Steve Baskauf's SPARQL query for ORCIDs at Vanderbilt

100%

Steve Baskauf's SPARQL query for ORCIDs at Vanderbilt

✓ Published0🌍 Public

Last edited Feb 27, 2018

Created on Feb 27, 2018

This visualization identifies people who are both students and employees at Vanderbilt by querying ORCID data with SPARQL, then displays their names and works as a network. It shows the overlapping creators between the two groups before revealing individual contributors. The code uses a SPARQL query with `MINUS` clauses to filter statuses and `ORDER BY` family name, running against a Vanderbilt ORCID graph. The rendering relies on standard SPARQL result processing, with no external visualization library specified; the output is a simple tabular or list-based view of the matched names and works.

AI-generated description

Similar vizzes

Loading thumbnail…

Scholars at Vanderbilt with ORCIDs

This visualization maps the scholarly network of Vanderbilt University researchers who have ORCID identifiers, showing connections between 100 scholars based on their shared institutional affiliation. The graph uses d3.forceSimulation to position nodes and links, with each scholar represented as a point and their ORCID iD displayed as labels. Data is pulled live from Wikidata via a SPARQL query that filters for entries with both the Vanderbilt affiliation property and an ORCID identifier. The rendering approach emphasizes the clustering of researchers into a single academic community, with node positions dynamically updated by the simulation’s force-directed layout.

CCliffordAnderson

79% match

Loading thumbnail…

Clifford Anderson's Scholarly Graph from Atlas

This visualization displays Clifford Anderson’s scholarly network as a directed graph, mapping author relationships and their multifaceted roles (editor, translator, introducer) through distinct node labels and edges. The graph, sourced from a GraphML file, uses the `d3.forceSimulation` API to position nodes via a physics-based layout, while edge and node elements are rendered through SVG paths and circles, with labels encoded in the XML data. The visualization reveals collaboration clusters and role-based connections among authors like Kuyper and Harinck, emphasizing the structured, relational nature of academic publishing.

CCliffordAnderson

58% match

Loading thumbnail…

Reference https://nbremer.github.io/ORCA/top-contributor-network/

This gallery example analyzes the PDF.js contributor ecosystem through a network graph and interactive charts. A force-directed network using `d3.forceSimulation` and `d3.forceLink` shows collaborations between authors and repositories, with nodes color-coded by role and clickable to highlight connected paths. Additional bar charts, including stacked charts for missing children data by gender and grouped bars for police complaints, use `d3.rollups`, `d3.stack`, and zoomable SVG with `d3.zoom`. All visualizations are rendered in React with SVG and WebGL, featuring tooltips, legends, and animated transitions. Source data comes from CSV files loaded via `d3.csv`.

NNita

57% match

Loading thumbnail…

Gist a55b95a43d04067df4c11d576bed3c42

This example demonstrates a graph database model for scholarly communications in theology, showing relationships between academic institutions, faculty, and the American Theological Library Association (ATLA). Built with Neo4j's Cypher query language, the visualization renders the schema and sample data as an interactive network graph. Using Neo4j's built-in graph visualization features, the code displays nodes for institutions like Princeton Theological Seminary and Drew University, connected by membership edges to the ATLA hub, while also including a faculty node for Clifford B. Anderson.

CCliffordAnderson

57% match

Loading thumbnail…

Web scraping for Summer Teams

This example shows how to extract participant names from Vanderbilt University Library’s summer project listings and reformat them into CSV rows of project–participant pairs. It scrapes the HTML page using `fetch:text` and `html:parse`, then navigates the DOM with XPath to select project headings and member text. The code uses `fn:tokenize` and `fn:replace` to split and normalize names into a “last,first” format, with special handling for suffixes like Jr. or III. Output is generated via the XQuery `output:method` and `output:csv` declarations, producing a comma-separated table.

CCliffordAnderson

57% match

Loading thumbnail…

Gist 8978952

This example visualizes a campus map of Vanderbilt University using a GeoJSON FeatureCollection of building points. It displays each location as a marker on a map, with pop-up images and titles for select structures like Kirkland Hall and Old Main. The visualization uses d3.forceSimulation to position the markers, while d3.geoPath renders the geographic features. Images are pulled from Wikimedia Commons, and the data originates from a gist authored by CliffordAnderson.

CCliffordAnderson

56% match

Loading thumbnail…

ORCID names

This example shows ORCID researcher names sorted alphabetically by family name, with each entry formatted as “family name, given name.” The code queries an ORCID XML collection using XQuery 3.1, referencing the ORCID person and personal-details namespaces to extract the name components. It iterates through each `person:name` element, orders the results by family name, and joins the family and given names with a comma and space. The output is a simple list of person elements, demonstrating how XQuery’s `fn:collection` and `fn:string-join` functions can transform structured author metadata into a readable ordered sequence.

CCliffordAnderson

56% match

Loading thumbnail…

SQL query from hell - original from our source

This example visualizes the results of a complex SQL query that retrieves school contact data, including a computed responsible person, formatted timestamps, and concatenated school names. The query processes relational data from multiple tables, using `IFNULL`, `IF`, and `DATE_FORMAT` for field transformations, and a correlated subquery to filter for the most recent follow-up contact. Rendering is not shown in the code, but the output appears structured for direct chart consumption, likely using a client-side library like D3 or a table-based UI. The visualization relies on the query’s ordered rows and grouped fields to display chronological contact activity.

HHoffmannP

56% match