Jun Saito

1.1k citations
82 papers · 806 · h-index 15

Impact in

Papers in

Jun Saito

77 papers receiving 741 citations

Peers

Jun Saito
Comparison fields: 5 of 115
  • Molecular Medicine 76
  • Surfaces, Coatings and Films 54
  • Microbiology 37
  • Computer Vision and Pattern Recognition 107
  • Political Science and International Relations 122
Replace Andrew K. Forrest with:
Andrew K. Forrest United Kingdom
Nicolae Goga Romania
Wenlong Huang China
Lilin Liu China
А. Н. Панин Russia
Kosmas Kosmidis Greece
Takao Abé Japan
William D. Palmer United States
Michelle M. Kuttel South Africa
Jun Saito relative to Andrew K. Forrest United Kingdom Andrew K. Forrest's profile →
Citations per field
00.5×50×122×
Andrew K. Forrest · 1×
Citations per year

Countries citing papers authored by Jun Saito

Since Specialization
Citations

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

Fields of papers citing papers by Jun Saito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jun Saito, 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 Jun Saito Line = papers co-authored together Jun Saito 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 200390
2 198774
3 200756
4 200846
5 200739
6 201433
7 200231
8 202024
9 200621
10 201819
11 202018
12 200416
13 201216
14 200216
15 200515
16 201514
17 200712
18 200412
19 200612
20 201010

About Jun Saito

Jun Saito is a scholar working on Electrical and Electronic Engineering, Political Science and International Relations, Computer Vision and Pattern Recognition, Computational Mechanics and Spectroscopy, having authored 82 papers that have together received 806 indexed citations. Recurring topics across this work include Advancements in Photolithography Techniques (12 papers), Optical measurement and interference techniques (9 papers), Electoral Systems and Political Participation (8 papers), Advanced X-ray Imaging Techniques (7 papers), Surface Roughness and Optical Measurements (7 papers), Silicon Carbide Semiconductor Technologies (6 papers), Advanced Measurement and Metrology Techniques (5 papers) and Local Government Finance and Decentralization (5 papers). The work is most often cited by research in Molecular Medicine (76 citations), Surfaces, Coatings and Films (54 citations), Microbiology (37 citations), Computer Vision and Pattern Recognition (107 citations) and Political Science and International Relations (122 citations). Jun Saito has collaborated with scholars based in Japan, United States and Germany. Frequent co-authors include Yusaku Horiuchi, Mototsugu Yamada, Masatoshi Sato, Hiroyuki Matsumoto, Hideo Kitagawa, Sho Takahata, Tomohiro Ozawa, Maiko Iida, Takashi Watanabe and Yasuo Takeuchi. Their work appears in journals such as Japanese Journal of Applied Physics, Journal of East Asian Studies, Journal of the Japan Petroleum Institute, Chemistry Letters and American Journal of Political Science.

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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