An article A Perfect Storm: The Record of a Revolution by Eric-Jan Wagenmakers, a mathematical psychologist from the Unversity of Amsterdam, describes a reproducibility revolution, which is taking place in psychology:

The dynamics of political revolutions are in some ways similar to the academic revolution that has recently gripped the field of psychology. Over the last two decades, increasing levels of competition for scarce research funding have created a working environment that rewards productivity over reproducibility; this perverse incentive structure has caused some of the findings in the psychological literature to be spectacular and counter-intuitive, but likely false […] The general dissatisfaction with the state of the field was expressed in print only occasionally, until in 2011 two major events ignited the scientific revolution that is still in full force today.

The article was published this month by the Inquisitive Mind (In-Mind) magazine.

Eric-Jan concludes:

Although some researchers are less enthusiastic about the “replicability movement” than others, it is my prediction that the movement will grow until its impact is felt in other empirical disciplines including the neurosciences, biology, economy, and medicine. The problems that confront psychology are in no way unique, and this affords an opportunity to lead the way and create dependable guidelines on how to do research well. Such guidelines have tremendous value, both to individual scientists and to society as a whole.

Yesterday (April 1, 2015) a group of computer scientists from UK (Neil Chue Hong, Tom Crick, Ian Gent, and Lars Kotthoff) announced a seminal paper Top Tips to Make Your Research Irreproducible.

Here are the tips that the authors share:

1. Think “Big Picture”. People are interested in the science, not the dull experimental setup, so don’t describe it. If necessary, camouflage this absence with brief, high-level details of insignificant aspects of your methodology.
2. Be abstract. Pseudo-code is a great way of communicating ideas quickly and clearly while giving readers no chance to understand the subtle implementation details (particularly the custom toolchains and manual interventions) that actually make it work.
3. Short and sweet. Any limitations of your methods or proofs will be obvious to the careful reader, so there is no need to waste space on making them explicit\footnote. However much work it takes colleagues to fill in the gaps, you will still get the credit if you just say you have amazing experiments or proofs.
4. The deficit model. You’re the expert in the domain, only you can define what algorithms and data to run experiments with. In the unhappy circumstance that your methods do not do well on community curated benchmarks, you should create your own bespoke benchmarks and use those (and preferably not make them available to others).
5. Don’t share. Doing so only makes it easier for other people to scoop your research ideas, understand how your code actually works instead of why you say it does, or worst of all to understand that your code doesn’t actually work at all.

These tips will be undoubtedly embraced by all scientists trying to make their research irreproducible. The paper ends with an important conjecture:

We make a simple conjecture: an experiment that is irreproducible is exactly equivalent to an experiment that was never carried out at all. The happy consequences of this conjecture for experts in irreproducibility will be published elsewhere, with extremely impressive experimental support.