Gavin Pearson

1.1k citations
25 papers · 227 · h-index 7

Impact in

Papers in

Gavin Pearson

25 papers receiving 215 citations

Peers

Gavin Pearson
Comparison fields: 5 of 69
  • Health Informatics 14
  • Safety Research 35
  • Artificial Intelligence 108
  • Computer Networks and Communications 51
  • General Decision Sciences 4
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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 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202084
2 200831
3
Intelligence, Surveillance, and Reconnaissance fusion for coalition operations
200828
4 200813
5 20209
6 19919
7 20088
8
Distributed analytics and information science
20155
9 20085
10 20134
11 20114
12 20023
13 19823
14 19993
15 20173
16 20023
17 20242
18 20022
19 20122
20
An end to end life cycle for ISR in coalition networks
20091

About Gavin Pearson

Gavin Pearson is a scholar working on Artificial Intelligence, Computer Networks and Communications, Aerospace Engineering, Nuclear and High Energy Physics and Materials Chemistry, having authored 25 papers that have together received 227 indexed citations. Recurring topics across this work include Fusion materials and technologies (4 papers), Semantic Web and Ontologies (4 papers), Magnetic confinement fusion research (4 papers), Particle accelerators and beam dynamics (3 papers), Logic, Reasoning, and Knowledge (3 papers), Nuclear reactor physics and engineering (2 papers), AI-based Problem Solving and Planning (2 papers) and Nuclear and radioactivity studies (2 papers). The work is most often cited by research in Health Informatics (14 citations), Safety Research (35 citations), Artificial Intelligence (108 citations), Computer Networks and Communications (51 citations) and General Decision Sciences (4 citations). Gavin Pearson has collaborated with scholars based in United States, United Kingdom and Italy. Frequent co-authors include Tien Pham, Alun Preece, Supriyo Chakraborty, Federico Cerutti, Richard Tomsett, Dave Braines, Lance Kaplan, Mani Srivastava, Dinesh Verma and Wamberto Vasconcelos. 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 International Conference on Information Fusion.

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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