Collecting Information about UCC Churches

Data from Jesse Wills finding aid that needs to be extracted into individual components

This visualization extracts individual letters from Jesse Wills’ XML finding aid and plots them as nodes in a network graph using d3.forceSimulation. Each node represents a single correspondence, positioned by recipient and date, with color or grouping revealing clusters of frequent correspondents. The code parses the XML’s unittitle and unitdate elements to create structured records, then draws the graph with SVG circles and links. It relies on d3’s force layout for spatial arrangement and d3.xml for data loading.

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Exploring your Collection

This visualization shows a collection of TEI-encoded texts by querying their titles and full-text content, highlighting matches for terms like “vin” or co-occurring words like “mal” and “je” within the same sentence. It first demonstrates how to list all titles in the collection, then filters those titles using XQuery Full-Text expressions via the `contains text` operator. The code uses the BaseX or eXist-db XQuery 3.0 engine, leveraging the `fn:collection()` function to access documents and the TEI namespace for element traversal, with no external visualization libraries—the output is plain text.

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For Zach Johnson

This example transforms a raw XML extract of the Jesse Wills finding aid into structured archival units using XQuery 3.1. The code loads a source document from a GitHub gist, then uses a tumbling window to pair adjacent XML elements into `c02` records. It tokensizes `unittitle` text by semicolons and periods, classifies segments as folder containers or dates, and constructs `did` elements with normalized `unitdate` and `unittitle` values. The result is a cleaned, hierarchical data structure suitable for downstream visualization, though no rendering library is invoked here.

Practice XPath expressions for USC

This example demonstrates XPath expression practice through a visual interface, showing how distinct values and hierarchical navigation can be explored interactively. It uses the USC document structure to highlight element names and path syntax, including `self::element()` for text-bearing nodes and child-axis selectors like `/child::usc:uscDoc/child::usc:main`. The visualization is driven by a gist from CliffordAnderson, with the code relying on XQuery (implied via the `.xqy` file) to compute distinct names and parent-child relationships. The rendering approach is not explicitly shown, but the focus remains on illustrating XPath’s filtering and traversal capabilities against a sample XML document.

Gist bdb4994137a7b0f75652f01bb4c170d2

This visualization converts a tab-separated dataset of Stolpersteine memorial stones into an interactive GeoJSON FeatureCollection. It shows each victim’s name, address, deportation, murder, and death details as a point location on a map. The XQuery script parses the TSV file using `csv:parse` with a tab separator, then constructs a GeoJSON structure with point geometries. It uses `fetch:text` to load the remote data and `json:serialize` to output the final GeoJSON, which can then be rendered by a mapping library.

Wikidata for Theological Librarians

This example demonstrates how theological librarians can use Wikidata to explore and visualize interconnected scholarly data. It shows a SPARQL query interface that retrieves information about theologians, their works, and associated religious concepts, rendered as an interactive network graph. The visualization uses D3.js’s force-directed layout to position nodes and links, with hover tooltips revealing entity details. Data is sourced live from Wikidata’s public endpoint, while the code employs the `d3.forceSimulation` API for physics-based layout and custom SVG rendering for clarity.

Tricky Metadata Problem

This visualization demonstrates the geographic distribution of artists' books by filtering MARCXML records according to publication location codes, then organizing them into a simple table. It shows which US states and territories are represented in the source collection, with each row containing the record’s leader, normalized title, and three-letter location code. The XQuery script uses the `fn:doc()` function to fetch remote XML, the `marc` namespace to parse MARC fields, and `csv:serialize()` from the BaseX or eXist-db library to generate a CSV output. Titles are cleaned by stripping trailing slashes and normalizing whitespace via string functions.

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XQuery to clean up data from an Excel-generated webpage & generate an EAD document

This example shows how XQuery 3.1 can clean data scraped from an Excel-generated HTML table and use it to generate an EAD (Encoded Archival Description) finding aid. The `fetch:text()` and `html:parse` functions extract rows from a legacy webpage, normalize the text content, and repackage the fields into JSON maps. A second function transforms those entries into EAD `<c02>` components, inserting metadata like titles, abstracts, and thumbnail image links from the Lantern Slide collection at Vanderbilt. Finally, the script rebuilds a complete EAD document, injecting the new `<c02>` elements into the existing archival description hierarchy, preserving the original structure while enriching it with parsed data.