Quick-start guide

Making data FAIR can require a substantial amount of time, depending on the initial state of the data. This can seem daunting and raises the threshold to start. But every step towards FAIR is already a great improvement!

The FAIR evaluation tool that we introduce with this guide provides an easy access to FAIR, as it evaluates the state of your data and points you to the sections in this guide that can help you to further enhance the data. You therefore do not have to go through this guide A to Z!

If your data is however still in a very original state, the tool will point you to the full workflow. If you do not have time to go through everything, we encourage you to follow at least the following two steps, as they are most important for ensuring data persistence, understandability and reuse:

  1. Describe your data: Describing your data in a way that others can understand them is crucial to allow others (or your future self) to understand and interpret the data. This description can in the simplest form be a README text file that contains information on the data contents, the data collection, the data files and their structure, and licensing information.
  • See Chapter 4.1 for more details and a template to create a README.
  1. Store your data: Data that only exists locally are at high risk to be lost and inaccessible for others. Storing your data persistently in an online repository, together with the description you created in step 1 (= metadata), ensures the longevity of your data and makes them visible to others. Sharing the data online does not necessarily mean that they are openly available to everyone! If you do not want to make the dataset open, upload your data to a repository anyway, e.g., under restricted access. As long as your metadata is openly available, this is fully compliant with FAIR and allows other users to find your data and contact you for access options.
  • See Chapter 5 for more details and options where to store your data.

Following these two steps is a great start! If you now want to make your data even more reusable, follow the remaining steps of the workflow or use the Evaluation tool to be pointed to the remaining chapters that can be useful for you.