{$doc/RecordTitle/text()}

XQuery Conversion for MDID

This example demonstrates an XQuery conversion that transforms a collection of XML records into a structured CSV file, preserving field mappings for VRA and VU metadata. The code iterates over all records in a collection, extracts specific child elements and attributes, and constructs new `<record>` elements with standardized tags. It then wraps the records in a `<csv>` root element and uses the `csv:serialize` function with a map specifying `'header': true()` to generate a CSV with column headers. Finally, it writes the output to a file named "vrc.csv". The process relies on XQuery functions like `fn:collection()`, `fn:data()`, and `file:write()`, along with the `csv` module’s serialization API.

66% match

{$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.

65% match

<title>{$title}

This example shows a data transformation that converts TEI-encoded metadata into MODS format, preserving bibliographic details such as title, author, language, publication origin, and abstract. The XQuery script, running on the eXist-db platform, uses the `fn:doc` function to read an XML file and XPath expressions to extract specific elements like `tei:titleStmt` and `tei:publicationStmt`. It then constructs a MODS record by mapping these values into the appropriate MODS elements, such as `<titleInfo>` and `<originInfo>`. The code relies on the standard XQuery 3.0 language features, with no external libraries or visualization APIs involved.

65% match

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.

{$articleTitle}

This example identifies journal articles from the BPP Quarterly collection whose full text mentions “jury” without also mentioning “law,” using the MarkLogic XQuery full-text search with stemming via `ftand ftnot`. It outputs matching article titles, journal names, and the boolean result as CSV, using the `output:csv` declaration to format the query results. The visualization highlights the distribution of such jury-related articles across journals, relying on XQuery’s text search and the `fn:collection` function to access the source documents. The code does not specify a rendering library, focusing instead on the data extraction and serialization step.

Some books

This example visualizes a collection of 20 books from a CSV file, grouped by binding type into hardcover and paperback categories. It shows the distribution of books across these two formats, with each group’s count displayed as an attribute. The code uses XQuery 3.1 with `fetch:text` and `csv:parse` to load and parse the data from a Gist URL, then groups records by the `Binding` field and orders them alphabetically. The rendering approach is plain text output, with no graphical libraries, relying solely on XQuery’s element construction to list the grouped records.

CVS to JSONL example

This example shows a practical data-transformation workflow that converts a CSV file into JSONL format, producing one JSON object per line. It fetches a remote CSV file from a GitHub gist using the `fetch:text` function, parses it into records with `csv:parse`, and iterates through each record to build key-value pairs. The code distinguishes between integer and string values using `castable as xs:integer`, then constructs JSON maps with `xml-to-json` using XQuery 3.1. The output is unindented JSONL, suitable for streaming or line-oriented processing.

Parse CSV with Day of DH Quotations as XML

This example shows how to randomly select and display a single quotation from a CSV file of "Day of DH" responses, rendering it as a styled HTML page. It first parses the remote CSV with the `csv:parse` function and `fetch:text`, then uses `random:integer` to pick a random record. The code generates a Bootstrap-based webpage that displays the quote, author, and date. The XQuery scripts include a plain parsing version, a random-selection version, and two HTML-rendering versions, one using the RESTXQ module for HTTP GET requests.