Research data management is the organization, documentation, storage, and preservation of the data resulting from the research process, where data can be broadly defined as the outcome of experiments or observations that validate research findings, and can take a variety of forms including numerical output (quantitative data), qualitative data, documentation, images, audio, and video.
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
A practical “how to” guidance to go FAIR can be found in the Three-point FAIRification Framework.
Download an overview of the FAIR Principles as a pdf file here
For a downloadable format of the FAIR Principles, visit the FAIR-nanopubs page on GitHub.
Sources:
Go Fair (n.d.). FAIR Principles. Retrieved March 24, 2023, from https://www.go-fair.org/fair-principles/
National Center for Data Services (n.d.). Research Data Management. Retrieved March 24, 2023, from https://www.nnlm.gov/guides/data-glossary/research-data-management
See also Boston University's Mugar Library's NIH 2023 Data Management and Sharing Policy
A data management plan (DMP) documents how data will be collected, secured, stored, organized, and shared for a research project, a lab, a department, or an entire organization. DMPs can be as long or as short as needed. They are often required by funding agencies as part of a research proposal.
Data management prevent future headaches, like mistakenly deleting valuable research results, misplacing files, or having to redo the analysis of your data. When done properly, data management will keep your work organized and intelligible to others as well as to your future self. This will make your work reproducible and increase your data’s value after the completion of the research.
Taken from Kotula, J. D. (n.d.). Research: Data Management: Data Management Plans. Retrieved March 24, 2023, from https://library.bu.edu/c.php?g=1167844&p=8528997