Data Documentation

Data documentation during research is keeping organised notes on how the data were collected, what the resulting data files comprise and how they were processed. Concise and clear documentation helps you as well as current and future users to understand the context in which your data were collected. It increases the chance your data can be understood and reused.

Your documentation should answer the following questions:

  1. What does my dataset contain? What abbreviations were used and what do they mean? How much data was collected? What software is needed to read them?
  2. How was my dataset collected? Who collected the data? On which dates were they collected?
  3. How were my data processed?

See the page Publishing your dataset in a repository for more information on what data documentation you need when publishing a dataset.

Keeping digital research notes

There are various ways of organising digital files in a systematic way (see Organizing files and folders). There are also pieces of software available for keeping and organising your digital research notes. There is a wide offer of so-called 'Electronic Laboratory Notebooks (ELNs)'. For an overview, see for example Dirnagl, U. and Przesdzing, I., A pocket guide to electronic laboratory notebooks in the academic life sciences. Note that an ELN is not the same as a 'Laboratory Information Management System' (LIMS). A LIMS is meant to track all the measurements, samples and protocols that are processed day by day in a laboratory. In an ELN, a note may be stored that certain samples were sent to the laboratory for a specific research project on a specific date, when the results were received back and where these files can be found.

A number of groups have developed an adequate way of working with general-purpose notes applications. We here share several "Tips & trips" documents for OneNote that were kindly made available by the Food Process Engineering Group:

Preparing data documentation for publication

Data Management Support can help you prepare dataset documentation. We will also help you compile relevant metadata for your files. Metadata enable the identification and discovery of published datasets.

See also: