David M. Thal
Impact in
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- Neuropeptides and Animal Physiology
- Neuroscience and Neuropharmacology Research
- Structural Biology top 2%
Papers in
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- Neuropeptides and Animal Physiology 16
- Neuroscience and Neuropharmacology Research 4
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- Adenosine and Purinergic Signaling 4
- Co-authors
- Arthur ChristopoulosPatrick M. SextonAlisa GlukhovaDenise WoottenRadostin DanevJ.J.G. TesmerSebastian G. B. FurnessMaryam Khoshouei
- Journals
- Nature (5 papers)Nature Communications (4 papers)Journal of Biological Chemistry (4 papers)ACS Chemical Neuroscience (3 papers)Science Advances (2 papers)
- Partner nations
- AustraliaUnited StatesUnited Kingdom
In The Last Decade
David M. Thal
37 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Cellular and Molecular Neuroscience 1.1k
- Structural Biology 83
- Physiology 243
- Molecular Biology 2.2k
- Computational Theory and Mathematics 250
Countries citing papers authored by David M. Thal
This map shows the geographic impact of David M. Thal'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 David M. Thal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David M. Thal more than expected).
Fields of papers citing papers by David M. Thal
This network shows the impact of papers produced by David M. Thal. 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 David M. Thal. The network helps show where David M. Thal may publish in the future.
Co-authors
The 25 scholars most cited alongside David M. Thal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 7 | |
| 6 | 2024 | 2 | |
| 7 | 2023 | 11 | |
| 8 | 2023 | 24 | |
| 9 | 2023 | 0 | |
| 10 | 2023 | 4 | |
| 11 | 2023 | 20 | |
| 12 | 2023 | 28 | |
| 13 | 2022 | 4 | |
| 14 | 2022 | 14 | |
| 15 | 2021 | 47 | |
| 16 | 2019 | 53 | |
| 17 | 2018 | 17 | |
| 18 | 2018 | 235 | |
| 19 | Phase-plate cryo-EM structure of a class B GPCR–G-protein complex Hit paper breakdown → | 2017 | 362 |
| 20 | 2016 | 264 |
About David M. Thal
David M. Thal is a scholar working on Cellular and Molecular Neuroscience, Physiology, Structural Biology, Molecular Biology and Spectroscopy, having authored 41 papers that have together received 2.5k indexed citations. Recurring topics across this work include Receptor Mechanisms and Signaling (33 papers), Neuropeptides and Animal Physiology (16 papers), Monoclonal and Polyclonal Antibodies Research (8 papers), Protein Kinase Regulation and GTPase Signaling (7 papers), Mass Spectrometry Techniques and Applications (6 papers), Pharmacological Receptor Mechanisms and Effects (5 papers), Neuroscience and Neuropharmacology Research (4 papers) and Adenosine and Purinergic Signaling (4 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (1.1k citations), Structural Biology (83 citations), Physiology (243 citations), Molecular Biology (2.2k citations) and Computational Theory and Mathematics (250 citations). David M. Thal has collaborated with scholars based in Australia, United States and United Kingdom. Frequent co-authors include Arthur Christopoulos, Patrick M. Sexton, Alisa Glukhova, Denise Wootten, Radostin Danev, J.J.G. Tesmer, Sebastian G. B. Furness, Maryam Khoshouei, Wolfgang Baumeister and Yi-Lynn Liang. Their work appears in journals such as Nature, Nature Communications, Journal of Biological Chemistry, ACS Chemical Neuroscience and Science Advances.
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.