Eva Mayr

48 papers receiving 504 citations

Peers

Eva Mayr
Comparison fields: 5 of 95
  • Computer Vision and Pattern Recognition 284
  • Human-Computer Interaction 75
  • Museology 42
  • Neuropsychology and Physiological Psychology 18
  • Ecological Modeling 26
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Ray Lc Hong Kong
Angeliki Antoniou Greece
Mitchell Whitelaw Australia
Florian Windhager Austria
Rossana Damiano Italy
Vincenzo Lombardo Italy
Trevor Collins United Kingdom
Laia Pujol Greece
Florian Block United Kingdom
Athanasios Manitsaris Greece
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Citations per year

Countries citing papers authored by Eva Mayr

Since Specialization
Citations

This map shows the geographic impact of Eva Mayr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Eva Mayr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eva Mayr more than expected).

Fields of papers citing papers by Eva Mayr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eva Mayr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Eva Mayr. The network helps show where Eva Mayr may publish in the future.

Co-authors

The 25 scholars most cited alongside Eva Mayr, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eva Mayr Line = papers co-authored together Eva Mayr links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018114
2 200968
3 200928
4 200927
5 201127
6 201223
7 201921
8 201320
9 201919
10 200919
11 201818
12
In-sights into mobile learning: An exploration of mobile eye tracking methodology for learning in museums
200915
13 202114
14 201610
15
Potentials and Challenges of Mobile Media in Museums
200710
16 20169
17
A Review of Information Visualization Approaches and Interfaces to Digital Cultural Heritage Collections.
20167
18 20126
19 20106
20
The Game Lies in the Eye of the Beholder: The Influence of Expertise on Watching Soccer
20105

About Eva Mayr

Eva Mayr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Experimental and Cognitive Psychology, Museology and Sociology and Political Science, having authored 54 papers that have together received 531 indexed citations. Recurring topics across this work include Data Visualization and Analytics (26 papers), Museums and Cultural Heritage (8 papers), Video Analysis and Summarization (8 papers), Digital Humanities and Scholarship (7 papers), Visual and Cognitive Learning Processes (7 papers), Advanced Text Analysis Techniques (6 papers), Multimedia Communication and Technology (5 papers) and Semantic Web and Ontologies (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (284 citations), Human-Computer Interaction (75 citations), Museology (42 citations), Neuropsychology and Physiological Psychology (18 citations) and Ecological Modeling (26 citations). Eva Mayr has collaborated with scholars based in Austria, Germany and Switzerland. Frequent co-authors include Florian Windhager, Michael Smuc, Silvia Miksch, Günther Schreder, Paolo Federico, Ulrike Sirsch, Ulrike Willinger, Marian Dörk, Hanna Risku and Daniel Wessel. Their work appears in journals such as IEEE Computer Graphics and Applications, IEEE Transactions on Visualization and Computer Graphics, Journal of Adolescent Research, Visual Informatics and Journal of Multimedia.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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