Daniel Saakes

1.1k citations
66 papers · 840 · h-index 16

Impact in

Papers in

Daniel Saakes

63 papers receiving 792 citations

Peers

Daniel Saakes
Comparison fields: 5 of 103
  • Human-Computer Interaction 377
  • Sensory Systems 55
  • Social Psychology 235
  • Computer Vision and Pattern Recognition 233
  • Medical Laboratory Technology 12
Replace Itiro Siio with:
Itiro Siio Japan
Giandomenico Caruso Italy
Jorge Alcaide-Marzal Spain
Umberto Cugini Italy
Marino Menozzi Switzerland
José Antonio Diego-Más Spain
Nobuji Tetsutani Japan
Richard Skarbez Australia
Francisco R. Ortega United States
Judith Amores United States
Daniel Saakes relative to Itiro Siio Japan Itiro Siio's profile →
Citations per field
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Itiro Siio · 1×
Citations per year

Countries citing papers authored by Daniel Saakes

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Saakes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021113
2 201656
3 200555
4 201337
5 201636
6 201834
7 201932
8 201329
9 201827
10 201623
11 201923
12 200723
13 201921
14 201220
15 201817
16 202115
17 201415
18 202015
19 201814
20 200913

About Daniel Saakes

Daniel Saakes is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition, Social Psychology, Mechanical Engineering and Automotive Engineering, having authored 66 papers that have together received 840 indexed citations. Recurring topics across this work include Interactive and Immersive Displays (25 papers), Augmented Reality Applications (15 papers), Virtual Reality Applications and Impacts (13 papers), Innovative Human-Technology Interaction (12 papers), Color perception and design (8 papers), Tactile and Sensory Interactions (8 papers), Additive Manufacturing and 3D Printing Technologies (8 papers) and Design Education and Practice (7 papers). The work is most often cited by research in Human-Computer Interaction (377 citations), Sensory Systems (55 citations), Social Psychology (235 citations), Computer Vision and Pattern Recognition (233 citations) and Medical Laboratory Technology (12 citations). Daniel Saakes has collaborated with scholars based in South Korea, Netherlands and Japan. Frequent co-authors include Shuping Xiong, Woojoo Kim, Chunxi Huang, Thomas J. L. van Rompay, Thomas van Rompay, Anna Fenko, Paul Hekkert, Masahiko İnami, Takeo Igarashi and Alexander Plopski. Their work appears in journals such as Food Quality and Preference, Artificial intelligence for engineering design analysis and manufacturing, Computers & Graphics, Personal and Ubiquitous Computing and Proceedings of the ACM on Human-Computer Interaction.

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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