Daniel Barber

1.7k citations
65 papers · 1.1k · 1 hit paper · h-index 16

Impact in

Papers in

Daniel Barber

57 papers receiving 1.0k citations

Daniel Barber's Hit Papers

Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management 2016 · 287 citations
2870+3+6Years since publication50100150200250

Peers

Daniel Barber
Comparison fields: 5 of 97
  • Social Psychology 671
  • Radiological and Ultrasound Technology 140
  • Human-Computer Interaction 95
  • Safety Research 112
  • Health Informatics 14
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Barber

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Barber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Barber, 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 Daniel Barber Line = papers co-authored together Daniel Barber links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Intelligent Agent Transparency in Human–Agent Teaming for Multi-UxV Management
Hit paper breakdown →
2016287
2 2014201
3 200644
4 201739
5 201633
6
The Mixed Initiative Experimental (MIX) Testbed for Human Robot Interactions With Varied Levels of Automation
200826
7 200524
8 201324
9 201521
10 201620
11 201419
12 201618
13 201718
14 201416
15 201615
16 201315
17 201914
18 201914
19 201413
20 201513

About Daniel Barber

Daniel Barber is a scholar working on Social Psychology, Radiological and Ultrasound Technology, Cognitive Neuroscience, Artificial Intelligence and Human-Computer Interaction, having authored 65 papers that have together received 1.1k indexed citations. Recurring topics across this work include Human-Automation Interaction and Safety (39 papers), Occupational Health and Safety Research (13 papers), Speech and dialogue systems (8 papers), Healthcare Technology and Patient Monitoring (8 papers), Safety Warnings and Signage (6 papers), Social Robot Interaction and HRI (6 papers), Risk and Safety Analysis (5 papers) and Hand Gesture Recognition Systems (5 papers). The work is most often cited by research in Social Psychology (671 citations), Radiological and Ultrasound Technology (140 citations), Human-Computer Interaction (95 citations), Safety Research (112 citations) and Health Informatics (14 citations). Daniel Barber has collaborated with scholars based in United States, Germany and Australia. Frequent co-authors include Lauren Reinerman-Jones, Julian Abich, Gerald Matthews, Michael A. Rupp, Jessie Y. C. Chen, Joseph Mercado, Katelyn Procci, Travis J. Wiltshire, Stephen M. Fiore and Denise Nicholson. Their work appears in journals such as Human Factors The Journal of the Human Factors and Ergonomics Society, Lecture notes in computer science, Ergonomics, The Journal of Strength and Conditioning Research and Interactive Learning Environments.

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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