Mimi Shin

783 total citations · 1 hit paper
17 papers, 529 citations indexed

About

Mimi Shin is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Genetics. According to data from OpenAlex, Mimi Shin has authored 17 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cellular and Molecular Neuroscience, 5 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in Mimi Shin's work include Neurobiology and Insect Physiology Research (8 papers), Insect and Arachnid Ecology and Behavior (5 papers) and Physiological and biochemical adaptations (3 papers). Mimi Shin is often cited by papers focused on Neurobiology and Insect Physiology Research (8 papers), Insect and Arachnid Ecology and Behavior (5 papers) and Physiological and biochemical adaptations (3 papers). Mimi Shin collaborates with scholars based in United States and China. Mimi Shin's co-authors include B. Jill Venton, Ying Wang, Jeffrey M. Copeland, Yong Zeng, Tanyu Wang, Nickolay V. Lavrik, Qun Cao, Yajun Zhang, Sunlei Pan and Jie Zhu and has published in prestigious journals such as Angewandte Chemie International Edition, Nano Letters and Nature Neuroscience.

In The Last Decade

Mimi Shin

16 papers receiving 519 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A genetically encoded sensor for measuring serotonin dynamics

2021 184 citations Jinxia Wan, Wanling Peng et al. Nature Neuroscience profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mimi Shin United States	12	279	158	152	92	89	17	529
Elyse C. Dankoski United States	8	285 1.0×	133 0.8×	214 1.4×	37 0.4×	165 1.9×	8	552
Paul L. Walsh United States	10	241 0.9×	147 0.9×	207 1.4×	52 0.6×	170 1.9×	16	553
Keeley L. Baker United States	9	210 0.8×	84 0.5×	69 0.5×	55 0.6×	46 0.5×	13	387
Teresa A. Murray United States	15	212 0.8×	205 1.3×	90 0.6×	97 1.1×	49 0.6×	39	603
Laura Borland United States	9	378 1.4×	297 1.9×	178 1.2×	120 1.3×	163 1.8×	10	815
Scott T. Lee United States	10	233 0.8×	140 0.9×	295 1.9×	125 1.4×	229 2.6×	11	630
J. P. Bourgeois France	14	273 1.0×	397 2.5×	251 1.7×	37 0.4×	130 1.5×	21	1.1k
Kevin M. Wood United States	11	297 1.1×	122 0.8×	129 0.8×	19 0.2×	114 1.3×	12	613
Annette Harsch United Kingdom	9	332 1.2×	91 0.6×	112 0.7×	127 1.4×	34 0.4×	12	672
Ada Dormann Israel	10	421 1.5×	160 1.0×	73 0.5×	90 1.0×	20 0.2×	13	542

Countries citing papers authored by Mimi Shin

Since Specialization

Citations

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

Fields of papers citing papers by Mimi Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mimi Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Mimi Shin. A scholar is included among the top collaborators of Mimi Shin 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 Mimi Shin. Mimi Shin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

1.

Zhang, Yajun, et al.. (2025). Coding principles and mechanisms of serotonergic transmission modes. Molecular Psychiatry. 30(8). 3430–3442. 1 indexed citations

2.

Shin, Mimi & B. Jill Venton. (2022). Fast‐Scan Cyclic Voltammetry (FSCV) Reveals Behaviorally Evoked Dopamine Release by Sugar Feeding in the Adult Drosophila Mushroom Body. Angewandte Chemie. 134(44). 2 indexed citations

3.

Shin, Mimi & B. Jill Venton. (2022). Fast‐Scan Cyclic Voltammetry (FSCV) Reveals Behaviorally Evoked Dopamine Release by Sugar Feeding in the Adult Drosophila Mushroom Body. Angewandte Chemie International Edition. 61(44). e202207399–e202207399. 20 indexed citations

4.

Wan, Jinxia, Wanling Peng, Xuelin Li, et al.. (2021). A genetically encoded sensor for measuring serotonin dynamics. Nature Neuroscience. 24(5). 746–752. 184 indexed citations breakdown →

5.

Wang, Ying, et al.. (2020). CD73 or CD39 Deletion Reveals Different Mechanisms of Formation for Spontaneous and Mechanically Stimulated Adenosine and Sex Specific Compensations in ATP Degradation. ACS Chemical Neuroscience. 11(6). 919–928. 13 indexed citations

6.

Shin, Mimi, Jeffrey M. Copeland, & B. Jill Venton. (2020). Real-Time Measurement of Stimulated Dopamine Release in Compartments of the Adult Drosophila melanogaster Mushroom Body. Analytical Chemistry. 92(21). 14398–14407. 15 indexed citations

7.

Shin, Mimi, Daniel Friedman, Deborah M. Gordon, & B. Jill Venton. (2020). Measurement of natural variation of neurotransmitter tissue content in red harvester ant brains among different colonies. Analytical and Bioanalytical Chemistry. 412(24). 6167–6175. 1 indexed citations

8.

Cao, Qun, Mimi Shin, Nickolay V. Lavrik, & B. Jill Venton. (2020). 3D-Printed Carbon Nanoelectrodes for In Vivo Neurotransmitter Sensing. Nano Letters. 20(9). 6831–6836. 58 indexed citations

9.

Shin, Mimi, et al.. (2019). Electrochemistry at the Synapse. Annual Review of Analytical Chemistry. 12(1). 297–321. 65 indexed citations

10.

Shin, Mimi, et al.. (2018). Nicotinic acetylcholine receptor (nAChR) mediated dopamine release in larval Drosophila melanogaster. Neurochemistry International. 114. 33–41. 25 indexed citations

11.

Shin, Mimi, Jeffrey M. Copeland, & B. Jill Venton. (2018). Drosophila as a Model System for Neurotransmitter Measurements. ACS Chemical Neuroscience. 9(8). 1872–1883. 33 indexed citations

12.

Field, Thomas M., et al.. (2018). Electrochemical Measurement of Dopamine Release and Uptake in Zebrafish Following Treatment with Carboplatin. ChemPhysChem. 19(10). 1192–1196. 14 indexed citations

13.

Shin, Mimi & B. Jill Venton. (2018). Electrochemical Measurements of Acetylcholine-Stimulated Dopamine Release in Adult Drosophila melanogaster Brains. Analytical Chemistry. 90(17). 10318–10325. 23 indexed citations

14.

Shin, Mimi, et al.. (2017). Ex Vivo Measurement of Electrically Evoked Dopamine Release in Zebrafish Whole Brain. ACS Chemical Neuroscience. 8(9). 1880–1888. 19 indexed citations

15.

Shin, Mimi, et al.. (2015). Simultaneous measurement and quantitation of 4-hydroxyphenylacetic acid and dopamine with fast-scan cyclic voltammetry. The Analyst. 140(9). 3039–3047. 5 indexed citations

16.

Shin, Mimi, et al.. (2013). Conference Report: Pittcon 2013: Microfluidics, Electrochemistry, and Much Much More. Bioanalysis. 5(16). 1965–1967.

17.

Zeng, Yong, Mimi Shin, & Tanyu Wang. (2012). Programmable active droplet generation enabled by integrated pneumatic micropumps. Lab on a Chip. 13(2). 267–273. 51 indexed citations

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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