modifique el script que hizo kimchy para su charla de París en el 2011

Edits to Rebecca Koeser's script to generate dataset from DH Q&A archive (preliminary)

This example shows a Python script that parses the Digital Humanities Q&A archive into a structured dataset, extracting questions, responses, authors, tags, dates, and best-answer flags from static HTML pages and linked RSS feeds. The script uses BeautifulSoup to locate page elements and pandas to organize post metadata into merged DataFrames, while the archive's source repository provides the raw HTML files. It also demonstrates a data-cleaning step that removes social-media widgets from post content before saving the final dataset.

ZZoeLeBlanc

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XQuery for Partitioning the Blue Mountain Project

This example demonstrates a pipeline for cleaning, partitioning, and exporting articles from the Blue Mountain Project’s TEI transcriptions into language-specific databases. It shows how XQuery 3.1 scripts transform raw periodical XML by removing facsimile and hyphenation artifacts, splitting documents into individual articles, and grouping them by `mainLang` attributes into new databases. The workflow then exports French-language texts as plain .txt files, and R code using the `tm` and `SnowballC` libraries performs text mining, including term-document matrices and stemming.

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Gist d57b4594607d7185a88e

This example demonstrates parent-child relationships in Elasticsearch by indexing stores, departments, and products as nested document types. It shows how to query hierarchical data using the `has_child` filter to find stores containing departments whose products match a specific trademark. The code uses Elasticsearch’s REST API with `curl` commands to define mappings, index sample data, and execute filtered queries. It illustrates the `_parent` field for linking document types and `_routing` to enforce shard placement, while the final search combines term queries with nested `has_child` filters to retrieve relevant parent documents.

CCBasis

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XQuery Blue Mountain Project - Part III

This example shows a pipeline for transforming a single TEI XML document from the Blue Mountain Project—a digitized 1920 issue of the French journal *Action*—into clean, article-level TEI files. The first script removes facsimile elements, end-hyphenation markers, and line breaks, replacing `<lb>` elements with spaces. A second script extracts metadata and article content into a fresh TEI template, while a third builds individual TEI files for each article. The code uses XQuery 3.1 with the `fn:doc` API for document access and node operations like `copy`, `modify`, and `replace node`.

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XQuery Samples for Blue Mountain Project

This example shows a sequence of XQuery scripts that analyze the language metadata of TEI-encoded journals from the Blue Mountain Project, a digital archive of avant-garde publications. The scripts query a collection called `bluemountain-transcriptions` using the `fn:collection` function, counting documents, extracting distinct languages from `tei:langUsage`, and identifying journals with multiple `tei:textLang/@mainLang` attributes. Later scripts partition documents by language, using `db:create` to store French texts in a new database, and verify the results by listing the URIs of the partitioned files. The code relies on the XQuery 3.1 standard and the eXist-db database API for collection access and database creation.

XQuery Full Text

This example demonstrates the evolution of text search in XQuery, from basic string matching to full-text querying with linguistic processing. It shows how `fn:contains` and `fn:matches` perform literal pattern matching, while the XQuery Full Text 3.1 features—`contains text`, `using stemming`, `ft:mark`, and the `score` clause—enable case-insensitive, stem-aware, ranked search results. The code queries a collection of TEI-encoded texts (`vwwp_tei`) and returns matched paragraphs, with scoring wrapped in XML output and string formatting via `format-number`.

Bash / JQ for Cloudant Bulk Upload & Download

This example demonstrates a command-line workflow for bulk uploading and downloading JSON documents to and from a Cloudant database, using bash scripts that combine HTTPie and JQ. It shows how to aggregate multiple local JSON files into a single `_bulk_docs` payload via `jq -s`, upload that payload with an HTTP POST request, and retrieve data from a Cloudant view—transforming the response into either a plain document array or a GeoJSON FeatureCollection. The scripts also convert view output back into a file, enabling round-trip data migration and export without a custom application.

Gist 9ea54f64f11961c494767a8384892e83

This example demonstrates how to debug a geocoding failure by comparing two CSV files sent to the French government's geocoding API (api-adresse.data.gouv.fr). It shows that renaming the first column from `geocoder__line` to `geocoder__line_number` prevents rows from being silently dropped during the batch geocoding request. The shell script (`debug-geocoder-related-issue.sh`) uses `curl` to POST both files to the API, then displays the first eight geocoded results. The output files include latitude, longitude, and match scores, revealing how column naming affects API behavior.

TThomasG77