Amber T. Levine

19 total papers · 1.1k total citations
17 papers, 820 citations indexed

About

Amber T. Levine is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Amber T. Levine has authored 17 papers receiving a total of 820 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Cellular and Molecular Neuroscience, 5 papers in Cognitive Neuroscience and 4 papers in Molecular Biology. Recurrent topics in Amber T. Levine's work include Neuroscience and Neuropharmacology Research (9 papers), Memory and Neural Mechanisms (4 papers) and Epilepsy research and treatment (3 papers). Amber T. Levine is often cited by papers focused on Neuroscience and Neuropharmacology Research (9 papers), Memory and Neural Mechanisms (4 papers) and Epilepsy research and treatment (3 papers). Amber T. Levine collaborates with scholars based in United States and Finland. Amber T. Levine's co-authors include An T. Dao, Munder Zagaar, Karim A. Alkadhi, Samina Salim, Ibrahim A. Alhaider, Kechun Yang, John I. Broussard, John A. Dani, Iiris Hovatta and Craig T. Vollert and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Neuroscience and Scientific Reports.

In The Last Decade

Amber T. Levine

17 papers receiving 801 citations

Author Peers

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

Author Last Decade Papers Cites
Amber T. Levine 327 278 208 188 96 17 820
N Naghdi 267 0.8× 320 1.2× 176 0.8× 141 0.8× 117 1.2× 26 732
An T. Dao 288 0.9× 199 0.7× 353 1.7× 154 0.8× 127 1.3× 19 1.0k
Jan N. Keijser 253 0.8× 385 1.4× 175 0.8× 218 1.2× 111 1.2× 24 950
Valérie Bertaina‐Anglade 330 1.0× 290 1.0× 90 0.4× 324 1.7× 159 1.7× 19 965
Alan Jung Park 546 1.7× 466 1.7× 112 0.5× 258 1.4× 135 1.4× 20 954
Alfredo Manfridi 278 0.9× 391 1.4× 125 0.6× 285 1.5× 101 1.1× 19 942
Danielle Gulick 228 0.7× 280 1.0× 165 0.8× 268 1.4× 163 1.7× 38 790
Camilla L. Patti 384 1.2× 383 1.4× 160 0.8× 171 0.9× 122 1.3× 32 891
Deanna L. Wallace 357 1.1× 403 1.4× 92 0.4× 195 1.0× 67 0.7× 15 977
Diana C. Ferrari 258 0.8× 288 1.0× 140 0.7× 231 1.2× 78 0.8× 16 914

Countries citing papers authored by Amber T. Levine

Since Specialization
Citations

This map shows the geographic impact of Amber T. Levine'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 Amber T. Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amber T. Levine more than expected).

Fields of papers citing papers by Amber T. Levine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amber T. Levine. 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 Amber T. Levine. The network helps show where Amber T. Levine may publish in the future.

Co-authorship network of co-authors of Amber T. Levine

This figure shows the co-authorship network connecting the top 25 collaborators of Amber T. Levine. A scholar is included among the top collaborators of Amber T. Levine 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 Amber T. Levine. Amber T. Levine 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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