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  3. Visualization Tools, Libraries, Data Sets, and Further Resources
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Visualization Tools, Libraries, Data Sets, and Further Resources

  • Existing Tools
  • Programming Libraries
  • Data Sets
  • Methodology, Tips, and Tricks
  • Further Resources

Existing Tools for Visualization and Data Analysis

Visualization tools — data exploration and visualization in dashboards:

  • Tableau (Visualization environment, Dashboards)
  • Visualize Free (Online, Dashboards)
  • Glue (Python; multi-dimensional linked-data exploration)
  • InstantClue (Visualization design and data exploration environment)
  • Voyager 2 (Online, data exploration tool)
  • QlikView (Visualization environment, Dashboards)
  • Power BI (Visualization environment, Dashboards)
  • Keshif Online (Online, Dashboards)

Visualization tools — authoring of individual charts:

  • DataWrapper (Visualization environment, single chart)
  • RawGraphs (Visualization environment, single chart)
  • Make-a-vis (Visualization environment, single or multiple charts)
  • Lyra (Online, visualization design environment)

Visualization tools — graph and network data:

  • yFiles (+ yEd) (Graph drawing libraries and tools; several platforms)
  • Gephi (Network analysis and visualization environment)
  • Graphia (Network analysis and visualization environment)
  • Cytoscape (Environment for complex networks and multivariate data analysis; several platforms)
  • OriGraph (Online graph data wrangling tool)
  • kepler.gl (Environment for large-scale geospatial and network analysis; online)
  • Nomic Atlas (Environment for AI/ML-based analysis of text, image, and multidimensional data collections; online)

Visualization tools — other specific data types and representations:

  • VoyantTools (Environment for text analysis; online)
  • StoryMapJS (Authoring tool for map-based storytelling; online)
  • TimelineJS (Authoring tool for timeline-based storytelling; online)
  • Heatmapper (Environment for geographical map and heatmap visualization; online)
  • EdWordle (Authoring tool for word clouds; online)
  • Jigsaw (Visual analytic environment for document and multivariate data; several platforms, including WebJigsaw)
  • Inviwo (Scientific visualization environment; several platforms, including C++ and Python)

Data analysis and visualization tools:

  • Word Rain (Python; semantic-preserving word cloud-like visualization of texts and document collections)
  • OpenRefine (Java; data preprocessing/wrangling)
  • Datasette (Python; data preprocessing/wrangling and exploratory data analysis)
  • Apache Superset (Python; data exploration and visualization platform for SQL DBs)

Interactive data mining / ML / AI tools:

  • Orange (Machine learning and visual analysis environment; Python)
  • Textable (Text mining extensions for Orange)
  • KNIME Analytics Platform (Interactive data mining environment; several platforms)
  • Weka (Interactive data mining environment; Java)
  • ELKI (Interactive data mining environment; Java)

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Programming Libraries for Visualization and Data Analysis

Data analysis and visualization libraries:

  • Word Rain (Python; semantic-preserving word cloud-like visualization of texts and document collections)
  • pyLDAvis (Python; visualization of topic models)
  • Scattertext (Python; visualization of text document collections)
  • t-SNE (Python and many other languages; dimensionality reduction for multivariate/high-dimensional data)
  • UMAP (Python; a recent DR approach comparable, or in some cases, preferred to t-SNE)
  • Further manifold learning and DR techniques (scikit-learn) (Python)
  • densVis (Python; DR for density-preserving data visualization)
  • NetworkX (Python; analysis and visualization of network data)
  • py3plex (Python; analysis and visualization of multiplex and multilayer network data)
  • muxViz (R; analysis and visualization of multilayer network data)
  • ScanPy (Python; analysis and visualization of biological data)
  • Yellowbrick (Python; visualization for machine learning)

Visualization libraries — JavaScript (general-purpose):

  • D3 (JavaScript)
  • D3 Discovery (Plugin catalog for D3)
  • Observable Plot (JavaScript; from the authors of D3)
  • plotly.js (JavaScript; based on D3)
  • billboard.js (JavaScript; based on D3)
  • vis.js (JavaScript)
  • Chart.js (JavaScript)
  • ApexCharts.js (JavaScript; based on D3)
  • Google Charts (JavaScript)
  • Regl Scatterplot (JavaScript; high-performance WebGL-based scatterplots)
  • Vega (JavaScript; based on D3)
  • Vega-Lite (JavaScript; based on D3 and Vega)
  • nivo (JavaScript; based on D3 and React)
  • Victory (JavaScript; based on D3 and React)
  • Highcharts (JavaScript)

Visualization libraries — JavaScript (specific data types and representations):

  • Rickshaw (Temporal data; JavaScript; based on D3)
  • Leaflet (Geospatial data (maps); JavaScript)
  • Mapbox GL JS (Geospatial data (maps); JavaScript)
  • Cytoscape.js (Graph/network data; JavaScript)
  • Sigma (Graph/network data; JavaScript)
  • WebCoLa (cola.js) (Graph/network data; JavaScript; compatible with D3 and Cytoscape.js)

Visualization libraries — Python:

  • Dash (Python; generates a web-based dashboard using plotly.js; also available for R, Julia, and F#)
  • plotly.py (Python; generates a web-based visualization using plotly.js; also R, Julia, F#, and Matlab)
  • HoloViz (Python; a set of high-level packages for interactive visualization)
  • Bokeh (Python; generates a web-based bashboard)
  • Chartify (Python; generates a web-based visualization using Bokeh)
  • Altair (Python; generates a web-based visualization using Vega and Vega-Lite)
  • mpld3 (Python; generates a web-based visualization using D3)
  • Folium (Python; generates a web-based geographical map using Leaflet.js)
  • Geoplot (Python; geospatial data (maps))

Visualization libraries — other languages and platforms:

  • Processing (Environment for graphics programming; several platforms)
  • Improvise (Java)
  • JFreeChart (Java)
  • Shiny (R; generates a web-based visualization)
  • Shiny Dashboard (R; generates a web-based visualization using Shiny)

Visualization libraries — statistical and scientific visualizations:

  • ggplot2 (R; statistical graphics)
  • Matplotlib (Python; statistical graphics and scientific plotting, static plots with very limited interaction options)
  • Seaborn (Python; statistical graphics, based on Matplotlib)
  • Chaco (Python; statistical graphics and scientific plotting, based on Matplotlib, further interaction options)
  • Veusz (Python; statistical graphics and scientific plotting)
  • PyVista (Python; scientific visualization)

Visualization libraries no longer in active development (not recommended for assignments):

  • JavaScript InfoVis Toolkit (JavaScript)
  • Flare - Data Visualization for the Web (ActionScript)
  • JUNG Java Universal Network/Graph Framework (Java)
  • InfoVis Toolkit (Java)
  • JChart (Java)
  • XmdvTool (Qt)

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Data Sets

Scientific publication data:

  • Vispubdata: Visualization Publication Dataset
  • VIS30K (31+ thousand images from IEEE VIS conferences)
  • arXiv Dataset
  • hdblp: historical data of the dblp collection

Text data:

  • The Brexit Blog Corpus
  • Project Gutenberg eBooks - Offline Catalogs and Feeds
  • 4000 CNN articles from 2023
  • OpinRank Data - Reviews From TripAdvisor & Edmunds
  • Enron Email Dataset
  • News Commentary Corpus
  • PHEME Rumour Scheme Dataset
  • Multi-Domain Sentiment Dataset
  • 200,000+ Jeopardy! Questions
  • European Parliament Proceedings Parallel Corpus 1996-2011
  • Open Parallel Corpora
  • The Language Archive
  • CLARIN corpora of academic texts
  • Språkbanken (University of Gothenburg) language resources
  • 1 Billion Word Language Model Benchmark
  • CC-100: Monolingual Datasets from Web Crawl Data
  • The Pile: An 800GB Dataset of Diverse Text for Language Modeling
  • OpenWebTextCorpus
  • Nordic Tweet Stream
  • 20 Open Datasets for Natural Language Processing

Graph and network data:

  • Gephi sample datasets
  • Graphia example datasets
  • YoutubeGraph-Dyn: An evolving graph dataset from YouTube interactions
  • Stanford Large Network Dataset Collection
  • BigDND: Big Dynamic Network Data
  • ICON: Colorado Index of Complex Networks
  • Network Repository: An Interactive Scientific Network Data Repository

Other and mixed data types:

  • Towards A Quantitative Survey of Dimension Reduction Techniques - Datasets
  • IMDb Non-Commercial Datasets
  • Amazon Review Data
  • Yelp Open Dataset
  • Netflix Prize Data
  • Stack Exchange Data Explorer
  • OpenStreetMap data
  • SuiteSparse Matrix Collection (formerly the University of Florida Sparse Matrix Collection)

Miscellaneous data set collections and search tools:

  • Visual Analytics Benchmarks Repository
  • UC Irvine Machine Learning Repository
  • Awesome Public Datasets
  • KDNuggets Datasets for Data Science, Machine Learning, AI & Analytics
  • Kaggle Datasets
  • Google Research Datasets
  • Google Research Dataset Search
  • OpenML data sets and collections
  • Harvard Dataverse
  • European Data
  • Swedish National Data Service
  • Sveriges Dataportal (Swedish open data)

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Methodology, Tips, Tricks, and Further Tools

Literature review/survey methodology:

  • A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies
  • Literature Review as a Research Methodology: an Overview and Guidelines

Overall methodology for visualization design and evaluation:

  • Embracing Disciplinary Diversity in Visualization
  • A Nested Model for Visualization Design and Validation
  • Design Study Methodology: Reflections from the Trenches and the Stacks
  • A Matter of Time: Applying a Data–Users–Tasks Design Triangle to Visual Analytics of Time-oriented Data

Tools and papers for choosing a color map:

  • ColorBrewer 2.0
  • Diverging Color Maps for Scientific Visualization
  • Rainbow Color Map (Still) Considered Harmful
  • Rainbow Colormaps Are Not All Bad
  • Rainbow Colormaps: What are They Good and Bad for?
  • Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps + Viridis color map in D3.js
  • Optimizing Colormaps With Consideration for Color Vision Deficiency to Enable Accurate Interpretation of Scientific Data + cmaputil + Cividis color map in D3.js
  • Good Colour Maps: How to Design Them + PerceptualColourMaps package in Julia
  • Color Blindness Simulator
  • Coblis — Color Blindness Simulator

Evaluation/validation, usability studies, questionnaires:

  • Evaluating Information Visualizations
  • Patterns for Visualization Evaluation
  • Measuring Usability: Are Effectiveness, Efficiency, and Satisfaction Really Correlated?
  • SUS Analysis Toolkit + paper
  • UMUX-Lite Questionnaire + paper

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Further Interesting URLs

  • InfoVis:Wiki, the Information Visualization community platform
  • A Tour through the Visualization Zoo
  • Information is Beautiful
  • A Visual Bibliography of Tree Visualization
  • A Visual Survey of Visualization Techniques for Time-Oriented Data
    • Visualization of Time-Oriented Data (2nd Edition, Open Access Book)
  • A Visual Survey of Text Visualization Techniques
  • A Visual Survey in Enhancing Trust in Machine Learning (ML) Models with Visualization
  • Quantified Self Viz Contest Entries
  • Overview of Data Visualizations and Infographics
  • From Data to Viz + The D3.js Graph Gallery
  • Selection of Data Visualisation Tools
  • Data Visualization for Human Perception
  • Visual Perception
  • Exploring Preattentive Attributes
  • Nightingale — The Journal of the Data Visualization Society
  • Save the Pies for Dessert (critique of the pie chart visualization technique)
  • Visualization Resources
  • Data Visualization Teaching and Learning Materials
  • Mastering the Information Age: Solving Problems with Visual Analytics (Free Book)
  • Progressive Data Analysis: Roadmap and Research Agenda (Free Book)
  • Search User Interfaces (Free Book)

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