Scientific reproducibility and Open Science may seem like a “new" approach to Science, but these concepts are in fact fundamental principles of the Scientific Method, established a thousand years ago. 

By definition, scientists want to follow the Scientific Method. Then why have different studies demonstrated that researchers have difficulties reproducing even their own methods? Clues can be found in a system where science is being drowned by numerical ranking, favouring productivity over discovery. Given the current epoch of economic crisis, where researchers are forced into a competitive game of pandering to panelists in quests for funds, it seems to be a good time for deep reflection on the entire scientific system, and on how to support good ideas versus good marketing.

The challenge will be harder when facilities like the Square Kilometre Array Observatory start generating data volumes reaching the exa-scale. Not only will computing, networking or storage resources reach their limits, but the biggest challenge will be scientific: Extracting knowledge will become an impossible task without a fundamental change in methodology and policies in the short-term. Computing facilities enabling science in Big Data oriented facilities should be key in supporting astronomers to build reproducible methods. 

I will discuss how different research communities (such as individual researchers, large collaborations, data centres, journals and their referees) as well as policy makers, view the challenges/barriers and rewards of reproducibility. For scientific facilities, adoption of Open Science is both a need and a duty. Open Science not only enhances scientific collaboration and knowledge interchange in a transparent way. It also brings a wider impact in other areas, by encouraging the democratisation of science, contributing to achieving UN’s Sustainable Development Goals. 

With this talk I aim to provoke extra critical thinking among the different actors involved in our system (applicants for jobs/grants,  referees, or evaluators), while trying to discuss state of the art and possible future tools and metrics in order to empower scientists to achieve reproducibility in their work. The end game should not be "good" science but just Science, the one that follows the Scientific Method.