MIMIC-IV, a freely accessible electronic health record dataset
MIMIC-IV, a freely accessible electronic health record dataset is a dataset published in Scientific Data (2023). On theSindex it has a DataRank of 13.0, placing it in the top 15.4% of the data-sharing corpus. It has been cited 2,640 times, with 190 citing works in its 1-hop citation network. Its calibrated FAIR score is 59/100.
Abstract
Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research. These systems are often inaccessible to researchers and structured for optimal storage, rather than interpretability and analysis. Here we present MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Center. Information available includes patient measurements, orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. MIMIC-IV is intended to support a wide array of research studies and educational material, helping to reduce barriers to conducting clinical research.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Indexed in repositories
- Open Access
- DataCite relations
- Linked datasets
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.2
From this paper's citation signal
Citation Network Contribution
11.9
From 190 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- MIMIC-III, a freely accessible critical care databaseScientific Data20168,052 citationsDataRank 18.2Top 8%
- The eICU Collaborative Research Database, a freely available multi-center database for critical care researchScientific Data20181,803 citationsDataRank 18.6Top 8%
- Best Practices for Scientific ComputingPLoS Biology2014710 citationsDataRank 0.985
- The MIMIC Code Repository: enabling reproducibility in critical care researchJournal of the American Medical Informatics Association2017454 citationsDataRank 13.7Top 15%
- Automated de-identification of free-text medical recordsBMC Medical Informatics and Decision Making2008417 citationsDataRank 0.905
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 (190 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.
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