DatAR: Supporting Neuroscience Literature Exploration by Finding Relations Between Topics in Augmented Reality

Publication date

2024

Authors

Xu, Boyu
Tanhaei, Ghazaleh
Hardman, Lynda
Hurst, WolfgangISNI 000000035205226X

Editors

Rudinac, Stevan
Worring, Marcel
Liem, Cynthia
Hanjalic, Alan
Jónsson, Björn Pór
Yamakata, Yoko
Liu, Bei

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

We present DatAR, an Augmented Reality prototype designed to support neuroscientists in finding fruitful directions to explore in their own research. DatAR provides an immersive analytics environment for exploring relations between topics published in the neuroscience literature. Neuroscientists need to analyse large numbers of publications in order to understand whether a potential experiment is likely to yield a valuable contribution. Using a user-centred design approach, we have identified useful tasks in collaboration with neuroscientists and implemented corresponding functionalities in DatAR. This facilitates querying and visualising relations between topics. Participating neuroscientists have stated that the DatAR prototype assists them in exploring and visualising seldom-mentioned direct relations and also indirect relations between brain regions and brain diseases. We present the latest incarnation of DatAR and illustrate the use of the prototype to carry out two realistic tasks to identify fruitful experiments.

Keywords

Augmented Reality, Data Visualisation, Human-centered Computing, Topic-based Literature Exploration, Taverne, Theoretical Computer Science, General Computer Science

Citation

Xu, B, Tanhaei, G, Hardman, L & Hürst, W 2024, DatAR : Supporting Neuroscience Literature Exploration by Finding Relations Between Topics in Augmented Reality. in S Rudinac, M Worring, C Liem, A Hanjalic, B P Jónsson, Y Yamakata & B Liu (eds), MultiMedia Modeling - 30th International Conference, MMM 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14557 LNCS, Springer, pp. 295-300, 30th International Conference on MultiMedia Modeling, MMM 2024, Amsterdam, Netherlands, 29/01/24. https://doi.org/10.1007/978-3-031-53302-0_24, conference