Crystal Qian

400 citations
5 papers · 9 · h-index 2

Impact in

Papers in

    • Research Data Management Practices 1
    • Software Engineering Research 1
    • Natural Language Processing Techniques 1
    • Topic Modeling 1
    • Explainable Artificial Intelligence (XAI) 1

Crystal Qian

3 papers receiving 9 citations

Peers

Crystal Qian
Comparison fields: 5 of 13
  • General Social Sciences 1
  • Information Systems and Management 2
  • Human-Computer Interaction 1
  • Management Science and Operations Research 2
  • Safety Research 1
Replace Shripad V. Godbole with:
Shripad V. Godbole United States
Joshua Kerrigan United States
Jayesh Krishna United States
N. Stone United States
H. L. Dai United States
Milica Aleksić Serbia
Vasily Ershov Russia
Stephan Janosch Germany
M. L. Proffitt United States
L. Shang China
Crystal Qian relative to Shripad V. Godbole United States Shripad V. Godbole's profile →
Citations per field
00.5×
Shripad V. Godbole · 1×
Citations per year

Countries citing papers authored by Crystal Qian

Since Specialization
Citations

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

Fields of papers citing papers by Crystal Qian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 12 scholars most cited alongside Crystal Qian, 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 Crystal Qian Line = papers co-authored together Crystal Qian links everyone, so they are left out of the graph.

All Works

5 of 5 papers shown

About Crystal Qian

Crystal Qian is a scholar working on Information Systems, Artificial Intelligence, Molecular Biology, Information Systems and Management and Management Science and Operations Research, having authored 5 papers that have together received 9 indexed citations. Recurring topics across this work include Research Data Management Practices (1 paper), Scientific Computing and Data Management (1 paper), Biomedical Text Mining and Ontologies (1 paper), Natural Language Processing Techniques (1 paper), Software Engineering Research (1 paper), Machine Learning in Materials Science (1 paper), Topic Modeling (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in General Social Sciences (1 citation), Information Systems and Management (2 citations), Human-Computer Interaction (1 citation), Management Science and Operations Research (2 citations) and Safety Research (1 citation). Crystal Qian has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include James Wexler, Ryan Mullins, Mahima Pushkarna, Ian Tenney, Minsuk Kahng, Emily Reif, Aaron Parisi, Maël Lebreton, Lucas Dixon and Michael Xieyang Liu. Their work appears in journals such as HAL (Le Centre pour la Communication Scientifique Directe).

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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