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 adds to the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based evidence for the choices made in the energy transition. We comprehensively test FAIR data practices in the energy domain with the help of automated and manual tests. We 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 and open energy data and suggest how to prioritize activities.