Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Research Data Management: Managing research data

Resources for learning about best practices in research data management across a variety of disciplines.

What You'll Find On This Page

  • Describing Data
    • Metadata standards
    • Good data documentation
    • How to talk about variables
  • Computing, Storage, and Backup
  • Data Preservation & Archiving
    • Formats
    • Domain archives
  • Additional Resources
    • Data Conservancy
    • Data Curation Profiles
    • DataFlow
    • DataONE
    • re3data
  • Organizations Related to Data Management & Preservation

Describing data

Sharing data (with others or within a lab over time) is impossible without proper data documentation. "Metadata" is data about data. It's structured information that describes content and makes it easier to find or use. A metadata record can be embedded in data or stored separately. Any data file in any format can have metadata fields. In social science, this record is called the "codebook" or "data dictionary."

There are many metadata standards and which one is right for your data will depend on the type, scale, and discipline of your research project.

Some examples of metadata standards are:

For more examples, see the Research Data Alliance Metadata Directory.

If your field doesn't have a metadata standard (it may not be listed above) or if you just need a simpler system to keep track of data within your own lab, consider that there are three main types of metadata addressed by most standards:

  • descriptive: describes the resource for identification and discovery
  • structural: how objects are related or put together
  • administrative: creation date, file type, rights management

Also consider this advice from the UK Data Archive [pdf]:

Good data documentation includes information on:

  • the context of data collection: project history, aim, objectives and hypotheses
  • data collection methods: sampling, data collection process, instruments used, hardware and software used, scale and resolution, temporal and geographic coverage and secondary data sources used
  • dataset structure of data files, study cases, relationships between files
  • data validation, checking, proofing, cleaning and quality assurance procedures carried out
  • changes made to data over time since their original creation and identification of different versions of data files
  • information on access and use conditions or data confidentiality

 At the data-level, documentation may include:

  • names, labels and descriptions for variables, records and their values
  • explanation or definition of codes and classification schemes used
  • definitions of specialist terminology or acronyms used
  • codes of, and reasons for, missing values
  • derived data created after collection, with code, algorithm or command file
  • weighting and grossing variables created
  • data listing of annotations for cases, individuals or items

Computing, storage, and backup

Yale is working to meet the demand of researchers by offering high performance computing, long-term storage options, and secure back-up services. You may also have access to additional resources through your departmental infrastructure or through Marx Library's StatLab consulting services.

Here are some links to get started on computing, security, backup, and storage at Yale:

There are different options for different research needs. Contact your local IT specialist to learn more.

Data preservation and archiving

Preservation of data is different from simple storage of data. For preservation purposes, data will be migrated from format to format as new storage models come into use, and the data's integrity will be maintained through the process. A good example of data preservation is the Inter-university Consortium for Political and Social Research which is a social science data archive containing thousands of data sets from all over the world back to the 1800's. Data in ICPSR has been and will continue to be properly managed to ensure access and usability of data over time.

Not many individual labs are equipped to preserve data for long-term use, so domain archives like ICPSR can be a good alternative. Several journals and funding agencies require data deposit into a repository (such as GenBank) for long-term reliable preservation.

There are hundreds of domain repositories. Some will accept only certain data funded by certain agencies, and others will accept data that fits their collection policy. is a database of research repositories by discipline:

Check these out and see if a repository there matches the long-term home you envision for your data. Keep in mind that not every subject repository will accept your data and not every repository is suited for long-term preservation. If you need help identifying a suitable repository for your data, contact the Research Data Support Services group.

Additional resources on data management & curation

Organizations related to data management and preservation