When, at the end of 2014, the NOMAD Repository & Archive went online, it was the first data infrastructure in computational materials science that fulfilled what was later and independently defined by the acronym FAIR (Findable, Accessible, Interoperable, and Reusable). This definition and the request that scientific data should be FAIR was introduced in a very general scientific-data context by Wilkinson et al. in 2016 . As of today, the NOMAD Repository stores input and output files from more than 50 different atomistic (ab initio and molecular mechanics) codes and totals more than 13 million entries, uploaded by over 500 international authors from their local storage, or from other public databases. The NOMAD Archive stores the same information, but converted, normalized, and characterized by means of a metadata schema, the NOMAD Metainfo , which allows for the labeling of most of the raw data in a code-independent representation. One of the benefits of normalized data is that they are accessible in a format that makes them suitable for direct artificial-intelligence (AI) analysis. NOMAD also offers the AI toolkit , a JupyterHub-based platform for running notebooks on NOMAD servers, without the need for any registration or downloaded software. The data-science community has introduced several platforms for performing AI-based analysis of scientific data, typically by providing rich libraries for AI. General-purpose frameworks such as Binder and Google Colab , as well as materialsscience dedicated frameworks such as pyIron , AiidaLab , and MatBench are the most used by the community. In all these cases, a big effort is devoted to education via online and in-person tutorials. The main specificity of the NOMAD AI toolkit is its connection with the extensive NOMAD Archive. Moreover, together with the NOMAD Oasis , users can work with their private as well as community data within the same software platform and using the same API.