🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge
Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB

Nucleic Acids Research(2007)10.1093/nar/gkm864Source: DataRank Database

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.

Top 4%percentile
21.3DataRank
21.3Top 4%
Dataset Open Access6872 citations · base score 8.8
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

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
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (1/2)
  • Has DOI
Accessible (1/2)
  • Open Access
Interoperable (0/2)
    Reusable (1/3)
    • Dataset classification

    FAIR checklist signals are shown for context only and do not affect DataRank scoring.

    72FAIR score
    F Findable
    100
    A Accessible
    70
    I Interoperable
    50
    R Reusable
    67
    Top 1% by FAIRdeterministic✓ full text read

    Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

    DataRank Breakdown

    Base Score 6%Citation Network 94%

    Base Score Contribution

    1.3

    From this paper's citation signal

    Citation Network Contribution

    20.0

    From 191 citing papers with measurable signal

    Learn more about DataRank methodology →

    Top 5 citers driving the network score

    Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.

    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.

    Read the full methodology →

    Click a node to highlight its connections. Use scroll to zoom. Drag to pan.

    Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank

    Authors (10)

    K. Knittel,B. M. Fuchs,W. Ludwig,Elmar PruesseORCID,Christian QuastORCID

    Related Papers (10)

    Proceedings of the National Academy of Sciences(2010)
    co-cited
    10.1073/pnas.1000080107
    'Omics Data Sharing
    N/A
    7.5DataRank · unranked
    Science(2009)
    co-cited
    10.1126/science.1180598