FAIR: Reusable

This section will describe what it means for research to be reusable

For data & software to be reusable:

File naming best practices

A file name should be unique, consistent and descriptive. This allows for increased visibility and discoverability and can be used to easily classify and sort files. Remember, a file name is the primary identifier to the file and its contents.




Directory structures and README files

A clear directory structure will make it easier to locate files and versions and this is particularly important when collaborating with others. Consider a hierarchical file structure starting from broad topics to more specific ones nested inside, restricting the level of folders to 3 or 4 with a limited number of items inside each of them.

The UK data services offers an example of directory structure and naming: https://ukdataservice.ac.uk/manage-data/format/organising.aspx

For others to reuse your research, it is important to include a README file and to organize your files in a logical way. Consider the following file structure examples from Dryad:

Dryad File Structures

It is also good practice to include README files to describe how the data was collected, processed, and analyzed. In other words, README files help others correctly interpret and reanalyze your data. A README file can include file names/directory structure, glossary/definitions of acronyms/terms, description of the parameters/variables and units of measurement, report precision/accuracy/uncertainty in measurements, standards/calibrations used, environment/experimental conditions, quality assurance/quality control applied, known problems, research date information, description of relationships/dependencies, additional resources/references, methods/software/data used, example records, and other supplemental information.

Disciplinary Data Formats

Many disciplines have developed formal metadata standards that enable re-use of data; however, these standards are not universal and often it requires background knowledge to indentify, contextualize, and interpret the underlying data. Interoperability between disciplines is still a challenge based on the continued use of custom metadata schmes, and the development of new, incompatiable standards. Thankfully, DataCite is providing a common, overarching metadata standard across disciplinary datasets, albeit at a generic vs granular level.

In the meantime, the Research Data Alliance (RDA) Metadata Standards Directory - Working Group developed a collaborative, open directory of metadata standards, applicable to scientific data, to help the research community learn about metadata standards, controlled vocabularies, and the underlying elements across the different disciplines, to potentially help with mapping data elements from different sources.

Metadata Standards Directory
Features: Standards, Extensions, Tools, and Use Cases

Quality Control

Quality control is a fundamental step in research, which ensures the integrity of the data and could affect its use and reuse and is required in order to identify potential problems.

It is therefore essential to outline how data collection will be controlled at various stages (data collection,digitisation or data entry, checking and analysis).


In order to keep track of changes made to a file/dataset, versioning can be an efficient way to see who did what and when, in collaborative work this can be very useful.

A version control strategy will allow you to easily detect the most current/final version, organize, manage and record any edits made while working on the document/data, drafting, editing and analysis.

Consider the following practices:

Example: UK Data service version control guide: https://www.ukdataservice.ac.uk/manage-data/format/versioning.aspx

Research vocabularies