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The structure is similar to Kingfisher Process, but a bit simpler. The main table is the projects table, which has one project per row in jsonb format in the data column. You can use the collection table to identify the collection you want to work with and then filter the projects table using the collection_id column.
you can find the data review results for each collection in collection_check , the coverage for each collection in field_counts (same format as field_counts in Kingfisher Summarize) and the coverage for each project in project_fields (same format as the new field_list columns in Kingfisher Summarize).
There is also a tabular copy of the OC4IDS schema in oc4ids_schema
`collection` - equivalent to the collection table in Kingfisher Process with one row per collection.
`projects` - equivalent to the join of the release and data tables in Kingfisher Process with one row per project.
`collection_check` - similar to release_check in Kingfisher Process, but with one row per collection.
`field_counts` - equivalent to field_counts in Kingfisher Views
`oc4ids_schema` - a flattened version of the OC4IDS schema, for use in coverage queries
The text was updated successfully, but these errors were encountered:
Some rough notes:
The structure is similar to Kingfisher Process, but a bit simpler. The main table is the
projects
table, which has one project per row in jsonb format in thedata
column. You can use thecollection
table to identify the collection you want to work with and then filter the projects table using thecollection_id
column.you can find the data review results for each collection in
collection_check
, the coverage for each collection infield_counts
(same format asfield_counts
in Kingfisher Summarize) and the coverage for each project inproject_fields
(same format as the newfield_list
columns in Kingfisher Summarize).There is also a tabular copy of the OC4IDS schema in
oc4ids_schema
The text was updated successfully, but these errors were encountered: