ST9. Data Visualization and Engaging Science (DataViz & Science)

Overview

Scientific knowledge and naturalistic inquiry are expanding exponentially, and demand an ability to digest information that is complex, interconnected, and conceptually challenging. The pressing grand challenges and “wicked” problems presented by living ecosystems, technology, resource distribution, and human health – to name a few –require an understanding and appreciation of, and even direct engagement with complex and large scale data sets. Drawing meaning and practical conclusions from data at this scale and scope is beyond the capacity of most people. While many techniques such as artificial intelligence, algorithmic analyses, and computer-assisted techniques, along with mapping and the creation of infographic displays have emerged as means for humans to better make sense of Big Data, Immersive Environments, with their rich and varied forms of display, situated learning capabilities, and ever broadening possibilities of human-computer interactions, offer a promising means by which we can meet the challenges that scientific datasets present.

What are the emerging immersive augmented, virtual reality, and mixed reality techniques for visualizing, manipulating, and interacting with scientific datasets? How can we visualize complexity in immersive environments and encourage people to engage in simulating and exploring data toward novel solutions? What are the ontological and epistemic issues surrounding particular scientific challenges that accompany large sets of data that immersive environments are uniquely poised to resolve? The Data Visualization and Engaging Science Special Track at iLRN 2019 will bring together scholars, developers, philanthropists, and designers interested in these and other related questions. Hands-on demos, workshops, demonstrations of working prototypes, and creative solutions within this problem area are encouraged.

List of Topics:

This special session welcomes submissions on (but not limited to) the following topics:

Analytics, Open Data, Citizen Science
Data Visualization, Complexity, Interactive Maps
Data Stories, Accessibility
Journalism, Big Data, Biometrics
Internet of Things, Predictive Analytics
Structured vs. Unstructured Data
Living Ecosystems, Artificial Intelligence
Machine Learning, Resource Distribution
Human Health, Economics

Author Info

Contributing papers have to undergo a double-blind peer review process and will be included in the conference proceedings, depending on the overall quality and track chair’s decision, either as long paper (10–12 pages) or as a short paper (6–8 pages). A selection of the best papers from Special Tracks will be considered to be published in the Springer proceedings. In order to keep the quality of full papers on a high standard, less than 35% are considered to be added to the Springer proceedings. The other papers will be added to a non-index proceedings volume assigned by an ISSN number and only online accessible.

Submitted papers must follow the same guidelines as the main conference submissions. Please visit https://immersivelrn.org/ilrn2019/authors-info/ for guidelines and templates. For submitting a paper to this special track, please use the submission system https://www.easychair.org/conferences/?conf=ilrn2019 , log in with an account or register, and select the track “ST9: Data Visualization and Engaging Science” to add your submission.

Special Track Chair

Jonathon Richter, United States Salish Kootenai College
Eliza Reilly, United States National Center for Science and Civic Engagement

Programme Committee

Eliza Reilly (@ElizaJReilly)
Brian W (@ImmersionXR)
Suzanne Borders (@SuzanneBorders)

Contact

For more information, please contact Jonathon_Richter@skc.edu