Georeferenced checklist and occurrence dataset of slime moulds (Eumycetozoa) across Central and Eastern Europe emphasising forest ecosystems

A continental-scale, georeferenced checklist of slime moulds (Eumycetozoa) for Central and Eastern Europe, supplemented with standardised environmental covariates and with a particular emphasis on forest ecosystems, has not previously been available. The absence of a harmonised corpus has constrained statistically supported tests of habitat- and substrate-related patterns and limited objective gap-mapping, particularly within forest ecosystems, where microclimatic buffering, dead-wood continuity and stand history are expected to be decisive; it has also hindered rigorous evaluation of slime moulds’ role as bioindicators of forest habitat types, substrate associations and gradients in anthropogenic pressure (naturalness).Literature discovery spanned multidisciplinary and domain-specific platforms; inclusion required a determinable taxon, a locality at least to country level and a year. Records were de-duplicated conservatively; names were harmonised to a single authority (Eumycetozoa.com) with GBIF Species backbone as a fallback and higher taxonomy was filled consistently. The resource comprises presence-only occurrences, a taxonomically standardised checklist and a reference set; the curated bibliography comprises 528 bibliographic entries. Coverage spans Austria, Belarus, Czechia, Estonia, Germany, Hungary, Latvia, Liechtenstein, Lithuania, Moldova, Poland, Russia (European part), Slovakia, Slovenia, Switzerland and Ukraine. Event dates range from 1857 to 2025-08-01 and support ranges and mixed precision. Environmental content includes elevation, consolidated forest class, substrate category, habitat pressure, microhabitat, pH, air temperature, annual precipitation and stand age; controlled vocabularies comprise eight consolidated forest classes, ten substrate categories and seven habitat-pressure classes. The dataset is released under CC-BY-4.0 (Creative Commons Attribution 4.0 International), employs reproducible DwC mapping and stable identifier versioning and is suited to ecological and biogeographic analyses, including forest-focused modelling and gap analyses.