MHA Herbarium: Eastern European collections of vascular plants.

Abstract

BackgroundWorld herbaria with 387.5M specimens (Thiers 2019) are being rapidly digitised. At least 79.9M plant specimens (20.6%) are already databased throughout the globe in the standard form of GBIF-mediated data. The contribution of smaller herbaria has been steadily growing over the last few years due to cost reduction, usage of platforms and solutions developed by the leaders. A web-resource the Moscow Digital Herbarium (Seregin 2020b) was launched by the Lomonosov Moscow State University in October, 2016 for publication of specimens imaged and databased in the Moscow University Herbarium (MW). As of 31 December 2018, the web-portal included 968,031 images of 971,732 specimens digitised in MW. This dataset is available in GBIF (Seregin 2020). The global trend is largely the same in Russia, where a dozen herbaria started to scan their holdings after imaging of the nation’s second largest herbarium (Kislov et al. 2017, Kovtonyuk et al. 2019, Seregin 2020a). In 2019, we started to use Moscow Digital Herbarium as a web-repository for digitised herbarium specimens from some Russian collections, starting with the Herbarium of Tsitsin Main Botanical Gaden, Russian Academy of Sciences (MHA). Due to this, a single-university system became a multi-institutional consortium in April 2019 (Seregin 2020a). The dataset of the Moscow collections and partly of the Eastern European collections of the MHA Herbarium is now available in GBIF (Seregin and Stepanova 2020).New informationMHA Herbarium imaged 64,008 specimens from Moscow Region and partly from other regions of Eastern Europe at 600 dpi and provided key metadata. These data are now fully available in the Moscow Digital Herbarium and GBIF. Complete georeferencing of the specimens from the City of Moscow was a key task in 2020. As of May 2020, 50,324 specimens, including 49,732 specimens from Russia, have been georeferenced (78.6%) and 39,448 specimens have fully-captured label transcriptions (61.6%). Based on these data, we give a detailed overview of the collections including spatial, temporal and taxonomic description of the dataset.