One-stop shopping: processing, prefuse, protovis, D3, Tableau, Mondrian, ggplot2, ggobi.

• General systems
  • Tableau: powerful and flexible database vis
    (commercial version of Polaris). Trial version downloadable.
  • Mondrian: statistical graphics package in java
    histograms, boxplots, scatterplots, barcharts, mosaic plots, missing value plots, parallel coordinates/boxplots, SPLOMs, maps. Connects with R.
  • ggplot2: statistical graphics / plotting system for R, based on Wilkinson Grammar of Graphics

• Toolkits
  • Heer toolkits (from newest to oldest)
    • D3: data driven documents, javascript
    • protovis: declarative infovis, javascript/svg
      Example apps:
      • Gremlin
      • NapkinVis (2009 class project)
    • flare actionscript/flash version of prefuse
    • prefuse java toolkit: hierarchies, graphs, timelines, scatterplots, etc
      Example apps:
      • Cerebral
      • FlowMap
      • Small World Graphs (2005 class project)
      • DocuBurst
  • processing java toolkit: vis programming sandbox, huge user community of artists
    Example apps:
    • MizBee
    • Pathline
    • MulteeSum
  • ILOG java, .net, flex, c++
    supports parallel coordinates, parallel histograms, graphs/histograms/bar charts, dynamic queries, grids (small multiples), treemaps. Trial version downloadable.
  • JIT: JavaScript InfoVis Toolkit
  • Improvise: java, multiple coordinated views

• Dimensionality reduction
  • ggobi DR and statistical graphics package (windows, mac, linux)
    fully documented with its own book, MDS, grand tours, linked views, scatterplots, bar charts, parallel coords. links with R.
  • DimStiller, java DR framework includes MDS, PCA, and dimensionality estimators
  • Glimmer MDS, C++. GPU version requires recent nVidia graphics card, CPU version runs anywhere
  • MDS FSMvis tool from Morrison and Chalmers
  • Isomap matlab
  • LLE matlab
  • SOM

• Graph drawing
  • graphviz static layouts
    strengths: solid and robust (has been available for years); popular; easy to use; many layout algorithms including dot for small graphs and sfdp for large graphs
    weaknesses: no interaction, batch only; labels hard to read with all but very small graphs with dot
  • Gephi interactive viewing/filtering, forcedirected and multilevel layouts, metrics
  • Tulip handles large data
    strengths: scalability; power; free
    weaknesses: complexity/learning curve; installation can be irritating
  • Large Graph Layout
  • yfiles (evaluation version available)
  • AiSee (noncommercial/trial version available)
  • ILOG JViews commercial
  • Tom Sawyer commercial, trial available
  • Pajek, Windows-only
  • H3, 3D hyperbolic graphs through spanning trees, free for noncommercial use

• Zoomable Interfaces
  • Jazz/Piccolo: toolkits for zoomable interfaces (successor to Pad++), written in Java
    Example apps:
    • PhotoMesa
    • TimeSearcher
    • FaceFinder, 2003 course project, Rhian Davies
  • ZVTM/ZGRViewer Zoomable Visual Transformation Machine, another Java zoomable interface toolkit/viewer
    Example apps:
    • Prawn, An Interactive Tool for Software Visualization 2003 course project, Andrew Chan / Reid Holmes

• Scientific visualization packages
  • vtk: powerful and heavyweight scientific visualization dataflow toolkit, open source
    • vtk graph drawing library docs, software
    • book
  • OpenDX: powerful and heavyweight scientific visualization dataflow toolkit
    • book

• Math visualization packages
  • Mathematica (commercial) huge user base
  • Maple (commercial)
  • Geomview: (open source) general/math vis system, supports noneuclidean geometry and 4D viewing

• General programming environments
  • Java: 2D graphics with Java2D, UI components with Swing
    Example apps:
    • Excentric Labels
    • Fisheye Menus
    • Table Lens
    • LifeLines
  • Flash:
    Example apps:
    • MuniMap Berkeley student project
    • MusicMaps Berkeley student project
  • OpenGL: extremely powerful and flexible graphics library, high learning curve if you don't already know it, simple UI with GLUT:
    Example apps:
    • XmdvTool: multivariate dataset exploration
    • H3: 3D hyperbolic graphs
  • MATLAB (commercial)
  • R (open source)
  • X Windows: (huge learning curve, only use if you already know it)
    Example apps:
    • XGobi (high-dimensional data) [Tutorial]
    • VisDB (pixel-oriented database viz) [no source, no documentation]

See also the InfoVis04 Workshop on InfoVis Software Infrastructures page

Data

One-stop shopping: Visual Analytics Benchmark Repository, data.gov, GapMinder

• IEEE VisWeek Contests are great resources because they have not only data, but also tasks and examples of how others have tackled the problem. See the HCIL Benchmark Repository for a complete list.
  • VAST Contests
  • VAST Challenge 2011: geospatial and microblogging, cybersecurity, text
  • VAST Challenge 2010: text, medical records, gene sequences.
  • VAST Challenge 2009: internet traffic, social network with geographic component, video.
  • VAST Challenge 2008: Grand challenge: phone records for social network analysis, geotemporal records, wikipedia edit data/history (unstructured text analysis), location tracking (evacuation modelling).
  • VAST Contest 2007: Blue Iguanodon: News stories, blog entries, background info, multimedia materials.
  • VAST Contest 2006: Alderwood: News stories, background info, multimedia materials.
  • InfoVis Contests
    • InfoVis Contest 2007: Hollywood/IMDB
    • InfoVis Contest 2006: US Census PUMS Data
    • InfoVis Contest 2005: Market Data (not so useful since data not available)
    • InfoVis Contest 2004: Publication History
    • InfoVis Contest 2003: Tree Comparison
  • Vis (scivis) Contests
    • Vis Contest 2011: fluid dynamics, centrifugal pump simulation
    • Vis Contest 2010: multimodal vis for neurosurgical test planning (DTI MRI/CT)
    • Vis Contest 2008: multifield 3D scalar data, galaxy formation

• Other Contests
  • Health 2.0 Developer Challenge
  • visualizing.org challenge list
  • WikiVis 2011: Wikipedia Data Visualization Challenge
  • Mozilla Labs Open Data Visualization Competition Fall 2010: How do people use Firefox?
    • A Day In The Life of a Browser - Version 2 a variety of general browsing data, such as: startup/shutdown events, session restore information, memory usage statistics, profile age, history size, and more. Also user demographic data, e.g. gender, age, self-reported technical level, etc.
    • Firefox 4 Beta Interface - Version 2 user interactions with the main Firefox UI, from the Back and Forward buttons, to smaller controls like the Web Feed icon. Demographic data as above.
    • also, dozens of other test case datasets at Mozilla Labs Test Pilot

• Other Dataset Lists
  • Government/Demographic
    • data.vancouver.ca Open Data Vancouver
    • data.gc.ca Open Data Canada
    • data.gov: US Government open data
    • data.gov.uk UK Government open data
    • Sunlight Foundation USA open government clearinghouse
    • Open Government Data worldwide hub
    • GapMinder data is all available for download
  • Eamonn Keogh's Time Series Classification/Clustering Archive
  • Jeff Klingner's List of Online Databases
  • Heidi Lam Time Series Data list
  • Harvard CS171 Visualization data sources list

• Networking project ideas
  • Tor Anonymity Network: See me if you're interested, I'll put you in contact with the folks at Tor. In their own words:
    The short version is that we're trying to visualize the changes in usage of the Tor anonymity network. We've been collecting a variety of (not terribly well documented yet) data sets. We use them to generate graphs like total network capacity and per-country usage, including censorship events: example 1 example 2 (red dots are 'statistical drop' whereas blue dots are 'statistical spike'). The challenge is that we have people who want to see pretty visualizations of exactly who is benefiting from Tor at that moment. Alas, since Tor is an anonymity system, we intentionally only collect aggregated data -- so the idea that they keep suggesting of "just put a dot on the map for each IP address that's using Tor right now" is not something we can (or should) support. We could imagine an alternative approach of doing a little video of "how growth has changed in that country over the past month", since while they ask for "right now" I bet that's not really what they most want to see. Or a map where the size of the country is based on how much Tor use it's seen in some time period. But I bet there are other better ideas out there.
  • Visual Tcpdump: Tcpdump is a powerful tool that shows all network traffic on a link, but it can be quite hard to understand what's going on when confronted with the raw tcpdump output. "Visual tcpdump" would ideally run off either log file of a past tcpdump session or in realtime with live tcpdump connection. There are several tasks one might target from this dataset. First, visually characterizing traffic patterns - for example, showing the distribution of session lengths or packet types. Second, highlighting dangerous packets that could occur in a stream - for example, passwords sent in plain text. Third, characterizing protocols - for example, showing the TCP window size changes over the course of a session. Some previous knowledge of networking will be helpful for this project.
  • Intrusion Detection: Noticing that a network is under attack is difficult because of the sheer volume of benign traffic, and the number of attack methods. The two main tasks are real-time detection that an attack is occurring, and forensic analysis of a past attack. There is a publicly available dataset of network traces with four different simulated attacks plus a control baseline with no attacks. Previous knowledge of networking and security issues will be helpful for this project.
  • Internet Backbone: One way to map "the Internet" is to consider the structure of the backbone router interconnections. Bill Cheswick has been keeping archives of the daily changes in the roughly 100,000 core reachable routers for over three years. Even the static dataset from a single day is a difficult challenge to show comprehensibly, and showing growth and changes over time is an even more interesting problem. The H3 browser for large graphs is a potential resource. This project should be feasible without previous knowledge of networking.

• Other project ideas
  • Graph sparsification New theory faculty member Nick Harvey has some interesting and cool ideas on how to approach graph sparsification in a very different direction from previous work on multilevel graph coarsening. See me to discuss further.

• Fell In Love With Data, Enrico Bertini
• Visual Business Intelligence, Stephen Few
• Eager Eyes, Robert Kosara
• infovis wiki, Vienna
• infovis.net, Juan C. Durstler
• Statistical Graphics, Martin Theus
• Functional Color, Maureen Stone
• Well-Formed Data, Moritz Stefaner
• Flowing Data, Nathan Yau
• information aesthetics, Andrew Vande Moere
• visual complexity, Manuel Lima
• Ask ET, Edward Tufte
• Information Wants to be Seen, TJ Jankun-Kelly
• Visuale, Enrico Bertini (no longer updated)
• Atlas of Cyberspaces, Martin Dodge (no longer updated)

• visualizing.org
• Many Eyes
• Gallery of Data Visualization
• Data Visualization Milestones