Hardwicke, Tom E and Serghiou, Stylianos and Janiaud, Perrine and Danchev, Valentin and Crüwell, Sophia and Goodman, Steven N and Ioannidis, John PA (2020) Calibrating the Scientific Ecosystem Through Meta-Research. Annual Review of Statistics and Its Application, 7 (1). pp. 11-37. DOI https://doi.org/10.1146/annurev-statistics-031219-041104
Hardwicke, Tom E and Serghiou, Stylianos and Janiaud, Perrine and Danchev, Valentin and Crüwell, Sophia and Goodman, Steven N and Ioannidis, John PA (2020) Calibrating the Scientific Ecosystem Through Meta-Research. Annual Review of Statistics and Its Application, 7 (1). pp. 11-37. DOI https://doi.org/10.1146/annurev-statistics-031219-041104
Hardwicke, Tom E and Serghiou, Stylianos and Janiaud, Perrine and Danchev, Valentin and Crüwell, Sophia and Goodman, Steven N and Ioannidis, John PA (2020) Calibrating the Scientific Ecosystem Through Meta-Research. Annual Review of Statistics and Its Application, 7 (1). pp. 11-37. DOI https://doi.org/10.1146/annurev-statistics-031219-041104
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | meta-research, meta-science, methodology, bias, reproducibility, openscience |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Sociology, Department of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Aug 2020 14:31 |
Last Modified: | 06 Jan 2022 14:15 |
URI: | http://repository.essex.ac.uk/id/eprint/28079 |