Abstract With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It compounds the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based targets for the choices made in the energy transition. The FAIR data guiding principles are widely acknowledged as a way forward, but their operationalization is yet to be agreed upon within different research domains. We comprehensively test FAIR data practices in the low carbon energy research domain. 80 databases representative for data needed to support the low carbon energy transition are screened. Automated and manual tests are used to document the state-of-the art and provide insights on bottlenecks from the human and machine perspectives. We propose action items for overcoming the problem with FAIR energy data and suggest how to prioritize activities.