Documenting research data involves naming files consistently and adding additional information that describes the research project and the collection process—known as metadata. Accompanying materials relating to the research process are also an essential part of the documentation. Essentially, the documentation serves as a description of the data and functions similarly to an instruction manual.

Why?

The following objectives should be achieved with data documentation [1]:

• Preservation of the interpretability and traceability of data
• Visibility and retrievability of data (e.g., in data catalogs)

Documentation enables third parties to assess the reusability of research data. It ensures the traceability and reconstructability of decisions. For this reason, documentation is also indispensable for project staff, especially in projects with several team members and in the event of members leaving the project. Nevertheless, queries may arise, so a permanent contact option for the data controllers must be provided.

The use of comprehensive data descriptions enables more effective working methods and increases the findability of research data. It creates clarity and accountability. The use of metadata standards and controlled vocabulary (e.g., through the use of a thesaurus) as well as the implementation of data management software, such as an electronic lab notebook, offers the following advantages, among others:

• significant time savings in the archiving and publication process
• minimization of file mix-ups
• ensuring appropriate and careful documentation that complies with the principles of good scientific practice

How?

Data documentation includes a detailed description of how data is collected, processed, analyzed, and archived. This description also contains information about the use of metadata standards and the vocabulary used. In addition, an explanation of the coding of the data is provided. These descriptions are based on the requirements and standards of the respective discipline.

Data documentation can be achieved in various ways. These include, for example, an accompanying ReadMe file, a metadata database, an internal project wiki, an (electronic) lab notebook, a data management plan (DMP), appropriate file naming within the folder structure, or appropriate documentation within the research data file itself or in the file’s meta information.

FAIR Research Data Management

The FAIR principles provide guidelines for organizing data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. The FAIR principles apply to data management, infrastructure, and services. The first step toward (re)using data and complying with the FAIR principles is to make it possible to find this data.

A systematic filing system ensures that files can be found easily in different systems. Accessibility is regulated by defining clear access rules so that only authorized persons can access the files. Interoperability is ensured by allowing files and their contents to be used in different systems without impairment. Finally, reusability is increased by preparing the files in such a way that they can be easily reused by other users.

All relevant documents and files must therefore be carefully structured. It is not only about FAIR data, but also about FAIR files. The following are therefore particularly important:

• a clear folder structure
• clear naming conventions
• consistent versioning

Folder and file management

Folder and file management is an important step for successful research data management. The following key questions regarding the establishment of a folder and file naming system should be answered within a research project:

• What should be considered or is helpful when defining a folder structure?
• What types of data should be stored?
• Are there subprojects that require their own folders?

A folder and file naming system is planned at the beginning of the project (jointly by the team) and should be meaningful and as simple as possible. Below are some general tips for establishing a folder structure:

• Balance between a folder structure that is as flat as possible but sufficiently deep. A deep folder structure requires many clicks to reach the required file, while a structure that is too flat can lead to overcrowding of the folders.
• Overlapping categories should be avoided and understandable folder names should be used.