Evidence map›Paper›PMID 35462676›Full record

ArticleBiodiversity Information Science and Standards2021

Wanted: Standards for FAIR taxonomic concept representations and relationships.

Beckett Sterner, Nathan Upham, Prashant Gupta, Caleb Powell, Nico M Franz

Abstract read
In one paragraph

Article in Biodiversity Information Science and Standards, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Beckett SternerArizona State University, Tempe, United States of America.
Nathan UphamArizona State University, Tempe, United States of America.
Prashant GuptaArizona State University, Tempe, United States of America.
Caleb PowellArizona State University, Tempe, United States of America.
Nico M FranzArizona State University, Tempe, United States of America.

Funding

Intelligently predicting viral spillover risks from bats and other wild mammalsR21AI164268 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI REEDER, DEEANN, UPHAM, NATHAN STEADMAN · 2021 to 2022
$433k
NIAID NIH HHS R21 AI164268
6 · The paper itself

Abstract

Making the most of biodiversity data requires linking observations of biological species from multiple sources both efficiently and accurately (Bisby 2000, Franz et al. 2016). Aggregating occurrence records using taxonomic names and synonyms is computationally efficient but known to experience significant limitations on accuracy when the assumption of one-to-one relationships between names and biological entities breaks down (Remsen 2016, Franz and Sterner 2018). Taxonomic treatments and checklists provide authoritative information about the correct usage of names for species, including operational representations of the meanings of those names in the form of range maps, reference genetic sequences, or diagnostic traits. They increasingly provide taxonomic intelligence in the form of precise description of the semantic relationships between different published names in the literature. Making this authoritative information Findable, Accessible, Interoperable, and Reusable (FAIR; Wilkinson et al. 2016) would be a transformative advance for biodiversity data sharing and help drive adoption and novel extensions of existing standards such as the Taxonomic Concept Schema and the OpenBiodiv Ontology (Kennedy et al. 2006, Senderov et al. 2018). We call for the greater, global Biodiversity Information Standards (TDWG) and taxonomy community to commit to

Indexed as

FAIR Principlesopen datataxonomic intelligence

Identifiers

PMID35462676
PMCPMC9028594

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.