In a lab notebook, the researcher records all kinds of project related information – from hypothesis to results of experiments. It often serves as the most important piece of documentation of the researchers’ work. Lab notebooks are crucial for ensuring accountability and reproducibility of research, and they are often needed to enable the re-usability of data. | Larsen, 2018
Clear and detailed data documentation increases the data quality and increases the chance that the data will be understood by (future) others. Data documentation is therefore essential to enable the reproducibility of research and the reuse of research data. In this section you will find a number of examples of data documentation.
In order to make data usable for other researchers who have not yet worked with it, data documentation that is as complete and detailed as possible is essential. Data documentation describes the characteristics of a dataset at various levels, such as:
- A description of the data itself
Think of an overview of all files of the dataset with a description of the content per file in a README file. Here you will find answers to questions such as:
- What is the data format?
- Which software can you use to read the data?
- Which codes and variables are used and what do they mean?
- A description of the data collection process and the tools used
Think of instruments such as a codebook, lab journal, logbook, diary, questionnaires, manuals, etc.
- A description of the changes of the dataset over time
A so-called historical report of the wanderings and processing of the research data in time is necessary to understand the origin of the data. In data jargon this is called data provenance. To be able to make a historical report, a description of the data collection process and of the data itself is also necessary.
Because of the great diversity of datasets, the choices to document data are not always standard.
High-level documentation is very important. A good README file does part of the job, but documentation and a user manual are also important. Any information (e.g. equations, model) behind the software also needs to be shared. | Workshop software reproducibility, 2018
The area between data, data documentation and metadata is a grey area. For example, certain data formats also have metadata in their data. Think of digital photos. As soon as you save them, data are automatically stored with information about the circumstances under which you took the photo: aperture, lighting, etc.
Ultimately, it is not a question of whether something is called data, metadata or data documentation, but of focusing on the underlying goal: to describe the data in such detail that the chance of reproducibility and reuse increases.
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4TU.ResearchData. (2020). Guidelines for creating a README file. https://data.4tu.nl/info/fileadmin/user_upload/Documenten/Guidelines_for_creating_a_README_file.pdf
CESSDA (2017). Data Management Expert Guide. Documentation and metadata. https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/2.-Organise-Document/Documentation-and-metadata
Cruz, M.J., Kurapati, S., der Velden, Y.T. (2018, July 2018). Software Reproducibility: How to put in into practice? https://doi.org/10.31219/osf.io/z48cm
Lantsoght, E. (2013, October 10). Keeping your spreadsheets under control. [blog]. http://phdtalk.blogspot.nl/2013/10/keeping-your-spreadsheets-under-control.html
Larsen (2018). OpenAIRE. Electronic Lab Notebooks - should you go "e"? [blog]. https://www.openaire.eu/blogs/electronic-lab-notebooks-should-you-go-e-1
Wageningen University & Research (2018, 27 August). WUR Data Champions Katharina Hanika & Eliana Papoutsoglou: actively promoting good data management practices. OpenScience blog [blog]. https://weblog.wur.eu/openscience/wur-data-champions-electronic-lab-notebook/
Wageningen University & Research (2017, 8 September). Documenting your research data along the way: tips and tools. OpenScience blog [blog]. https://weblog.wur.eu/openscience/documenting-research-data-along-way-tips-tools/