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.

Detecting bottlenecks in serial production lines – a focus on interdeparture time variance

International Journal of Production Research(2012)10.1080/00207543.2011.596847Source: DataRank Database

Detecting bottlenecks in serial production lines – a focus on interdeparture time variance is a research paper published in International Journal of Production Research (2012). On theSindex it has a DataRank of 4.6. It has been cited 70 times, with 69 citing works in its 1-hop citation network.

N/A

4.6DataRank · unranked

4.6

70 citations · base score 4.3

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 (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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

Run a calibrated FAIR evaluation for this paper →

DataRank Breakdown

Base Score 14%Citation Network 86%

Base Score Contribution

0.639

From this paper's citation signal

Citation Network Contribution

3.9

From 56 citing papers with measurable signal

Learn more about DataRank methodology →

Top 2 citers driving the network score

Ranked by citation count — the same ordering the engine uses when summing log1p(C_q) over citers.

A statistical framework of data-driven bottleneck identification in manufacturing systems
International Journal of Production Research201647 citationsDataRank 2.8
Reliable Shop Floor Bottleneck Detection for Flow Lines through Process and Inventory Observations
Procedia CIRP201437 citationsDataRank 1.8

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 14% comes from its base citations and 86% from the citation network (56 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(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 →

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank