Research findings advance science only if they are significant, reliable and reproducible. Scientists and journals must publish robust data in a way that renders it optimally reproducible. Reproducibility has to be incentivized and supported by the research infrastructure but without dampening innovation.
In collaboration with the University of Washington (UW) and Berkeley, and under the sponsorship of the Moore and Sloan foundations, NYU is working on a new initiative to 'harness the potential of data scientists and big data'. As part of this initiative, we aim to increase awareness of sharing, preservation, provenance, and reproducibility best practices across UW, NYU, Berkeley campuses and encourage their adoption.
Research at Duke University in genomics that involved fighting cancer by looking for gene patterns that would determine which drugs would best attack a particular cancer (no more trial-and-error treatment, considered a breakthrough). This research turned out to be wrong, due to flaws in the research (found by statisticians); if the research was reproducible, errors could have been found earlier and the patients could have continued their treatment.
NY article discussing the issues with scientific reproducibility: "Why? One simple answer is that it takes a lot of time to look back over other scientists’ work and replicate their experiments. Scientists are busy people, scrambling to get grants and tenure. As a result, papers that attract harsh criticism may nonetheless escape the careful scrutiny required if they are to be refuted."