Richard G. Sherlock

577 total citations
8 papers, 287 citations indexed

About

Richard G. Sherlock is a scholar working on Genetics, Agronomy and Crop Science and Animal Science and Zoology. According to data from OpenAlex, Richard G. Sherlock has authored 8 papers receiving a total of 287 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Genetics, 4 papers in Agronomy and Crop Science and 3 papers in Animal Science and Zoology. Recurrent topics in Richard G. Sherlock's work include Genetic and phenotypic traits in livestock (8 papers), Reproductive Physiology in Livestock (3 papers) and Cancer-related molecular mechanisms research (2 papers). Richard G. Sherlock is often cited by papers focused on Genetic and phenotypic traits in livestock (8 papers), Reproductive Physiology in Livestock (3 papers) and Cancer-related molecular mechanisms research (2 papers). Richard G. Sherlock collaborates with scholars based in New Zealand, Australia and United States. Richard G. Sherlock's co-authors include Kathryn Tiplady, Mathew D. Littlejohn, Richard Spelman, Stephen R. Davis, Thomas Johnson, Thomas Lopdell, Dorian J. Garrick, Chad Harland, Russell G. Snell and Michael Keehan and has published in prestigious journals such as Nature Communications, Scientific Reports and Journal of Dairy Science.

In The Last Decade

Richard G. Sherlock

8 papers receiving 287 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard G. Sherlock New Zealand 7 218 89 83 61 51 8 287
Kathryn Tiplady New Zealand 13 459 2.1× 133 1.5× 167 2.0× 139 2.3× 117 2.3× 25 573
Pingxian Wu China 11 205 0.9× 61 0.7× 24 0.3× 98 1.6× 63 1.2× 32 283
Brian Karisa Canada 7 188 0.9× 84 0.9× 89 1.1× 43 0.7× 65 1.3× 10 250
A. N. Rosa Brazil 7 174 0.8× 78 0.9× 61 0.7× 45 0.7× 36 0.7× 10 203
Diogo Anastácio Garcia Brazil 11 252 1.2× 117 1.3× 81 1.0× 52 0.9× 32 0.6× 25 320
Taiane da Silva Martins Brazil 9 103 0.5× 169 1.9× 127 1.5× 22 0.4× 65 1.3× 16 333
Ruslana Stavetska Ukraine 8 136 0.6× 64 0.7× 46 0.6× 56 0.9× 79 1.5× 19 274
Marina Mortati Dias Barbero Brazil 10 214 1.0× 39 0.4× 111 1.3× 51 0.8× 25 0.5× 19 263
Masoumeh Naserkheil South Korea 10 242 1.1× 78 0.9× 51 0.6× 83 1.4× 28 0.5× 19 282
Laís Grigoletto Brazil 10 218 1.0× 53 0.6× 76 0.9× 48 0.8× 10 0.2× 16 244

Countries citing papers authored by Richard G. Sherlock

Since Specialization
Citations

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

Fields of papers citing papers by Richard G. Sherlock

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard G. Sherlock. 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 Richard G. Sherlock. The network helps show where Richard G. Sherlock may publish in the future.

Co-authorship network of co-authors of Richard G. Sherlock

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

All Works

8 of 8 papers shown
1.
Tiplady, Kathryn, Thomas Lopdell, Richard G. Sherlock, et al.. (2022). Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins. Journal of Dairy Science. 105(12). 9763–9791. 11 indexed citations
2.
Lopdell, Thomas, Yu Wang, Kathryn Tiplady, et al.. (2022). Non-additive QTL mapping of lactation traits in 124,000 cattle reveals novel recessive loci. Genetics Selection Evolution. 54(1). 5–5. 11 indexed citations
3.
Tiplady, Kathryn, et al.. (2022). Pregnancy status predicted using milk mid-infrared spectra from dairy cattle. Journal of Dairy Science. 105(4). 3615–3632. 6 indexed citations
4.
Tiplady, Kathryn, Thomas Lopdell, Richard G. Sherlock, et al.. (2021). Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle. Genetics Selection Evolution. 53(1). 62–62. 22 indexed citations
5.
Tiplady, Kathryn, Richard G. Sherlock, Mathew D. Littlejohn, et al.. (2019). Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle. Journal of Dairy Science. 102(7). 6357–6372. 27 indexed citations
6.
Johnson, Thomas, Kathryn Tiplady, Richard G. Sherlock, et al.. (2018). Mating strategies to maximize genetic merit in dairy cattle herds. Journal of Dairy Science. 101(5). 4650–4659. 8 indexed citations
7.
Littlejohn, Mathew D., Kathryn Tiplady, Klaus Lehnert, et al.. (2016). Sequence-based Association Analysis Reveals an MGST1 eQTL with Pleiotropic Effects on Bovine Milk Composition. Scientific Reports. 6(1). 25376–25376. 91 indexed citations
8.
Littlejohn, Mathew D., Kristen Henty, Kathryn Tiplady, et al.. (2014). Functionally reciprocal mutations of the prolactin signalling pathway define hairy and slick cattle. Nature Communications. 5(1). 5861–5861. 111 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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