As David Shaywitz accounts today in today’s Forbes, science is hard. Communication is hard (especially for scientists).
This is becoming increasingly apparent in the era of big data science. The scientific complexities are compounded by current modes of communication, specifically two dimensional text representations of complex analytics, which have become wholly inefficient in allowing appropriate peer review and understanding of scientific claims.
As was explored by Kesselheim et al (pay wall), funding disclosures have a profound impact on how physicians interpret research findings. But ultimately an individuals’ perception of research results comes down to trust. And the further one is removed from the science itself, the more assumptions one has to make, and ultimately the more one has to rely on preconceived notions to guide their opinion. Under the current model of scientific communication, interpretations require not only trust but a full on leap of faith. Allowing consumers a bridge across this vast chasm between scientific claims and the scientific process itself is one way to gain trust. Narrowing this gap is important specifically because it mitigates preconceived notions which are otherwise necessary to form one’s opinion, such as study funding sponsors.
The clearScience pilot, funded by the Alfred P. Sloan Foundation, is specifically positioning itself to build infrastructure for more effective scientific communication. By leveraging the open APIs of GitHub, Amazon Web Services, and Synapse, clearScience demonstrates how scientists can easily transition from exploring data—executing science—and providing the scientific community all the resources and artifacts to recreate analyses. Conducting research in this manner allows reproducibility to be a byproduct of the process rather than a burden. And more importantly, provides a framework for the science to be extended upon instead of publication as a a finite endpoint for research.