Calibrating the Scientific Ecosystem Through Meta-Research is a research paper published in Annual Review of Statistics and Its Application (2020). On theSindex it has a DataRank of 2.4. It has been cited 98 times, with 77 citing works in its 1-hop citation network.
While some scientists study insects, molecules, brains, or clouds, other scientists study science itself. Meta-research, or research-on-research, is a burgeoning discipline that investigates efficiency, quality, and bias in the scientific ecosystem, topics that have become especially relevant amid widespread concerns about the credibility of the scientific literature. Meta-research may help calibrate the scientific ecosystem toward higher standards by providing empirical evidence that informs the iterative generation and refinement of reform initiatives. We introduce a translational framework that involves ( a) identifying problems, ( b) investigating problems, ( c) developing solutions, and ( d) evaluating solutions. In each of these areas, we review key meta-research endeavors and discuss several examples of prior and ongoing work. The scientific ecosystem is perpetually evolving; the discipline of meta-research presents an opportunity to use empirical evidence to guide its development and maximize its potential.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.689
From this paper's citation signal
Citation Network Contribution
1.7
From 60 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 29% comes from its base citations and 71% from the citation network (60 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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