Dan Knights Google Scholar: h-index, Total Citations, and Citation Map
Dan Knights's h-index is 82 (148 i10-index, 154,073+ total citations across 249+ publications) according to Google Scholar as of October 2026. Dan Knights is affiliated with University of Minnesota.
Dan Knights is a researcher affiliated with University of Minnesota, specializing in Computational Biology, Machine Learning, Bioinformatics. Their work has been cited 154,073 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Dan Knights's Citation Metrics
Bibliometric impact based on 249 indexed publications.
- H-Index
- 82
- i10-Index
- 148
- Total Citations
- 154,073
- Citing Countries
- 71
As of October 2026.
Dan Knights has an h-index of 82 and 154,073 total citations across 249 publications, with research cited by institutions in 71 countries.
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Global Impact Map
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QIIME allows analysis of high-throughput community sequencing data
201039,332
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed foundational methods for predictive functional profiling of microbial communities and established scalable, reproducible data science frameworks for microbiome analysis.
The researcher developed QIIME, a foundational software framework enabling the analysis of high-throughput community sequencing data, as evidenced by its publication in Nature Methods and extensive citation record.
The researcher established a foundational framework for characterizing the structure, function, and diversity of the healthy human microbiome through a seminal, highly cited publication.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Dan Knights's research
Dan Knights is a researcher in Computational Biology, Machine Learning and Bioinformatics at University of Minnesota. Their work has been cited 154,073 times across 249 publications (h-index 82), according to Google Scholar.
Their most-cited work, “QIIME allows analysis of high-throughput community sequencing data” (2010), has accumulated 39,332 citations. Other influential works include “QIIME allows analysis of high-throughput community sequencing data” (2010) with 39,332 citations and “Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2” (2019) with 27,127 citations.
Citations of Dan Knights's research come primarily from United States, China and India, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
