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.

Statsmodels: Econometric and Statistical Modeling with Python

Proceedings of the Python in Science Conference(2010)10.25080/majora-92bf1922-011Source: DataRank Database

Statsmodels: Econometric and Statistical Modeling with Python is a research paper published in Proceedings of the Python in Science Conference (2010). On theSindex it has a DataRank of 1.3. It has been cited 6,339 times. Its calibrated FAIR score is 68/100.

N/A

1.3DataRank · unranked

1.3

Open Access6339 citations · base score 8.8

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Statsmodels is a library for statistical and econometric analysis in Python. This paper discusses the current relationship between statistics and Python and open source more generally, outlining how the statsmodels package fills a gap in this relationship. An overview of statsmodels is provided, including a discussion of the overarching design and philosophy, what can be found in the package, and some usage examples. The paper concludes with a look at what the future holds.

›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 (0/3)

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

68FAIR score

F Findable

100

A Accessible

I Interoperable

R Reusable

Top 4% by FAIRdeterministic⚠ abstract only

Estimated from the abstract only. The agent couldn't read this paper's full text, so body-dependent criteria (data-availability statement, formats, license) are inferred. For a confident score, upload the PDF or supply full text →

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

1.3

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

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(C_q) ÷ max(outdegree_q, 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 →