Loading data through SPARQL

100%

Loading data through SPARQL

✓ Published1🌍 Public

Last edited Oct 16, 2019

Created on Oct 4, 2019

This example demonstrates how to query a live SPARQL endpoint using d3.json, fetching up to 1000 records created in 1893 from the Dutch Network of Digital Heritage. The results are displayed as raw JSON text in a paragraph element. The code encodes a SPARQL query with encodeURIComponent and appends it to the endpoint URL, then parses the response with d3’s JSON loader. It relies on d3.v5 and the public API endpoint, which must remain active for the example to function.

AI-generated description

A simple example showing how to load data from SPARQL into d3.

Note: this example will only work as long as the endpoint used is still up and public.

MIT Licensed

Similar vizzes

Loading thumbnail…

Loading data through SPARQL

This example demonstrates a SPARQL query against the Network of Digital Heritage (NDE) API, displaying the results as raw JSON text in a paragraph element. The visualization shows counts of cultural heritage objects grouped by material type and African place names. The code uses D3 v5’s `d3.json()` helper to fetch data from the SPARQL endpoint, with the query wrapped in template literals and passed as a URL-encoded parameter. The results are rendered by directly setting the innerText of the paragraph, without any further visual encoding.

Jjoanpadolina

82% match

Loading thumbnail…

Transforming data using d3

This example demonstrates how to clean, group, and enrich SPARQL query results about ethnographic artefacts. It loads data from a Dutch heritage endpoint using `d3.json`, maps each row to extract plain values with `cleanData`, then applies `d3.nest()` to group artefacts by their parent country. For each country group, it calculates the total count of artefacts as a new `amount` property. The code uses d3 v5 APIs such as `d3.nest()` and `d3.json`, and logs the raw, cleaned, and transformed data to the console for inspection.

LLaurens

75% match

Loading thumbnail…

Transforming data using d3

This example demonstrates how to transform SPARQL query results about ethnographic artefacts by cleaning and nesting the data per country using d3.nest(), then calculating the number of records per country. It loads data from a live SPARQL endpoint via d3.json and applies a custom cleanData function to extract property values. The transformed data structure, with country keys and value counts, is logged to the console, showing the grouping and aggregation process using d3 v5.

RRoyCsuka

74% match

Loading thumbnail…

Transforming data using d3

This example demonstrates how to group a collection of ethnographic artefact records by date using the `d3.nest()` method. The visualization is generated from SPARQL query results, accessed via the Web API using `d3.json`, and the data is subsequently cleaned and transformed to calculate a count of records per entry. The final visualization is not rendered; instead, the process focuses on logging the transformed, nested data structure to the console, showcasing the data preparation stage before any visual encoding takes place. The code relies on the D3.js v5 library and its built-in nesting and data-loading utilities.

Kkimgarrard

69% match

Loading thumbnail…

Transforming data using d3

This example demonstrates how D3's `nest()` function and custom mapping can transform SPARQL query results about ethnographic artefacts into a hierarchical, country-based dataset with calculated counts. The visualization first loads data from a SPARQL endpoint using `d3.json`, cleans the raw bindings by extracting property values, then groups entries by country label and computes each country’s total number of artefacts. The code uses D3 v5’s `nest().key().entries()` API to perform the grouping, and logs the raw, cleaned, and transformed data to the console for inspection.

RRoyCsuka

68% match

Loading thumbnail…

Collection data filter and sort

This example demonstrates how to filter and sort collection data from the Dutch digital heritage network API. It uses a SPARQL query to retrieve object counts per geographic origin and object type, then processes the JSON result. The visualization focuses on ranking origins by quantity, showing the top ten objects per continent. Built with D3.js v5 and vanilla JavaScript, it fetches data from the NMVW dataset using the `loadApiData` function and displays the results in a text-based interface.

EEyob Westerink

67% match

Loading thumbnail…

Live API + d3 coding for fame

This example fetches live parking location data from the RDW open data API and processes it using custom JavaScript functions. It loads JSON from the endpoint `https://opendata.rdw.nl/resource/t5pc-eb34.json`, extracts values from columns such as `areaid` and `usageid`, and lists unique values or counts empty entries in the arrays. The page displays a large text label reading "Hello Js" and relies on a bundled script (`bundle.js`) with no D3 or other visualization library; it only demonstrates API fetching and array manipulation.

LLaurens

65% match

Loading thumbnail…

Nu het inladen van de nieuwe data

This visualization displays a world map with small circles indicating the geographic locations of museum objects, with each point colored according to its associated object function or type. The data is retrieved via a SPARQL query from the Network Digital Heritage (Netwerk Digitaal Erfgoed) API. Using D3 v5, the code employs `d3.geoOrthographic` and `d3.geoPath` to render the globe, together with `d3.zoom` for interactive panning and zooming. A `d3.scaleOrdinal` color scheme maps object categories to a legend.

BBryan Ray

64% match