Good documentation makes material understandable, verifiable and reusable (by you or by others). Good documentation ensures that:
John MacInnes, Professor of Sociology, The University of Edinburgh, talks about the importance of good documentation in secondary data analysis:
‘Metadata’ is another form of documentation and is simply ‘data about data’. It is related to the broader contextual information that describes your data, but is usually more structured in that it conforms to set standards and is machine readable. One typical use of metadata is to create a catalogue record for a dataset held in an archive. By using a standard set of tags, an automatic system can tell where to locate the information about the title, creator, description, etc. This in turn helps to raise the visibility of your research by making it easier for others to learn about it (e.g. via a search engine or online catalogue), cite it and use it.
You need to consider how you will create or capture these metadata, what form the metadata will take, to what extent the metadata creation will be automated, and which metadata standard you will use.
Information about a file or dataset can be included within the data or document itself. For digital data sets, this means that the documentation can sit in separate files (for example text files) or be integrated into the data file(s), as a header or at specified locations in the file. Examples of embedded documentation include:
This is information in separate files that accompanies data in order to provide context, explanation, or instructions on confidentiality and data use or reuse. Examples of supporting documentation include:
Lab notebooks, whether in print or electronic form, are a critical component of tracking and recording research. Consistent documentation of your research methods, calculations, and results is important not only for your personal use, but will help when you publish or otherwise share research, and when others want to reproduce what you have done.
Listed below are links to several guidelines. Please let us know if there are other guidelines that are used in your lab, School, or Research Institute:
Documentation is best created alongside the data project, as it is easier to capture it then, rather than trying to remember to do things at a later stage. Make sure that there are strong links between your data and the associated documentation, e.g.:
There are three broad categories of metadata:
Metadata Standards provide specific data fields or elements to be used in describing data for a particular use.
Some research fields have predefined metadata standards. See further resources below to find a suitable metadata standard for your data.
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