Skip to Main Content
University of Texas University of Texas Libraries

Data Management

This guide is to help you prepare a data management plan.

Data Sharing & FAIR

Why Share Data?

  • Increase your impact
    • Data are increasingly recognized as a scholarly product in their own right. Making them discoverable, available, and citable can increase the visibility and impact of your work.
  • Move science forward
    • Reproducibility is a cornerstone of scientific inquiry. By making your data openly available and usable, you allow other researchers to verify your results and build on them. This helps reduce fraud and redundancy and generally adds to a global pool of scientific knowledge.
    • Many journals are requiring a Data Availability or Data Archiving Policy as a prerequisite for publication. These vary according to publisher, but may require that you submit supporting data, deposit your data in a repository for which you provide a persistent link, or make your own contact information available for data access requests.

             

Sharing your data

Regardless of what your funder requires, there are some best practices to consider when making a plan for your data.

  • Plan ahead - Put in place a robust data management and sharing plan to ensure that your data collection practices align with policies and standards for you institution, your funding agency, and your archiving and sharing platform.
  • Create a usage rights statement - Usage rights statements should include appropriate data uses, how to contact the data creators, and citation information for the data source.
  • Copyright and intellectual property - If you use data from other sources, you should review your rights to use the data and be sure you have the appropriate licenses and permissions.
  • Metadata standards - Use robust metadata formatted appropriately for your community and/or repository. ICPSR recommends using XML to create structured documentation compliant with the Data Documentation Initiative (DDI) metadata specification. See more on the Data Collection page of this guide.
  • Make filenames descriptive - File names should reflect the contents of the file and include enough information to uniquely identify the data file.
  • Describe the quality of your data - Flag data that have been identified as questionable by including a flagging column next to the column of data values.
  • Define roles and assign responsibilities for data management - By clearly defining the roles and responsibilities of the parties involved, data are more likely to be available for use by the primary researchers and anyone re-using the data.

You may have to format, describe, clean, and de-identify your data to ensure that other researchers will find the datasets useful and understandable and in order to protect, if applicable, the privacy of human subjects.

Cleaning your data

Data cleaning is the process of detecting, diagnosing, and editing faulty data. The tools listed below will help you clean your data and prepare it for sharing.

FAIR Data Principles

The FAIR Data Principles are community developed principles for making data Findable, Accessible, Interoperable, and Reusable.

  • Findable – Assign persistent IDs, provide rich metadata, register in a searchable resource.
  • Accessible – Retrievable by ID using a standard protocol, allows for authentication/authorization, metadata remain accessible even if data aren’t.
  • Interoperable – Use standard vocabularies, qualified references, shared and broadly applicable language for knowledge representation.
  • Reusable – Rich, accurate metadata, clear licenses, provenance, use of community standards.

Informed Consent

There may be ethical considerations when it comes to sharing data collected from human participants. It's important to think through these issues early and getting consent from participants up front.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 2.0 Generic License.