gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data
gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data is a dataset published in Nucleic Acids Research (2018). On theSindex it has a DataRank of 3.6, placing it in the top 31.1% of the data-sharing corpus. It has been cited 111 times, with 83 citing works in its 1-hop citation network. Its calibrated FAIR score is 53/100.
Abstract
Meta-omics approaches have been increasingly used to study the structure and function of the microbial communities. A variety of large-scale collaborative projects are being conducted to encompass samples from diverse environments and habitats. This change has resulted in enormous demands for long-term data maintenance and capacity for data analysis. The Global Catalogue of Metagenomics (gcMeta) is a part of the 'Chinese Academy of Sciences Initiative of Microbiome (CAS-CMI)', which focuses on studying the human and environmental microbiome, establishing depositories of samples, strains and data, as well as promoting international collaboration. To accommodate and rationally organize massive datasets derived from several thousands of human and environmental microbiome samples, gcMeta features a database management system for archiving and publishing data in a standardized way. Another main feature is the integration of more than ninety web-based data analysis tools and workflows through a Docker platform which enables data analysis by using various operating systems. This platform has been rapidly expanding, and now hosts data from the CAS-CMI and a number of other ongoing research projects. In conclusion, this platform presents a powerful and user-friendly service to support worldwide collaborative efforts in the field of meta-omics research. This platform is freely accessible at https://gcmeta.wdcm.org/.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.705
From this paper's citation signal
Citation Network Contribution
2.9
From 66 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2Genome Biology201497,097 citationsDataRank 1.7
- Basic local alignment search toolJournal of Molecular Biology199093,553 citationsDataRank 1.7
- The Sequence Alignment/Map format and SAMtoolsBioinformatics200966,179 citationsDataRank 1.7
- Fast and accurate short read alignment with Burrows–Wheeler transformBioinformatics200962,117 citationsDataRank 1.7
- Fast gapped-read alignment with Bowtie 2Nature Methods201259,681 citationsDataRank 1.6
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 20% comes from its base citations and 80% from the citation network (66 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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