Charley M. Wu

121 total papers · 1.3k total citations
27 papers, 515 citations indexed

About

Charley M. Wu is a scholar working on Developmental and Educational Psychology, Sociology and Political Science and Artificial Intelligence. According to data from OpenAlex, Charley M. Wu has authored 27 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Developmental and Educational Psychology, 7 papers in Sociology and Political Science and 7 papers in Artificial Intelligence. Recurrent topics in Charley M. Wu's work include Child and Animal Learning Development (7 papers), Evolutionary Game Theory and Cooperation (7 papers) and Decision-Making and Behavioral Economics (6 papers). Charley M. Wu is often cited by papers focused on Child and Animal Learning Development (7 papers), Evolutionary Game Theory and Cooperation (7 papers) and Decision-Making and Behavioral Economics (6 papers). Charley M. Wu collaborates with scholars based in Germany, United States and United Kingdom. Charley M. Wu's co-authors include Eric Schulz, Björn Meder, Azzurra Ruggeri, Maarten Speekenbrink, Jonathan D. Nelson, Timothy J. Pleskac, Simon Ciranka, Flavia Filimon, Mona M. Garvert and Nicolas W. Schuck and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Charley M. Wu

26 papers receiving 508 citations

Author Peers

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

Author						Papers	Cites
Charley M. Wu	186	136	124	98	90	27	515
Jean-Paul Caverni	140 0.8×	118 0.9×	69 0.6×	129 1.3×	63 0.7×	21	504
N. E. Wetherick	172 0.9×	156 1.1×	164 1.3×	170 1.7×	27 0.3×	53	538
Colleen F. Surber	122 0.7×	208 1.5×	109 0.9×	49 0.5×	91 1.0×	26	487
Dries Trippas	274 1.5×	102 0.8×	63 0.5×	156 1.6×	91 1.0×	16	597
Pauline Austin Adams	254 1.4×	121 0.9×	99 0.8×	57 0.6×	25 0.3×	24	566
Lena Nadarevic	289 1.6×	67 0.5×	91 0.7×	105 1.1×	196 2.2×	23	512
Jean Baratgin	91 0.5×	148 1.1×	83 0.7×	240 2.4×	31 0.3×	46	571
Jonathan F. Kominsky	247 1.3×	265 1.9×	106 0.9×	49 0.5×	87 1.0×	35	540
Lisbeth S. Fried	88 0.5×	209 1.5×	104 0.8×	109 1.1×	64 0.7×	22	461
Brian D. Glass	196 1.1×	179 1.3×	180 1.5×	90 0.9×	64 0.7×	19	541

Countries citing papers authored by Charley M. Wu

Since Specialization

Citations

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

Fields of papers citing papers by Charley M. Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charley M. Wu

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