Evidence map›Paper›PMID 37731725›Full record

ArticlemLife2022

gcCov: Linked open data for global coronavirus studies.

Wenyu Shi, Guomei Fan, Zhihong Shen, Chuan Hu, Juncai Ma, Yuanchun Zhou, Zhen Meng, Songnian Hu, Yuhai Bi, Liang Wang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. 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

17 authors.

Wenyu ShiMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Guomei FanMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Zhihong ShenComputer Network Information Center, Chinese Academy of Sciences Beijing China.
Chuan HuComputer Network Information Center, Chinese Academy of Sciences Beijing China.
Juncai MaMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Yuanchun ZhouComputer Network Information Center, Chinese Academy of Sciences Beijing China.
Zhen MengComputer Network Information Center, Chinese Academy of Sciences Beijing China.
Songnian HuMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Yuhai BiCAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Liang WangCAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Haiying YuMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Siru LinMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Xiuqiang SunMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Xinjiao ZhangMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Dongmei LiuMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Qinlan SunMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.
Linhuan WuMicrobial Resource and Big Data Center, Institute of Microbiology Chinese Academy of Sciences Beijing China.ORCID 0000-0002-5255-1846

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a method of mapping data from publicly available genomics and publication resources to the Resource Description Framework (RDF) and implement a server to publish linked open data (LOD). As one of the largest and most comprehensive semantic databases about coronaviruses, the resulted gcCov database demonstrates the capability of using data in the LOD framework to promote correlations between genotypes and phenotypes. These correlations will be helpful for future research on fundamental viral mechanisms and drug and vaccine designs. These LOD with 62,168,127 semantic triplets and their visualizations are freely accessible through gcCov at https://nmdc.cn/gccov/.

Identifiers

PMID37731725
PMCPMC9088579

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.