Aim to create a dataset you wish you had received
Clear documentation is important for making your research data understandable, reusable, and shareable, whether for yourself, your collaborators, peer reviewers, or future researchers. Properly documented data ensures transparency, supports reproducibility, and increases the likelihood that your data can be reused and cited.
Metadata:
Metadata is data about data. It provides standardized, machine-readable information that allows others (and systems) to discover, access, and interpret your data.
Whereas README files are primarily for human users, metadata is structured to support automated processes like searching, indexing, and repository storage.
Types of metadata:
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Descriptive metadata – Title, authors, abstract, keywords
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Administrative metadata – File type, creation date, rights or licenses
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Technical metadata – Software or hardware used, file format specifications
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Structural metadata – Relationships between parts of the dataset (e.g., multiple files)
Metadata standards and tools:
Different disciplines and repositories may require specific metadata standards. Examples include:
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Dublin Core – A general-purpose metadata standard used widely across disciplines.
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DataCite – Often used for citing and sharing datasets; supports DOI creation.
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Ecological Metadata Language (EML) – Used in environmental and ecological sciences.
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ISO 19115 – Used for geospatial datasets.
Some metadata can be automatically generated by repositories or data management tools, but researchers are often responsible for providing descriptive and contextual information. At UBC, we tend to use DDI metadata for our Borealis repository (small to medium data) and Dublin Core metadata for the FRDR repository (big data)
README files: describing your dataset
A README file is a plain-text file that accompanies your dataset and provides human-readable information about the contents, structure, and context of your data. It is a key part of documentation and is often the first place users will look to understand how to interpret and reuse your files.
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Keep your README file updated throughout your project, not just at the end.
Resources and Support
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UBC Library’s Research Data Team offers workshops, templates, and consultations on documentation and metadata. For example:
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: These open-source, self-paced learning modules are built on GitHub and aligned with the Carpentries’ pedagogy. Topics include file naming, data organization, READMEs, sharing data, Data Management Plans, data deposits in Borealis, and more. Free, self-paced, reproducible, and licensed for reuse.
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Various one-page guides:
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Need help? Contact research.data@ubc.ca
If you are deciding which metadata standard to use, you can also consult your subject librarian.