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Data Management Planning for NMSU Researchers

Tutorials & Guides

Mantra: Research Data Management Training - "MANTRA is a free online course for those who manage digital data as part of their research project." - Web site

New England Collaborative Data Management Curriculum - "NECDMC is an instructional tool for teaching data management best practices to undergraduates, graduate students, and researchers in the health sciences, sciences, and engineering disciplines." -Web site 

Penn State Data Management Plan Tutorial - Five lessons. Printer-friendly version is available. 

Data Management Case Studies (Stanford University Libraries) - Case studies include data persistence, file formats, file naming, basic and advanced metadata, and data storage and backup. 



"Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.  Metadata is often called data about data or information about information."  (2004, NISO, Understanding Metadata).

Metadata provides information about your data's contents, structure, permissions, and other important descriptive characteristics. Metadata makes it possible for others to find and use your data. If you don't have it, you might not be able to find your data again. 

Essentially, metadata is:

  • Descriptive and aids discovery
  • Readable and retrievable by humans and machines
  • Follows disciplinary standards 

Stanford University's Data Management Services has several excellent, case studies -  including some cautionary tales - that explain why adhering to the file and metadata principles above are important.

Gilles, C. (2010). Metadata [Online Image]. Retrieved from [CC BY 2.0]

Citing Data

If you are reusing a dataset in your research, you will want to make sure that you are providing proper recognition. Datasets are scholarly products and should be cited as such. If you are using a dataset that was deposited in a disciplinary data repository, you may find that the repository has a recommended citation standard.

ICPSR provides useful guidance on data citations and suggests that a citation for a dataset should include the following basic elements:

  • Title
  • Author
  • Date
  • Version
  • Persistent identifier

For general information about citing a dataset, see the following resources: