Ryan Shaw

37 papers receiving 446 citations

Peers

Ryan Shaw
Comparison fields: 5 of 83
  • Computer Vision and Pattern Recognition 162
  • Artificial Intelligence 245
  • Signal Processing 52
  • Information Systems 108
  • Geography, Planning and Development 22
Replace Albert Meroño-Peñuela with:
Albert Meroño-Peñuela United Kingdom
Luis Martínez-Uribe Spain
Monica Lestari Paramita United Kingdom
Rao Shen United States
Ulrich Thiel Germany
Daniel Tunkelang United States
Neill A. Kipp United States
Roxane Segers Netherlands
Costantino Thanos Italy
Bert R. Boyce United States
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Citations per field
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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 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009173
2 200760
3 200630
4 201129
5 202023
6 201619
7 201518
8 201615
9 200515
10
LODE: Linking Open Descriptions of Events
200915
11 201511
12
Events and Periods as Concepts for Organizing Historical Knowledge
20109
13 20068
14 20127
15 20147
16 20087
17 20074
18
A Semantic Tool for Historical Events
20133
19
The missing profession: towards an institution of critical technical practice.
20193
20 20233

About Ryan Shaw

Ryan Shaw is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Computer Vision and Pattern Recognition and Sociology and Political Science, having authored 39 papers that have together received 491 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (17 papers), Natural Language Processing Techniques (7 papers), Biomedical Text Mining and Ontologies (7 papers), Data Quality and Management (4 papers), Video Analysis and Summarization (4 papers), Digital Humanities and Scholarship (4 papers), Advanced Database Systems and Queries (3 papers) and Service-Oriented Architecture and Web Services (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (162 citations), Artificial Intelligence (245 citations), Signal Processing (52 citations), Information Systems (108 citations) and Geography, Planning and Development (22 citations). Ryan Shaw has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Raphaël Troncy, Lynda Hardman, David A. Shamma, Patrick Schmitz, Yiming Liu, Marc Davis, Anindya Datta, Kaushik Dutta, Eric Kansa and Debra VanderMeer. Their work appears in journals such as Proceedings of the American Society for Information Science and Technology, PeerJ Computer Science, IEEE Transactions on Knowledge and Data Engineering, Bulletin of the Institute of Classical Studies and Biology of Blood and Marrow Transplantation.

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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