Another similar story recently was a bug found in analysis software used in many 'brain scan' papers. Then there are a continuous stream of scientific retractio

Another similar story recently was a bug found in analysis software used in many 'brain scan' papers. Then there are a continuous stream of scientific retractions going on (see retractionwatch.com). We do science as follows: data is collected, run through some analytic and/or statistical method, and then the results are published. Then those results get cited by other papers. It's a dependency graph. If some flaw is found in the data or the analysis it would be natural to invalidate this paper and automatically flag all downstream dependencies in the chain of inference. Unfortunately the publishing model does not allow this. I think if we'd developed the internet and software engineering practises before we'd developed scientific publishing, what scientists produce would look less like papers and more like Git repositories. Under this fantasy scenario, we'd have metadata that included statistical methods and software for all analyses. The raw, open data in the repositories could be re-analyzed by anyone else at any time. If there were doubt thrown on a method, or if some data were fraudulent, we could instantly assess the impact on all of current knowledge. I wonder if these recurring problems in curating and re-assessing knowledge will eventually lead to a restructuring of science. Researchers are starting to take the idea of a 'post journal world' seriously (http://blogs.bmj.com/bmj/2016/06/15/richard-smith-what-will-the-post-journal-world-look-like/ http://blogs.bmj.com/bmj/2016/06/15/richard-smith-what-will-...)