Daniela Trevisan

1.2k citations
102 papers · 860 · h-index 15

Impact in

Papers in

Daniela Trevisan

83 papers receiving 796 citations

Peers

Daniela Trevisan
Comparison fields: 5 of 112
  • Human-Computer Interaction 339
  • Human Factors and Ergonomics 32
  • Computer Vision and Pattern Recognition 234
  • Neurology 108
  • Occupational Therapy 23
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Ian Williams United Kingdom
Cecília Sik‐Lányi Hungary
Adalberto L. Simeone Belgium
Edmund LoPresti United States
Dayang Rohaya Awang Rambli Malaysia
Fátima L. S. Nunes Brazil
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Citations per field
00.5×8.3×
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Citations per year

Countries citing papers authored by Daniela Trevisan

Since Specialization
Citations

This map shows the geographic impact of Daniela Trevisan'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 Daniela Trevisan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Trevisan more than expected).

Fields of papers citing papers by Daniela Trevisan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniela Trevisan. 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 Daniela Trevisan. The network helps show where Daniela Trevisan may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniela Trevisan, 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 Daniela Trevisan Line = papers co-authored together Daniela Trevisan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1991109
2 201792
3 200966
4 199061
5 200832
6 201031
7 202125
8 202123
9 200722
10 202022
11
UsiXML: A User Interface Description Language for Context-Sensitive User Interfaces
200421
12 202020
13 201719
14 201117
15 202016
16 201914
17 201513
18 200612
19
Detecting Interaction Variables in a Mixed Reality System for Maxillofacial-guided Surgery
200612
20 200411

About Daniela Trevisan

Daniela Trevisan is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Information Systems and Human Factors and Ergonomics, having authored 102 papers that have together received 860 indexed citations. Recurring topics across this work include Virtual Reality Applications and Impacts (32 papers), Augmented Reality Applications (28 papers), Interactive and Immersive Displays (24 papers), Tactile and Sensory Interactions (11 papers), Digital Accessibility for Disabilities (8 papers), Usability and User Interface Design (7 papers), Data Visualization and Analytics (6 papers) and Visual Attention and Saliency Detection (6 papers). The work is most often cited by research in Human-Computer Interaction (339 citations), Human Factors and Ergonomics (32 citations), Computer Vision and Pattern Recognition (234 citations), Neurology (108 citations) and Occupational Therapy (23 citations). Daniela Trevisan has collaborated with scholars based in Brazil, Belgium and France. Frequent co-authors include Esteban Clua, Cristina Nader Vasconcelos, Jean Vanderdonckt, Francesco Paolo Di Maio, Alberto Di Renzo, Ettore Beghi, Luis Valente, José Viterbo, Flávia Bernardini and Valter Torri. Their work appears in journals such as Virtual Reality, Lecture notes in computer science, Epilepsia, Powder Technology and International Journal of Human-Computer Studies.

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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