The psychology/social psychology community has made replication a huge focus over the last year. One reason is the recent, public blow-up over a famous study that did not replicate. There are also concerns about the experimental and conceptual design of these studies that go beyond simple lack of replication. In genomics, a similar scandal occurred due to what amounted to “data fudging”. Although, in the genomics case, much of the blame and focus has been on lack of reproducibility or data availability.
Reproducibility has been a hot topic for the last several years among computational scientists. A study is reproducible if there is a specific set of computational functions/analyses (usually specified in terms of code) that exactly reproduce all of the numbers in a published paper from raw data. It is now recognized that a critical component of the scientific process is that data analyses can be reproduced. This point has been driven home particularly for personalized medicine applications, where irreproducible results can lead to delays in evaluating new procedures that affect patients’ health.