Xiaodan Ni

939 citations
15 papers · 599 · h-index 12

Impact in

Papers in

    • RNA and protein synthesis mechanisms 2
    • RNA modifications and cancer 2
    • Alzheimer's disease research and treatments 3

Xiaodan Ni

15 papers receiving 597 citations

Peers

Xiaodan Ni
Comparison fields: 5 of 82
  • Structural Biology 19
  • Neurology 127
  • Physiology 186
  • Molecular Biology 336
  • Molecular Medicine 19
Replace Olga M. Selivanova with:
Olga M. Selivanova Russia
Elizabeth A. Sweeny United States
Claudia Parrini Italy
Benedikt Frieg Germany
Roberto Maya‐Martinez United Kingdom
Marc Nadal France
Karthikeyan Annamalai Germany
Qinghua Luo China
Nikolaos Louros Belgium
Jiho Yoo South Korea
Xiaodan Ni relative to Olga M. Selivanova Russia Olga M. Selivanova's profile →
Citations per field
00.5×5.5×
Olga M. Selivanova · 1×
Citations per year

Countries citing papers authored by Xiaodan Ni

Since Specialization
Citations

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

Fields of papers citing papers by Xiaodan Ni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

15 of 15 papers shown
#Work
1 2019109
2 201786
3 201964
4 202160
5 201756
6 202052
7 201637
8 201932
9 202226
10 202324
11 202120
12 201213
13 20239
14 20219
15 20232

About Xiaodan Ni

Xiaodan Ni is a scholar working on Molecular Biology, Physiology, Genetics, Infectious Diseases and Pathology and Forensic Medicine, having authored 15 papers that have together received 599 indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (3 papers), Bacterial Genetics and Biotechnology (3 papers), Parkinson's Disease Mechanisms and Treatments (2 papers), Botulinum Toxin and Related Neurological Disorders (2 papers), COVID-19 Clinical Research Studies (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), RNA and protein synthesis mechanisms (2 papers) and RNA modifications and cancer (2 papers). The work is most often cited by research in Structural Biology (19 citations), Neurology (127 citations), Physiology (186 citations), Molecular Biology (336 citations) and Molecular Medicine (19 citations). Xiaodan Ni has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Jiansen Jiang, Jennifer C. Lee, Ryan P. McGlinchey, Joaquín Ortega, Bryan VanSchouwen, Istvan Botos, Susan K. Buchanan, Giuseppe Melacini, Rashik Ahmed and Jared A. Shadish. Their work appears in journals such as Journal of the American Chemical Society, Nucleic Acids Research, PLoS Biology, ACS Central Science and Nature Communications.

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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