Mayu Ukai

93 total papers · 816 total citations
45 papers, 604 citations indexed

About

Mayu Ukai is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Obstetrics and Gynecology. According to data from OpenAlex, Mayu Ukai has authored 45 papers receiving a total of 604 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Cellular and Molecular Neuroscience, 17 papers in Molecular Biology and 9 papers in Obstetrics and Gynecology. Recurrent topics in Mayu Ukai's work include Neuropeptides and Animal Physiology (18 papers), Receptor Mechanisms and Signaling (15 papers) and Neurotransmitter Receptor Influence on Behavior (8 papers). Mayu Ukai is often cited by papers focused on Neuropeptides and Animal Physiology (18 papers), Receptor Mechanisms and Signaling (15 papers) and Neurotransmitter Receptor Influence on Behavior (8 papers). Mayu Ukai collaborates with scholars based in Japan, United States and Switzerland. Mayu Ukai's co-authors include Tsutomu Kameyama, S G Holtzman, T Kameyama, Michinori Mayama, Jiro Itoh, Yasuyuki Kishigami, Hidenori Oguchi, Sho Tano, Masato Yoshihara and Takayoshi Mamiya and has published in prestigious journals such as Annals of the New York Academy of Sciences, American Journal of Obstetrics and Gynecology and Journal of Pharmacology and Experimental Therapeutics.

In The Last Decade

Mayu Ukai

38 papers receiving 592 citations

Author Peers

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

Author						Papers	Cites
Mayu Ukai	344	290	100	86	82	45	604
Mohammad Hadi Gharedaghi	103 0.3×	137 0.5×	42 0.4×	92 1.1×	21 0.3×	30	590
Réka Brubel	342 1.0×	165 0.6×	81 0.8×	46 0.5×	83 1.0×	34	549
A. Di Lieto	185 0.5×	160 0.6×	55 0.6×	47 0.5×	11 0.1×	32	547
Maria Zubrzycka	140 0.4×	170 0.6×	53 0.5×	83 1.0×	63 0.8×	34	652
María Sol Kruse	209 0.6×	225 0.8×	20 0.2×	47 0.5×	27 0.3×	30	541
Zhiqiang Liu	81 0.2×	127 0.4×	49 0.5×	61 0.7×	40 0.5×	48	672
Makiko Kuwagata	97 0.3×	176 0.6×	20 0.2×	49 0.6×	47 0.6×	51	674
Noriko Tagawa	85 0.2×	144 0.5×	21 0.2×	46 0.5×	44 0.5×	44	581
Robyn M. Amos‐Kroohs	142 0.4×	64 0.2×	26 0.3×	38 0.4×	65 0.8×	26	540
Theodora Kalpachidou	147 0.4×	185 0.6×	8 0.1×	218 2.5×	58 0.7×	26	619

Countries citing papers authored by Mayu Ukai

Since Specialization

Citations

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

Fields of papers citing papers by Mayu Ukai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mayu Ukai. 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 Mayu Ukai. The network helps show where Mayu Ukai may publish in the future.

Co-authorship network of co-authors of Mayu Ukai

This figure shows the co-authorship network connecting the top 25 collaborators of Mayu Ukai. A scholar is included among the top collaborators of Mayu Ukai 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 Mayu Ukai. Mayu Ukai 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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