Hannah M. Batchelor

1.1k total citations
11 papers, 621 citations indexed

About

Hannah M. Batchelor is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, Hannah M. Batchelor has authored 11 papers receiving a total of 621 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Cellular and Molecular Neuroscience, 6 papers in Molecular Biology and 5 papers in Cognitive Neuroscience. Recurrent topics in Hannah M. Batchelor's work include Receptor Mechanisms and Signaling (5 papers), Memory and Neural Mechanisms (4 papers) and Neuroscience and Neuropharmacology Research (3 papers). Hannah M. Batchelor is often cited by papers focused on Receptor Mechanisms and Signaling (5 papers), Memory and Neural Mechanisms (4 papers) and Neuroscience and Neuropharmacology Research (3 papers). Hannah M. Batchelor collaborates with scholars based in United States, Australia and Germany. Hannah M. Batchelor's co-authors include Geoffrey Schoenbaum, Melissa J. Sharpe, Lauren Mueller, Chun Yun Chang, Yael Niv, Melissa Liu, Joshua L. Jones, Bing Liu, Marisela Morales and Yuji K. Takahashi and has published in prestigious journals such as Nature Communications, Neuron and SHILAP Revista de lepidopterología.

In The Last Decade

Hannah M. Batchelor

11 papers receiving 617 citations

Peers

Hannah M. Batchelor
Jorge Miranda‐Barrientos United States
Judith Joyce Balcita‐Pedicino United States
Konstantin Kaganovsky United States
Faïza Benaliouad Canada
Ann E. Fink United States
Xiusong Wang China
Barbara Gisabella United States
Jessica K. Shaw United States
Muhammad O. Chohan United States
Satoshi Yawata Japan
Jorge Miranda‐Barrientos United States
Hannah M. Batchelor
Citations per year, relative to Hannah M. Batchelor Hannah M. Batchelor (= 1×) peers Jorge Miranda‐Barrientos

Countries citing papers authored by Hannah M. Batchelor

Since Specialization
Citations

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

Fields of papers citing papers by Hannah M. Batchelor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hannah M. Batchelor. 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 Hannah M. Batchelor. The network helps show where Hannah M. Batchelor may publish in the future.

Co-authorship network of co-authors of Hannah M. Batchelor

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

All Works

11 of 11 papers shown
1.
Belzer, Annika, Erin M. Yeagle, Hannah M. Batchelor, et al.. (2021). Medical Student Patient Outreach to Ensure Continuity of Care During the COVID-19 Pandemic. SHILAP Revista de lepidopterología. 2(1). 56–63. 2 indexed citations
2.
Sharpe, Melissa J., Hannah M. Batchelor, Lauren Mueller, Matthew Gardner, & Geoffrey Schoenbaum. (2021). Past experience shapes the neural circuits recruited for future learning. Nature Neuroscience. 24(3). 391–400. 27 indexed citations
3.
Crouse, Richard B., Kristen K.O. Kim, Hannah M. Batchelor, et al.. (2020). Acetylcholine is released in the basolateral amygdala in response to predictors of reward and enhances the learning of cue-reward contingency. eLife. 9. 57 indexed citations
4.
Sharpe, Melissa J., Hannah M. Batchelor, Lauren Mueller, et al.. (2020). Dopamine transients do not act as model-free prediction errors during associative learning. Nature Communications. 11(1). 106–106. 51 indexed citations
5.
Müller, Frank, Hannah M. Batchelor, Zhou Zhou, et al.. (2020). The RabGAPs TBC1D1 and TBC1D4 Control Uptake of Long-Chain Fatty Acids Into Skeletal Muscle via Fatty Acid Transporter SLC27A4/FATP4. Diabetes. 69(11). 2281–2293. 18 indexed citations
6.
Woodworth, Hillary L., Juliette A. Brown, Hannah M. Batchelor, Raluca Bugescu, & Gina M. Leinninger. (2018). Determination of neurotensin projections to the ventral tegmental area in mice. Neuropeptides. 68. 57–74. 29 indexed citations
7.
Woodworth, Hillary L., et al.. (2017). Lateral Hypothalamic Neurotensin Neurons Orchestrate Dual Weight Loss Behaviors via Distinct Mechanisms. Cell Reports. 21(11). 3116–3128. 50 indexed citations
8.
Takahashi, Yuji K., et al.. (2017). Dopamine Neurons Respond to Errors in the Prediction of Sensory Features of Expected Rewards. Neuron. 95(6). 1395–1405.e3. 127 indexed citations
9.
Sharpe, Melissa J., Chun Yun Chang, Melissa Liu, et al.. (2017). Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nature Neuroscience. 20(5). 735–742. 184 indexed citations
10.
Woodworth, Hillary L., Hannah M. Batchelor, Raluca Bugescu, et al.. (2017). Neurotensin Receptor-1 Identifies a Subset of Ventral Tegmental Dopamine Neurons that Coordinates Energy Balance. Cell Reports. 20(8). 1881–1892. 43 indexed citations
11.
Sharpe, Melissa J., Hannah M. Batchelor, & Geoffrey Schoenbaum. (2017). Preconditioned cues have no value. eLife. 6. 33 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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