Computer Supported Cooperative Work (CSCW)

, Volume 26, Issue 4–6, pp 693–731 | Cite as

Collaborative Exploration and Sensemaking of Big Environmental Sound Data

  • Tshering Dema
  • Margot Brereton
  • Jessica L. Cappadonna
  • Paul Roe
  • Anthony Truskinger
  • Jinglan Zhang


Many ecologists are using acoustic monitoring to study animals and the health of ecosystems. Technological advances mean acoustic recording of nature can now be done at a relatively low cost, with minimal disturbance, and over long periods of time. Vast amounts of data are gathered yielding environmental soundscapes which requires new forms of visualization and interpretation of the data. Recently a novel visualization technique has been designed that represents soundscapes using dense visual summaries of acoustic patterns. However, little is known about how this visualization tool can be employed to make sense of soundscapes. Understanding how the technique can be best used and developed requires collaboration between interface, algorithm designers and ecologists. We empirically investigated the practices and needs of ecologists using acoustic monitoring technologies. In particular, we investigated the use of the soundscape visualization tool by teams of ecologists researching endangered species detection, species behaviour, and monitoring of ecological areas using long duration audio recordings. Our findings highlight the opportunities and challenges that ecologists face in making sense of large acoustic datasets through patterns of acoustic events. We reveal the characteristic processes for collaboratively generating situated accounts of natural places from soundscapes using visualization. We also discuss the biases inherent in the approach. Big data from nature has different characteristics from social and informational data sources that comprise much of the World Wide Web. We conclude with design implications for visual interfaces to facilitate collaborative exploration and discovery through soundscapes.


Collaborative sensemaking Environmental soundscapes Boundary object Visualization Collaborative exploration Soundmarks Interfaces Big data 


  1. Annenberg Learner (2017). The Habitable Planet: A Systems Approach to Environmental Science. Accessed March 20 2017.
  2. Argenta, Chris, Benson Jordan, Nathan Bos, Susannah B F Paletz, William Pike, and Aaron Wilson (2014). Sensemaking in Big Data Environments. HCBDR ‘14: Proceedings of the 2014 Workshop on Human Centered Big Data Research, Raleigh, NC, USA, 1–3 April 2014. New York: ACM Press, pp. 53–55.Google Scholar
  3. Boyd, Danah, and Kate Crawford. (2011). Six Provocations for Big Data. A Decade. In Internet Time: Symposium on the Dynamics of the Internet and Society. 21 September 2011. pp. 1–17.
  4. Brown, John Seely, Allan Collins, and Paul Duguid (1989). Situated Learning and the Culture of Learning. Education Researcher, vol. 18, no. 1, February 1989, pp. 32–42.Google Scholar
  5. Butchart, Stuart H. M., Matt Walpole, Ben Collen, Arco van Strien, Jörn P. W. Scharlemann, Rosamunde E. A. Almond, Jonathan E. M. Baillie, et al (2010). Global Biodiversity: Indicators of Recent Declines. Science, vol. 328, no. 5982, May 2010, pp. 1164–1168.Google Scholar
  6. Chamberlain, Alan; and Chloe Griffiths (2013). Moths at Midnight: Design Implications for Supporting Ecology-Focused Citizen Science. MUM ‘13: Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, Luleå, Sweden, 2–5 December, 2013, New York: ACM Press, art. 25.Google Scholar
  7. Chan, Joel, Steven Dang, and Steven P Dow (2016). Comparing Different Sensemaking Approaches for Large-Scale Ideation. CHI ‘16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, 7-12 May 2016, New York: ACM Press, pp. 2717–2728.Google Scholar
  8. Cottman-Fields, Mark, Margot Brereton, Jason Wimmer, and Paul Roe (2014). Collaborative Extension of Biodiversity Monitoring Protocols in the Bird Watching Community. PDC ‘14: Proceedings of the 13th Participatory Design Conference, Windhoek, Namibia, 6-10 October, 2014, New York: ACM Press, pp. 111–114.Google Scholar
  9. Digby, Andrew, Michael Towsey, Ben D. Bell, and Paul D. Teal (2013). A practical comparison of manual and autonomous methods for acoustic monitoring. Methods in Ecology and Evolution, vol. 4, no. 7, July 2013, Bristish Ecological Society, pp. 675–683.Google Scholar
  10. Dörk, Marian, Sheelagh Carpendale, and Carey Williamson (2011). The Information Flaneur. CHI' 11: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, Vancouver, BC, 7-12 May 2011, Newyork: ACM Press, pp. 1215–1224.Google Scholar
  11. Dörk, Marian; Rob Comber; and Martyn Dade-Robertson (2014). Monadic Exploration: Seeing the Whole through Its Parts. CHI' 14: Proceedings of the 32nd Conference on Human Factors in Computing Systems, Toronto, Canada, 26 April – 1 May 2014. New York: ACM Press, pp. 1535–1544.Google Scholar
  12. Fisher, Kristie, Scott Counts, and Aniket Kittur (2012). Distributed Sensemaking: Improving Sensemaking by Leveraging the Efforts of Previous Users. CHI ‘12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Austin, Texas, 5–10 May 2012, New York: ACM Press, pp. 247–256.Google Scholar
  13. Frommolt, Karl-Heinz, and Klaus-Henry Tauchert (2013). Applying bioacoustic methods for long-term monitoring of a nocturnal wetland bird. Ecological Informatics, vol. 14, December 2013, pp. 4–12.Google Scholar
  14. Goyal, Nitesh, and Susan R Fussell (2016). Effects of Sensemaking Translucence on Distributed Collaborative Analysis. CSCW ‘16: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, San Francisco, CA, USA, 27 February – 2 March, 2016. New York: ACM Press. pp. 288–302.Google Scholar
  15. Goyal, Nitesh, Gilly Leshed; Dan Cosley, and Susan R. Fussell (2014). Effects of Implicit Sharing in Collaborative Analysis. CHI ‘14: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, Canada, 26 April – 1 May, 2014, New York: ACM Press, pp. 129–138.Google Scholar
  16. Goyal, Nitesh, Gilly Leshed, and Susan R Fussell (2013). Effects of Visualization and Note-Taking on Sensemaking and Analysis. CHI ‘13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 27 April – 2 May 2013, New York: ACM Press, pp. 2721–2724.Google Scholar
  17. Gutwin, Carl, Gwen Stark, and Saul Greenberg (1995). Support for Workspace Awareness in Educational Groupware. CSCL’ 95: Proceedings of the First International Conference on Computer Support for Collaborative Learning, Indiana Univ., Bloomington, Indiana, USA, 17-20 October, 1995, Hillsdale, New Jersey: Lawrence Erlbaum Associates Inc. pp. 147–56.Google Scholar
  18. Isenberg, Petra, Niklas Elmqvist, Jean Scholtz, Daniel Cernea, Kwan-Liu Ma, and Hans Hagen (2011). Collaborative visualization: Definition, challenges, and research agenda. Information Visualization, vol. 10, no. 4, October 2011, pp. 310–326.Google Scholar
  19. Kidd, Alison (1994). The Marks Are on the Knowledge Worker. CHI' 94: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, Massachusetts, 24-28 April 1994, New York: ACM Press, pp. 186–191.Google Scholar
  20. Krause, Bernard L. (1993). The Niche Hypothesis: A Virtual Symphony of Animal Sounds, the Origins of Musical Expression and the Health of Habitats. The Soundscape Newsletter, June, 1993, pp. 6–10.
  21. Lagoze, Carl (2014). eBird: Curating Citizen Science Data for Use by Diverse Communities. International Journal of Digital Curation, vol. 9, no. 1, February 2014, pp. 71–82.Google Scholar
  22. Latour, Bruno (2005). Reassembling the social: An introduction to actor-network theory. Oxford University Press.Google Scholar
  23. Latour, Bruno, Pablo Jensen, Tommaso Venturini, Sébastian Grauwin, and Dominique Boullier (2012). The Whole Is Always Smaller than Its Parts’ - a Digital Test of Gabriel Tardes’ Monads. British Journal of Sociology, vol. 63, no. 4, December 2012, pp. 590–615.Google Scholar
  24. Lau, Lydia; Fan Yang-Turner; and Nikos Karacapilidis (2014). Requirements for big data analytics supporting decision making: A Sensemaking Perspective, In Mastering Data-Intensive Collaboration and Decision Making, Big Data Studies, Vol. 5, Springer International Publishing. pp. 49–70.Google Scholar
  25. Light, Ann, Margot Brereton, and Paul Roe (2015). Some Notes on the Design of “World Machines.” OzCHI ‘15: In Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction, Melbourne, Australia, 7-10 December 2015, New York: ACM Press, pp. 289–293.Google Scholar
  26. Ludwig, Thomas; Tino Hilbert; and Volkmar Pipek (2015). Collaborative Visualization for Supporting the Analysis of Mobile Device Data. ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, Oslo, Norway, 19-23 September 2015, Cham: Springer International Publishing. pp. 305–316.Google Scholar
  27. Lycett, Mark. (2013). Datafication: making sense of (big) data in a complex world. European Journal of Information Systems, vol. 22, no. 4, July 2013, Cham: Springer. pp. 381–386.Google Scholar
  28. Newbold, Tim, Lawrence N Hudson, Samantha L L Hill, Sara Contu, Igor Lysenko, Rebecca A Senior, Luca Borger, et al (2015). Global effects of land use on local terrestrial biodiversity. Nature, vol. 520, no. 7545, April 2015, Nature Publishing Group. pp. 45–50.Google Scholar
  29. Paul, Sharoda A, and Madhu C Reddy (2010). Understanding Together: Sensemaking in Collaborative Information Seeking. CSCW ‘10: In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, Savannah, Georgia, USA, 6-10 February 2010, New York: ACM Press. pp. 321–330.Google Scholar
  30. Peirce, Charles Sanders (1903). Peirce on Signs: Writings on Semiotic. UNC Press Books, 1991.Google Scholar
  31. Pijanowski, Bryan C., Luis J. Villanueva-Rivera, Sarah L. Dumyahn, Almo Farina, Bernie L. Krause, Brian M. Napoletano, Stuart H. Gage, and Nadia Pieretti (2011). Soundscape Ecology: The Science of Sound in the Landscape. Bio Science, vol. 61, no. 3, March 2011, Oxford University Press. pp. 203–216.Google Scholar
  32. Potamitis, Ilyas (2014). Automatic Classification of a Taxon-Rich Community Recorded in the Wild. PLoS ONE, vol. 9, no. 5, May 2014, pp. 1–12.Google Scholar
  33. Qu, Yan, and George W Furnas (2005). Sources of Structure in Sensemaking. CHI EA ‘05: Proceedings of Extended Abstracts in Human Factors in Computing Systems, Portland, Oregon, USA, 2–7 April 2005. New York: ACM Press. pp. 1989–1892.Google Scholar
  34. Robertson, Toni, Margot Brereton, Frank Vetere, and Steve Howard (2012). Knowing Our Users : Scoping Interviews in Design Research with Ageing Participants. OzCHI ‘12, Melbourne, Australia, 26-30 November, 2012. New York: ACM Press. pp. 517–520.Google Scholar
  35. Rost, Mattias; Louise Barkhuus; Henriette Cramer; and Barry Brown (2013). Representation and Communication : Challenges in Interpreting Large Social Media Datasets. In CSCW ‘13: Proceedings of the 2013 conference on Computer Supported Cooperative Work, San Antonio, Texas, 23-27 February 2013, New York: ACM Press.Google Scholar
  36. Rotman, Dana, Jenny Preece, Jen Hammock, Kezee Procita, Derek Hansen, Cynthia Parr, Darcy Lewis, and David Jacobs (2012). Dynamic Changes in Motivation in Collaborative Citizen-Science Projects. In CSCW’12:Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, Seattle, Washington, 11-15 February 2012, New York: ACM Press. pp. 217–226.Google Scholar
  37. Russell, Daniel M, Mark J Stefik, Peter Pirolli, and Stuart K Card (1993). The Cost Structure of Sensemaking. CHI ‘93: In Proceedings of the INTERCHI ‘93 Conference on Human Factors in Computing Systems, Amsterdam, The Netherlands, 24-29 April 1993, New York: ACM Press, pp. 269–276.Google Scholar
  38. Schafer, R. Murray (1993). The Soundscape: Our Sonic Environment and the Tuning of the World. Rochester: Inner Traditions/Bear & Co.Google Scholar
  39. Servick, Kelly (2014). Eavesdropping on Ecosystems. Science, vol. 343, no. 6173, February 2014, pp. 834–37.Google Scholar
  40. Star, Susan L. (1989). The Structure of Ill-Structured Solutions: Boundary Objects and Heterogeneous Distributed Problem Solving. Distributed Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc. pp. 37–54.Google Scholar
  41. Star, Susan L. (2010). This is Not a Boundary Object: Reflections on the Origin of a Concept. Science, Technology & Human Values, vol. 35, no. 5, September 2010, pp. 601–617.Google Scholar
  42. Suchman, Lucy (2003). Located Accountabilities in Technology Production. Scandinavian Journal of Information Systems, vol. 14, no. 2, September 2002, pp. 91–105Google Scholar
  43. Suchman, Lucy (2010). Agencies at the Interface: Expanding Frames and Accountable Cuts. CHI ‘10: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA, 10–15 April, 2010, New York: ACM Press.Google Scholar
  44. Tao, Yihan, and Anastasios Tombros (2013). An Exploratory Study of Sensemaking in Collaborative Information Seeking. ECIR’13: In Proceedings of the 35th European Conference on Advances in Information Retrieval, Moscow, Russia 24–27 March, Berlin, Heidelberg: Springer-Verlag. pp. 26–37Google Scholar
  45. Tee, Kimberly, Saul Greenberg, and Carl Gutwin (2009). Artifact Awareness through Screen Sharing for Distributed Groups. International Journal of Human Computer Studies, vol. 67, no. 9, pp. 677–702.Google Scholar
  46. Towsey, Michael, Liang Zhang, Mark Cottman-Fields, Jason Wimmer, Jinglan Zhang, and Paul Roe (2014). Visualization of Long-Duration Acoustic Recordings of the Environment. Procedia Computer Science, vol. 29. pp. 703–712.Google Scholar
  47. Truskinger, Anthony; Mark Cottman-fields; Daniel Johnson; and Paul Roe (2013). Rapid Scanning of Spectrograms for Efficient Identification of Bioacoustic Events in Big Data, 2013 I.E. 9th International Conference on eScience, Beijing, China, 22–25 October 2013. IEEE, pp. 270–277.Google Scholar
  48. Venter, Oscar, Eric W Sanderson, Ainhoa Magrach, James R Allan, Jutta Beher, Kendall R Jones, Hugh P Possingham, et al (2016). Sixteen Years of Change in the Global Terrestrial Human Footprint and Implications for Biodiversity Conservation. Nature Communications, vol. 7, no. 12558, August 2016,Google Scholar
  49. Virginia, Braun and Victoria Clarke (2006). Using Thematic Analysis in Psychology. Qualitative Research in Psychology, vol. 3, no. 2, May 2006, pp. 77–101.Google Scholar
  50. Weick, Karl E., Kathleen M Sutcliffe, and David Obstfeld (2005) Organizing and the Process 16, no. 4, July-August 2005, pp. 409–421.Google Scholar
  51. Weick, Karl E. (1995). Sensemaking in Organizations. Thousand Oaks, CA: SAGE Publications.Google Scholar
  52. Whittaker, Steve (2008). Making Sense of Sense making. In T. Erickson and D. W. McDonald (eds): HCI Remixed: Reflections on Works That Have Influenced the HCI Community. Cambridge, Massachusetts, London, MIT Press. pp. 173-178Google Scholar
  53. Wiggins, Andrea, and Kevin Crowston (2011). From Conservation to Crowdsourcing: A Typology of Citizen Science. HICSS ‘11:In Proceedings of the 2011 44th Hawaii International Conference on System Sciences. Washington, DC: IEEE Computer Society, pp. 1-10.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Tshering Dema
    • 1
  • Margot Brereton
    • 1
  • Jessica L. Cappadonna
    • 1
  • Paul Roe
    • 1
  • Anthony Truskinger
    • 1
  • Jinglan Zhang
    • 1
  1. 1.Computer Human Interaction, Science and Engineering FacultyQueensland University of TechnologyBrisbaneAustralia

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