Dan Tulpan

2.5k citations
82 papers · 1.4k · 1 hit paper · h-index 21

Impact in

Papers in

    • Effects of Environmental Stressors on Livestock 18
    • Meat and Animal Product Quality 10
    • Animal Nutrition and Physiology 8
    • Genetic and phenotypic traits in livestock 21
    • Genetic Mapping and Diversity in Plants and Animals 6

Dan Tulpan

72 papers receiving 1.4k citations

Dan Tulpan's Hit Papers

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean 2021 · 169 citations
1690+1+3Years since publication50100150

Peers

Dan Tulpan
Comparison fields: 5 of 132
  • Animal Science and Zoology 263
  • Small Animals 134
  • Plant Science 446
  • Analytical Chemistry 98
  • Agronomy and Crop Science 82
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Citations per field
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Citations per year

Countries citing papers authored by Dan Tulpan

Since Specialization
Citations

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

Fields of papers citing papers by Dan Tulpan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Tulpan. 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 Dan Tulpan. The network helps show where Dan Tulpan may publish in the future.

Co-authors

The 25 scholars most cited alongside Dan Tulpan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Tulpan Line = papers co-authored together Dan Tulpan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 82 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean
Hit paper breakdown →
2021169
2 201197
3 202167
4 202064
5 200362
6 202160
7 201956
8 201851
9 200548
10 202048
11 202145
12 202144
13 201640
14 202238
15 200532
16 200931
17 202228
18 200328
19 202223
20 202321

About Dan Tulpan

Dan Tulpan is a scholar working on Animal Science and Zoology, Genetics, Molecular Biology, Small Animals and Plant Science, having authored 82 papers that have together received 1.4k indexed citations. Recurring topics across this work include Genetic and phenotypic traits in livestock (21 papers), Effects of Environmental Stressors on Livestock (18 papers), Animal Behavior and Welfare Studies (14 papers), Advanced biosensing and bioanalysis techniques (10 papers), Meat and Animal Product Quality (10 papers), Animal Nutrition and Physiology (8 papers), DNA and Biological Computing (8 papers) and Genetic Mapping and Diversity in Plants and Animals (6 papers). The work is most often cited by research in Animal Science and Zoology (263 citations), Small Animals (134 citations), Plant Science (446 citations), Analytical Chemistry (98 citations) and Agronomy and Crop Science (82 citations). Dan Tulpan has collaborated with scholars based in Canada, United States and Switzerland. Frequent co-authors include Milad Eskandari, Mohsen Yoosefzadeh-Najafabadi, Holger H. Hoos, Serge Léger, Hugh J. Earl, John Sulik, Anne Condon, Miroslava Čuperlović‐Culf, Adrian S. Culf and Shadi Nayeri. Their work appears in journals such as Journal of Animal Science, Journal of Dairy Science, BMC Bioinformatics, Computers and Electronics in Agriculture and animal.

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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