Sachit Butail

1.6k total citations
59 papers, 1.2k citations indexed

About

Sachit Butail is a scholar working on Ecology, Evolution, Behavior and Systematics, Cell Biology and Nature and Landscape Conservation. According to data from OpenAlex, Sachit Butail has authored 59 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Ecology, Evolution, Behavior and Systematics, 16 papers in Cell Biology and 14 papers in Nature and Landscape Conservation. Recurrent topics in Sachit Butail's work include Zebrafish Biomedical Research Applications (16 papers), Animal Behavior and Reproduction (14 papers) and Fish Ecology and Management Studies (13 papers). Sachit Butail is often cited by papers focused on Zebrafish Biomedical Research Applications (16 papers), Animal Behavior and Reproduction (14 papers) and Fish Ecology and Management Studies (13 papers). Sachit Butail collaborates with scholars based in United States, India and Italy. Sachit Butail's co-authors include Maurizio Porfiri, T Bartolini, Derek A. Paley, Violet Mwaffo, Simone Macrı̀, Nicholas C. Manoukis, José M. C. Ribeiro, Moussa Diallo, Davide Spinello and Tovi Lehmann and has published in prestigious journals such as PLoS ONE, Scientific Reports and Journal of Theoretical Biology.

In The Last Decade

Sachit Butail

57 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sachit Butail United States 19 337 313 278 138 130 59 1.2k
Alfonso Pérez‐Escudero Spain 15 191 0.6× 196 0.6× 468 1.7× 133 1.0× 254 2.0× 34 1.6k
Robert C. Hinz Portugal 5 209 0.6× 162 0.5× 259 0.9× 92 0.7× 138 1.1× 6 926
Giovanni Polverino Italy 22 279 0.8× 475 1.5× 524 1.9× 50 0.4× 299 2.3× 44 1.3k
Nicole Abaid United States 18 141 0.4× 127 0.4× 161 0.6× 169 1.2× 75 0.6× 60 1.0k
Violet Mwaffo United States 16 225 0.7× 141 0.5× 116 0.4× 109 0.8× 46 0.4× 44 649
José Halloy France 24 211 0.6× 123 0.4× 367 1.3× 100 0.7× 119 0.9× 68 2.2k
Donato Romano Italy 27 83 0.2× 114 0.4× 495 1.8× 356 2.6× 147 1.1× 101 1.9k
Kolbjørn Tunstrøm Sweden 8 71 0.2× 108 0.3× 309 1.1× 78 0.6× 122 0.9× 10 1.1k
Jolyon J. Faria United Kingdom 12 64 0.2× 130 0.4× 515 1.9× 72 0.5× 192 1.5× 12 1.2k
John A. Bender United States 13 113 0.3× 151 0.5× 609 2.2× 137 1.0× 218 1.7× 25 1.5k

Countries citing papers authored by Sachit Butail

Since Specialization
Citations

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

Fields of papers citing papers by Sachit Butail

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sachit Butail

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

All Works

20 of 20 papers shown
1.
Butail, Sachit, et al.. (2024). Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19. Chaos An Interdisciplinary Journal of Nonlinear Science. 34(3). 1 indexed citations
2.
Burbano, Daniel, Lorenzo Zino, Sachit Butail, et al.. (2022). Activity-driven network modeling and control of the spread of two concurrent epidemic strains. Applied Network Science. 7(1). 66–66. 3 indexed citations
3.
Zino, Lorenzo, Sachit Butail, Emanuele Caroppo, et al.. (2021). Predicting the effects of waning vaccine immunity against COVID-19 through high-resolution agent-based modeling. arXiv (Cornell University). 13 indexed citations
4.
Norrlöf, Mikael, et al.. (2021). A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots. 2(1). 2 indexed citations
5.
Kim, Yanghee, Cynthia D’Angelo, Francesco Cafaro, et al.. (2020). Multimodal Data Analytics for Assessing Collaborative Interactions. IUScholarWorks (Indiana University). 2547–2554. 3 indexed citations
6.
Müller, Rolf, et al.. (2019). Extracting Interactions between Flying Bat Pairs Using Model-Free Methods. Entropy. 21(1). 42–42. 15 indexed citations
7.
Jain, Puneet, et al.. (2017). Effect of Leader Placement on Robotic Swarm Control. Adaptive Agents and Multi-Agents Systems. 1387–1394. 2 indexed citations
8.
Mwaffo, Violet, Sachit Butail, & Maurizio Porfiri. (2017). In-silico experiments of zebrafish behaviour: modeling swimming in three dimensions. Scientific Reports. 7(1). 39877–39877. 21 indexed citations
9.
Macrı̀, Simone, et al.. (2017). Three-dimensional scoring of zebrafish behavior unveils biological phenomena hidden by two-dimensional analyses. Scientific Reports. 7(1). 1962–1962. 42 indexed citations
10.
Butail, Sachit, Violet Mwaffo, & Maurizio Porfiri. (2016). Model-free information-theoretic approach to infer leadership in pairs of zebrafish. Physical review. E. 93(4). 42411–42411. 76 indexed citations
11.
Bartolini, T, et al.. (2016). Zebrafish response to 3D printed shoals of conspecifics: the effect of body size. Bioinspiration & Biomimetics. 11(2). 26003–26003. 42 indexed citations
12.
Bartolini, T, et al.. (2015). Live Predators, Robots, and Computer-Animated Images Elicit Differential Avoidance Responses in Zebrafish. Zebrafish. 12(3). 205–214. 57 indexed citations
13.
Mwaffo, Violet, Sachit Butail, Mario di Bernardo, & Maurizio Porfiri. (2015). Measuring Zebrafish Turning Rate. Zebrafish. 12(3). 250–254. 14 indexed citations
14.
Bartolini, T, Violet Mwaffo, Sachit Butail, & Maurizio Porfiri. (2015). Effect of acute ethanol administration on zebrafish tail-beat motion. Alcohol. 49(7). 721–725. 20 indexed citations
15.
Butail, Sachit, et al.. (2014). Influence of robotic shoal size, configuration, and activity on zebrafish behavior in a free-swimming environment. Behavioural Brain Research. 275. 269–280. 34 indexed citations
16.
Shishika, Daigo, Nicholas C. Manoukis, Sachit Butail, & Derek A. Paley. (2014). Male motion coordination in anopheline mating swarms. Scientific Reports. 4(1). 6318–6318. 24 indexed citations
17.
Butail, Sachit, et al.. (2014). Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling. Behavior Research Methods. 47(4). 1020–1031. 11 indexed citations
18.
Butail, Sachit, Nicholas C. Manoukis, Moussa Diallo, José M. C. Ribeiro, & Derek A. Paley. (2013). The Dance of MaleAnopheles gambiaein Wild Mating Swarms. Journal of Medical Entomology. 50(3). 552–559. 32 indexed citations
19.
Manoukis, Nicholas C., Sachit Butail, Moussa Diallo, José M. C. Ribeiro, & Derek A. Paley. (2013). Stereoscopic video analysis of Anopheles gambiae behavior in the field: Challenges and opportunities. Acta Tropica. 132. S80–S85. 18 indexed citations
20.
Butail, Sachit, Nicholas C. Manoukis, Moussa Diallo, et al.. (2011). 3D tracking of mating events in wild swarms of the malaria mosquito Anopheles gambiae. PubMed. 2011. 720–723. 11 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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