Nathaniel J. Hall

1.3k total citations
76 papers, 867 citations indexed

About

Nathaniel J. Hall is a scholar working on Genetics, Small Animals and Sensory Systems. According to data from OpenAlex, Nathaniel J. Hall has authored 76 papers receiving a total of 867 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Genetics, 32 papers in Small Animals and 23 papers in Sensory Systems. Recurrent topics in Nathaniel J. Hall's work include Human-Animal Interaction Studies (38 papers), Animal Behavior and Welfare Studies (29 papers) and Olfactory and Sensory Function Studies (23 papers). Nathaniel J. Hall is often cited by papers focused on Human-Animal Interaction Studies (38 papers), Animal Behavior and Welfare Studies (29 papers) and Olfactory and Sensory Function Studies (23 papers). Nathaniel J. Hall collaborates with scholars based in United States, Canada and United Kingdom. Nathaniel J. Hall's co-authors include Clive D. L. Wynne, Alexandra Protopopova, Monique A. R. Udell, David W. Smith, Darlene A. Kertes, Emily E. Bray, Evan L. MacLean, Cynthia M. Otto, Angie M. Johnston and Jingwen Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and International Journal of Environmental Research and Public Health.

In The Last Decade

Nathaniel J. Hall

72 papers receiving 845 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathaniel J. Hall United States 19 557 277 175 175 139 76 867
Lucia Lazarowski United States 16 427 0.8× 246 0.9× 119 0.7× 142 0.8× 152 1.1× 46 659
Federica Pirrone Italy 19 486 0.9× 230 0.8× 140 0.8× 40 0.2× 189 1.4× 56 933
Patrick Pageat France 19 573 1.0× 518 1.9× 272 1.6× 202 1.2× 28 0.2× 83 1.1k
Claire Guest United Kingdom 15 338 0.6× 100 0.4× 78 0.4× 242 1.4× 355 2.6× 35 907
Paolo Baragli Italy 19 497 0.9× 431 1.6× 210 1.2× 52 0.3× 72 0.5× 75 1.0k
Robert Hubrecht United Kingdom 15 499 0.9× 564 2.0× 325 1.9× 42 0.2× 66 0.5× 28 1.3k
Anindita Bhadra India 22 691 1.2× 149 0.5× 196 1.1× 26 0.1× 28 0.2× 53 961
L. Paul Waggoner United States 13 233 0.4× 126 0.5× 56 0.3× 107 0.6× 167 1.2× 20 464
Therese Rehn Sweden 15 620 1.1× 441 1.6× 302 1.7× 60 0.3× 28 0.2× 19 882
Paul Waggoner United States 14 254 0.5× 97 0.4× 123 0.7× 120 0.7× 124 0.9× 27 487

Countries citing papers authored by Nathaniel J. Hall

Since Specialization
Citations

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

Fields of papers citing papers by Nathaniel J. Hall

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathaniel J. Hall. 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 Nathaniel J. Hall. The network helps show where Nathaniel J. Hall may publish in the future.

Co-authorship network of co-authors of Nathaniel J. Hall

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

All Works

20 of 20 papers shown
1.
Medrano, Alba, et al.. (2025). The effect of training paradigm on dogs’ (Canis familiaris) acquisition and generalization of smokeless powders. Applied Animal Behaviour Science. 284. 106527–106527. 1 indexed citations
2.
Pedretti, G, Teddy Lazebnik, Anna Zamansky, et al.. (2025). Does the tail show when the nose knows? Artificial intelligence outperforms human experts at predicting detection dogs finding their target through tail kinematics. Royal Society Open Science. 12(8). 250399–250399. 1 indexed citations
3.
Hall, Nathaniel J., et al.. (2024). Group Identification and Employer Support Predict Lower Occupational Stress in Animal Shelter Employees. Society and Animals. 1–23. 1 indexed citations
4.
Hall, Nathaniel J., et al.. (2024). Evaluation of the capability of oil specific discrimination in detection dogs. Behavioural Processes. 216. 105014–105014. 1 indexed citations
5.
Nita, Mizuho, et al.. (2024). Dogs can detect powdery mildew (Erysiphe necator) in grapevine (Vitis vinifera) leaves. Journal of Veterinary Behavior. 77. 19–29. 1 indexed citations
6.
Hall, Nathaniel J., et al.. (2024). Chemical Characterization of Human Body Odor Headspace Components. Separations. 11(3). 85–85. 1 indexed citations
7.
Prada‐Tiedemann, Paola A., et al.. (2024). Development of an automated human scent olfactometer and its use to evaluate detection dog perception of human scent. PLoS ONE. 19(3). e0299148–e0299148. 3 indexed citations
8.
DeGreeff, Lauryn E., et al.. (2024). Evaluation of non-detonable canine training aids for explosives by headspace analysis and canine testing. Forensic Chemistry. 37. 100553–100553. 4 indexed citations
9.
Buckley, Patricia E., et al.. (2024). Calibrating canines—a universal detector calibrant for detection dogs. SHILAP Revista de lepidopterología. 5. 1366596–1366596. 4 indexed citations
10.
Hall, Nathaniel J., et al.. (2024). Dogs' ability to detect an inflammatory immune response in cattle via olfaction. Frontiers in Veterinary Science. 11. 1393289–1393289.
11.
Thompson, Ryan, et al.. (2023). Effect of rapid changes in environmental conditions on canine detection of methyl benzoate. Applied Animal Behaviour Science. 264. 105924–105924. 2 indexed citations
12.
Nita, Mizuho, et al.. (2023). Olfactory threshold of dogs (Canis lupus familiaris) to cold-killed spotted lantern fly eggs. Applied Animal Behaviour Science. 261. 105880–105880. 4 indexed citations
13.
Prada‐Tiedemann, Paola A., et al.. (2023). A laboratory model of canine search vigilance decrement, I. Journal of the Experimental Analysis of Behavior. 120(1). 103–119. 6 indexed citations
14.
Prada‐Tiedemann, Paola A., et al.. (2023). A laboratory model of canine search vigilance decrement, II: Noncontingent reward and Pavlovian appetitive stimuli. Journal of the Experimental Analysis of Behavior. 120(1). 120–136. 1 indexed citations
15.
Prada‐Tiedemann, Paola A., et al.. (2023). Part, III: Increasing odor detection performance after training with progressively leaner schedules of odor prevalence. Journal of the Experimental Analysis of Behavior. 120(1). 137–152. 2 indexed citations
16.
Prada‐Tiedemann, Paola A., et al.. (2022). The use of an intermittent schedule of reinforcement to evaluate detection dogs’ generalization from smokeless-powder. Animal Cognition. 25(6). 1609–1620. 5 indexed citations
17.
Hall, Nathaniel J., et al.. (2022). The effect of repeated testing on judgement bias in domestic dogs (Canis familiaris). Animal Cognition. 26(2). 477–489. 5 indexed citations
18.
Hall, Nathaniel J., et al.. (2021). Case Study: An Evaluation of Detection Dog Generalization to a Large Quantity of an Unknown Explosive in the Field. Animals. 11(5). 1341–1341. 14 indexed citations
19.
Hall, Nathaniel J., et al.. (2021). Evaluating and re-evaluating intra- and inter-species social transmission of food preferences in domestic dogs. Behavioural Processes. 191. 104471–104471. 1 indexed citations
20.
Hall, Nathaniel J.. (2017). Persistence and resistance to extinction in the domestic dog: Basic research and applications to canine training. Behavioural Processes. 141(Pt 1). 67–74. 25 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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