The present database compiles and standardize fish occurrence datasets from federal agencies, research institutes, and conservation organizations, integrating data from sources such as the Global Biodiversity Information Facility, the Joint Danube Surveys, and the European Fish Index.  It contains 133,103 occurrence records across 116 fish species, representing 30 families and 17 orders, with a temporal range from 1856 to 2024, organized into 38 columns. In total, 506,290 entries were collected and subsequently subjected to quality checks and cleaning procedures. To facilitate data collation, formatting, and quality control, an R package, danubeoccurR , was developed, which streamlined the entire process. Additional R packages, hydrographr and specleanr, were incorporated into the workflow to aid in data manipulation and geospatial analysis.

The dataset provides essential data to address conservation challenges for fish in Europe and the Danube River Basin. It can be used to identify suitable habitats for native species in projects aimed at prioritizing habitat reconnection, taking into account estimated dam removal and fish pass construction costs, while also minimizing the spread of invasive species.

Geospatial Considerations: Species occurrences are georeferenced based on available locality information. Users should be aware that some records may have variable spatial precision, particularly historical occurrences. It is recommended to apply spatial filtering techniques suited to the intended analysis. For example, the coordinate uncertainty provided for records sourced from GBIF can help determine whether a record is suitable for a given analysis. Additionally, the function snap_points_on_map() in danubeoccurR allows users to manually adjust occurrence points for greater precision.

Taxonomic Standardization: Users are advised to cross-check species names with updated taxonomic databases if taxonomic revisions occur after the dataset's publication.

Data Quality and Potential Limitations: While efforts were made to standardize and clean the data, users should consider potential sources of bias, including sampling effort variations, taxonomic misidentifications, or incomplete historical records. Some records have been flagged as environmental outliers based on inconsistencies between species occurrence and expected environmental conditions. These flagged records should be reviewed carefully and may require further investigation or validation before inclusion in analyses.

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