Exploring Relations in Neuroscientific Literature using Augmented Reality: A Design Study

To support scientists in maintaining an overview of disciplinary concepts and their interrelations, we investigate whether Augmented Reality can serve as a platform to make automated methods more accessible and integrated into current literature exploration practices. Building on insights from text and immersive analytics, we identify information and design requirements. We embody these in DatAR, a system design and implementation focussed on analysis of co-occurrences in neuroscientific text collections. We conducted a scenario-based video survey with a sample of neuroscientists and other domain experts, focusing on participants’ willingness to adopt such an AR system in their regular literature review practices. The AR-tailored epistemic and representational designs of our system were generally perceived as suitable for performing complex analytics. We also discuss several fundamental issues with our chosen 3D visualisations, making steps towards understanding in which ways AR is a suitable medium for high-level conceptual literature exploration.

[1]  D. Swanson.  Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge , 2015, Perspectives in biology and medicine.

[2]  Tiziana di Matteo,et al.  Graph Theory Enables Drug Repurposing – How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action , 2013, PloS one.

[3]  Mohammad Chuttur,et al.  Overview of the Technology Acceptance Model: Origins, Developments and Future Directions , 2009 .

[4]  Nicholas J. Belkin,et al.  Investigation of users' knowledge change process in learning‐related search tasks , 2019, ASIST.

[5]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[6]  Ross T. Smith,et al.  Situated Analytics , 2018, Immersive Analytics.

[7]  Lynda Hardman,et al.  DatAR: Your Brain, Your Data, On Your Desk - A Research Proposal , 2019, 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR).

[8]  Albrecht Schmidt,et al.  Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality , 2020, CHI.

[9]  Marc Bron,et al.  Exploration and contextualization through interaction and concepts , 2013 .

[10]  Silvia Miksch,et al.  A Survey on Visual Approaches for Analyzing Scientific Literature and Patents , 2017, IEEE Transactions on Visualization and Computer Graphics.

[11]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[12]  Tovi Grossman,et al.  Ivy: Exploring Spatially Situated Visual Programming for Authoring and Understanding Intelligent Environments , 2017, Graphics Interface.

[13]  Bruce H. Thomas,et al.  Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display? , 2017, IEEE Transactions on Visualization and Computer Graphics.

[14]  Adrien Fonnet,et al.  Survey of Immersive Analytics , 2021, IEEE Transactions on Visualization and Computer Graphics.

[15]  K. Bretonnel Cohen,et al.  Visualization and Language Processing for Supporting Analysis across the Biomedical Literature , 2010, KES.

[16]  Greg Welch,et al.  Situated Analytics , 2018, Immersive Analytics.

[17]  Antonio Rizzo,et al.  Scenarios: Heuristics for action , 2004 .

[18]  Lynda Hardman,et al.  DatAR: An Immersive Literature Exploration Environment for Neuroscientists , 2020, 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR).

[19]  Maria-Esther Vidal,et al.  Towards a Knowledge Graph for Science , 2018, WIMS.

[20]  Emilie M. Roth,et al.  Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis , 1999, Cognition, Technology & Work.

[21]  Ernestina Menasalvas Ruiz,et al.  New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification , 2016, Inf. Vis..

[22]  Jian Chen,et al.  Immersive Analytics: Time to Reconsider the Value of 3D for Information Visualisation , 2018, Immersive Analytics.

[23]  Daniel Baier,et al.  How augmented reality apps are accepted by consumers : a comparative analysis using scales and opinions , 2017 .

[24]  Paul T. Groth,et al.  A Semantic Web Primer. - 3rd ed. , 2012, CoopIS 2012.

[25]  Jason Dykes,et al.  Criteria for Rigor in Visualization Design Study , 2019, IEEE Transactions on Visualization and Computer Graphics.

[26]  Alan R. Hevner,et al.  POSITIONING AND PRESENTING DESIGN SCIENCE RESEARCH FOR MAXIMUM IMPACT 1 , 2013 .

[27]  Aidong Zhang,et al.  A survey on literature based discovery approaches in biomedical domain , 2019, J. Biomed. Informatics.

[28]  Matthias Klapperstück,et al.  Immersive Analytics , 2015, 2015 Big Data Visual Analytics (BDVA).

[29]  Jiawan Zhang,et al.  Galex: Exploring the Evolution and Intersection of Disciplines , 2020, IEEE Transactions on Visualization and Computer Graphics.

[30]  Bruce H. Thomas,et al.  ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate Data Visualisation , 2017, UIST.

[31]  Jennifer Ann Rode,et al.  Looking past yesterday's tomorrow: using futures studies methods to extend the research horizon , 2013, CHI.

[32]  Won-Ki Jeong,et al.  DXR: A Toolkit for Building Immersive Data Visualizations , 2019, IEEE Transactions on Visualization and Computer Graphics.