Background: Knowledge about the distribution of organisms on Earth is important backbone of biological sciences and especially for deeper understanding of biogeography. However, much of the existing distributional data are scattered throughout a multitude of sources (including in different languages), such as taxonomic publications, checklists and natural history collections and often, bringing them together is difficult. Development of the digital storage facilities may prevent loss of important data (Ruchin et al. 2020). Project GBIF is a good example of a successful data storage facility, which allows investigators to publish biodiversity data in one safe place in one uniform format. Our dataset describes the degree of the investigation of the fish fauna of the inland water of the Murmansk Region. Murmansk Region is a Euro-Arctic Region with a heterogeneous landscape, which determines diversity of the habitats for the fish occurrence. Our dataset contains valid information about distribution of the fish species. This dataset was built upon information obtained by the members of a Laboratory of the aquatic ecosystems of the Institute of North Industrial Ecology Problems of Kola Science Center of the Russian Academy of Science (INEP KSC RAS). The dataset includes 18,509 records about 16 fish species from 14 genera (eight families) collected from 1972 to 2021. A total of 67 water bodies from 15 different basins (rivers from basins of the White and Barents Seas) was screened in order to characterise ichthyocenoses. The main purpose of publishing a database is to make our data available in the global biodiversity system to a wide range of users. The data can be used by researchers, as well as helping the authorities to manage their territory more efficiently.
New information: All occurrences are published in GBIF for the first time. We would like to make this data available to everyone by adding it in the global biodiversity database (GBIF).
Keywords: Murmansk Region; database; fishes; inland water.
Elena Zubova, Nikolay Kashulin, Petr Terentyev, Alexey Melekhin, Roman Konstantinovich Fedorov, Sergei Shalygin.