| Summary: | Sedimentary microfossil assemblage data can provide a crucial baseline of biodiversity prior to human influence, reveal species turnoverdynamics across time scales inaccessible using direct observations and are essential for quantitative palaeoclimatology.Many micropalaeontological studies rely on merging data from different sources. This merging requires that the data are findable,accessible, interoperable and reusable, i.e. comply with the FAIR data principles. As a community we are in the favourable position thatdata sharing through established repositories is common practice. However, challenges remain to make micropalaeontological data trulyfair: data sets need additional information (metadata) in order to streamline reusability. In addition, the biggest challenge to reusability is the semantically complex nature of species assemblage data. This is because of theexistence of different taxonomic schools and evolving taxonomic insights, which both render standardisation difficult. As a consequencereusing micropalaeontological data is cumbersome, even when they are findable, accessible and interoperable. Moreover, semanticcomplexity leads to confusion and archiving errors, further hampering data reusability. Thus, to make micropalaeontological data FAIRer,we, as a community, need data standards to increase the value of our data and to make our science reproducible. Within the framework of the German National Research Data Infrastructure (NFDI) we are developing tools and processing pipelines toharmonise taxonomic data. At the same time we are starting a community engagement process to collectively definemicropalaeontological data requirements. To this end we invite you to take part in a survey. As an illustration of why we need data standards, we here report on common problems associated with the standardisation of taxonomicdata identified in a large number of micropalaeontological datasets publicly available at PANGAEA. Our assessment focussed on planktonicforaminifera because of the relatively simple ...
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