Scientific Visualization Research

Below is the VISTAS’ reading list/bibliography, relating to Scientific Visualization.  We note if the item was included in the VISTAS reading group (RG: date).

  1. Abdullah, Kulsoon, Chris Lee, Gergory Conti, John A. Copeland.  Visualizing Network Data for Intrusion Detection, June 16, 2005.
  2. Mike Bailey.  2013. Using GPU Shaders for Visualization Part I, Part 2, Part 3.   Visualization Viewpoints,  IEEE  Computer Graphics and Applications. #1: xxx, #2: March/April 2011 (vol. 31 no. 2), pp. 67-73,#3: May-June 2013.  Posted by Judy March 11, for Reading Group Friday, March 15, 2013.  See alsoTeaching a Shader-Based Introduction to Computer Graphics, www.computer.org/csdl/mags/cg/2011/02/mcg2011020009.pdf, by S Owen, Feb 10, 2011.  (RG:  3/15/2013 – 6/14/2013)
  3. Becker, Richard A., Stephen G. Eick (eick@research.bell-labs.com), Allan R. Wilks,  Visualizing Network Data, IEEE Transactions on Visualizationand Computer Graphics, Vol. 1, No. 1,pages 16-21,March 1995.
  4. BELIV 2012:  Beyond Time and Errors:  Novel Evaluation Methods for Visualization, an IEEE VisWeek 2012 Workshop, Seattle, WA, October 14-15, 2012.
  5. Ecological Reflections:  A network of sites dedicated to long-term, collaborative science and art inquiry into particular sites of great ecological or cultural importance.
  6. Michael Dubakov, Targetprocess Founder, Visual Encoding, September 2012.  A useful description of the concept.
  7. Jean-Daniel Fekete, “Visual Analytics Infrastructures: From Data Management to Exploration,” IEEE Computer, vol. 46, no. 7, pp. 22-29, July, 2013
  8. Jon D. Genetti, M.Bailey, D. Laidlaw, R. Moorhead, R. Whitaker.  What Should We Teach in a Scientific Visualization Class? IEEE Visualization 2004, Austin, Texas, USA. 0-7803-8788-0/04, IEEE.
  9. Grey, Jim, and Alex Szakay (2004).  Where the Rubber Meets the Sky:  Bridging the Gap between Databases and Science.  Microsoft Technical Report (MSR-TR-2004-110).
  10. Grey, Jim, and Alex Szakay.  20 Questions Requirements Gathering for Data Science.  Reposted by Bill Howe on Novembe 1, 2007.
  11. Guzy,Michael R. ,  Courtland L. Smith, John P. Bolte, David W. Hulse, and Stanley V. Gregory (2008).  Policy Research Using Agent-Based Modeling to Assess Future Impacts of Urban Expansion into Farmlands and Forests,  Ecology and Society 13(1): 37.  Posted by Judy Cushing January 17, 2013, for January 2013 Reading Group.
  12. HANA Immersive Visualization Environment (HIVE), Stanford University, June 5, 2014.  accessed 6/6/2014.
  13. Daniel Halperin, Konstantin Weitz, Bill Howe, Francois Ribalet, E. Virginia Armbrust.  Real-Time Collaborative Analysis with (Almost) Pure SQL: A Case Study in Biogeochemical Oceanography.  Scientific and Statistical Database Conference, Baltimore, MD.  July 2013.   (RG:  5/17/2013.  This paper presents a methodology for a collaborative users’ problem solving session using prototype software.)
  14. Hinsen, Konrad. Daydreaming about Scientific Programming, IEEE Computing Today, Sept./Oct. 2013 (Vol. 15, No. 5) pp. 77-79. 1521-9615/13/$31.00 © 2013 IEEE Published by the IEEE Computer Society.
  15. Hornbaek, Kasper.  Current practice in measuring usability: Challenges to usability studies and research, International Journal of Human-Computer Studies, Int. J. Human-Computer Studies 64 (2006) 79–102. http://www.cse.chalmers.se/research/group/idc/ituniv/courses/11/hcd/literatures/Hornbaek%202006%20usability%20measurement%20methods.pdf
  16. Petra Isenberg, Danyel Fisher, Meredith Ringel Morris, Kori Inkpen, and Mary Czerwinski. An Exploratory Study of Co-located Collaborative Visual Analytics around a Tabletop Display. In Proceedings of Visual Analytics Science and Technology (VAST), pages 179–186, Los Alamitos, CA, USA, 2010. IEEE. Received a best paper honorable mention. (doi) (PDF, 8 pages, 3.51 MB)
  17. Kosara, Robert. Storytelling: The Next Step for Visualization, IEEE Computer, May 2013 (Vol. 46, No. 5) pp. 44-50 0018-9162/13/$31.00 © 2013 IEEE. See this site for an on-line version.  (RG:  1/31/2014)
  18. Denise Lach, An Experiment in Post-Normal Science: Building a Knowledge to Action Network in Idaho.  For background, see : S.Funtowicz and J. Ravetz, Post-Normal Science,  International Society for Ecological Economics ; Internet Encyclopaedia of Ecological Economics.  (RG: 1/16/2015)
  19. Longo, João S. C., and Claudia Bauzer Medeiros. 2013.  Providing multi-scale consistency for multi-scale
    geospatial data.  SSDBM 2013, p. 60-71.
  20. John Lukasiewicz, Scientific Visualization Using Pixar’s RenderMan, Computer Science M.S. Thesis, Rochester Institute of Technology, June 29, 2011.
  21. K.-L. Ma et al., “Scientific Storytelling Using Visualization,” IEEE Computer Graphics and Applications, vol. 32, no. 1, 2012, pp. 12-19.
  22. McGaughey, R.J. Fusion/LDV: Software for LiDAR Data Analysis and Visualization; USDA Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 2012. Available online: Accessed:January 2012.
  23. James D.A. Millington, David Demeritt & Raúl Romero-Calcerrada (2011): Participatory evaluation of agent-based land-use models, Journal of Land Use Science, 6:2-3, 195-210, http://dx.doi.org/10.1080/1747423X.2011.558595  (RG: 2/21/2014)
  24. Monroe, M., Lan, R., Morales del Olmo, J., Shneiderman, B., Plaisant, C., Millstein, J. (October 2012).  The Challenges of Specifying Intervals and Absences in Temporal Queries: A Graphical Language Approach
    Published     in Proc. Of ACM Conference on Human-Computer Interaction (CHI 2013)
  25. Paar, P. 2006. Landscape visualizations: Applications and requirements of 3D visualization software for environmental planning. Computers, Environment and Urban Systems 30: 815-839.http://ssl.tamu.edu/personnel/s_popescu/TreeVaW/download.htm.
  26. Deana Denise Pennington, Imme Ebert-Uphoff, Natalie Freed, Suzanne A. Pierce. 2019.  Bridging sustainability science, earth science, and data science through interdisciplinary education, Sustainability Science, September 2019. 10.1007/s11625-019-00735-3.
  27. Popescu, S.C. TreeVaW: Tree Variable Window; Spatial Sciences Laboratory, Texas A&M University: College Station,TX, USA. Available online: Accessed 7thJuly 7 2012
  28. Rhyne, Theresa-Marie and Min Chen.  A Snapshot of Current Trends in Visualization – “5 articles that exemplify current trends in computer-generated visualization” – Computing Now, vol. 7, no. 1, Jan. 2014, IEEE Computer Society [online]; http://www.computer.org/portal/web/computingnow/archive/january2014.
  29. Bernice Rogowitz and Lloyd Treinish, Why Should Engineers and Scientists Be Worried About Color?
  30. Schultz, Nick, and Michael Bailey (2012). Using Extruded Volumes to Visualize Time-series Datasets. In Expanding the Frontiers of Visual Analytics and Visualization, J. Dill et al (eds), Springer-Verlag, Berlin, 127–148.  Posted by Judy Cushing October 11, 2011.
  31. Sherren, K., J. Fischer, H. Clayton, A. Hauldren, and S. Dovers. Lessons from visualizing the landscape and habitat implications of tree decline—and its remediation through tree planting—in Australia’s grazing landscapes. Landscape and Urban Planning 103: 248-258.
  32. Ben Shneiderman1, Catherine Plaisant, and Bradford W. Hesse.  Improving health and healthcare with interactive visualization methods, IEEE Computer Special Issue on Challenges in Information Visualization (2013).
  33. Jared M. Spool, C. Perfetti, and D.Brittan.  2004.  Designing for the Scent of Information.  User Interface Engineering.   copyright held by www.uie.com.
  34. Michael Sedlmair, Petra Isenberg, Dominikus Baur, and Andreas Butz. Evaluating Information Visualization in Large Companies: Challenges, Experiences and Recommendations. In Proceedings of the CHI Workshop Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (BELIV), pages 79–86, New York, NY, USA, 2010. ACM Press. Received the best paper award at BELIV 2010
  35. Thomas CK, Smoot AR.  2012.   An effective, economic, aspirated radiation shield for air temperature observations and its spatial gradients, J. Atmos. Ocean. Technol. inpress.
  36. L. Treinish. Task-Specific Visualization Design:  A Case Study in Operational Weather Forecasting.
  37. P. Skraba, Bei Wang, G. Chen, P. Rosen. “2D Vector Field Simplification Based on Robustness,” In Proceedings of the 2014 IEEE Pacific Visualization Symposium. (“Best Paper” at PacificVis 2014)
  38. Wang, W., B. Song, J. Chen, D. Zheng and T.R. Crow. 2006. Visualizing forest landscapes using public data sources. Landscape and Urban Planning 75: 111-124.
  39. Matt Williams and Tamara Munzner. Steerable, Progressive Multidimensional Scaling. Proc. InfoVis 2004, pp 57-64.
  40. Sherry Yang, M. M. Burnett, E. DeKoven, MosheZloof.  1997. Representation Design Benchmarks: A Design-Time Aid for VPL Navigable Static Representations. Journal of Visual Languages and Computing 8, 563-599. (RG: 4/4/4014)