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Data Management: Data Management Plans

Data Management Plans, Support, and Data Repository Recommendations

Data Management, Defined

Data Management Plans are formal documents encompassing key elements of good data management. Data Management Plans (DMPs) are increasingly required by federal funders and major foundations. While the exact format and contents of a data management plan can vary, a data management plan typically requires a description of:

  • Data collection:
    • What data will be collected or created
    • The software and hardware used
    • File formats for data
    • Expected volume of data
    • Standards or methods you'll use for data collection
    • How versioning or quality control will be handled
  • Data and metadata standards
    • The additional files that must be created or maintained for data to be understandable (e.g. data dictionaries, a ReadMe file with information about the study and who was involved)
    • Discipline-specific standards that will be employed (e.g. using Darwin Core for biodiversity data)
  • Access and sharing
    • Whether or not data may be shared outside of the study team--typically, compelling barriers to sharing are ethical or legal, though occasionally there are technical barriers as well
    • If data may be shared without ethical or legal barriers, how it will be shared (e.g. via a data repository or only upon direct request)
    • How much of the data will be shared (e.g. just a subset of all the data collected)
    • When data will be shared
    • Who will be able to use your data and under what circumstances (e.g. some researchers restrict use to noncommercial purposes only)
    • The file formats for any final data sets that will be shared (e.g. converting spreadsheets to CSV format or converting survey instruments to PDF files to allow maximum access)
  • Data sharing restrictions, licensing, or use limitations
    • How data from human research participants will be anonymized
    • How sensitive data will be handled throughout the research life cycle
    • How consent for data sharing and reuse will be attained and ensured if sharing data
    • Ownership of the data--if you've used third-party data or collected data using a copyrighted research instrument, you may not be able to share the data or provide the research instrument along with the dataset
    • Licensing of data
  • Archiving and preservation
    • How long data will be retained
    • Where data will be locally stored at the end of a research project
    • The file formats that will be used to make sure data is accessible for the long term (e.g. using PDF, XML, TXT, CSV, TIFF files that don't require specific software)

DMPTool.org provides more elaborated general guideline for writing clear and sufficient data management plans. 

Why does a DMP matter? Plan ahead to avoid the "Data Sharing and Management Snafu in 3 Short Acts"

(Courtesy of the NYU Health Sciences Library)

Templates and Samples

Guides and Checklists

Metadata Standards

Generic Metadata Schemata

If you cannot use a discipline-specific metadata standard, try to use a generic metadata schema such as Dublin Core or DataCite Metadata Schema.‚Äč

Additional guidance on documenting your data: