Abstract

BackgroundThe "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people were involved in the data collection.New informationWithin 20 months, the participants accumulated 750,143 photo observations of 6,857 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 87% of all project data, i.e. 652,285 observations, are available under free licences (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities.

Highlights

  • Since 2008, iNaturalist has been crowdsourcing biodiversity observations made by citizen scientists, as well as their taxonomic identifications

  • With 50M observations accompanied by photo or audio evidence, the global iNaturalist dataset is one of the largest online collections of biodiversity data. It is partially represented in the GBIF, with the exclusion of observations which remain unidentified or have unconfirmed or missing licence information

  • The GBIF data usage counter shows that iNaturalist GBIF-mediated data gained 781 citations making it one of the most commonly-used datasets amongst the GBIF (Ueda 2020)

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Summary

Background

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people were involved in the data collection. Within 20 months, the participants accumulated 750,143 photo observations of 6,857 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 87% of all project data, i.e. 652,285 observations, are available under free licences (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

Introduction
Findings
Sampling methods
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