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Research Data Services

Guidance, tools, and training to support faculty and students working with research data.

File Naming

Establishing file naming conventions and folder hierarchies at the beginning of a research project will make it easier for you and your collaborators to navigate and find specific files, and avoid file duplication and accidental deletion. The most important rule is to be consistent. Best practices include:

  • Keeping files names short but meaningful
  • Using capital letters to delimit words, avoiding spaces, hyphens and underscores
  • Avoiding the use of non-alphanumeric characters
  • Denoting dates using the "YYYYMMDD" format

For further guidance and examples of best practices, review this mini-course from UBC Library. 

File Organization

To keep your files organized, creating a meaningful folder structure can support findability, collaboration, and productivity. A well structured folder hierarchy will make it easier to locate and share your files.

Some recommended practices include:

  • Restrict level of sub-folders to three or four deep
  • Restrict the number of sub-folders within each folder to ten
  • Include a README file that describes your folder structure, including:
    • Description of folder contents
    • Naming conventions
    • Data structure
  • Include a folder within the folder structure for your project documentation. This might include:
    • Project proposals/protocols
    • Consent and approval forms
    • Methodology documents
    • Data management plans
    • Code used for analysis and outputs
    • Codebooks or guides

For further guidance and examples of best practices, review this mini-course from UBC Library. 

Documentation

For research data to be read and interpreted correctly, it requires sufficient description and documentation. Consider the information needed to make your data ‘independently understandable’, now and in the future. For example:

Remember that it is best to document your data throughout the research process, rather than at a later stage.

For guidance on how to write readme-style metadata, review this mini-course from UBC Library.

File Formats

Typically, file format selection will be determined by the software you use for data collection or analysis. However, because technology changes, storing and sharing research data over the long-term requires it be kept in a format that is widely accessible and readable. Researchers should also consider whether their data needs to be converted to an open file format for archiving once their project is completed. 

Some examples of file formats:

  • Statistical data files e.g. SPSS, SAS, Stata, R
  • Spreadsheets e.g. Excel or Google Sheets
  • Text data e.g. .txt text files, .csv comma separated values, etc
  • Documents e.g. MS Word, Google Docs, PDFs
  • Image files e.g. .tiff, .jpeg files
  • Geospatial files e.g. Esri Shapefiles, GEOJSON, etc.
  • Scientific files e.g. .NetCDF, .MAT

For further guidance and examples of best practices, review this mini-course from UBC Library. 

For authoritative recommendations on file formats consult these resources:

Metadata Standards

Metadata is "structured information associated with an object for purposes of discovery, description, use, management, and preservation. Metadata are often called data about data or information about information." (NISO, 2004)

Metadata should follow defined standards in make it findable, accessible and reusable. These standards vary according to the research discipline. For help identifying an appropriate metadata standard for your project, contact UVic Libraries. You can also search for metadata standards in your discipline through the DCC open directory:

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This work by The University of Victoria Libraries is licensed under a Creative Commons Attribution 4.0 International License unless otherwise indicated when material has been used from other sources.