Evidence map›Paper›PMID 30012108›Full record

ArticleBMC bioinformatics2018

CEDAR OnDemand: a browser extension to generate ontology-based scientific metadata.

Syed Ahmad Chan Bukhari, Marcos Martínez-Romero, Martin J O' Connor, Attila L Egyedi, Debra Willrett, John Graybeal, Mark A Musen, Kei-Hoi Cheung, Steven H Kleinstein

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

5 citing papers in PubMed, 18 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Syed Ahmad Chan BukhariDepartment of Pathology, Yale School of Medicine, New Haven, CT, USA. ahmad.chan@yale.edu.ORCID 0000-0002-6517-5261
Marcos Martínez-RomeroStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Martin J O' ConnorStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Attila L EgyediStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Debra WillrettStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
John GraybealStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Mark A MusenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Kei-Hoi CheungInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. kei.cheung@yale.edu.
Steven H KleinsteinDepartment of Pathology, Yale School of Medicine, New Haven, CT, USA. steven.kleinstein@yale.edu.
Stanford University · USYale University · USYale Cancer Center · US

Funding

TrainingU54AI117925 · NIAID · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2014 to 2018
$12.9M
NIAID NIH HHS U54 AI117925NIH BIG DATA TO KNOWLEDGE U54AI117925
6 · The paper itself

Abstract

backgroundPublic biomedical data repositories often provide web-based interfaces to collect experimental metadata. However, these interfaces typically reflect the ad hoc metadata specification practices of the associated repositories, leading to a lack of standardization in the collected metadata. This lack of standardization limits the ability of the source datasets to be broadly discovered, reused, and integrated with other datasets. To increase reuse, discoverability, and reproducibility of the described experiments, datasets should be appropriately annotated by using agreed-upon terms, ideally from ontologies or other controlled term sources.

resultsThis work presents "CEDAR OnDemand", a browser extension powered by the NCBO (National Center for Biomedical Ontology) BioPortal that enables users to seamlessly enter ontology-based metadata through existing web forms native to individual repositories. CEDAR OnDemand analyzes the web page contents to identify the text input fields and associate them with relevant ontologies which are recommended automatically based upon input fields' labels (using the NCBO ontology recommender) and a pre-defined list of ontologies. These field-specific ontologies are used for controlling metadata entry. CEDAR OnDemand works for any web form designed in the HTML format. We demonstrate how CEDAR OnDemand works through the NCBI (National Center for Biotechnology Information) BioSample web-based metadata entry.

conclusionCEDAR OnDemand helps lower the barrier of incorporating ontologies into standardized metadata entry for public data repositories. CEDAR OnDemand is available freely on the Google Chrome store https://chrome.google.com/webstore/search/CEDAROnDemand.

Indexed as

Biological OntologiesInternetMetadataSoftwareAlgorithmsHumansBioPortalCEDARFAIRMetadataNCBINCBOOntology

Identifiers

PMID30012108
PMCPMC6048706
OpenAlexW2883686584

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.