🏆 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.

Construction of <i>Escherichia coli</i> K‐12 in‐frame, single‐gene knockout mutants: the Keio collection

Molecular Systems Biology(2006)10.1038/msb4100050Source: DataRank Database

Construction of <i>Escherichia coli</i> K‐12 in‐frame, single‐gene knockout mutants: the Keio collection is a dataset published in Molecular Systems Biology (2006). On theSindex it has a DataRank of 19.2, placing it in the top 6.3% of the data-sharing corpus. It has been cited 8,210 times, with 194 citing works in its 1-hop citation network. Its calibrated FAIR score is 41/100.

Top 6%percentile
19.2DataRank
19.2Top 6%
Dataset Open Access8210 citations · base score 9.0
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants-the 'Keio collection'-provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).

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.

    41FAIR score
    F Findable
    53
    A Accessible
    55
    I Interoperable
    25
    R Reusable
    33
    Top 79% by FAIRLLM-assessed✓ full text read

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

    DataRank Breakdown

    Base Score 7%Citation Network 93%

    Base Score Contribution

    1.3

    From this paper's citation signal

    Citation Network Contribution

    17.8

    From 194 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 7% comes from its base citations and 93% 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.

    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)

    Takeshi AraORCID,Miki HasegawaORCID,Yuki TakaiORCID,Yoshiko Okumura,Miki Baba

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    Journal of Molecular Biology(1990)
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