Visualisierung 2: Data, Libraries, and other Resources

Data

Data can - but does not need to be - acquired from one of these sources: 

• IEEE SciVis Contests 2004-now
  https://scivis2021.netlify.app/previous/
  Each year, SciVis contests provide a challenging scientific data set to be visualized; list of all SciVis contests from 2004 until now.

• Visualization Group Data Sets
  http://www.cg.tuwien.ac.at/research/vis/datasets/
  3D volume data sets from our institute.

• Stefan Roettgers Volume Library
  http://www9.informatik.uni-erlangen.de/External/vollib/
  A collection of free 3D volume data sets.

• The Stanford Volume Data Archive
  http://graphics.stanford.edu/data/voldata/
  3D volume data sets from CT.

• Osirix DICOM Data
  http://www.osirix-viewer.com/datasets/
  Medical volume data sets in DICOM format.

• Protein Data Bank
  http://www.rcsb.org/
  3D shapes of proteins, nucleic acids, and compelx assemblies.

• Open Scientific Visualization Data Sets
  by SCI, Utah; including CT, MR etc. 
  https://klacansky.com/open-scivis-datasets/

• VAST challenge archive
  https://vast-challenge.github.io/2024/
  Archive of all Visual Analytics Science and Technology (VAST) Challenges from 2006.

• UCI Machine Learning Repository
  https://archive.ics.uci.edu/ 
  Provides hundreds of data sets mainly for the machine learning community, but also interesting for visualization!

• sci-kit learn Datasets
  https://scikit-learn.org/stable/datasets.html
  Provides some interesting data sets for machine learning, but also interesting for visualization 

• Papers with Code 
  https://paperswithcode.com/datasets
  Machine learning data sets (mainly images, texts, and videos), that could also be interesting for visualization or visual analytics 

• Kaggle Datasets
  https://www.kaggle.com/datasets
  Ten thousands of public datasets, including Bitcoin, YouTube statistics, transports etc.

• FiveThirtyEight Data
  https://data.fivethirtyeight.com/
  Data used and provided by the FiveThirtyEight opinion poll analysis / politics, economics, and sports blog website.

• data.gv.at
  https://www.data.gv.at/
  offene Daten Österreich

• Gapminder world data
  https://www.gapminder.org/data/
  Provides hundrets of country indicators, such as population, infant mortality rates, life expectancy at birth, employment rates etc., over many years.

• Tableau sample data sets
  https://public.tableau.com/s/resources?qt-overview_resources=1#qt-overview_resources
  Dozens of data sets about education, science -- and even characteristics of Star Wars characters.

• CIA world factbook
  https://www.cia.gov/the-world-factbook/
  Country comparisons of different geographic or economic statistics can be downloaded.

• Data.Gov
  https://catalog.data.gov/dataset
  Hundrets thousands of data sets (e.g., demographic statistics on zip code level) provided by the U.S. government.

• Google public data explorer
  http://www.google.com/publicdata/home
  Search through publicly available data sets, such as world bank, eurostat, WTO etc.

• Statista
  https://www.statista.com/
  Large collection of statistics (TU Wien has a license)

• Stanford Large Network Dataset Collection
  http://snap.stanford.edu/data/
  Provides any kinds of network data.

• The Koblenz Network Collection
  https://www.re3data.org/repository/r3d100011612
  Large network datasets of all types collected by the University of Koblenz-Landau.

• GMap Graph Datasets
  http://gmap.cs.arizona.edu/datasets
  Collection of graphs and networks used for demonstrating GMap.

• graphdrawing.org Benchmark Data
  http://www.graphdrawing.org/data.html
  Benchmark data for various classes of graph drawing algorithms.

• Network Data Collection
  http://www-personal.umich.edu/~mejn/netdata/
  Network data collected by Mark Newman.

• Visualization publications dataset
  https://sites.google.com/site/vispubdata/home
  Contains all IEEE VIS publications from 1990 to 2015 with titles, authors etc. and citations to previous VIS papers.

• IMDbPY
  https://imdbpy.sourceforge.io/
  Python package to retrieve and manage data of the IMDb movie database.

• Car Evaluation Data Set
  http://archive.ics.uci.edu/ml/datasets/Car+Evaluation
  A larger car datset.

• Plants Data Set
  http://archive.ics.uci.edu/ml/datasets/Plants
  A classic and quite large multivariate data set about plants.

Tools

• D3.js - JavaScript library for data-driven visualization documents
  https://d3js.org/

• Vega Light - high-level visualization grammar based on JSON
  https://vega.github.io/vega-lite/

• Python Visualization Libraries - Python offers a variety of visualization libraries, like Bokeh, Plotly, Seaborn, Altair, or Folium. Here is an overview:
  https://mode.com/blog/python-data-visualization-libraries

• R Visualization Packages - R provides some visualization packages as well:
  https://mode.com/blog/r-data-visualization-packages

• three.js - JavaScript 3D scenegraph library based on WebGL
  https://threejs.org/

• babylon.js - JavaScript 3D rendering engine based on WebGL (and partially supporting WebGPU) 
  https://www.babylonjs.com/

• Stardust: GPU-based Visualization Library
  https://stardustjs.github.io/

• DataShader - Python library for large-scale data visualization
  https://datashader.org/

• Leaflet - JavaScript library for mobile-friendly interactive maps
  http://leafletjs.com/

• CesiumJS - JavaScript library for 3D geospatial visualization 
  https://cesium.com/platform/cesiumjs/

• Google Visualization API for the creation of visualization
  https://developers.google.com/chart/interactive/docs/reference

• Qt Data Visualization Module
  http://doc.qt.io/qt-5/qtdatavisualization-index.html

• Visualization Toolkit (VTK) -- 3D graphics, image processing, and visualization library
  https://www.vtk.org/

• Kitware toolkits and applications (including VTK, ParaView, ITK, 3D Slicer etc.)
  https://www.kitware.com/platforms/

• Unity3D - game engine; sometimes used for 3D visualization
  https://unity3d.com/

• Unreal Engine - game engine; sometimes used for 3D visualization
  https://www.unrealengine.com

• A Frame - framework for web VR applications 
  https://aframe.io/

• R Shiny - web-based dashboards based on R
  https://shiny.rstudio.com/

• Collection of Vis-Tools
  http://selection.datavisualization.ch/

• Crossfilter - JavaScript library for efficient handling of large tabular datasets
  http://square.github.io/crossfilter/

• Pandas - Python library for data manipulation and analysis
  https://pandas.pydata.org/

• OpenRefine - tool for cleaning and tranforming messy data
  http://openrefine.org/

• NetworkX - Python package for graph creation, transformation, analysis
  https://networkx.github.io/

• scikit-learn - machine learning in Python
  https://scikit-learn.org/stable/

• TensorFlow - open source machine learning framework (especially for deep learning) for Python, Java, C, Go, and JavaScript
  https://www.tensorflow.org/

• CUDA - parallel computing platform for NVIDIA GPUs
  https://developer.nvidia.com/cuda-zone

• OpenCL - open, cross-platform parallel programming framework
  https://www.khronos.org/opencl/

• Numba - JIT compiler translating subset of Python / NumPy into fast machine code
  http://numba.pydata.org/

Links

• A Tour Through the Visualization Zoo (Heer et al., 2010)
  https://cacm.acm.org/magazines/2010/6/92482-a-tour-through-the-visualization-zoo

• A Survey of Surveys in Information Visualization
  https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13212

• Data Visualization Milestones
  http://www.datavis.ca/milestones/

• Survey on text visualization techniques
  http://textvis.lnu.se/

• Survey on tree visualization techniques
  http://treevis.net/

• Survey on BioVis techniques
  http://biovis.lnu.se/

• Survey on set visualization techniques
  https://www.cvast.tuwien.ac.at/SetViz

• Survey on trust in machine learning visualization 
  https://trustmlvis.lnu.se/

• Information Visualization community platform
  https://infovis-wiki.net/wiki/Main_Page
  The InfoVis:Wiki project is intended to provide a community platform and forum integrating recent developments and news on all areas and aspects of Information Visualization.

• The Python Graph Gallery
  https://python-graph-gallery.com/
  Hundrets of charts, each with corresponding python code.

• TED-Talk by Hans Rosling
  https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen?subtitle=en&geo=de (Video)
  A very good example how InfoVis can be used to explore large and high-dimensional data sets.

• TED-Talk by Gary Flake
  https://www.ted.com/talks/gary_flake_is_pivot_a_turning_point_for_web_exploration?subtitle=en&geo=de (Video)
  Talk about Pivot - an interesting application to browse the web.

Contact

If you discover any broken link, or you think something has to be added to this list, contact Manuela Waldner.