Gavin Pearson

29 papers receiving 336 citations

Peers

Gavin Pearson
Comparison fields: 5 of 73
  • Health Informatics 18
  • Safety Research 44
  • Artificial Intelligence 177
  • Computer Networks and Communications 97
  • Information Systems 80
Replace Parus Khuwaja with:
Parus Khuwaja Pakistan
Doris Xin United States
Julia Badger United States
Ghadah Aldabbagh Saudi Arabia
Toru Nakamura Japan
Salam Al-E’mari Jordan
Ziyi Kou United States
José M. Álvarez Spain
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Citations per field
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Citations per year

Countries citing papers authored by Gavin Pearson

Since Specialization
Citations

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

Fields of papers citing papers by Gavin Pearson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020111
2 200871
3 200839
4
Intelligence, Surveillance, and Reconnaissance fusion for coalition operations
200829
5 200815
6 202012
7 19919
8 20088
9
Distributed analytics and information science
20157
10 20085
11 20115
12 20025
13 19824
14 20024
15 20174
16 20134
17 20174
18 19993
19 20193
20 20242

About Gavin Pearson

Gavin Pearson is a scholar working on Artificial Intelligence, Computer Networks and Communications, Aerospace Engineering, Sociology and Political Science and Information Systems, having authored 29 papers that have together received 356 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (5 papers), Fusion materials and technologies (4 papers), Magnetic confinement fusion research (4 papers), Service-Oriented Architecture and Web Services (3 papers), AI-based Problem Solving and Planning (3 papers), Logic, Reasoning, and Knowledge (3 papers), Particle accelerators and beam dynamics (3 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Health Informatics (18 citations), Safety Research (44 citations), Artificial Intelligence (177 citations), Computer Networks and Communications (97 citations) and Information Systems (80 citations). Gavin Pearson has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Alun Preece, Tien Pham, Lance Kaplan, Richard Tomsett, Federico Cerutti, Dave Braines, Mani Srivastava, Supriyo Chakraborty, Geeth de Mel and Thomas La Porta. Their work appears in journals such as Journal of Vacuum Science & Technology A Vacuum Surfaces and Films, Review of Scientific Instruments, Journal of Nuclear Materials, Patterns and Lecture notes in computer science.

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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