ecological-data-foundation

To clean Darwin Core occurrence records, this Skill ingests raw data from GBIF, iNaturalist, OBIS, and IUCN, applies coordinate QA, duplicate detection, and taxonomic harmonization, and outputs analysis-ready datasets with quality metadata.

Yes, you can prepare occurrence data for species distribution models by running the end-to-end cleaning workflow, which standardizes records and generates QA reports suitable for SDMs, occupancy analyses, and conservation prioritization workflows.

Yes, standardizing Darwin Core occurrence data requires Python and R-based tooling to execute data ingestion, quality assurance, and taxonomic harmonization for reproducible ecological workflows.

The best way to harmonize taxonomy across multiple ecological occurrence datasets is to ingest records from GBIF, iNaturalist, OBIS, and IUCN, then apply automated taxonomic harmonization to align species names into a standardized format.

Yes, the workflow supports coordinate QA and duplicate detection for biodiversity records, automatically cleaning and validating raw occurrence data to ensure high-quality inputs for downstream conservation and ecological analyses.

You can generate metadata reports after cleaning ecological occurrence data by running the end-to-end workflow, which writes both the cleaned dataset and a detailed quality assurance report to your output directory.