Stephen M. Selkirk

31 total papers · 558 total citations
18 papers, 396 citations indexed

About

Stephen M. Selkirk is a scholar working on Pathology and Forensic Medicine, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Stephen M. Selkirk has authored 18 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Pathology and Forensic Medicine, 6 papers in Cellular and Molecular Neuroscience and 6 papers in Neurology. Recurrent topics in Stephen M. Selkirk's work include Neuroinflammation and Neurodegeneration Mechanisms (6 papers), Neuroscience and Neural Engineering (5 papers) and Muscle activation and electromyography studies (5 papers). Stephen M. Selkirk is often cited by papers focused on Neuroinflammation and Neurodegeneration Mechanisms (6 papers), Neuroscience and Neural Engineering (5 papers) and Muscle activation and electromyography studies (5 papers). Stephen M. Selkirk collaborates with scholars based in United States. Stephen M. Selkirk's co-authors include Robert H. Miller, Jeffrey R. Capadona, Madhumitha Ravikumar, Andrew V. Caprariello, Jiong Shi, Smrithi Sunil, Deborah S. Barkauskas, James Black, Robert L. Ruff and Patrick D. Smith and has published in prestigious journals such as Biomaterials, Annals of Neurology and Genomics.

In The Last Decade

Stephen M. Selkirk

17 papers receiving 390 citations

Author Peers

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

Author Last Decade Papers Cites
Stephen M. Selkirk 209 111 98 94 67 18 396
Stefano Lai 157 0.8× 157 1.4× 178 1.8× 51 0.5× 25 0.4× 13 431
Cassie Bennett 149 0.7× 66 0.6× 79 0.8× 101 1.1× 15 0.2× 12 360
John P. Woock 135 0.6× 53 0.5× 28 0.3× 21 0.2× 44 0.7× 9 375
Ana M. Lucas‐Osma 174 0.8× 46 0.4× 120 1.2× 31 0.3× 215 3.2× 14 429
Ute Neubacher 102 0.5× 129 1.2× 197 2.0× 41 0.4× 24 0.4× 13 425
A. Mellström 134 0.6× 50 0.5× 177 1.8× 37 0.4× 107 1.6× 19 425
Mark A. Liker 125 0.6× 62 0.6× 41 0.4× 238 2.5× 19 0.3× 30 337
Jun Zhang 83 0.4× 46 0.4× 37 0.4× 82 0.9× 67 1.0× 29 385
David Satzer 145 0.7× 85 0.8× 44 0.4× 189 2.0× 30 0.4× 23 382
Jan T. Hachmann 169 0.8× 69 0.6× 39 0.4× 82 0.9× 107 1.6× 13 376

Countries citing papers authored by Stephen M. Selkirk

Since Specialization
Citations

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

Fields of papers citing papers by Stephen M. Selkirk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen M. Selkirk

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