<div class="csl-bib-body">
<div class="csl-entry">Keskin, M., Qin, T., & Liu, B. (2023). A Framework and Practical Guidelines for Sharing Open Benchmark Datasets in Cartographic User Research Utilizing Neuroscientific Methods. In <i>Proceedings of the 18th International Conference on Location Based Services</i> (pp. 194–203). https://doi.org/10.34726/5701</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/194708
-
dc.identifier.uri
https://doi.org/10.34726/5701
-
dc.description.abstract
This paper presents a structured approach to creating benchmark datasets and proposes guidelines for open-data sharing in the field of cartographic user research using neuroscientific methods including but not limited to eye tracking, EEG, and fMRI. The unique complexities introduced by geospatial data and maps make geospatial tasks fundamentally different from those encountered in the experimental psychology or cognitive visualization domains. We argue that datasets capable of addressing specific cartographic problems possess significant value and hold the potential to become benchmarks. For instance, studying the cognitive load and strategies employed by map users during various map tasks can provide valuable insights for map design and serve as benchmarks in developing complexity algorithms for cartography. We emphasize that benchmarks should be tailored to specific scientific issues rather than solely focusing on data standards. Such benchmarks not only contribute to map usability research but also play a pivotal role in developing predictive models that consider the visual attention and map use capabilities of users. Researchers across domains bear the responsibility of actively seeking concrete methods to encourage the open sharing of experimental data, complemented by high-quality metadata. By fostering the creation of benchmark datasets and promoting open-data sharing, collaboration is enhanced, cartographic research advances, and the scientific community is empowered to effectively address cartographic challenges.
en
dc.language.iso
en
-
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
-
dc.subject
Eye tracking
en
dc.subject
EEG
en
dc.subject
fMRI
en
dc.subject
cartographic user research
en
dc.subject
open data benchmark dataset guidelines
en
dc.title
A Framework and Practical Guidelines for Sharing Open Benchmark Datasets in Cartographic User Research Utilizing Neuroscientific Methods
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/5701
-
dc.contributor.affiliation
University of Salzburg, Austria
-
dc.contributor.affiliation
Ghent University, Belgium
-
dc.contributor.affiliation
BYD (China), China
-
dc.relation.doi
10.34726/5400
-
dc.description.startpage
194
-
dc.description.endpage
203
-
dc.rights.holder
Authors
-
dc.type.category
Full-Paper Contribution
-
tuw.booktitle
Proceedings of the 18th International Conference on Location Based Services
-
tuw.relation.ispartof
10.34726/5400
-
tuw.researchTopic.id
I5
-
tuw.researchTopic.name
Visual Computing and Human-Centered Technology
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E000 - Technische Universität Wien
-
dc.identifier.libraryid
AC17202931
-
dc.description.numberOfPages
10
-
dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
18th International Conference on Location Based Services (LBS 2023)