DatAR: An Immersive Literature Exploration Environment for Neuroscientists

Ivar Troost; Ghazaleh Tanhaei; Lynda Hardman; Wolfgang Hürst

doi:10.1109/AIVR50618.2020.00020

DatAR: An Immersive Literature Exploration Environment for Neuroscientists

Ivar Troost, Ghazaleh Tanhaei, Lynda Hardman, Wolfgang Hürst

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system's design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details.

Original language	English
Title of host publication	IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
Publisher	IEEE
Pages	55-56
Number of pages	2
ISBN (Electronic)	978-1-7281-7463-1
DOIs	https://doi.org/10.1109/AIVR50618.2020.00020
Publication status	Published - Dec 2020
Event	3rd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020 - Virtual, Utrecht, Netherlands Duration: 14 Dec 2020 → 18 Dec 2020

Conference

Conference	3rd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020
Country/Territory	Netherlands
City	Virtual, Utrecht
Period	14/12/20 → 18/12/20

Keywords

augmented reality
Immersive analytics
linked data
literature exploration
neuroscience

Access to Document

10.1109/AIVR50618.2020.00020

DatAR_An_Immersive_Literature_Exploration_Environment_for_NeuroscientistsFinal published version, 77.8 KBLicence: Taverne

Cite this

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title = "DatAR: An Immersive Literature Exploration Environment for Neuroscientists",

abstract = "Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system's design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details. ",

keywords = "augmented reality, Immersive analytics, linked data, literature exploration, neuroscience",

author = "Ivar Troost and Ghazaleh Tanhaei and Lynda Hardman and Wolfgang H{\"u}rst",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020 ; Conference date: 14-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

doi = "10.1109/AIVR50618.2020.00020",

language = "English",

pages = "55--56",

booktitle = "IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)",

publisher = "IEEE",

address = "United States",

}

Troost, I, Tanhaei, G, Hardman, L & Hürst, W 2020, DatAR: An Immersive Literature Exploration Environment for Neuroscientists. in IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)., 9319103, IEEE, pp. 55-56, 3rd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020, Virtual, Utrecht, Netherlands, 14/12/20. https://doi.org/10.1109/AIVR50618.2020.00020

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T1 - DatAR

T2 - 3rd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020

AU - Troost, Ivar

AU - Tanhaei, Ghazaleh

AU - Hardman, Lynda

AU - Hürst, Wolfgang

PY - 2020/12

Y1 - 2020/12

N2 - Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system's design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details.

AB - Maintaining an overview of publications in the neuroscientific field is challenging, especially with an eye to finding relations at scale; for example, between brain regions and diseases. This is true for well-studied as well as nascent relationships. To support neuroscientists in this challenge, we developed an Immersive Analytics (IA) prototype for the analysis of relationships in large collections of scientific papers. In our video demonstration we showcase the system's design and capabilities using a walkthrough and mock user scenario. This companion paper relates our prototype to previous IA work and offers implementation details.

KW - augmented reality

KW - Immersive analytics

KW - linked data

KW - literature exploration

KW - neuroscience

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DO - 10.1109/AIVR50618.2020.00020

M3 - Conference contribution

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BT - IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

PB - IEEE

Y2 - 14 December 2020 through 18 December 2020

ER -

DatAR: An Immersive Literature Exploration Environment for Neuroscientists

Abstract

Conference

Keywords

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