[praeparātiō ex automatīs] MVP of idea of automation to pre-process external data to LSF internal file format

Related issues, but not equal:
- MVP of read access to Wikidata #3
- Automate SPARQL query generation to Wikidata by items with P #40
Related concepts
Projects with scrappers in a very structured way (many external referential data)
- https://github.com/OCHA-DAP?q=scraper
- https://github.com/datasets
- Beyond data, most also contain the scripts to process such data. Some may use this https://github.com/datahq/dataflows

This point is an minimal viable product of a one or more "crawlers" or "scripts" or "conversors" that transform external dictionaries (aka the ones we would label origo_per_automata, origin trough automation, vs origo_per_amanuenses, origin trough manuenses, the way we mostly optimized now) into the working format.

Focuses

The data we're interested are already referential data, which is smaller subset of what is shared
- Is more important have less, but actively updated with primary source and very high quality than do data hoarding and ignore the important ones.
We're really interested in referential data we can document how to use
- This also means we may intentionally name the data fields in ways that make easier to document; even if this means automatically generate user documentation
- The entire idea must allow ways to receive collaborators help to translate documentation (not need be on sort term, but at least be planned from very start)
Referential data can be public; but most information managers will deal with sensitive data
- The best potential end user, aka the information managers, are likely to ingest all the data as soon as new emergency happens.
- Even if information managers have good data proeficiency, or know some programming language, they're likely to be overloaded; so we need to make as easier as possible to mitigate human error (on the reference tables)
We're interested on reference data useful to disaster preparedness
- This makes even more important the idea of optimize for faster releases, user documentation, care about make less likely users would leak sensitive data, and to make data schema interoperable at international level

External examples of type of reference data

International Federation of Red Cross and Red Crescent Societies | IFRC Data initiatives

https://data.ifrc.org/

Common Operational datasets (overview)

2007 reference (somewhat outdated)

From https://interagencystandingcommittee.org/system/files/legacy_files/Country%20Level%20OCHA%20and%20HIC%20Minimum%20Common%20Operational%20Datasets%20v1.1.pdf

Table One :Minimum Common Operational Datasets

Category	Datalayer	Recommendedscaleof sourcematerial
Political/Administrativeboundaries	CountryboundariesAdmin level1Adminlevel2Adminlevel3Adminlevel4	1:250K
Populated places (with attributes including:latitude/longitude,alternativenames,populationfigures,classification)	Settlements
1:100K–1:250K
Transportationnetwork	RoadsRailways	1:250K
Transportationinfrastructure	Airports/HelipadsSeaports	1:250K
Hydrology	RiversLakes	1:250K
Citymaps	Scannedcitymaps	1:10K

Table Two: Optional Datasets

Category	Datalayer	Recommendedscaleofsourcematerial
Marine	Coastlines	1:250K
Terrain	Elevation	1:250K
Nationalmapseries	Scannedtoposheets	1:50K-1:250K
Satelliteimagery	Landsat,ASTER,Ikonos, Quickbirdimagery	Various
Naturalhazards2	Various	Various
Thematic	Various	Various

Okay. Doing some dogfooding with previous step on #41.

999999999_268072.py is a script to preprocess IBGE (Brazil) related data (at least COD-ABs) from primary source at https://servicodados.ibge.gov.br/api/docs
999999999_10263485.py is a script to preprocess CNES codes (brazil) related to mostly healtcare sites.

Both cases are what would be considered referential data.

On the issues to convert files: not a problem at all

I think the file converters (and later do the pipeline with GitHub actions or equivalent) is quite feasible. This is not what taking more time (at least, considering everything already done with the HXLTM and Numerodinatio).

However, most of the time, it was testing from data already added to HDX. Not from scratch (which have far more data to potentially document)

The new challenge: the MAPPINGS to full blow linked data

The numbers of identifiers which already have an Wikidata P are quite limited (see https://www.wikidata.org/wiki/Wikidata:Database_reports/List_of_properties/all). The IBGE municipality code is an perfect example. From this post https://dadosabertos.social/t/lista-de-codigos-de-referencia-para-intercambio-de-dados/1138/2, we're starting to get more data, which would need to be mapped.

In sort: things that already have an Wikidata P code are much simpler to deal with. But we need to be smarter on how to make it viable. I'm not saying that is not possible. But at this point, the level of abstraction of the converters (which now means even RDF, but at same time it should work if is on relational databases) is such that not only this would allow convert the reference data to pretty much any format out there, but also make easier to do automated discovery of final datasets.

(For what already is referential data) moving the logic to non code file

The print screen is very early stage, but to generalize better, since is "easier" to do software to convert whatever source was to something in tabular format, then after the tabular format is done (think raw CSV headers) an strategy to explain what it means will take time.

Captura de tela de 2022-05-16 23-34-46

Since at some point this will take far more thinking than just create more crawlers, the idea of move the logic to an YAML configuration makes sense.

EticaAI / lexicographi-sine-finibus