Occurrence of the amphibians in the Volga, Don River basins and adjacent territories (Russia): research in 1996-2020

© 2020. Ruchin A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. All rights reserved. Background Knowledge about the distribution of living organisms on Earth is very important for many areas of biological science and understanding of the surrounding world. However, much of the existing distributional data are scattered throughout a multitude of sources, such as taxonomic publications, checklists and natural history collections and often, bringing them together is difficult. A very successful attempt to solve this problem is the GBIF project, which allows a huge number of researchers to publish data in one place in a single standard. Our dataset represents a significant addition to the occurrences of amphibians in the Volga, Don riverine basins and adjacent territories. The dataset contains up-to-date information on amphibian occurrences in the Volga river basin and adjacent territories, located for the most part on the Russian plain of European Russia. The dataset is based on our own studies that were conducted in the years 1996-2020. The dataset consists of 5,030 incident records, all linked to geographical coordinates. A total of 13 amphibian species belonging to nine genera and six families have been registered within the studied territory, although the distribution of amphibian species in this region of Russia has not yet been fully studied. This is especially relevant with the spread of cryptic species that can only be identified using molecular genetic research methods. 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