We developed a new probabilistic model to assess the impact of recommendations rectifying the reproducibility crisis (by publishing both positive and 'negative' results and increasing statistical power) on competing objectives, such as discovering causal relationships, avoiding publishing false positive results, and reducing resource consumption. In contrast to recent publications our model quantifies the impact of each single suggestion not only for an individual study but especially their relation and consequences for the overall scientific process. We can prove that higher-powered experiments can save resources in the overall research process without generating excess false positives. The better the quality of the pre-study information and its exploitation, the more likely this beneficial effect is to occur. Additionally, we quantify the adverse effects of both neglecting good practices in the design and conduct of hypotheses-based research, and the omission of the publication of 'negative' findings. Our contribution is a plea for adherence to or reinforcement of the good scientific practice and publication of 'negative' findings.
It is important for research users to know how likely it is that reported research findings are true. The Social Science Replication Project finds that, in highly powered experiments, only 13 of 21 high-profile reports could be replicated. Investigating the factors that contribute to reliable results offers new opportunities for the social sciences.
If the results in a published study can’t be replicated in subsequent experiments, how can you trust what you read in scientific journals? One international group of researchers is well aware of this reproducibility crisis, and has been striving to hold scientists accountable. For their most recent test, they attempted to reproduce 21 studies from two of the top scientific journals, Science and Nature, that were published between 2010 and 2015. Only 13 of the reproductions produced the same results as the original study.
The paucity of major scientific breakthroughs leading to new or improved treatments, and the inability to identify valid and reproducible biomarkers that improve clinical management, has produced a crisis in confidence in the validity of our pathogenic theories and the reproducibility of our research findings. This crisis in turn has driven changes in standards for research methodologies and prompted calls for the creation of open‐access data repositories and the preregistration of research hypotheses. Although we should embrace the creation of repositories and registries, and the promise for greater statistical power, reproducibility, and generalizability of research findings they afford, we should also recognize that they alone are no substitute for sound design in minimizing study confounds, and they are no guarantor of faith in the validity of our pathogenic theories, findings, and biomarkers. One way, and maybe the only sure way, of knowing that we have a valid understanding of brain processes and disease mechanisms in human studies is by experimentally manipulating variables and predicting its effects on outcome measures and biomarkers.
We refer to the recent guidelines for experimental models of myocardial ischemia and infarction , and aim to provide now practical guidelines to ensure rigor and reproducibility in preclinical and clinical studies on cardioprotection. In line with the above guidelines , we define rigor as standardized state-of-the-art design, conduct and reporting of a study, which is then a prerequisite for reproducibility, i.e. replication of results by another laboratory when performing exactly the same experiment.
Reproducibility in the computational sciences has been stymied because of the complex and rapidly changing computational environments in which modern research takes place. While many will espouse reproducibility as a value, the challenge of making it happen (both for themselves and testing the reproducibility of others' work) often outweigh the benefits. There have been a few reproducibility solutions designed and implemented by the community. In particular, the authors are contributors to ReproZip, a tool to enable computational reproducibility by tracing and bundling together research in the environment in which it takes place (e.g. one's computer or server). In this white paper, we introduce a tool for unpacking ReproZip bundles in the cloud, ReproServer. ReproServer takes an uploaded ReproZip bundle (.rpz file) or a link to a ReproZip bundle, and users can then unpack them in the cloud via their browser, allowing them to reproduce colleagues' work without having to install anything locally. This will help lower the barrier to reproducing others' work, which will aid reviewers in verifying the claims made in papers and reusing previously published research.