Ryan Shaw

33 papers receiving 246 citations

Peers

Ryan Shaw
Comparison fields: 5 of 66
  • Computer Vision and Pattern Recognition 90
  • Space and Planetary Science 5
  • Artificial Intelligence 95
  • Computer Science Applications 15
  • Information Systems 48
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Tanya Clement United States
Leo Iaquinta Italy
Xiaoguang Wang China
Stefan Boddie New Zealand
Albert Meroño-Peñuela United Kingdom
Ingo Frommholz United Kingdom
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Citations per year

Countries citing papers authored by Ryan Shaw

Since Specialization
Citations

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

Fields of papers citing papers by Ryan Shaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200746
2 200628
3 201127
4 202023
5 201618
6 201615
7 201515
8
LODE: Linking Open Descriptions of Events
200915
9 200514
10 201510
11 20068
12 20126
13 20146
14 20074
15
The missing profession: towards an institution of critical technical practice.
20193
16 20233
17 20133
18 20143
19 20132
20 20112

About Ryan Shaw

Ryan Shaw is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Molecular Biology and Sociology and Political Science, having authored 35 papers that have together received 267 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (14 papers), Natural Language Processing Techniques (7 papers), Biomedical Text Mining and Ontologies (5 papers), Video Analysis and Summarization (4 papers), Data Quality and Management (3 papers), Web Data Mining and Analysis (3 papers), Music and Audio Processing (3 papers) and Digital Humanities and Scholarship (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (90 citations), Space and Planetary Science (5 citations), Artificial Intelligence (95 citations), Computer Science Applications (15 citations) and Information Systems (48 citations). Ryan Shaw has collaborated with scholars based in United States, United Kingdom and France. Frequent co-authors include Patrick Schmitz, David A. Shamma, Yiming Liu, Kaushik Dutta, Debra VanderMeer, Anindya Datta, Marc Davis, Eric Kansa, Lynda Hardman and Raphaël Troncy. Their work appears in journals such as Proceedings of the American Society for Information Science and Technology, PeerJ Computer Science, Journal of the Association for Information Science and Technology, Big Data & Society and Transplantation and Cellular Therapy.

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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