Daniel M. Kemp

2.3k citations
32 papers · 1.5k · h-index 19

Impact in

Papers in

    • Receptor Mechanisms and Signaling 4
    • Muscle Physiology and Disorders 4
    • Metabolism, Diabetes, and Cancer 3
    • CRISPR and Genetic Engineering 3
    • Pancreatic function and diabetes 9

Daniel M. Kemp

31 papers receiving 1.4k citations

Peers

Daniel M. Kemp
Comparison fields: 5 of 100
  • Endocrine and Autonomic Systems 113
  • Genetics 160
  • Endocrinology, Diabetes and Metabolism 232
  • Genetics 384
  • Surgery 562
Replace Catherine Seva with:
Catherine Seva France
Patrick W. Kleyn United States
Luca Grumolato France
Alena Shostak United States
Carolina M. Greco United States
Lynda Elghazi United States
Kyoung-Han Kim Canada
Tomohiko Okuda Japan
David M. Flavell United Kingdom
Miguel Lucas Spain
Daniel M. Kemp relative to Catherine Seva France Catherine Seva's profile →
Citations per field
00.5×2.9×
Catherine Seva · 1×
Citations per year

Countries citing papers authored by Daniel M. Kemp

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Kemp

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel M. Kemp. 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 Daniel M. Kemp. The network helps show where Daniel M. Kemp may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004315
2 2006129
3 2002104
4 201199
5 200598
6 200985
7 200465
8 201257
9 200556
10 200656
11 201247
12 201242
13 196642
14 201742
15 200130
16 200529
17 200324
18 200324
19 200520
20 200214

About Daniel M. Kemp

Daniel M. Kemp is a scholar working on Molecular Biology, Surgery, Genetics, Physiology and Endocrinology, Diabetes and Metabolism, having authored 32 papers that have together received 1.5k indexed citations. Recurring topics across this work include Pancreatic function and diabetes (9 papers), Adipose Tissue and Metabolism (5 papers), Receptor Mechanisms and Signaling (4 papers), Genetic Associations and Epidemiology (4 papers), Diabetes Treatment and Management (4 papers), Muscle Physiology and Disorders (4 papers), Metabolism, Diabetes, and Cancer (3 papers) and CRISPR and Genetic Engineering (3 papers). The work is most often cited by research in Endocrine and Autonomic Systems (113 citations), Genetics (160 citations), Endocrinology, Diabetes and Metabolism (232 citations), Genetics (384 citations) and Surgery (562 citations). Daniel M. Kemp has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Joel F. Habener, Melissa K. Thomas, Mariano Ubeda, Penelope A. Kosinski, Gung‐Wei Chirn, Stephen J. Elliman, Danielle M. Greenawalt, Lee M. Kaplan, Radu Dobrin and Eric E. Schadt. Their work appears in journals such as Endocrinology, Journal of Biological Chemistry, Biochemical and Biophysical Research Communications, SLAS DISCOVERY and BioData Mining.

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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