Deborah Stacey

53 papers receiving 672 citations

Peers

Deborah Stacey
Comparison fields: 5 of 129
  • Agronomy and Crop Science 83
  • Computer Vision and Pattern Recognition 116
  • Artificial Intelligence 168
  • Physical Therapy, Sports Therapy and Rehabilitation 21
  • Infectious Diseases 85
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Karin Anna Hummel Austria
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Citations per year

Countries citing papers authored by Deborah Stacey

Since Specialization
Citations

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

Fields of papers citing papers by Deborah Stacey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 60 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200488
2
Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: A survey
201479
3 200661
4 200350
5 200345
6 202144
7 202143
8 200141
9 199832
10 200429
11 202322
12 201914
13 198613
14 201413
15 201911
16 19949
17 20228
18 20067
19 20037
20 20046

About Deborah Stacey

Deborah Stacey is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Public Health, Environmental and Occupational Health and Computer Networks and Communications, having authored 60 papers that have together received 708 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (8 papers), Neural Networks and Applications (7 papers), Zoonotic diseases and public health (6 papers), Data-Driven Disease Surveillance (5 papers), Animal Disease Management and Epidemiology (5 papers), Service-Oriented Architecture and Web Services (4 papers), Data Quality and Management (4 papers) and Viral gastroenteritis research and epidemiology (4 papers). The work is most often cited by research in Agronomy and Crop Science (83 citations), Computer Vision and Pattern Recognition (116 citations), Artificial Intelligence (168 citations), Physical Therapy, Sports Therapy and Rehabilitation (21 citations) and Infectious Diseases (85 citations). Deborah Stacey has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Stephen D. Prentice, A.E. Patla, Chaomin Luo, Simon X. Yang, Stefan C. Kremer, Rozita Dara, Shannon E. Majowicz, Susan Read, Kathryn Doré and Paul Sockett. Their work appears in journals such as Revue Scientifique et Technique de l OIE, Journal of Computational Science, Frontiers in Veterinary Science, Research in Veterinary Science and Journal of the American Veterinary Medical Association.

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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