Rajat Kumar Jha

31 total papers · 663 total citations
16 papers, 443 citations indexed

About

Rajat Kumar Jha is a scholar working on Molecular Biology, Computational Theory and Mathematics and Molecular Medicine. According to data from OpenAlex, Rajat Kumar Jha has authored 16 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 9 papers in Molecular Medicine. Recurrent topics in Rajat Kumar Jha's work include Computational Drug Discovery Methods (10 papers), Antibiotic Resistance in Bacteria (9 papers) and SARS-CoV-2 and COVID-19 Research (4 papers). Rajat Kumar Jha is often cited by papers focused on Computational Drug Discovery Methods (10 papers), Antibiotic Resistance in Bacteria (9 papers) and SARS-CoV-2 and COVID-19 Research (4 papers). Rajat Kumar Jha collaborates with scholars based in India, United States and Ethiopia. Rajat Kumar Jha's co-authors include Amit Kumar Singh, Rameez Jabeer Khan, Jayaraman Muthukumaran, Gizachew Muluneh Amera, Monika Jain, Ekampreet Singh, Amita Pathak, Rashmi Prabha Singh, Ankit Kumar and Changyou Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Microbial Pathogenesis and Journal of Biomolecular Structure and Dynamics.

In The Last Decade

Rajat Kumar Jha

16 papers receiving 436 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Rajat Kumar Jha 256 205 157 84 39 16 443
Rameez Jabeer Khan 262 1.0× 206 1.0× 165 1.1× 85 1.0× 40 1.0× 15 449
Gizachew Muluneh Amera 250 1.0× 200 1.0× 163 1.0× 79 0.9× 38 1.0× 11 445
Amita Pathak 210 0.8× 160 0.8× 165 1.1× 64 0.8× 35 0.9× 13 397
Ekampreet Singh 233 0.9× 196 1.0× 136 0.9× 78 0.9× 40 1.0× 15 401
Tebelay Dilnessa 163 0.6× 229 1.1× 112 0.7× 57 0.7× 19 0.5× 17 428
Sona Lyndem 173 0.7× 98 0.5× 139 0.9× 61 0.7× 58 1.5× 14 401
Shalini Mathpal 236 0.9× 118 0.6× 158 1.0× 68 0.8× 60 1.5× 30 485
Vemula Sarojamma 118 0.5× 175 0.9× 128 0.8× 27 0.3× 37 0.9× 13 427
Rashmi Prabha Singh 210 0.8× 189 0.9× 121 0.8× 91 1.1× 36 0.9× 16 486
David Ortega-Alarcón 156 0.6× 107 0.5× 188 1.2× 41 0.5× 50 1.3× 19 462

Countries citing papers authored by Rajat Kumar Jha

Since Specialization
Citations

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

Fields of papers citing papers by Rajat Kumar Jha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rajat Kumar Jha. 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 Rajat Kumar Jha. The network helps show where Rajat Kumar Jha may publish in the future.

Co-authorship network of co-authors of Rajat Kumar Jha

This figure shows the co-authorship network connecting the top 25 collaborators of Rajat Kumar Jha. A scholar is included among the top collaborators of Rajat Kumar Jha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rajat Kumar Jha. Rajat Kumar Jha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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