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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.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

Nucleic Acids Research(2021)10.1093/nar/gkab1038Source: DataRank Database

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences is a dataset published in Nucleic Acids Research (2021). On theSindex it has a DataRank of 12.1, placing it in the top 17.2% of the data-sharing corpus. It has been cited 6,690 times, with 196 citing works in its 1-hop citation network. Its calibrated FAIR score is 70/100.

Top 17%percentile
12.1DataRank
12.1Top 17%
Dataset Open Access6690 citations · base score 8.8
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

›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.

    70FAIR score
    F Findable
    90
    A Accessible
    80
    I Interoperable
    50
    R Reusable
    58
    Top 1% 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 11%Citation Network 89%

    Base Score Contribution

    1.3

    From this paper's citation signal

    Citation Network Contribution

    10.8

    From 196 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.

    1. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest
      Nucleic Acids Research20228,502 citationsDataRank 9.4Top 23%
    2. The PRIDE database and related tools and resources in 2019: improving support for quantification data
      Nucleic Acids Research20187,380 citationsDataRank 15.0Top 13%
    3. SARS-CoV-2 variants evolve convergent strategies to remodel the host response
      Cell2023113 citationsDataRank 2.1
    4. CancerProteome: a resource to functionally decipher the proteome landscape in cancer
      Nucleic Acids Research202417 citationsDataRank 0.565Top 49%
    5. Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression
      Cancer Cell202516 citationsDataRank 0.474
    Why this DataRank?

    DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 11% comes from its base citations and 89% from the citation network (196 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 (16)

    Jingwen Bai,Chakradhar BandlaORCID,David García-Seisdedos,Suresh HewapathiranaORCID,Selvakumar KamatchinathanORCID

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