Abstract
To respond to the NIH's policy for rigor and reproducibility in preclinical research, many journals have implemented guidelines and checklists to guide authors in improving the rigor and reproducibility of their research. Transparency in developing detailed prospective experimental designs and providing raw data are essential premises of rigor and reproducibility. Standard peer reviews and journal-specific technical and statistical reviews are critical factors for enhancing rigor and reproducibility. This brief review also shares some experience from Arteriosclerosis, Thrombosis, and Vascular Biology, an American Heart Association journal, that has implemented several mechanisms to enhance rigor and reproducibility for preclinical research.
Highlights
In the past 50 years, the number of scientific publications has grown from ∼300 K per year to above 1,000 K every year
One illustration is a survey published in Nature that found only six (11%) of 53 selected publications from academic laboratories being reproduced by a pharmaceutical company, raising serious concerns on rigor and reproducibility [1]
The survey concluded that the studies they were able to reproduce were characterized by authors being attentive to controls, reagents, investigator bias, and describing complete datasets
Summary
In the past 50 years, the number of scientific publications has grown from ∼300 K per year to above 1,000 K every year. In 2012, the United States National Institute of Neurological Disorders and Stroke convened a meeting to develop recommendations for improving preclinical research [2]. This initial call for transparent reporting prompted subsequent discussion and implementation of rigorous experimental design. In 2016, the NIH announced a Rigor and Reproducibility policy and guidelines for scientific research, including transparency in reporting detailed methods and rigorous statistical analyses. Multiple guidelines have been launched for appropriate study design and data analysis as well as transparent reporting of animal research (Table 1). For journals that require a declaration, the guidelines provide three levels for data transparency: 1. For journals that require a declaration, the guidelines provide three levels for data transparency: 1. Level 1 requires that authors disclose whether data are available and, if so, where to access them
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