Evidence map›Paper›PMID 24595056›Full record

ArticlePloS one2014

Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies.

Ramona L Walls, John Deck, Robert Guralnick, Steve Baskauf, Reed Beaman, Stanley Blum, Shawn Bowers, Pier Luigi Buttigieg, Neil Davies, Dag Endresen and 15 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

  1. RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
  3. Article
  4. Russian Biodiversity Collections: A Professional Opinion Survey.Animals : an open access journal from MDPI · 2023
    Article
  5. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.Mammalian genome : official journal of the International Mammalian Genome Society · 2023
    Article
  6. Article
  7. Review
  8. CRENO: An ontology to model concepts relating to culture, race, ethnicity, and nationality for health data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Ramona L WallsThe iPlant Collaborative, University of Arizona, Tucson, Arizona, United States of America.
John DeckUniversity of California, Berkeley, Berkeley, California, United States of America.
Robert GuralnickDepartment of Ecology and Evolutionary Biology and the CU Museum of Natural History, University of Colorado at Boulder, Boulder, Colorado, United States of America.
Steve BaskaufDepartment of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
Reed BeamanUniversity of Florida, Florida Museum of Natural History, Gainesville, Florida, United States of America.
Stanley BlumResearch Informatics, California Academy of Sciences, San Francisco, California, United States of America.
Shawn BowersGonzaga University, Computer Science, Spokane, Washington, United States of America.
Pier Luigi ButtigiegAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany.
Neil DaviesUniversity of California, Berkeley, Gump South Pacific Research Station, Moorea, French Polynesia.
Dag EndresenGBIF Norway, Natural History Museum, University in Oslo, Oslo, Norway.
Maria Alejandra GandolfoLH Bailey Hortorium, Department of Plant Biology, Cornell University, Ithaca, New York, United States of America.
Robert HannerBiodiversity Institute of Ontario, University of Guelph, Guelph, ON, Canada.
Alyssa JanningSchool of Information Resources and Library Science, University of Arizona, Tucson, Arizona, United States of America.
Leonard KrishtalkaBiodiversity Institute and Ecology & Evolutionary Biology, The University of Kansas, Lawrence, Kansas, United States of America.
Andréa MatsunagaUniversity of Florida, Gainesville, Florida, United States of America.
Peter MidfordEcology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America.
Norman MorrisonThe BioVeL Project, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
Éamonn Ó TuamaGBIF Secretariat, Copenhagen, Denmark.
Mark SchildhauerNational Center for Ecological Analysis and Synthesis, Santa Barbara, California, United States of America.
Barry SmithDepartment of Philosophy, University at Buffalo, Buffalo, New York, United States of America.
Brian J StuckyDepartment of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America.
Andrea ThomerGraduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America.
John Wieczorek3101 VLSB, Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, California, United States of America.
Jamie WhitacreInformatics Branch, Information Technology Office, National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America.
John WooleyUniversity of California San Diego, La Jolla, California, United States of America.

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
NHGRI NIH HHS U54 HG004028
6 · The paper itself

Abstract

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

Indexed as

BiodiversityKnowledgeSemantics

Identifiers

PMID24595056
PMCPMC3940615

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.