Data Management Plan


A data management plan grows out of an understanding of how data should be collected, normalized, processed, analyzed, preserved, used and re-used over their lifetime. Data management plans are often required by grant funding agencies such as the National Science Foundation. A data management plan that is associated with a research study must include comprehensive information about the data such as the types of data, the metadata standards used, the policies for access and sharing, and the plans for archiving and preserving data so that it is accessible over time. Data management plans ensure that data will be properly documented and available for use by other researchers in the future. Librarians can assist researchers to formulate a data management plan based on strong foundational knowledge working with metadata, archiving, preservation, and scholarly communication.

For librarians, it is important to know what types of information are needed to be included when writing a data management plan for faculty, researchers or students. According to the requirements from the National Science Foundation Data Management Plan, the information needed to complete a data management plan may include:

  • The types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of a project;
  • The standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies);
  • Policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements;
  • Policies and provisions for re-use, re-distribution, and the production of derivatives; and
  • Plans for archiving data, samples, and other research products, and for preservation of access to them.

The librarian can address these requirements by completing a series of data interviews with the researcher – similar to a traditional reference interview. Information gathered from these interviews will help to inform the librarian as to the proper organization and management of the data that has been generated by the user. Data can then be arranged in a logical manner by subject, experiment or other organizational format discussed with the data creator, and be stored for future use.

Further Resources


Bardyn TP, Resnick T, Camina SK. (2012). Translational Researchers’ Perceptions of Data Management Practices and Data Curation Needs: Findings from a Focus Group in an Academic Health Sciences Library(link is external). Journal of Web Librarianship, 6(4):274–87.

Creamer A, Morales M, Kafel D, Crespo J, Martin E. (2012). An Assessment of Needed Competencies to Promote the Data Curation and Management Librarianship of Health Sciences and Science and Technology Librarians in New England(link is external). Journal of eScience Librarianship, 1(1):18–26.

Donnelly M. (2012). Data management plans and planning. Managing Research Data. London: Facet Publishing.

Dulock M. (2013). Technical Services Report: Data Management: What is the library’s role?(link is external) A Report of the LITA/ALCTS Metadata Interest Group Program, American Library Association Annual Conference, Anaheim, June 2012. Technical Services Quarterly, 30(1):107–13.


Burnette M, Williams S, & Imker H. (2016). From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project(link is external). Journal of eScience Librarianship, e1101.

Davis HM, Cross WM. (2015). Using a Data Management Plan Review Service as a Training Ground for Librarians(link is external). Journal of Librarianship and Scholarly Communication, 3(2), eP1243.

Ferreira F, Coimbra ME, Bairrão R, Viera R, Freitas AT, Russo LMS, & Borbinha J. (2014). Data Management in Metagenomics: A Risk Management Approach(link is external). International Journal of Digital Curation, 9(1).

Johnson A, Knuth S. (2016). Data Management Plan Requirements for Campus Grant Competitions: Opportunities for Research Data Services Assessment and Outreach(link is external). Journal of eScience Librarianship, e1089.

Johnston L, Lafferty M, & Petsan B. (2012). Training Researchers on Data Management: A Scalable, Cross-Disciplinary Approach(link is external). Journal of eScience Librarianship, 1(2).

Raboin R, Reznik-Zellen RC, & Salo D. (2012). Forging New Service Paths: Institutional Approaches to Providing Research Data Management Services(link is external). Journal of eScience Librarianship, 1(3).

Starr J, Willett P, Federer L, Horning C, & Bergstrom M. (2012). A Collaborative Framework for Data Management Services: The Experience of the University of California(link is external). Journal of eScience Librarianship, 1(2):109–14.

Thoegersen J. (2015). Examination of Federal Data Management Plan Guidelines(link is external). Journal of eScience Librarianship.

Michener WK. (2015). Ten Simple Rules for Creating a Good Data Management Plan(link is external). PLoS Computational Biology.

Search for a Term

Send us your feedback or suggestions for new terms

Contact information
CAPTCHA This question is to prevent spam submissions. Contact for any accessibility issues.
2 + 13 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.