Jonatan Ginés

575 citations
13 papers · 295 · h-index 7

Impact in

Papers in

Jonatan Ginés

13 papers receiving 282 citations

Peers

Jonatan Ginés
Comparison fields: 5 of 50
  • Computer Vision and Pattern Recognition 140
  • Control and Systems Engineering 78
  • Aerospace Engineering 79
  • Social Psychology 44
  • Artificial Intelligence 61
Replace Raymond Sheh with:
Raymond Sheh Australia
E. Gil Jones United States
Beom-Su Seo South Korea
Dominik Jain Germany
Melonee Wise United States
Yoichiro Endo United States
Ruffin White United States
Èric Pairet United Kingdom
Nate Koenig United States
Shu Jiang United States
Jonatan Ginés relative to Raymond Sheh Australia Raymond Sheh's profile →
Citations per field
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Citations per year

Countries citing papers authored by Jonatan Ginés

Since Specialization
Citations

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

Fields of papers citing papers by Jonatan Ginés

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside Jonatan Ginés, 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 Jonatan Ginés Line = papers co-authored together Jonatan Ginés links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2020168
2 202342
3 201920
4 202018
5 201913
6 202011
7 20186
8 20194
9 20213
10 20223
11 20213
12 20202
13 20212

About Jonatan Ginés

Jonatan Ginés is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Social Psychology and Control and Systems Engineering, having authored 13 papers that have together received 295 indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (4 papers), Reinforcement Learning in Robotics (3 papers), Social Robot Interaction and HRI (3 papers), Robotics and Sensor-Based Localization (3 papers), Context-Aware Activity Recognition Systems (2 papers), Video Surveillance and Tracking Methods (2 papers), Robotics and Automated Systems (2 papers) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (140 citations), Control and Systems Engineering (78 citations), Aerospace Engineering (79 citations), Social Psychology (44 citations) and Artificial Intelligence (61 citations). Jonatan Ginés has collaborated with scholars based in Spain, United States and India. Frequent co-authors include Francisco Martí­n, Steve Macenski, Ruffin White, Vicente Matellán Olivera, Francisco J. Rodríguez-Lera, Ángel Manuel Guerrero‐Higueras, Francisco J. Rodríguez and Manuel F. Fernández. Their work appears in journals such as Applied Sciences, Sensors, Robotics, Engineering Applications of Artificial Intelligence and Natural Computing.

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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