Sarah Boyd

31 papers receiving 635 citations

Peers

Sarah Boyd
Comparison fields: 5 of 140
  • Applied Microbiology and Biotechnology 18
  • Immunology and Allergy 55
  • Human-Computer Interaction 47
  • Algebra and Number Theory 34
  • Cancer Research 78
Replace John C. Schultz with:
John C. Schultz United States
Louis A. Kamentsky United States
Makoto Ishihara Japan
Seok Jin Kang South Korea
Sharon R. Lubkin United States
Falk Schubert Germany
Umesh Kumar India
Wenping Song China
James Shuttleworth United Kingdom
Yin Zhao China
Sarah Boyd relative to John C. Schultz United States John C. Schultz's profile →
Citations per field
00.5×11×
John C. Schultz · 1×
Citations per year

Countries citing papers authored by Sarah Boyd

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Boyd

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sarah Boyd, 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 Sarah Boyd Line = papers co-authored together Sarah Boyd 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 2017144
2 200581
3 200767
4 201763
5 198459
6 200851
7 200536
8 200430
9 199329
10 201816
11 201911
12 201710
13 199310
14 20227
15 20197
16 20187
17 20216
18 20184
19 20244
20 20233

About Sarah Boyd

Sarah Boyd is a scholar working on Microbiology, Molecular Biology, Infectious Diseases, Computer Vision and Pattern Recognition and Clinical Biochemistry, having authored 35 papers that have together received 664 indexed citations. Recurring topics across this work include Reproductive tract infections research (7 papers), Bacterial Identification and Susceptibility Testing (3 papers), Ubiquitin and proteasome pathways (2 papers), Data Visualization and Analytics (2 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), Advanced Operator Algebra Research (2 papers), Antimicrobial Resistance in Staphylococcus (2 papers) and Machine Learning in Bioinformatics (2 papers). The work is most often cited by research in Applied Microbiology and Biotechnology (18 citations), Immunology and Allergy (55 citations), Human-Computer Interaction (47 citations), Algebra and Number Theory (34 citations) and Cancer Research (78 citations). Sarah Boyd has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Michael H. Parsons, Andrew Smith, Oscar Meruvia-Pastor, Shiyao Wang, Minglun Gong, Peter Rogers, Iain Raeburn, T. D. Jickells, Richard E. Dodge and R. P. M. Bak. Their work appears in journals such as Ophthalmic Epidemiology, Journal of Biological Chemistry, International Health, Proceedings of the American Mathematical Society and Critical Care Medicine.

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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