David G. Lloyd
- Molecular Biology top 10%
- Organic Chemistry top 5%
- Ecology, Evolution, Behavior and Systematics top 2%
- Genetics top 5%
- Computational Theory and Mathematics top 1%
- Co-authors
- Mary J. MeeganAndrew J. S. KnoxDaniela M. ZistererDarren FayneM.J. CarrGiorgio CartaLaura CaboniD. Clive Williams
- Topics
- Estrogen and related hormone effects (25 papers)Computational Drug Discovery Methods (22 papers)Synthesis and biological activity (11 papers)
- Cited by
- Computational Theory and MathematicsEcology, Evolution, Behavior and SystematicsNature and Landscape Conservation
- Partner nations
- IrelandUnited StatesNew Zealand
In The Last Decade
David G. Lloyd
85 papers receiving 2.3k citations
Peers
Comparison fields: 5 of 138
- Molecular Biology 903
- Organic Chemistry 694
- Ecology, Evolution, Behavior and Systematics 550
- Genetics 490
- Computational Theory and Mathematics 460
Countries citing papers authored by David G. Lloyd
This map shows the geographic impact of David G. Lloyd'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 G. Lloyd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David G. Lloyd more than expected).
Fields of papers citing papers by David G. Lloyd
This network shows the impact of papers produced by David G. Lloyd. 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 G. Lloyd. The network helps show where David G. Lloyd may publish in the future.
Co-authorship network of co-authors of David G. Lloyd
This figure shows the co-authorship network connecting the top 25 collaborators of David G. Lloyd. A scholar is included among the top collaborators of David G. Lloyd 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 G. Lloyd. David G. Lloyd is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 53 | |
| 3 | 5 | |
| 4 | 38 | |
| 5 | 19 | |
| 6 | 6 | |
| 7 | 163 | |
| 8 | 43 | |
| 9 | 7 | |
| 10 | 23 | |
| 11 | 11 | |
| 12 | 39 | |
| 13 | 15 | |
| 14 | 78 | |
| 15 | The selection of social actions in families: II. Parental investment | 3 |
| 16 | The selection of social actions in families: I. A collective fitness approach | 1 |
| 17 | 22 | |
| 18 | 2 | |
| 19 | 4 | |
| 20 | 3 |
About David G. Lloyd
David G. Lloyd is a scholar working on Toxicology, Computational Theory and Mathematics and Genetics, having authored 85 papers that have together received 2.4k indexed citations. Recurring topics across this work include Estrogen and related hormone effects (25 papers), Computational Drug Discovery Methods (22 papers) and Synthesis and biological activity (11 papers). The work is most often cited by research in Computational Theory and Mathematics (460 citations), Ecology, Evolution, Behavior and Systematics (550 citations) and Nature and Landscape Conservation (325 citations). David G. Lloyd has collaborated with scholars based in Ireland, United States and New Zealand. Frequent co-authors include Mary J. Meegan, Andrew J. S. Knox, Daniela M. Zisterer, Darren Fayne, M.J. Carr, Giorgio Carta, Laura Caboni, D. Clive Williams, Lisa M. Greene and David Gubbins. Their work appears in journals such as Nature Biotechnology, The Journal of Physical Chemistry and Biochemical Journal.
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.