Scientists propose a modified critical incident reporting system to help combat the reproducibility crisis.When Dirnagl first considered that his lab might benefit from a formal incident reporting system, he was surprised to find that no such system existed for biomedical researchers. Other high-stakes fields, from clinical medicine to nuclear power research, have long had such systems in place, but for the preclinical space, "we had to create one, because there’s nothing like it," Dirnagl said. But once Dirnagl and colleagues introduced an anonymous, online system, people began submitting reports. At meetings, the team would discuss what had gone wrong and strategize how to fix it. After a short while, Dirnagl said, his team began voluntarily filing virtually all reports with their signatures on them.

The week at Retraction Watch featured a refreshingly honest retraction, and a big win for PubPeer. Here’s what was happening elsewhere.

Accumulating evidence indicates high risk of bias in preclinical animal research, questioning the scientific validity and reproducibility of published research findings. Systematic reviews found low rates of reporting of measures against risks of bias in the published literature (e.g., randomization, blinding, sample size calculation) and a correlation between low reporting rates and inflated treatment effects. That most animal research undergoes peer review or ethical review would offer the possibility to detect risks of bias at an earlier stage, before the research has been conducted.

It’s not a new story, although "the reproducibility crisis" may seem to be. For life sciences, I think it started in the late 1950s. Problems caused in clinical research burst into the open in a very public way then. But before we get to that, what is "research reproducibility"? It’s a euphemism for unreliable research or research reporting. Steve Goodman and colleagues (2016) say 3 dimensions of science that affect reliability are at play: Methods reproducibility – enough detail available to enable a study to be repeated; Results reproducibility – the findings are replicated by others; Inferential reproducibility – similar conclusions are drawn about results, which brings statistics and interpretation squarely into the mix. There is a lot of history behind each of those. Here are some of the milestones in awareness and proposed solutions that stick out for me.

The US National Institutes of Health (NIH) is now assessing all research grant submissions based on the rigor and transparency of the proposed research plans. Previously, efforts to strengthen scientific practices had been undertaken by individual institutes, beginning in 2011 with the National Institute on Aging, which partnered with APS and the NIH Office of Behavioral and Social Science Research to begin a conversation about improving reproducibility across science. These early efforts were noted and encouraged by Congress. Now, the entire agency has committed to this important goal: NIH's 2016–2020 strategic plan announces, "NIH will take the lead in promoting new approaches toward enhancing the rigor of experimental design, analysis, and reporting."

ReproZip (Rampin et al. 2014) is a tool aimed at simplifying the process of creating reproducible experiments. After finishing an experiment, writing a website, constructing a database, or creating an interactive environment, users can run ReproZip to create reproducible packages, archival snapshots, and an easy way for reviewers to validate their work.