Nathan D. Olson

12.1k total citations
34 papers, 1.4k citations indexed

About

Nathan D. Olson is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Nathan D. Olson has authored 34 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 6 papers in Genetics and 5 papers in Ecology. Recurrent topics in Nathan D. Olson's work include Genomics and Phylogenetic Studies (14 papers), Molecular Biology Techniques and Applications (8 papers) and Genomics and Rare Diseases (5 papers). Nathan D. Olson is often cited by papers focused on Genomics and Phylogenetic Studies (14 papers), Molecular Biology Techniques and Applications (8 papers) and Genomics and Rare Diseases (5 papers). Nathan D. Olson collaborates with scholars based in United States, Switzerland and Austria. Nathan D. Olson's co-authors include Jayne B. Morrow, Michael P. Lesser, C. L. Fiore, Jessica K. Jarett, Justin M. Zook, Marc Salit, Tracy D. Ainsworth, R. D. Gates, Misaki Takabayashi and Justin Wagner and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Nature Biotechnology.

In The Last Decade

Nathan D. Olson

32 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nathan D. Olson United States 16 577 421 210 186 122 34 1.4k
Ramana Madupu United States 18 1.4k 2.4× 791 1.9× 239 1.1× 81 0.4× 207 1.7× 29 2.3k
Man Kit Cheung Hong Kong 22 719 1.2× 515 1.2× 79 0.4× 145 0.8× 331 2.7× 54 1.4k
Xueming Wei China 18 723 1.3× 317 0.8× 279 1.3× 26 0.1× 238 2.0× 31 1.3k
Barbara Cardazzo Italy 28 937 1.6× 284 0.7× 238 1.1× 26 0.1× 177 1.5× 88 2.0k
Liam D. H. Elbourne Australia 26 1.1k 1.9× 494 1.2× 292 1.4× 67 0.4× 413 3.4× 50 2.3k
В. А. Рассказов Russia 16 592 1.0× 97 0.2× 89 0.4× 45 0.2× 91 0.7× 73 1.1k
Cristal Zúñiga United States 20 862 1.5× 248 0.6× 64 0.3× 30 0.2× 94 0.8× 43 1.3k
Dag Anders Brede Norway 29 1.1k 1.9× 175 0.4× 135 0.6× 13 0.1× 117 1.0× 74 2.0k
Tiffany Hsu United States 11 735 1.3× 254 0.6× 58 0.3× 15 0.1× 162 1.3× 14 1.3k
Cleber Ouverney United States 15 985 1.7× 905 2.1× 87 0.4× 207 1.1× 82 0.7× 19 1.8k

Countries citing papers authored by Nathan D. Olson

Since Specialization
Citations

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

Fields of papers citing papers by Nathan D. Olson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan D. Olson. 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 Nathan D. Olson. The network helps show where Nathan D. Olson may publish in the future.

Co-authorship network of co-authors of Nathan D. Olson

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan D. Olson. A scholar is included among the top collaborators of Nathan D. Olson based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nathan D. Olson. Nathan D. Olson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
McDaniel, Jennifer, Pilar Álvarez Jerez, Bharati Jadhav, et al.. (2024). The GIAB genomic stratifications resource for human reference genomes. Nature Communications. 15(1). 9029–9029. 12 indexed citations
2.
English, Adam C., Egor Dolzhenko, Sean K. McKenzie, et al.. (2024). Analysis and benchmarking of small and large genomic variants across tandem repeats. Nature Biotechnology. 43(3). 431–442. 24 indexed citations
3.
Tonner, Peter D., Abe Pressman, Nathan D. Olson, et al.. (2023). Precision engineering of biological function with large-scale measurements and machine learning. PLoS ONE. 18(3). e0283548–e0283548. 2 indexed citations
4.
Cleveland, Megan H., et al.. (2023). Rapid production and free distribution of a synthetic RNA material to support SARS-CoV-2 molecular diagnostic testing. Biologicals. 82. 101680–101680. 2 indexed citations
5.
6.
Olson, Nathan D., et al.. (2022). Evaluation of a Hands-On Wrist Fracture Simulator for Fracture Management Training in Emergency Medicine Residents. Cureus. 14(7). e27030–e27030. 1 indexed citations
7.
Tonner, Peter D., Abe Pressman, Nathan D. Olson, et al.. (2021). The genotype‐phenotype landscape of an allosteric protein. Molecular Systems Biology. 17(3). e10179–e10179. 39 indexed citations
8.
Olson, Nathan D., M. S. Binoj Kumar, Shan Li, et al.. (2020). A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.. Microbiome. 8(1). 35–35. 8 indexed citations
9.
Olson, Nathan D., et al.. (2019). metagenomeFeatures : an R package for working with 16S rRNA reference databases and marker-gene survey feature data. Bioinformatics. 35(19). 3870–3872. 3 indexed citations
10.
Wang, Ying‐Chih, Nathan D. Olson, Gintaras Deikus, et al.. (2019). High-coverage, long-read sequencing of Han Chinese trio reference samples. Scientific Data. 6(1). 91–91. 7 indexed citations
11.
Kong, Heidi H., Björn Andersson, Thomas Clavel, et al.. (2017). Performing Skin Microbiome Research: A Method to the Madness. Journal of Investigative Dermatology. 137(3). 561–568. 152 indexed citations
12.
Olson, Nathan D., Justin M. Zook, Jayne B. Morrow, & Nancy J. Lin. (2017). Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data. PeerJ. 5. e3729–e3729. 7 indexed citations
13.
Olson, Nathan D., Steven P. Lund, Zvi Kelman, et al.. (2016). Evaluation of microbial qPCR workflows using engineered Saccharomyces cerevisiae. SHILAP Revista de lepidopterología. 7. 27–33. 3 indexed citations
14.
Olson, Nathan D., Justin M. Zook, Daniel V. Samarov, Scott A. Jackson, & Marc Salit. (2016). PEPR: pipelines for evaluating prokaryotic references. Analytical and Bioanalytical Chemistry. 408(11). 2975–2983. 5 indexed citations
15.
Olson, Nathan D., Steven P. Lund, Rebecca E. Colman, et al.. (2015). Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Frontiers in Genetics. 6. 235–235. 118 indexed citations
16.
Olson, Nathan D., Steven P. Lund, Justin M. Zook, et al.. (2015). International interlaboratory study comparing single organism 16S rRNA gene sequencing data: Beyond consensus sequence comparisons. SHILAP Revista de lepidopterología. 3. 17–24. 3 indexed citations
17.
Olson, Nathan D. & Michael P. Lesser. (2013). Diazotrophic diversity in the Caribbean coral, Montastraea cavernosa. Archives of Microbiology. 195(12). 853–859. 31 indexed citations
18.
Olson, Nathan D. & Jayne B. Morrow. (2012). DNA extract characterization process for microbial detection methods development and validation. BMC Research Notes. 5(1). 668–668. 93 indexed citations
19.
Olson, Nathan D., et al.. (2010). RNA Deep Sequencing - Beyond Proof of Concept. Journal of Biomolecular Techniques JBT. 21.
20.
Fiore, C. L., Jessica K. Jarett, Nathan D. Olson, & Michael P. Lesser. (2010). Nitrogen fixation and nitrogen transformations in marine symbioses. Trends in Microbiology. 18(10). 455–463. 169 indexed citations

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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