DESCRIBING

In order for your data to be useful to yourself, team members, and to others, proper data documentation is necessary. Metadata, often described as "data about data," is structured information describing content and making it easier to find and use. Documentation and metadata standards exist for many disciplines.

TIPS

Be sure to include the following elements:

• Scope and structure. Do your best to describe the content, formats, and any internal relationships in your dataset.
• Glossaries and legends. Define any terms, codes, and variables that you use.
• Context. Provide details relevant for validation/replication (e.g. funding sources, timetables, collaborators, location, and environmental conditions).
• Methods. Describe techniques, software, and hardware used in data collection.
• Analysis. Describe steps you took in processing and analyzing your data.
• Attribution. Cite your data sources.
• Access. Provide details about confidentiality, access & use conditions of your data.

Documentation also includes well-structured and comprehensive data dictionaries, code or lab books, and field notes.

TOOLS

• Electronic Lab Notebooks are a useful tool for documenting and managing data throughout your project. There are many options (e.g., LabArchives, Evernote), each with unique features for various workflows.
• A metadata schema or standard, such as Dublin Core, is useful for standardizing your data and the associated metadata. There are also standards for certain disciplines.

CAMPUS RESOURCES

• UTA Libraries’ Metadata Librarian can assist with selecting a metadata standard and creating rules for projects.

Still stuck? Send us your questions or request a consultation at datahelp@uta.edu