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A Guide To...Digital and Emerging Scholarship

This guide is dedicated to the advancement of digital and emerging scholarship at the College of the Holy Cross.

Describing Data

Put simply, metdata is “data about data.” But that data is extremely important and essential information when it comes to research studies and experiments. No matter your area of study, other researchers will always be interested in how you got the data. For scientific inquiries, the context is just as important as the data itself.

Metadata Standards

As mentioned in the last section, much of this information should be covered in your file titles if you are following proper naming conventions. However, there may be additional information that is important to your particular field. Or you may not be able to fit the full breadth of metadata into your title alone.

Here are some metadata standards that may guide you in creating metadata specific to your project:



  • Digital Curation Centre – includes metadata standards for scientific fields such as biology, earth sciences, social sciences, and physical sciences

Social Sciences

  • DDI: An international standard for describing data from the social, behavioral, and economic sciences. Expressed in XML, the DDI metadata specification supports the entire research data life cycle.


  • CDWA: Categories for the Description of Works of Art, serves as a foundational framework for the description of cultural heritage materials.
  • VRA Core: Visual Resource Association Core Categories, a data standard for the description of works of visual culture as well as the images that document them.
  • TEI: Text Encoding Initiative, a standard for the digital encoding of literary and linguistic texts.


Any additional documentation related to your project is considered metadata, as well. Future researchers will be interested in your workflows and how to interpret any title codes or shorthand that your team may have developed during the project. Again, it is important to formalize your processes as much as possible both for your project team and for future researchers.

These documents may include project planning (history, objectives, potential outcomes), collection methods (data collection process, hardware used, environmental factors), data validation procedures, versioning explanations, or data confidentiality agreements.