage and data management
5. Roles and responsibilities
6. Budget

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Belmont Forum DDOMP

• Data and Digital Object Management Plan (DDOMP) has 16 criteria for DMPs in proposals that fit into 9 broad areas:
• the data itself;
• data storage and use;
• data management personnel;
• data security;
• data preservation concerns;
• restrictions (required only if necessary);
• intellectual property;
• supporting documentation; and
• long-term costs.

Bishop, B. W., Gunderman, H., Davis, R., Lee, T., Howard, R., Samors, R., Murphy, F., & Ungvari, J. (2020). Data curation profiling to assess data management training needs and practices to inform a toolkit. Data Science Journal, 19(4),1–8. https://doi.org/10.5334/dsj-2020-004

Goudeseune L., Le Roux X., Eggermont H., Bishop B.W., Bléry C., Brosens D., Coupremanne M., Davis R., Hautala H., Heughebaert A., Jacques C., Lee T., Rerig G., Ungvári J. (2019). Guidance document for scientists on data management, open data, and the production of Data Management Plans. BiodivERsA report. 48 pp. http://doi.org/10.5281/zenodo.3448251

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Where’s Your Data in 10 years?

• Do you care?
• Would anyone else ever use it?

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Here are other useful papers

• Bishop, B. W., Neish, P., Kim, J., Bats, R., Million, A.J., Carlson, J., Moulaison-Sandy, H, & Pham, N.-M. (2023). Data Management Plan Implementation, Assessments, and Evaluations: Implications and Recommendations. Data Science Journal, 22(1), 1-8. https://doi.org/10.5334/dsj-2023-027
• Pham, N.-M., Moulaison-Sandy, H., Bishop, B. W., & Gunderman, H. (2023). Data management plans: Implications for automated analyses. Data Science Journal, 22(1), 2. http://doi.org/10.5334/dsj-2023-002
• Bishop, B. W., Collier, H. R., Orehek, A. M., & Ihli, M. (2021). Closing the Research Data Management Gap at Universities: A Comparative Study of Data Services Librarians and Research Integrity Officers on Data Management Plans. Issues in Science & Technology Librarianship, (98). https://doi.org/10.29173/istl2602