Ural spider occurrences extracted from the literature

The dataset contains spider occurrences extracted from the scientific literature Attention! As far as these were published in print, we strongly recommend to cite the original work along with this GBIF dataset! Data were digitized within the Faunistics International project for the occurrence data extraction from the scientific literature: faunistic, taxonomical, ecological and other kinds. Full text is available here. Data are available here as well. Purpose Faunistics International aims to level up the classical background of ecology and biogeography. Background version 2.0 leads the literature data according to FAIR principles, so everyone can easily find and reuse it many times. Methodology Study extent The study is currently focused on the spiders of the Ural region and covers all the literature data concerning these. Sampling There were no samplings. The dataset is based on the literature data. Check its full text, please. Quality control All the records come from the literature data. Some articles don’t have properly described occurrences but were transformed into datasets in order to be found by search matching a species name. See more below about the literature gathering, data extraction, cleaning & uploading, and metadata assembling (section Method step description). In case of mistakes/mistypes finding, please, write to the [e-mail] or other contacts provided. Method steps Step 1: Literature gathering. An exhaustive arachnological bibliography was assembled by K.G. Mikhailov. The literature database, search, and file-sharing engine was developed by A.N. Sozontov over the bibliography, resulting in the Arachnolibrary resource. It has 5,345 references and over 2,800 full texts uploaded in total, as of 2025-08-01. Step 2: Data extraction by volunteers. Data extraction is carried out by the core team along with volunteers involved in the Faunistics International Citizen Science project, see more at the website and describing articles. All volunteers’ records were cross-verified with further merging to consensus by experts. Step 3: Data & metadata filling, cleaning & uploading. Each article digitized becomes a separated GBIF dataset. The database produces Darwin Core occurrence tables, which are being checked and cleaned up by the project team. Server-side utility gets local variables, constant for each article, global variables, constant for the whole digitization project, metadata meta.xml & eml.xml templates, generates metadata and occurrence files, and combines them into Darwin Core archive. Dataset name and citation follow the pattern: Faunistics International, %Authors%. (%year%). Spider occurrences extracted from %title%.