David H. Small
- Physiology top 0.2%
- Molecular Biology top 2%
- Pharmacology top 0.2%
- Cellular and Molecular Neuroscience top 1%
- General Economics, Econometrics and Finance top 0.5%
- Co-authors
- Domenico GiannoneLucrezia ReichlinJanelle NunanColin L. MastersSu San MokKonrad BeyreutherMarie‐Isabel AguilarJoel C. Bornstein
- Topics
- Alzheimer's disease research and treatments (107 papers)Cholinesterase and Neurodegenerative Diseases (56 papers)Computational Drug Discovery Methods (32 papers)
- Partner nations
- AustraliaUnited StatesGermany
In The Last Decade
David H. Small
182 papers receiving 7.8k citations
Hit Papers
Peers
Comparison fields: 5 of 184
- Physiology 4.0k
- Molecular Biology 3.3k
- Pharmacology 1.7k
- Cellular and Molecular Neuroscience 1.5k
- General Economics, Econometrics and Finance 1.0k
Countries citing papers authored by David H. Small
This map shows the geographic impact of David H. Small'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 H. Small with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David H. Small more than expected).
Fields of papers citing papers by David H. Small
This network shows the impact of papers produced by David H. Small. 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 H. Small. The network helps show where David H. Small may publish in the future.
Co-authorship network of co-authors of David H. Small
This figure shows the co-authorship network connecting the top 25 collaborators of David H. Small. A scholar is included among the top collaborators of David H. Small 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 David H. Small. David H. Small is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Why Interest Rates Rise When an Unexpectedly Large Money Stock is Announced | 2 |
| 2 | 44 | |
| 3 | 63 | |
| 4 | 30 | |
| 5 | 50 | |
| 6 | 39 | |
| 7 | 38 | |
| 8 | 89 | |
| 9 | 125 | |
| 10 | 108 | |
| 11 | 77 | |
| 12 | 44 | |
| 13 | Estimating the Interest Rate Sensitivity of Liquid Retail Deposit Values | 13 |
| 14 | 53 | |
| 15 | 3 | |
| 16 | 9 | |
| 17 | 47 | |
| 18 | Drawing the map : equality and efficacy of the vote in Canadian electoral boundary reform | 10 |
| 19 | Understanding the Behavior of M2 and V2 | 39 |
| 20 | 14 |
About David H. Small
David H. Small is a scholar working on Physiology, Pharmacology and Computational Theory and Mathematics, having authored 185 papers that have together received 8.1k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (107 papers), Cholinesterase and Neurodegenerative Diseases (56 papers) and Computational Drug Discovery Methods (32 papers). The work is most often cited by research in Physiology (4.0k citations), General Economics, Econometrics and Finance (1.0k citations) and Pharmacology (1.7k citations). David H. Small has collaborated with scholars based in Australia, United States and Germany. Frequent co-authors include Domenico Giannone, Lucrezia Reichlin, Janelle Nunan, Colin L. Masters, Su San Mok, Konrad Beyreuther, Marie‐Isabel Aguilar, Joel C. Bornstein, Edgar Dawkins and Robert D. Moir. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Neuron.
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.