a simple parser for simple eaf files

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.

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Gist 614119

This example processes interlinear linguistic annotation from standard input, splitting each line into words and glosses to build a structured data array. It uses Node.js `process.openStdin()` and `stdin.on('data')` to read chunks, with a `trim` function to clean whitespace and `extractLines` to split text by newlines. The code logs each line to the console, demonstrating a simple text-parsing workflow for linguistic data.

Aamundo

{$title || " - "}

This example displays a list of EAD (Encoded Archival Description) titles sorted alphabetically from a collection of finalized XML files, rendered as plain paragraph elements in an XHTML page. It uses XQuery 3.1 with the `fn:collection` function to gather titles from `titleproper[@type="filing"]` nodes, and a local template function `local:template` constructs the HTML output, setting a dynamic heading level and tab title. The code demonstrates a server-side, XML-to-HTML transformation pipeline without client-side JavaScript libraries or interactive visualization components.

Gist 302c3f0062de516f2d53

This example demonstrates filtering Atom feed entries by category using a StAX parser. It reads an XML file and extracts the text content of `<title>` and `<category>` elements, printing only titles whose category contains “HTML.” The code relies on the `javax.xml.stream` API, specifically `XMLInputFactory`, `XMLEventReader`, and `XMLStreamConstants`, to iterate through events. It handles `START_ELEMENT` cases to capture element text via `parser.getElementText()`, while skipping other event types. The data source is a local file path (`webtec.atom.xml`), and error handling uses a try-catch block that outputs any exception message.

Aamirothman

Documentation for Introduction to EAD Workshop at Vanderbilt University

This example documents a two-day introductory workshop on Encoded Archival Description (EAD), showing the training schedule, linked reference materials, and a hands-on XML markup exercise using a postcard from the Library of Congress. It demonstrates the use of XML schema validation, including a Relax NG compact schema (`postcard.rnc`) and a sample EAD template with namespace declarations. The code highlights specific APIs and standards, such as the `xsd:date` datatype, `xml-model` processing instruction, and the EAD XML schema (`ead.xsd`). The data originates from the workshop’s GitHub gist, featuring raw text files and images.

EAD template using MSS.102

An EAD (Encoded Archival Description) template renders a collection’s metadata from an XML file, displaying the finding aid’s title, publication statement, scope notes, and series listing. The visualization structures the archival hierarchy using the EAD XML schema, with fields for repository, unit ID, extent, and dates. The code relies on standard XML parsing to extract elements such as `<eadheader>`, `<archdesc>`, and `<scopecontent>`, presenting the collection’s linear feet and box count alongside its inclusive date range. The interface highlights the template’s reusable structure for archival description.

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Get finding aid

This example demonstrates how to fetch and transform an XML finding aid from a GitHub repository, isolating a specific portion of its hierarchical structure. The code defines a function that rebuilds the EAD document by extracting the header, archdesc attributes, and selected child elements while discarding others. It uses the XQuery language with a custom function `local:build-ead` to restructure the XML. The data source is a remote finding aid document loaded via `fn:doc` from a raw GitHub URL. The transformation explicitly copies the `c01` element and its preceding siblings, omitting the `c02` children, thereby illustrating selective XML reshaping for archival metadata.

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Parse Text Node as XML

This example demonstrates the transformation of CSV data containing organism records into structured XML, specifically parsing a text column of notes as embedded XML. It shows two versions of the process: the first uses a verbose `fn:parse-xml` call wrapped in a `<notes>` element, while the second simplifies the syntax with an `element notes` constructor. Both versions fetch remote CSV data via the `http:send-request` API, parse it with `csv:parse`, and iterate through records to repackage fields, replacing the raw notes text with parsed XML content. The data source is the Bioimages repository’s `organisms-small.csv` file.