Reproducibility of computational workflows is automated using continuous analysis is a research paper published in Nature Biotechnology (2017). On theSindex it has a DataRank of 7.0. It has been cited 161 times, with 137 citing works in its 1-hop citation network.
Replication, validation and extension of experiments are crucial for scientific progress. Computational experiments are scriptable and should be easy to reproduce. However, computational analyses are designed and run in a specific computing environment, which may be difficult or impossible to match using written instructions. We report the development of continuous analysis, a workflow that enables reproducible computational analyses. Continuous analysis combines Docker, a container technology akin to virtual machines, with continuous integration, a software development technique, to automatically rerun a computational analysis whenever updates or improvements are made to source code or data. This enables researchers to reproduce results without contacting the study authors. Continuous analysis allows reviewers, editors or readers to verify reproducibility without manually downloading and rerunning code and can provide an audit trail for analyses of data that cannot be shared.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.763
From this paper's citation signal
Citation Network Contribution
6.3
From 120 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 11% comes from its base citations and 89% from the citation network (120 citing papers contributed measurable signal).
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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