build_dwc_archive

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A file this tool reads, sent or fetched from the address given, is read as one dropped on our website: what the read finds (column headings, the shape of its rows, the terms they became, refusal codes) is kept so the reader learns, never the file or its values. Names and contacts are dropped or replaced by a code, and protected species' locations are made coarser. How do I publish a species file on GBIF? A Darwin Core archive (DwC-A) for GBIF, built from a species occurrence or checklist file (CSV, TSV, Excel, a zipped shapefile, ...) given as `file_url` (a public https address the door fetches) or `content_base64`, or from a list of names (`species`: a checklist). The converter is the one behind map_to_darwin_core: it maps every column, checks every value and writes occurrence.txt (or taxon.txt), meta.xml and eml.xml. The answer: `archive` (core, records, rows read and lost, terms, bytes, sha256, `publishable` with what GBIF still needs); the zip itself, as an embedded resource when it is at most 32 KB (a larger one comes whole, as base64, from POST /api/v1/dwc/archive with the same arguments); `could_not_read` = each thing the converter would not guess, coded (a column no term carries, a row with no date or position, a 0,0 coordinate, a name the naming authority does not match, repeated rows), with what it costs; and `notices` (records of species a conservation register lists, at the precision filed). GBIF indexes an archive only with a title, an abstract, a licence and a creator and a contact (given name, surname, organisation, email): give them in `metadata`, nothing is filled in. `chapman_category` (1-4: the data holder's own sensitivity category under Chapman 2020) coarsens the coordinates of listed species and says so in the archive. `columns` names the column for a term where your user decided it (map_to_darwin_core's reading, corrected); the converter still checks the values and refuses what they contradict. Our counter keeps how the file came, the core and the counts. When your user confirmed `columns` and you send columns_confirmed_by_user: true, each confirmed column's words and its term (never a value) are also read as a label to teach the converter, and the answer's `learned` says so. Large files take a minute.