Evidence map›Paper›PMID 34327299›Full record

ArticleFrontiers in research metrics and analytics2021

ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.

Elizabeth T Hobbs, Stephen M Goralski, Ashley Mitchell, Andrew Simpson, Dorjan Leka, Emmanuel Kotey, Matt Sekira, James B Munro, Suvarna Nadendla, Rebecca Jackson and 4 more

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 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. 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

14 authors.

Elizabeth T HobbsDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Stephen M GoralskiDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Ashley MitchellDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Andrew SimpsonDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Dorjan LekaDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Emmanuel KoteyDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
Matt SekiraDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.
James B MunroInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States.
Suvarna NadendlaInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States.
Rebecca JacksonInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States.
Aitor Gonzalez-AguirreBarcelona Supercomputing Center (BSC), Barcelona, Spain.
Martin KrallingerBarcelona Supercomputing Center (BSC), Barcelona, Spain.
Michelle GiglioInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States.
Ivan ErillDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analysis of high-throughput experiments in the life sciences frequently relies upon standardized information about genes, gene products, and other biological entities. To provide this information, expert curators are increasingly relying on text mining tools to identify, extract and harmonize statements from biomedical journal articles that discuss findings of interest. For determining reliability of the statements, curators need the evidence used by the authors to support their assertions. It is important to annotate the evidence directly used by authors to qualify their findings rather than simply annotating mentions of experimental methods without the context of what findings they support. Text mining tools require tuning and adaptation to achieve accurate performance. Many annotated corpora exist to enable developing and tuning text mining tools; however, none currently provides annotations of evidence based on the extensive and widely used Evidence and Conclusion Ontology. We present the ECO-CollecTF corpus, a novel, freely available, biomedical corpus of 84 documents that captures high-quality, evidence-based statements annotated with the Evidence and Conclusion Ontology.

Indexed as

annotationbiocurationcorpusevidenceliteraturetext- and data mining

Identifiers

PMID34327299
PMCPMC8313968

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.