Gina M. Many

52 total papers · 1.1k total citations
22 papers, 445 citations indexed

About

Gina M. Many is a scholar working on Physiology, Molecular Biology and Rehabilitation. According to data from OpenAlex, Gina M. Many has authored 22 papers receiving a total of 445 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Physiology, 9 papers in Molecular Biology and 4 papers in Rehabilitation. Recurrent topics in Gina M. Many's work include Muscle Physiology and Disorders (8 papers), Adipose Tissue and Metabolism (5 papers) and Exercise and Physiological Responses (4 papers). Gina M. Many is often cited by papers focused on Muscle Physiology and Disorders (8 papers), Adipose Tissue and Metabolism (5 papers) and Exercise and Physiological Responses (4 papers). Gina M. Many collaborates with scholars based in United States, Canada and Japan. Gina M. Many's co-authors include Marcas M. Bamman, Samuel T. Windham, S. Craig Tuggle, Neil Kelly, Michael J. Stec, Eric P. Hoffman, C. Scott Bickel, David L. Mayhew, Anna Thalacker‐Mercer and Brandon M. Roberts and has published in prestigious journals such as PLoS ONE, Medicine & Science in Sports & Exercise and American Journal Of Pathology.

In The Last Decade

Gina M. Many

22 papers receiving 436 citations

Author Peers

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

Author						Papers	Cites
Gina M. Many	209	166	119	83	66	22	445
José A. Arevalo	150 0.7×	168 1.0×	92 0.8×	81 1.0×	51 0.8×	31	405
C. R. Kirby	180 0.9×	250 1.5×	105 0.9×	38 0.5×	34 0.5×	15	404
Tatiane Gorski	158 0.8×	136 0.8×	51 0.4×	99 1.2×	32 0.5×	14	367
Cameron Hill	180 0.9×	196 1.2×	102 0.9×	92 1.1×	19 0.3×	22	423
Macsue Jacques	127 0.6×	144 0.9×	76 0.6×	85 1.0×	88 1.3×	27	378
Brittany C. Collins	244 1.2×	185 1.1×	68 0.6×	61 0.7×	52 0.8×	15	473
Andrew C. D’Lugos	206 1.0×	226 1.4×	203 1.7×	55 0.7×	36 0.5×	28	481
Anne Fredsted	113 0.5×	141 0.8×	72 0.6×	86 1.0×	99 1.5×	12	460
Sadayoshi Taguchi	133 0.6×	203 1.2×	59 0.5×	37 0.4×	62 0.9×	27	404
Kaylan M. Haizlip	139 0.7×	156 0.9×	68 0.6×	74 0.9×	46 0.7×	11	489

Countries citing papers authored by Gina M. Many

Since Specialization

Citations

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

Fields of papers citing papers by Gina M. Many

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gina M. Many

This figure shows the co-authorship network connecting the top 25 collaborators of Gina M. Many. A scholar is included among the top collaborators of Gina M. Many 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 Gina M. Many. Gina M. Many 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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