Danielle Lee

990 citations
62 papers · 628 · h-index 15

Impact in

Papers in

Danielle Lee

52 papers receiving 585 citations

Peers

Danielle Lee
Comparison fields: 5 of 106
  • Computer Science Applications 64
  • Information Systems 249
  • Statistics, Probability and Uncertainty 68
  • Statistical and Nonlinear Physics 70
  • Artificial Intelligence 149
Replace Chun-Hua Tsai with:
Chun-Hua Tsai United States
Nana Yaw Asabere Ghana
Emma L. Tonkin United Kingdom
Muh‐Chyun Tang Taiwan
Norbou Buchler United States
Thomas Maillart Switzerland
Sebastián Moreno Chile
Marios Poulos Greece
Yea‐Seul Kim United States
Jack Andersen Denmark
Danielle Lee relative to Chun-Hua Tsai United States Chun-Hua Tsai's profile →
Citations per field
00.5×6.8×
Chun-Hua Tsai · 1×
Citations per year

Countries citing papers authored by Danielle Lee

Since Specialization
Citations

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

Fields of papers citing papers by Danielle Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200755
2 201448
3 201839
4 201038
5 201237
6 201733
7 201831
8 201930
9 201724
10 201222
11 201120
12 201020
13 201717
14 200816
15 201014
16 200813
17 202412
18
Does trust influence information similarity
200911
19 201910
20 201510

About Danielle Lee

Danielle Lee is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Public Health, Environmental and Occupational Health and Statistical and Nonlinear Physics, having authored 62 papers that have together received 628 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (17 papers), Expert finding and Q&A systems (8 papers), Complex Network Analysis Techniques (8 papers), scientometrics and bibliometrics research (7 papers), Diverse Approaches in Healthcare and Education Studies (6 papers), Digital Marketing and Social Media (5 papers), Educational Systems and Policies (4 papers) and Computational and Text Analysis Methods (3 papers). The work is most often cited by research in Computer Science Applications (64 citations), Information Systems (249 citations), Statistics, Probability and Uncertainty (68 citations), Statistical and Nonlinear Physics (70 citations) and Artificial Intelligence (149 citations). Danielle Lee has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Peter Brusilovsky, Titus Schleyer, David Borsook, Michael Yudelson, Sergey Sosnovsky, Vladimir Zadorozhny, Lino Becerra, Rami Burstein, Linda Solstrand Dahlberg and Clas Linnman. Their work appears in journals such as Scientometrics, Current Developments in Nutrition, Journal of Nutrition Education and Behavior, The Electronic Library and Nanotoxicology.

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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