Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability is a research paper published in Journal of Biomedical Informatics (2020). On theSindex it has a DataRank of 2.3. It has been cited 77 times, with 70 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.654
From this paper's citation signal
Citation Network Contribution
1.7
From 57 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 70 citers.
NLM NIH HHS
Grant: R01 LM011369
NLM NIH HHS
Grant: R01 LM012895
NHGRI NIH HHS
Grant: U01 HG008680
NLM NIH HHS
Grant: R01 LM006910
NCATS NIH HHS
Grant: UL1 TR001873
NLM NIH HHS
Grant: R01 LM009886
NLM NIH HHS
Grant: R01 LM013061
National Institutes of Health
Grant: 5U01HG008680-03
Columbia GENIE (GENomic Integration with Ehr)
National Institutes of Health
Grant: 5R01LM009886-02
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data
National Institutes of Health
Grant: 2R01LM006910-14
Discovering and Applying Knowledge in Clinical Databases
National Institutes of Health
Grant: 5R01LM011369-07
From enrichment to insights
National Human Genome Research Institute
U.S. National Library of Medicine
FWCI
7.72
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Additional file 1 of Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth
Additional file 1 of Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth