Collaborative Exploration and Sensemaking of Big Environmental Sound Data Tshering Dema, Margot Brereton, Jessica L. Cappadonna, Paul Roe, Anthony Truskinger & Jinglan Zhang Computer Human Interaction, Science and Engineering Faculty, Queensland University of Technology, 2 George St, Brisbane, QLD 4000,, Australia (Phone: +61 7 3138 2240; E-mail: t3.dema@qut.edu.au) Abstract. 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 visuali- zation 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 investi- gated 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. Keywords: Collaborative sensemaking, Environmental soundscapes, Boundary object, Visualization, Collaborative exploration, Soundmarks, Interfaces, Big data 1. Introduction With changes in landscape and growing fragmentation of natural landscapes, mon- itoring biodiversity and ecosystems changes have been asserted as global priorities (Butchart et al. 2010; Newbold et al. 2015; Venter et al. 2016). Ecologists assert the need to monitor the environment at a higher temporal and spatial resolution (Newbold et al. 2015). Technology research has thus begun to explore new forms of monitoring and ways of summarising and interpreting the large amount of acoustic data emanating from networks of environmental sensors. This paper investigates collaborative exploration and sensemaking of big environmental acoustic data using Computer Supported Cooperative Work (CSCW) DOI 10.1007/s10606-017-9286-9 © Springer Science+Business Media Dordrecht 2017