SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB
SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB is a dataset published in Nucleic Acids Research (2007). On theSindex it has a DataRank of 21.3, placing it in the top 4% of the data-sharing corpus. It has been cited 6,872 times, with 191 citing works in its 1-hop citation network. Its calibrated FAIR score is 72/100.
Abstract
Sequencing ribosomal RNA (rRNA) genes is currently the method of choice for phylogenetic reconstruction, nucleic acid based detection and quantification of microbial diversity. The ARB software suite with its corresponding rRNA datasets has been accepted by researchers worldwide as a standard tool for large scale rRNA analysis. However, the rapid increase of publicly available rRNA sequence data has recently hampered the maintenance of comprehensive and curated rRNA knowledge databases. A new system, SILVA (from Latin silva, forest), was implemented to provide a central comprehensive web resource for up to date, quality controlled databases of aligned rRNA sequences from the Bacteria, Archaea and Eukarya domains. All sequences are checked for anomalies, carry a rich set of sequence associated contextual information, have multiple taxonomic classifications, and the latest validly described nomenclature. Furthermore, two precompiled sequence datasets compatible with ARB are offered for download on the SILVA website: (i) the reference (Ref) datasets, comprising only high quality, nearly full length sequences suitable for in-depth phylogenetic analysis and probe design and (ii) the comprehensive Parc datasets with all publicly available rRNA sequences longer than 300 nucleotides suitable for biodiversity analyses. The latest publicly available database release 91 (August 2007) hosts 547 521 sequences split into 461 823 small subunit and 85 689 large subunit rRNAs.
›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
1.3
From this paper's citation signal
Citation Network Contribution
20.0
From 191 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.
- Metagenomic biomarker discovery and explanationGenome Biology201116,383 citationsDataRank 1.5
- Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studiesNucleic Acids Research20128,835 citationsDataRank 1.4
- Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing PlatformApplied and Environmental Microbiology20137,478 citationsDataRank 1.3
- Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specificationsNature Biotechnology2011803 citationsDataRank 7.1Top 26%
- Novel primers for 16S rRNA-based archaeal community analyses in environmental samplesJournal of Microbiological Methods2011258 citationsDataRank 9.9
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 6% comes from its base citations and 94% from the citation network (191 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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