Sarah E. London

6.9k total citations · 1 hit paper
43 papers, 2.2k citations indexed

About

Sarah E. London is a scholar working on Developmental Biology, Ecology, Evolution, Behavior and Systematics and Ecology. According to data from OpenAlex, Sarah E. London has authored 43 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Developmental Biology, 34 papers in Ecology, Evolution, Behavior and Systematics and 27 papers in Ecology. Recurrent topics in Sarah E. London's work include Animal Vocal Communication and Behavior (35 papers), Animal Behavior and Reproduction (31 papers) and Marine animal studies overview (24 papers). Sarah E. London is often cited by papers focused on Animal Vocal Communication and Behavior (35 papers), Animal Behavior and Reproduction (31 papers) and Marine animal studies overview (24 papers). Sarah E. London collaborates with scholars based in United States, United Kingdom and Russia. Sarah E. London's co-authors include Stephanie Cacioppo, Angela J. Grippo, John T. Cacioppo, Luc Goossens, David F. Clayton, Barney A. Schlinger, Daniel H. Geschwind, Stephanie A. White, Lili C. Kudo and Ikuko Teramitsu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and Nature Neuroscience.

In The Last Decade

Sarah E. London

43 papers receiving 2.1k citations

Hit Papers

Loneliness 2015 2026 2018 2022 2015 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarah E. London United States 21 718 684 476 453 432 43 2.2k
Noah Snyder‐Mackler United States 30 774 1.1× 346 0.5× 1.3k 2.8× 368 0.8× 95 0.2× 112 2.8k
Mariko Hiraiwa‐Hasegawa Japan 23 744 1.0× 387 0.6× 880 1.8× 422 0.9× 71 0.2× 81 1.9k
Alfonso Troisi Italy 40 639 0.9× 498 0.7× 2.3k 4.8× 99 0.2× 57 0.1× 138 5.0k
Michelle Wilson United States 36 502 0.7× 201 0.3× 1.5k 3.1× 110 0.2× 43 0.1× 118 3.7k
Mark V. Flinn United States 29 443 0.6× 110 0.2× 1.2k 2.6× 299 0.7× 75 0.2× 62 3.5k
Fernando A. Campos United States 21 543 0.8× 258 0.4× 876 1.8× 294 0.6× 56 0.1× 33 1.4k
Jane B. Lancaster United States 22 246 0.3× 126 0.2× 1.1k 2.3× 192 0.4× 96 0.2× 36 3.0k
Leonard A. Rosenblum United States 37 496 0.7× 382 0.6× 2.5k 5.3× 109 0.2× 56 0.1× 106 5.2k
Yvon Delville United States 33 465 0.6× 139 0.2× 2.2k 4.6× 143 0.3× 47 0.1× 80 3.5k
Lewis Petrinovich United States 36 1.1k 1.5× 1.1k 1.6× 616 1.3× 778 1.7× 21 0.0× 83 4.1k

Countries citing papers authored by Sarah E. London

Since Specialization
Citations

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

Fields of papers citing papers by Sarah E. London

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sarah E. London. 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 Sarah E. London. The network helps show where Sarah E. London may publish in the future.

Co-authorship network of co-authors of Sarah E. London

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah E. London. A scholar is included among the top collaborators of Sarah E. London 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 Sarah E. London. Sarah E. London 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.
London, Sarah E., Márk E. Hauber, Matthew I. M. Louder, & Christopher N. Balakrishnan. (2025). Simultaneous RNA Sequencing and DNA Methylation Profiling Reveals Neural Mechanisms That Regulate Sensitive Period Behavioral Learning. Genes Brain & Behavior. 24(4). e70031–e70031. 1 indexed citations
2.
London, Sarah E., et al.. (2019). The variability of song variability in zebra finch ( Taeniopygia guttata ) populations. Royal Society Open Science. 6(5). 190273–190273. 6 indexed citations
3.
Barger, Brian, Jane Squires, Catherine E. Rice, et al.. (2019). State Variability in Diagnosed Conditions for IDEA Part C Eligibility. Infants & Young Children. 32(4). 231–244. 20 indexed citations
4.
Louder, Matthew I. M., et al.. (2019). An Acoustic Password Enhances Auditory Learning in Juvenile Brood Parasitic Cowbirds. Current Biology. 29(23). 4045–4051.e3. 18 indexed citations
5.
Berman, Marc G., Omid Kardan, Hiroki Kotabe, Howard C. Nusbaum, & Sarah E. London. (2019). The promise of environmental neuroscience. Nature Human Behaviour. 3(5). 414–417. 46 indexed citations
6.
London, Sarah E., et al.. (2018). A complex mTOR response in habituation paradigms for a social signal in adult songbirds. Learning & Memory. 25(6). 273–282. 4 indexed citations
7.
London, Sarah E., et al.. (2017). A reliable and flexible gene manipulation strategy in posthatch zebra finch brain. Scientific Reports. 7(1). 43244–43244. 14 indexed citations
8.
Louder, Matthew I. M., et al.. (2016). Shared neural substrates for song discrimination in parental and parasitic songbirds. Neuroscience Letters. 622. 49–54. 20 indexed citations
9.
London, Sarah E., et al.. (2014). Social Information Embedded in Vocalizations Induces Neurogenomic and Behavioral Responses. PLoS ONE. 9(11). e112905–e112905. 5 indexed citations
10.
Clayton, David F. & Sarah E. London. (2013). Advancing avian behavioral neuroendocrinology through genomics. Frontiers in Neuroendocrinology. 35(1). 58–71. 5 indexed citations
11.
Drnevich, Jenny, Kirstin Replogle, Peter V. Lovell, et al.. (2012). Impact of experience-dependent and -independent factors on gene expression in songbird brain. Proceedings of the National Academy of Sciences. 109(supplement_2). 17245–17252. 47 indexed citations
12.
Balakrishnan, Christopher N., Ya‐Chi Lin, Sarah E. London, & David F. Clayton. (2012). RNA-seq transcriptome analysis of male and female zebra finch cell lines. Genomics. 100(6). 363–369. 18 indexed citations
13.
Xie, Fang, Sarah E. London, Bruce R. Southey, et al.. (2010). The zebra finch neuropeptidome: prediction, detection and expression. BMC Biology. 8(1). 28–28. 36 indexed citations
14.
London, Sarah E. & David F. Clayton. (2010). Genomic and neural analysis of the estradiol-synthetic pathway in the zebra finch. BMC Neuroscience. 11(1). 46–46. 26 indexed citations
15.
London, Sarah E., et al.. (2010). Neural expression and post-transcriptional dosage compensation of the steroid metabolic enzyme 17β-HSD type 4. BMC Neuroscience. 11(1). 47–47. 22 indexed citations
16.
Clayton, David F., Christopher N. Balakrishnan, & Sarah E. London. (2009). Integrating Genomes, Brain and Behavior in the Study of Songbirds. Current Biology. 19(18). R865–R873. 43 indexed citations
17.
London, Sarah E. & David F. Clayton. (2008). Functional identification of sensory mechanisms required for developmental song learning. Nature Neuroscience. 11(5). 579–586. 166 indexed citations
18.
Smith, Shannon R., et al.. (2007). Proteomic Analyses of Songbird (Zebra finch; Taeniopygia guttata) Retina. Journal of Proteome Research. 6(3). 1093–1100. 6 indexed citations
19.
Schlinger, Barney A., Kiran K. Soma, & Sarah E. London. (2001). Neurosteroids and brain sexual differentiation. Trends in Neurosciences. 24(8). 429–431. 47 indexed citations
20.
Saldanha, Colin J., et al.. (2000). Telencephalic Aromatase but Not a Song Circuit in a Sub-Oscine Passerine, the Golden Collared Manakin <i>(Manacus vitellinus)</i>. Brain Behavior and Evolution. 56(1). 29–37. 19 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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