Data Management: Data Management Plans

Data Management Plans, Support, and Data Repository Recommendations

What is a Data Management Plan (DMP)?

(Courtesy of the Libraries of the University of Texas at Austin and Texas Tech University )

A DMP will help you to properly manage your data for your own use, meet funder requirements, and enable data sharing in the future. A DMP describes the structure and nature of the data as well as the activities and technical requirements to gather, merge, transfer, organize, document, analyze and preserve research data.

 

(Courtesy of the NYU Health Sciences Library)

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

Templates and Samples

 

  • DMPTool - "Guidance and Resources for your Data Management Plan" and a service of the University of California Curation Center of the California Digital Library.  Create ready-to-use data management plans for specific funding agencies. See templates at this link.
  • Data Management Plans, Generic templates and guides by agency are provided along the left side of this guide to Digital Data Management, Curation, and Archiving (UNM). 

Guides and Checklists

  • Data Management & Publishing (MIT): This Data Management and Publishing Guide provides guidance on a range of topics, including planning for data management, documentation/metadata, file formats, data organization, data security and backup, citing data, data integration, funder requirements, ethical and legal issues, and sharing and archiving data.

Metadata Standards

Metadata is used for search, retrieval, and preservation of your research data.

Discipline-Specific Metadata

If possible, use a metadata standard that is common to research in your field--for instance, FGDC for geospatial data or Darwin Core for biological studies. This resource provides links to metadata standards for a wide variety of disciplines:  Disciplinary Metadata.

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.

  • Dublin Core is one of the first and simplest metadata schema. Initially consisting of 15 elements used to specify basic descriptive metadata such as the name(s) of the creators of the asset, subject terms, publisher etc. This is often the best fallback when you don't know what else to use. For faculty not familiar with data standards, just recording this information in a text file is a way to get started annotating your data. The standard has since been expanded to allow greater granularity and clarity, known as Qualified Dublin Core.
  • The DataCite Metadata Schema is discipline-agnostic. The schema is available for download, and a variety of examples will help researchers know how to apply this schema to their data. One advantage in using DataCite's Schema is that these are required elements for generating a DOI through DataCite.

Additional guidance on documenting your data: