Organise and describe your research material to enable you and research collaborators to locate data efficiently when needed, and to understand the processes followed during data collection and analysis. Include documentation about data within headers and filenames or in a structured document. This contributes to efficient and long term access and enables collaboration.
This cartoon video (4mins 40 secs) from the New York University Health Sciences Library shows what can happen when research data is not described adequately and how this can impact future research.
Metadata refers to documentation about your data, ie documenting the elements someone else would need to find, understand and/or re-use and cite your data. Depending on data types, metadata includes title; name of creator or data collector; geographic location of data collection (eg, photos or transcript data); parameters or units of measurement; equipment used; access conditions. See the ANDS Metadata Guide for more detail.
Why use metadata standards?
Using a metadata standard means you can apply standardised elements and don’t have to come up with your own. You can select from a range of existing metadata schemes, and the Digital Curation Centre (DCC) provides many examples of discipline-based, or general research metadata schemes. Dublin Core is a widely used scheme.
If you create your own metadata scheme, document it in a table or text file (eg a ReadMe file) to accompany the data. Cornell University provides a useful Guide to writing “readme” style metadata.
What resources/tools are available?
An alphabetical list of metadata tools is available from the Digital Curation Centre, and the DCC metadata standards page includes examples of tools within disciplines. For more information and advice, contact the Library.