Many scientific "truths" are, in fact, false

In 2005, John Ioannidis, a professor of medicine at Stanford University, published a paper, “Why most published research findings are false,” mathematically showing that a huge number of published papers must be incorrect. He also looked at a number of well-regarded medical research findings, and found that, of 34 that had been retested, 41% had been contradicted or found to be significantly exaggerated. Since then, researchers in several scientific areas have consistently struggled to reproduce major results of prominent studies. By some estimates, at least 51%—and as much as 89%—of published papers are based on studies and experiments showing results that cannot be reproduced.

The Quest for Reproducible Science: Issues in Research Transparency and Integrity

A pre-conference event of the American Library Association's annual conference: "The credibility of scientific findings is under attack. While this crisis has several causes, none is more common or correctable than the inability to replicate experimental and computational research. This preconference will feature scholars, librarians, and technologists who are attacking this problem through tools and techniques to manage data, enable research transparency, and promote reproducible science. Attendees will learn strategies for fostering and supporting transparent research practices at their institutions."

Evaluating replicability of laboratory experiments in economics

The reproducibility of scientific findings has been called into question. To contribute data about reproducibility in economics, we replicate 18 studies published in the American Economic Review and the Quarterly Journal of Economics in 2011-2014. All replications follow predefined analysis plans publicly posted prior to the replications, and have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We find a significant effect in the same direction as the original study for 11 replications (61%); on average the replicated effect size is 66% of the original. The reproducibility rate varies between 67% and 78% for four additional reproducibility indicators, including a prediction market measure of peer beliefs.

Psychology’s reproducibility problem is exaggerated – say psychologists

In August 2015, a team of 270 researchers reported the largest ever single-study audit of the scientific literature. Led by Brian Nosek, executive director of the Center for Open Science in Charlottesville, Virginia, the Reproducibility Project attempted to replicate studies in 100 psychology papers. According to one of several measures of reproducibility, just 36% could be confirmed; by another statistical measure, 47% could. Not so fast, says Gilbert. Because of the way the Reproducibility Project was conducted, its results say little about the overall reliability of the psychology papers it tried to validate, he argues. "The number of studies that actually did fail to replicate is about the number you would expect to fail to replicate by chance alone — even if all the original studies had shown true effects."

Research Software Sustainability: Report on Knowledge Exchange workshop

The report introduces software sustainability, provides definitions, clearly demonstrates that software is not the same as data and illustrates aspects of sustainability in the software lifecycle. The recommendations state that improving software sustainability requires a number of changes: some technical and others societal, some small and others significant. We must start by raising awareness of researchers' reliance on software. This goal will become easier if we recognise the valuable contribution that software makes to research and reward those people who invest their time into developing reliable and reproducible software.

ACM SIGMOD 2016 Reproducibility Guidelines

SIGMOD Reproducibility has three goals: Highlight the impact of database research papers; Enable easy dissemination of research results; Enable easy sharing of code and experimentation set-ups. In short, the goal is to assist in building culture where sharing results, code, and scripts of database research is the norm rather than the exception. The challenge is to do this efficiently, which means building technical expertise on how to do better research via creating repeatable and shareable research. The SIGMOD Reproducibility Committee is here to help you with this.