The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools is a dataset published in Nucleic Acids Research (2012). On theSindex it has a DataRank of 20.7, placing it in the top 4.6% of the data-sharing corpus. It has been cited 33,628 times, with 194 citing works in its 1-hop citation network. Its calibrated FAIR score is 84/100.
Abstract
SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Indexed in repositories
- Open Access
- DataCite relations
- Linked datasets
- 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
1.6
From this paper's citation signal
Citation Network Contribution
19.1
From 194 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.
- MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger DatasetsMolecular Biology and Evolution201645,285 citationsDataRank 1.6
- Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial CommunitiesApplied and Environmental Microbiology200921,605 citationsDataRank 1.5
- Search and clustering orders of magnitude faster than BLASTBioinformatics201021,556 citationsDataRank 1.5
- Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARBApplied and Environmental Microbiology200611,229 citationsDataRank 24.1Top 2%
- VSEARCH: a versatile open source tool for metagenomicsPeerJ201610,782 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 8% comes from its base citations and 92% from the citation network (194 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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