Metadata means data about data. For Research Data Management, there are basically two types, the human-readable documentation that you create during your project such as readme files, lab notebooks, or headers inside your data files giving explanation, description and context. This is sometimes called informal metadata. Then, there is machine-readable metadata, sometimes called formal or standard metadata, that allow your datasets and documents to be discovered and retrieved through search engines, specify which users under which conditons have which rights to access your data, and create identifiers and citations for your data.
- Australian National Data Service (ANDS) provides a simple working-level view of the needs, the issues, and the processes around metadata collection and creation.
- NISO Press, 2015. Research Data Management: A Primer - This primer covers the basics of research data management, with the goal of helping researchers with planning, documenting, and preserving data to promote reproducible and transparent research data.
- UK Data Archive. Catalogue Metadata
- Edina. Documentation, Metadata, Citation
There are many standards or schema for metadata, some generic, and many more that are discipline specific. Your funder, intended journal for publication, or intended repository for deposit of your data may have standards that you need to follow. Please check early in your research for these, to avoid surprises later. Please check with your colleagues, and observe what metadata standards are most often used in your discipline.
- Disciplinary Metadata - This Digital Curation Centre page provides links to information about disciplinary metadata standards, including profiles, tools to implement the standards, and use cases of data repositories currently implementing them.
- RDA Metadata Directory - A list of metadata standards by discipline maintained by the Research Data Alliance Metadata Standards Directory Working Group.
The HKU Scholars Hub uses Dublin Core (DC) metadata to describe publications and datasets. DC is the most commonly used metadata schema, and there are many crosswalks between it and other schemas. This means that it can quickly be interpreted by search engines and machine programs for many different purposes. When you use the Hub deposit submission form, the backend will automatically change your input into DC. This deposit form will allow you to enter keywords of your choice, and ask you to choose subject terms from pull-down lists, from RGC (GRF, Annexes B1-B5) and Australia New Zealand Standard Research Classification (ANSRC). Beyond this, it will also allow you to enter subject terms from the schema or thesaurus of your choice, as long as you give the namespace or URI describing that schema or thesaurus.