NCBI Taxonomy: a comprehensive update on curation, resources and tools
Open accessAbstract
The National Center for Biotechnology Information (NCBI) Taxonomy includes organism names and classifications for every sequence in the nucleotide and protein sequence databases of the International Nucleotide Sequence Database Collaboration. Since the last review of this resource in 2012, it has undergone several improvements. Most notable is the shift from a single SQL database to a series of linked databases tied to a framework of data called NameBank. This means that relations among data elements can be adjusted in more detail, resulting in expanded annotation of synonyms, the ability to flag names with specific nomenclatural properties, enhanced tracking of publications tied to names and improved annotation of scientific authorities and types. Additionally, practices utilized by NCBI Taxonomy curators specific to major taxonomic groups are described, terms peculiar to NCBI Taxonomy are explained, external resources are acknowledged and updates to tools and other resources are documented. Database URL: https://www.ncbi.nlm.nih.gov/taxonomy.
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The National Library of Medicine (NLM) often manually curates biomedical literature databases using ontology and taxonomy concepts due to their established authenticity.
“The National Library of Medicine (NLM) often manually curates biomedical literature databases using ontology and taxonomy concepts due to their established authenticity.22”
From Safeguarding authenticity for mitigating the harms of generative AI: Issues, research agenda, and policies for detection, fact-checking, and ethical AI (2024) · cites this paper as [22]
The NLM employs two prominent taxonomies: the Medical Subject Headings (MeSH) and the NLM National Center of Biotechnology Information (NCBI) Taxonomy.
“Notably, the NLM employs two prominent taxonomies: (a) the Medical Subject Headings (MeSH)23 and (b) the NLM National Center of Biotechnology Information (NCBI) Taxonomy.24,25”
From Safeguarding authenticity for mitigating the harms of generative AI: Issues, research agenda, and policies for detection, fact-checking, and ethical AI (2024) · cites this paper as [25]
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- Authors
- Schoch, C. L., Ciufo, S., Domrachev, M., Hotton, C. L., Kannan, S., Khovanskaya, R., Leipe, D., Mcveigh, R., O’Neill, K., Robbertse, B., Sharma, S., Soussov, V., Sullivan, J. P., Sun, L., Turner, S., & Karsch-Mizrachi, I.
- Affiliations
- National Institutes of Health (US)
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, 9600 Rockville Pike, Bethesda, MD 20892, USA
- Corresponding author
- Schoch, Conrad L
- ORCID
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-01
- Volume
- 2020
- ISSN
- 1758-0463 (ISSN-L)
- PMC
- PMC7408187
- OpenAlex
- W3048090444
- Cited by
- 2,635
- References
- 9
- Citations by year
- 2026: 691 · 2025: 569 · 2024: 465 · 2023: 407 · 2022: 318 · 2021: 172 · 2020: 9 · 2019: 1
- FWCI
- 21.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Database (publishedVersion, public-domain)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research)
- Funders
- Intramural Research Program of the National Library of Medicine
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- NCBI taxonomy, taxonomic nomenclature, linked databases, scientific authority, type specimens, publication tracking, sequence databases, taxonomic classification
- MeSH terms
- Animals, Bacteria, Classification, Database Management Systems, Humans, National Library of Medicine (U.S.), Plants, United States, Viruses, Databases, Genetic
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