David Shaw

528 citations
20 papers · 398 · h-index 12

Impact in

Papers in

    • Viral Infectious Diseases and Gene Expression in Insects 10
    • CRISPR and Genetic Engineering 7
    • Protein purification and stability 5
    • Advanced biosensing and bioanalysis techniques 3
    • Monoclonal and Polyclonal Antibodies Research 4

David Shaw

20 papers receiving 364 citations

Peers

David Shaw
Comparison fields: 5 of 60
  • Molecular Biology 304
  • Radiology, Nuclear Medicine and Imaging 69
  • Genetics 88
  • Oncology 68
  • Biotechnology 20
Replace Leon P. Pybus with:
Leon P. Pybus United Kingdom
Jerry Wu United States
Yiren Xu China
Christine Lattenmayer Austria
Evelyn Trummer Austria
Anna A. Nushtaeva Russia
Richard J. Connolly United States
Alexander Mäder Austria
Nathalie van den Tempel Netherlands
David Shaw relative to Leon P. Pybus United Kingdom Leon P. Pybus's profile →
Citations per field
00.5×1.5×2.3×
Leon P. Pybus · 1×
Citations per year

Countries citing papers authored by David Shaw

Since Specialization
Citations

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

Fields of papers citing papers by David Shaw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 202055
2 202254
3 202143
4 201642
5 201742
6 201528
7 201828
8 201717
9 201715
10 201714
11 201513
12 201711
13 200411
14 200510
15 20194
16 20154
17 20172
18 20192
19
The API 1149 Update, Model-Based Leak Detection Uncertainty Assessment
20152
20 20241

About David Shaw

David Shaw is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Oncology and Genetics, having authored 20 papers that have together received 398 indexed citations. Recurring topics across this work include Viral Infectious Diseases and Gene Expression in Insects (10 papers), CRISPR and Genetic Engineering (7 papers), Protein purification and stability (5 papers), Monoclonal and Polyclonal Antibodies Research (4 papers), Microfluidic and Bio-sensing Technologies (3 papers), Advanced biosensing and bioanalysis techniques (3 papers), CAR-T cell therapy research (3 papers) and Structural Integrity and Reliability Analysis (2 papers). The work is most often cited by research in Molecular Biology (304 citations), Radiology, Nuclear Medicine and Imaging (69 citations), Genetics (88 citations), Oncology (68 citations) and Biotechnology (20 citations). David Shaw has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Brad Snedecor, Michael W. Laird, Shahram Misaghi, Zhilan Hu, Amy Shen, Benjamin Haley, Salina Louie, Danming Tang, Cynthia Lam and Steven Lang. Their work appears in journals such as Biotechnology Progress, Current Cancer Drug Targets, Analytical Chemistry, Biotechnology and Bioengineering and Biotechnology Journal.

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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