docs(data/posters): add README for poster data

Document file containing poster data as well
as OpenRefine data enrichment and data export
process (and obstacles).

data/posters/README.md

@@ -0,0 +1,97 @@
+# Thomas Bernhard posters base data
+
+[FTB_posters_initial_catalogue_refined.json](FTB_posters_initial_catalogue_refined.json) is a JSON-formatted export of an OpenRefine project used to clean up and enrich a catalogue of Thomas Bernhard-related posters ("Theaterplakate") in the possession of the [Thomas Bernhard Research Centre](https://www.oeaw.ac.at/acdh/research/literary-textual-studies/research/authors-editions/ftb-thomas-bernhard-research-centre).
+
+
+## Data set
+
+The poster data set includes information on the physical objects themselves, the events they were created for/meant to promote – primarily theatre productions and/or performances – as well as people and groups involved in those events.
+
+The cataloguing was originally done by hand; [OpenRefine](https://openrefine.org/) was used to add onto the raw data for `Work`, `Person`, `Group` entities via the [GND](https://www.dnb.de/EN/Professionell/Standardisierung/GND/gnd_node.html#doc147904bodyText5).
+
+
+## OpenRefine JSON export
+
+Not all export options provided by OpenRefine will export 100% of a project's data. E.g. CSV files don't include any linked data (metadata like unique identifiers added by external data endpoints). Excel or JSON files do.
+
+JSON files are generated with the "Templating" option. OpenRefine's default row template can be edited to name keys more sensibly (which otherwise mirror a project's column names) or to extract aforementioned metadata of _refined_ objects.
+
+
+### Row template
+
+For reproducability, the following is how OpenRefine's row template was customised:
+
+
+```python
+{
+  "signature": {{jsonize(cells["Signatur"].value)}},
+  "storage_location": {{jsonize(cells["Archivierung"].value)}},
+  "status": {{jsonize(cells["Status"].value)}},
+  "year": {{jsonize(cells["Jahr"].value)}},
+  "title": {{jsonize(cells["Titel"].value)}},
+  "work": {
+     "value": {{jsonize(cells["Werkbezug"].value)}},
+     "match": {{jsonize(cells["Werkbezug"].recon.match)}},
+     "candidates": {{jsonize(cells["Werkbezug"].recon.candidates)}}
+   },
+  "notes": {{jsonize(cells["Anmerkungen"].value)}},
+  "event_type": {{jsonize(cells["Veranstaltungstyp"].value)}},
+  "director": {
+     "value": {{jsonize(cells["Regie"].value)}},
+     "match": {{jsonize(cells["Regie"].recon.match)}},
+     "candidates": {{jsonize(cells["Regie"].recon.candidates)}}
+   },
+  "participants": [
+    {
+      "value": {{jsonize(cells["Beteiligte Person 1"].value)}},
+      "match": {{jsonize(cells["Beteiligte Person 1"].recon.match)}},
+      "candidates": {{jsonize(cells["Beteiligte Person 1"].recon.candidates)}}
+    },
+    {
+      "value": {{jsonize(cells["Beteiligte Person 2"].value)}},
+      "match": {{jsonize(cells["Beteiligte Person 2"].recon.match)}},
+      "candidates": {{jsonize(cells["Beteiligte Person 2"].recon.candidates)}}
+    },
+    {
+      "value": {{jsonize(cells["Beteiligte Person 3"].value)}},
+      "match": {{jsonize(cells["Beteiligte Person 3"].recon.match)}},
+      "candidates": {{jsonize(cells["Beteiligte Person 3"].recon.candidates)}}
+    }
+   ],
+  "group": {
+     "value": {{jsonize(cells["Institution"].value)}},
+     "match": {{jsonize(cells["Institution"].recon.match)}},
+     "candidates": {{jsonize(cells["Institution"].recon.candidates)}}
+   },
+  "country": {{jsonize(cells["Land"].value)}},
+  "start_date_written": {{jsonize(cells["Datum Anfang"].value)}},
+  "end_date_written": {{jsonize(cells["Datum Ende"].value)}},
+  "measurements": {{jsonize(cells["Maße"].value)}},
+  "quantity": {{jsonize(cells["Anzahl"].value)}}
+}
+```
+
+#### Variables reference
+
+Template variable/key combinations and the kinds of data they return:
+
+| **Variable/key**  | **JSON data type**  | **Data returned**   |
+|:--  |:--  |:--  |
+| `cells["COLUMN_NAME"].value`  | string or `null` | visible (text) value of a cell  |
+| `cells["COLUMN_NAME"].recon.match`  | object or `null`  | an object enriched with entity data from an external data source which was confirmed to be a match in OpenRefine  |
+| `cells["COLUMN_NAME"].recon.candidates`   | array of objects or `null`  | an array of objects enriched with entity data from an external data source which are potential matches; the object with the highest `score` is identical to what's returned for `recon.match` (where applicable)   |
+
+#### Example object
+
+Format of data returned for `recon` matches and candidates:
+
+```json
+{
+  "id": "4221007-0",
+  "name": "Heldenplatz",
+  "types": ["AuthorityResource","Work"],
+  "score": 74.80832
+}
+```
+
+`id` is the identifier for the entity at the external data source, `name` is its human-readable name, `types` includes information on what kind of entity it is, and `score` is an indicator for how well the entity is thought to match the original data.