Binayak Sinha

1.7k citations
56 papers · 1.1k · h-index 16

Impact in

Papers in

Binayak Sinha

48 papers receiving 1.0k citations

Peers

Binayak Sinha
Comparison fields: 5 of 105
  • Endocrinology, Diabetes and Metabolism 679
  • Modeling and Simulation 60
  • Reproductive Medicine 98
  • Behavioral Neuroscience 26
  • Pharmacology 114
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Fahim Ebrahimi Switzerland
Samit Ghosal India
Maria Inês Reinert Azambuja Brazil
Anna Gottschlich Canada
Erika Laurini de Souza Meyer Brazil
Jarred B. McAteer United States
Aaron Leong United States
Angeliki M. Angelidi United States
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Citations per year

Countries citing papers authored by Binayak Sinha

Since Specialization
Citations

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

Fields of papers citing papers by Binayak Sinha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002159
2 2004157
3 2020151
4 201978
5 202077
6 202166
7 202062
8 200336
9 202024
10 201321
11 201921
12 202221
13 202021
14 201717
15 201816
16 201315
17 201313
18 201913
19 202111
20 20219

About Binayak Sinha

Binayak Sinha is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology, Pharmacology, Surgery and Infectious Diseases, having authored 56 papers that have together received 1.1k indexed citations. Recurring topics across this work include Diabetes Treatment and Management (30 papers), Diabetes Management and Research (19 papers), Metabolism, Diabetes, and Cancer (12 papers), Pharmacology and Obesity Treatment (8 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (6 papers), Pancreatic function and diabetes (6 papers), Hormonal Regulation and Hypertension (5 papers) and COVID-19 Clinical Research Studies (5 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (679 citations), Modeling and Simulation (60 citations), Reproductive Medicine (98 citations), Behavioral Neuroscience (26 citations) and Pharmacology (114 citations). Binayak Sinha has collaborated with scholars based in India, United Kingdom and United States. Frequent co-authors include Samit Ghosal, Jeremy Tomlinson, Paul M. Stewart, Iwona Bujalska, Anoop Misra, Martin Hewison, Wiebke Arlt, Marcus Quinkler, Amerta Ghosh and Koel Dutta. Their work appears in journals such as Diabetes & Metabolic Syndrome Clinical Research & Reviews, Diabetes Therapy, Diabetes Research and Clinical Practice, Frontiers in Endocrinology and Scientific Reports.

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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