Evidence map›Paper›PMID 33719446›Full record

ArticleJournal of proteome research2021

A Second Look at FAIR in Proteomic Investigations.

J Harry Caufield, John Fu, Ding Wang, Vladimir Guevara-Gonzalez, Wei Wang, Peipei Ping

Abstract read
In one paragraph

Article in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

John Fu
Ding Wang
Vladimir Guevara-Gonzalez
Wei Wang

Funding

Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science PlatformR35HL135772 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PING, PEIPEI · 2017 to 2023
$6.4M
Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathwayR01HL146739 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI NG, CHUN MING DOMINIC, WANG, DING · 2019 to 2022
$2.6M
iDISCOVER: Integrated Data Science Training in CardioVascular MedicineT32HL139450 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX, PING, PEIPEI · 2018 to 2022
$1.3M
NHLBI NIH HHS R01 HL146739NHLBI NIH HHS R35 HL135772NHLBI NIH HHS T32 HL139450
6 · The paper itself

Abstract

Proteomics is, by definition, comprehensive and large-scale, seeking to unravel ome-level protein features with phenotypic information on an entire system, an organ, cells, or organisms. This scope consistently involves and extends beyond single experiments. Multitudinous resources now exist to assist in making the results of proteomics experiments more findable, accessible, interoperable, and reusable (FAIR), yet many tools are awaiting to be adopted by our community. Here we highlight strategies for expanding the impact of proteomics data beyond single studies. We show how linking specific terminologies, identifiers, and text (words) can unify individual data points across a wide spectrum of studies and, more importantly, how this approach may potentially reveal novel relationships. In this effort, we explain how data sets and methods can be rendered more linkable and how this maximizes their value. We also include a discussion on how data linking strategies benefit stakeholders across the proteomics community and beyond.

Indexed as

Proteomicsdata sharingFAIR principlesknowledgebasesontologiesstandardization

Identifiers

PMID33719446
PMCPMC8518219

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.