Consecutive IDs in DIMLI export

100%

Consecutive IDs in DIMLI export

✓ Published0🌍 Public

Last edited Apr 8, 2019

Created on Apr 8, 2019

The visualization shows the distribution of consecutive ID ranges extracted from a DIMLI database export, revealing gaps and clusters in the numeric sequence. It uses XQuery 3.1 with a tumbling window to group contiguous IDs, relying on `fn:base-uri` and `fn:replace` to parse start and end values from filenames. The result lists each contiguous block as an `<ids>` element, ordered by starting ID. This approach highlights the underlying data structure without plotting, focusing instead on logical grouping of identifier sequences.

AI-generated description

Similar vizzes

Loading thumbnail…

XQuery for Complex Migration

This example demonstrates XQuery scripts for cleaning and restructuring VRA Core XML metadata from the DIMLI collection, focusing on consolidating duplicate work records and image records by refid. The code identifies duplicate work entries, merges their associated images while removing redundant id attributes, and normalizes image records. It also runs diagnostic queries to count built works, locations without refids, and image records (targeting 40,420 image records), while filtering location names that reference museums, libraries, or geographic terms. The visualization highlights data-quality issues in cultural-heritage metadata and the programmatic transformations needed to reconcile legacy cataloging inconsistencies.

CCliffordAnderson

54% match

Loading thumbnail…

idempotence

This animated scatter plot cycles through the Iris dataset's four numeric columns, re-mapping the x-axis every two seconds to reveal different pairwise relationships. Using D3.js with the `scaleLinear` and `extent` APIs for axes, `scaleOrdinal` for species-based symbols, and `symbol()` to generate SVG paths, the visualization updates smoothly via `setInterval`. The `scatterPlot` higher-order function manages reusable chart components, while the CSS styles text with the Share Tech font.

AAnshul Rawat

53% match

Loading thumbnail…

Reusable scatter plot using idis dataset with different symbols

This reusable scatter plot visualizes the Iris dataset, mapping petal width to the x-axis and sepal length to the y-axis, with each species represented by a distinct D3 symbol (circle, diamond, cross, etc.). The visualization employs a modular scatterPlot function that leverages d3.scaleLinear, d3.scaleOrdinal, d3.axisLeft, d3.axisBottom, and d3.symbol to generate the SVG marks and axes. Data is loaded asynchronously via d3.csv from a GitHub gist, and the plot resizes to fill the browser window.

Vvidhyanamini

53% match

Loading thumbnail…

Fibonacci sequence in XQuery

This example visualizes the Fibonacci sequence by generating and plotting the first fifteen numbers through XQuery code. The visualization shows the numeric progression of the sequence, with each value computed recursively via the `local:fibo-numbers` function, which takes two starting integers and a limit. The rendering approach is plain text or tabular output, as the code only returns the sequence values without graphical mapping. It uses standard XQuery 3.0 functions and user-defined functions, with no external libraries or APIs, focusing purely on functional sequence generation. The data source is the code’s own hardcoded initial parameters, 0 and 1, and the result is a simple list of integers.

CCliffordAnderson

52% match

Loading thumbnail…

Sequences sunburst

This sunburst diagram summarizes sequences of ICT ticket categories, showing the hierarchical flow of events from an initial category through subsequent subcategories to a final outcome. The visualization uses a D3.js partition layout and arc rendering to display the data, with a breadcrumb trail that emphasizes the selected path and fades non-ancestor segments. Percentages are displayed explicitly in the center to clarify the relative sizes of each sequence, despite the radial distortion. The code imports visit-sequences.csv and builds the hierarchy using `d3.csv.parseRows` and `d3.layout.partition`.

MMisunoKitara

52% match

Loading thumbnail…

incls 8 shivani Fork of Vanilla D3 Vibecoding Template

This scatter plot visualizes Fisher’s Iris dataset, comparing sepal length against sepal width with species distinguished by color. The D3 v7 code uses `d3.scaleLinear`, `d3.scaleOrdinal`, `d3.extent`, `d3.axisBottom`, and `d3.axisLeft` to render an SVG chart, loading data via `d3.csv`. Interactive legend hover highlighting fades non-selected species, while a separate world map using `d3.geoNaturalEarth1`, `d3.geoPath`, and TopoJSON is also included in the files.

Bbodushivani2601@gmail.com

52% match

Loading thumbnail…

Untitled

This visualization shows the cumulative growth of a dataset from 1949 to 2019, tracking a running total that increases from 5 to nearly 15,000 over seven decades. The data is loaded from a CSV file using D3’s `csv()` function, though the rendering logic is incomplete in the provided `index.js`. The example demonstrates how to import and parse tabular data with D3 v3, including accessing SVG dimensions via `.attr()` and preparing the data for subsequent plotting. The chart’s design and marks are not yet implemented in the visible code.

Eelaine-xiao

51% match

Loading thumbnail…

Fork of Fork of ILINet Flu Surveillance Data

A multi-line chart displays annual ILI flu case totals across U.S. regions, with each line representing a year from 2015 to 2019 and encoded using an unordered color scale for the ordered year field, an improper choice that hampers temporal comparisons. The visualization aggregates weekly ILITOTAL values per year, region, and week, then plots them against week number using Vega-Lite’s API with markLine. Data originates from CDC FluView Interactive, loaded via d3.csv, and rendered with SVG through vega-tooltip for interactivity.

SShan Gao

51% match