🏆 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.

DrugBank 5.0: a major update to the DrugBank database for 2018

Nucleic Acids Research(2017)10.1093/nar/gkx1037Source: DataRank Database

DrugBank 5.0: a major update to the DrugBank database for 2018 is a dataset published in Nucleic Acids Research (2017). On theSindex it has a DataRank of 14.6, placing it in the top 13.6% of the data-sharing corpus. It has been cited 8,733 times, with 196 citing works in its 1-hop citation network. Its calibrated FAIR score is 60/100.

Top 14%percentile
14.6DataRank
14.6Top 14%
Dataset Open Access8733 citations · base score 9.0
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.

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 (1/2)
  • Open Access
Interoperable (2/2)
  • DataCite relations
  • Linked datasets
Reusable (1/3)
  • Dataset classification

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

60FAIR score
F Findable
65
A Accessible
68
I Interoperable
75
R Reusable
33
Top 7% 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 9%Citation Network 91%

Base Score Contribution

1.4

From this paper's citation signal

Citation Network Contribution

13.2

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. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
    Nature Communications201915,474 citationsDataRank 14.7Top 13%
  2. HMDB 5.0: the Human Metabolome Database for 2022
    Nucleic Acids Research20212,377 citationsDataRank 8.8Top 23%
  3. Constructing knowledge graphs and their biomedical applications
    Computational and Structural Biotechnology Journal2020266 citationsDataRank 0.838
  4. Machine learning for perturbational single-cell omics
    Cell Systems2021114 citationsDataRank 0.712
Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 9% comes from its base citations and 91% 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 (27)

Yannick D Feunang,An C Guo,Elvis J Lo,Ana MarcuORCID,Jason R. GrantORCID

Related Papers (10)

Nucleic Acids Research(2000)
co-citedsame journal
10.1093/nar/28.1.27
Nucleic Acids Research(2016)
co-citedsame journal
10.1093/nar/gkw377
The Protein Data Bank
Top 1%
32.3DataRank
Nucleic Acids Research(2000)
co-citedsame journal
10.1093/nar/28.1.235
Mammalian Genome(2015)
co-cited
10.1007/s00335-015-9573-z