Chaoqun Ni

2.6k citations
48 papers · 1.7k indexed · 1 hit paper · h-index 17

Chaoqun Ni

43 papers receiving 1.6k citations

Hit Papers

Bibliometrics: Global gender disparities in science9122013202620172021250500750

Peers

Chaoqun Ni
Comparison fields: 5 of 137
  • Statistics, Probability and Uncertainty 679
  • Gender Studies 461
  • Information Systems and Management 150
  • History and Philosophy of Science 97
  • Library and Information Sciences 28
Replace Gunnar Sivertsen with:
Gunnar Sivertsen Norway
Daryl E. Chubin United States
John P. Walsh United States
Lowell L. Hargens United States
Donald deB. Beaver United States
Mary R. Anderson United States
Juan Pablo Alperín Canada
Liv Langfeldt Norway
Brian Martin Australia
Sarah de Rijcke Netherlands
Chaoqun Ni relative to Gunnar Sivertsen Norway Gunnar Sivertsen's profile →
Citations per field
00.5×4.7×
Gunnar Sivertsen · 1×
Citations per year

Countries citing papers authored by Chaoqun Ni

Since Specialization
Citations

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

Fields of papers citing papers by Chaoqun Ni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20251
3 20241
4 20240
5 20245
6 20240
7 20231
8 20231
9 202311
10 20217
11 20210
12 20181
13 201586
14 201437
15 201313
16 201331
17 201211
18 201113
19 200829
20 20081

About Chaoqun Ni

Chaoqun Ni is a scholar working on Statistics, Probability and Uncertainty, Library and Information Sciences and Communication, having authored 48 papers that have together received 1.7k indexed citations. Recurring topics across this work include scientometrics and bibliometrics research (23 papers), Web visibility and informetrics (10 papers), Wikis in Education and Collaboration (6 papers), Complex Network Analysis Techniques (5 papers), Diversity and Career in Medicine (4 papers), Doctoral Education Challenges and Solutions (4 papers), Academic Publishing and Open Access (3 papers) and Web Data Mining and Analysis (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (679 citations), Gender Studies (461 citations) and Information Systems and Management (150 citations). Chaoqun Ni has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Cassidy R. Sugimoto, Vincent Larivière, Blaise Cronin, Yves Gingras, Jiepu Jiang, Jevin D. West, Alice Robbin, Ruimin Ma, Terrell Russell and Elise Smith. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and PLoS ONE.

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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