At the start of your project think about the sorts of documentation that you will need:
Document your data as you go along- it is much easier to do this than to try to remember what you have done at the end of your project!
There are different ways in which you can document your data depending on the context within which it is being collected:
When documenting your data, the aim is to provide enough information so that a fellow researcher who is familiar with your field, but not necessarily with your work, should be able to understand the data, interpret it and use it in new research, without the need to contact you directly about the dataset.
An overview of the data should include:
Specifically, you may need to include some of the following information:
You may be recording some of this information in a lab notebook or research journal. If so, you may find it convenient to maintain an index file that links data files to the corresponding page numbers until you have an opportunity to transfer the information into a documentation file.
A 'readme' file is a plain text file that is named 'readme' to encourage users to read it before looking at the remainder of the content. It can contain documentation directly or instruct the reader where to look to find more information. Even though it is free text, the file should be structured into sections as an aid to the reader. The following table summarises suggestions on what to include. There are some examples of readme files provided as links below the table.
|Section||What to include|
Information needed so that the reader can cite your dataset:
Describe how you collected the data:
|Third-party inputs||If you used third-party data, provide a data citation or a description of how you accessed the data.|
Provide details of the steps you took to process the data:
If your workflow generates auxiliary files as well as data files, explain which are which.
Relate the outputs of your workflow to the data files you have, or will be submitting, for archiving.
|Inventory of files||
Give the names of the files in the dataset, a short description of each, and how they interrelate.
Mention related data that was not selected for inclusion, such as auxiliary files generated by your workflow.
|File structure and conventions||
Provide details on how to interpret your data files:
Give a short statement about the terms under which others may use the dataset.
If necessary, the full text of the licence may be given in a separate plain-text file called 'licence.txt'.
|Relationships||If applicable, give links to related datasets, alternative records or publications.|
The University of Bath Research Data Archive contains some examples of readme files you can look at for inspiration. The University of Minnesota provides an example of a readme file template.
As a researcher, the three main types of metadata you will be asked to provide are contextual metadata, discovery data, and metadata for reuse.
This describes the context within which the project was conducted. You will provide this when you create create a record of a dataset in Pure. This helps to connect your data to your own research profile, and to your project, funding body and publications.
This helps other researchers to find your data, and as a result may help to increase the impact of your research. You will provide discovery metadata when you complete a record in the University of Bath Research Data Archive or another research data archive or repository.
The metadata you provide for reuse will depend on the field of your research.
The resources section of this guide has links to a number of subject-specific metadata standards and to catalogues of metadata standards.
Some subject areas have agreed on a common set of terminology to use when describing data. Metadata standards list the properties of the dataset that need to be known and vocabularies provide a standardised set of terms with which these properties can be recorded.
Some labs are now moving away from paper-based laboratory notebooks to electronic lab notebooks. At the University we are currently recommending the use of Signals Notebook to Chemistry students. More information on this software can be found on the Library Chemistry subject page. The Advancing Research Computing Service is recommending Jupyter Notebook for coding and bioinformatics. Jupyter is open-source software and is therefore freely available to use.
Harvard Biomedical Data Management Group have a created an Electronic Lab Notebook Matrix that contains information on a wide range of the currently available software.