Data management plan

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A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. [1] The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; [2] this may lead to data being well-managed in the present,[ citation needed ] and prepared for preservation in the future. [2]

Contents

DMPs were originally used in 1966 to manage aeronautical and engineering projects' data collection and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". [3] In the 2000s and later, E-research and economic policies drove the development and uptake of DMPs. [3]

Importance

Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. [4] This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector.

In the 2010s, [3] funding agencies increasingly required data management plans as part of the proposal and evaluation process, [5] despite little or no evidence of their efficacy. [3]

Major components

"There is no general and definitive list of topics that should be covered in a DMP for a research project", [6] and researchers are often left to their own devices as to how to fill out a DMP. [2]

Information about data & data format

Metadata content and format

Metadata are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as “data about data”. [10] Issues to be considered include:

Policies for access, sharing, and re-use

Long-term storage and data management

Budget

Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are

The data management plan should include how these costs will be paid.

NSF Data Management Plan

All grant proposals submitted to NSF must include a Data Management Plan that is no more than two pages. [11] This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following:

  1. The types of data
  2. The standards to be used for data and metadata format and content
  3. Policies for access and sharing
  4. Policies and provisions for re-use
  5. Plans for archiving data

Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results): [12]

  1. Promptly publish with appropriate authorship
  2. Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame
  3. Share software and inventions
  4. Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others
  5. Policies will be implemented via
    1. Proposal review
    2. Award negotiations and conditions
    3. Support/incentives

ESRC Data Management Plan

Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. [13]

ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. [14] [15]

ESRC has a longstanding arrangement with the UK Data Archive, based at the University of Essex, as a place of deposit for research data, with award holders required to offer data resulting from their research grants via the UK Data Service. [16] The Archive enables data re-use by preserving data and making them available to the research and teaching communities.

Benefits

There are three major themes identified in the literature in terms of benefits of DMPs: professional benefits, economic benefits and institutional benefits. [3] It has been argued that DMPs can form a catalyst for researchers to improve their data literacy and data management practices, often aided by the library. [3]

In practice

In practice, however, DMPs often fall short of their stated goals. A 2012 review of DMP policies by research funders found that policies were missing several elements from the Digital Curation Centre's list of criteria for a DMP. [17] Researchers shared DMP text. [18] DMPs are often regarded as an "administrative exercise rather than an integral part" of the research process, [19] and it has been acknowledged that DMPs do not guarantee good data management practices. [20] Most funders do not require a DMP after grants are awarded, thus robbing stakeholders of the powerful tool that an active DMP can be. Best practice would be to "require maintenance of the data management plan following award and during the active phase of a study." [6] At present, data sharing plans are more important than data management plans to funders. [6]

See also

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References

  1. ↑ "Data Management Plan". University of Virginia Library. Archived from the original on Nov 9, 2012.
  2. 1 2 3 Burnette, Margaret; Williams, Sarah; Imker, Heidi (16 September 2016). "From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project". Journal of eScience Librarianship. 5 (1): e1101. doi: 10.7191/jeslib.2016.1101 .
  3. 1 2 3 4 5 6 Smale, Nicholas; Unsworth, Kathryn; Denyer, Gareth; Barr, Daniel (17 October 2018). "The History, Advocacy and Efficacy of Data Management Plans". bioRxiv : 443499. doi:10.1101/443499. S2CID   91931719.
  4. ↑ "Why manage & share your data? - Data management". libraries.mit.edu.
  5. ↑ "Data Management & Sharing Frequently Asked Questions (FAQs)". Archived from the original on 2017-07-11. Retrieved 2018-04-06.
  6. 1 2 3 Williams, Mary; Bagwell, Jacqueline; Nahm Zozus, Meredith (July 2017). "Data management plans: the missing perspective". Journal of Biomedical Informatics . 71: 130–142. doi:10.1016/j.jbi.2017.05.004. PMC   6697079 . PMID   28499952.
  7. ↑ "Elements of a Data Management Plan". www.icpsr.umich.edu. Retrieved 2015-09-30.
  8. ↑ "Archived copy" (PDF). libraries.mit.edu. Archived from the original (PDF) on 4 May 2018. Retrieved 12 January 2022.{{cite web}}: CS1 maint: archived copy as title (link)
  9. ↑ Guns, Raf. "Tools for version control of research data" (PDF). University of Antwerp .
  10. ↑ Michener,WK and JW Brunt. 2000. Ecological Data: Design, Management and Processing. Blackwell Science, 180p.
  11. ↑ "GPG Chapter II". www.nsf.gov.
  12. ↑ "Dissemination and Sharing of Research Results - NSF - National Science Foundation". www.nsf.gov.
  13. ↑ ESRC Research Data Policy 2010
  14. ↑ Prepare and manage data: Guidance from the UK Data Service
  15. ↑ "Managing and Sharing Research Data - SAGE Publications Inc". www.sagepub.com. Archived from the original on 2014-04-07. Retrieved 2014-04-01.
  16. ↑ "UK Data Archive - WHO CAN DEPOSIT?". www.data-archive.ac.uk.
  17. ↑ Dietrich, Dianne; Adamus, Trisha; Miner, Alison; Steinhart, Gail (2012). "De-Mystifying the Data Management Requirements of Research Funders". Issues in Science and Technology Librarianship . 70 (70). doi:10.5062/F44M92G2.
  18. ↑ Parham, Susan Wells; Doty, Chris (October 2012). "NSF DMP content analysis: What are researchers saying?". Bulletin of the American Society for Information Science and Technology . 39 (1): 37–38. doi:10.1002/bult.2012.1720390113. hdl: 1853/44391 .
  19. ↑ Miksa, Tomasz; Simms, Stephanie; Mietchen, Daniel; Jones, Sarah (28 March 2019). "Ten principles for machine-actionable data management plans". PLOS Computational Biology . 15 (3): e1006750. Bibcode:2019PLSCB..15E6750M. doi: 10.1371/journal.pcbi.1006750 . PMC   6438441 . PMID   30921316. S2CID   85563774.
  20. ↑ Donelly, Martin (2012). "Data management plans and planning". In Pryor, Graham (ed.). Managing research data. London: Facet Publishing. pp. 83–104. ISBN   9781856048910.

Further reading

Pryor, Graham (2014). Delivering research data management services. Facet Publishing. ISBN   9781856049337.

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