🏆 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.

Comprehensive Analysis of the Lysine Succinylome and Protein Co-modifications in Developing Rice Seeds

Molecular & Cellular Proteomics(2019)10.1074/mcp.ra119.001426Source: DataRank Database

Comprehensive Analysis of the Lysine Succinylome and Protein Co-modifications in Developing Rice Seeds is a research paper published in Molecular & Cellular Proteomics (2019). On theSindex it has a DataRank of 1.4. It has been cited 53 times, with 39 citing works in its 1-hop citation network.

N/A
1.4DataRank · unranked
1.4
53 citations · base score 4.0
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

FAIR Checklist

Context only (not used in score)
Findable (2/2)
  • Has DOI
  • Indexed in repositories
Accessible (0/2)
    Interoperable (2/2)
    • DataCite relations
    • Linked datasets
    Reusable (0/3)

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

      DataRank Breakdown

      Base Score 43%Citation Network 57%

      Base Score Contribution

      0.598

      From this paper's citation signal

      Citation Network Contribution

      0.806

      From 27 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 43% comes from its base citations and 57% from the citation network (27 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 (7)

      Hana Mujahid,Yadong Zhang,Xiaojun Peng,Edilberto D. Redoña,Cailin Wang