Most of what scientists discovered about biodiversity and published, totaling a corpus of an estimated 500 million printed pages stacked up in our libraries and more recently, in digital format, is not known. It is an amount of information that can not be processed by humans, but even machines can’t cope with it because the publications are either not scanned, or are behind paywalls, or in formats that machines can’t read at scale. It is a tragedy that in this digital age we can’t make use of this data. But it doesn’t need to be like this. Scientific publications are structured, they use standard means to express the results. Arguments cite previous arguments building a network of knowledge. If represented digitally, this knowledge could be represented as a knowledge graph and analysed. The data in publications can be made FAIR: Figures, blocks of texts such as the descriptions of species, or material citations; named entities such as person or taxonomic names can be annotated and linked to reference vocabularies. They can be cited and reused irrespective whether a publication is behind closed doors. In an exemplary way this lecture will show the collaboration between the Biodiversity Literature Repository and the Global Biodiversity Information Facility that has made available data about 80,000 species known in GBIF only because they have been liberated from the publications, thereby enabling the sharing of this scientific knowledge with anybody, anywhere for any purpose. Open FAIR data * represent knowledge about biodiversity, i.e.g what we know about a gene, specimen, species as well as assess a gene, specimen in its scientific knowledge, and allows exploring by machine * is basis for conservation, biodiversity research, society, CBD’s post 2020 Global Biodiversity Framework * depends on complex infrastructures, funding, collaborations producing FAIR data that * can be used anywhere, at any time by anyone by human or machine. * increased by accelerating switching to advanced semantically enhanced publishing * needs massive amounts of teaching how to use the results and create innovations *Access and Benefit Sharing (ABS): Open FAIR access as part of benefit sharing