Towards a scientific blockchain framework for reproducible data analysis
This addresses the widespread problem of irreproducible analyses in science, particularly for life sciences and precision medicine, by offering an incremental solution focused on incentivizing verification.
The paper tackles the challenge of scientific reproducibility by proposing a blockchain framework to create a trustless ecosystem for researchers, funding bodies, and publishers, resulting in a mechanism that rewards verification efforts and quantifies reputation for ranking.
Publishing reproducible analyses is a long-standing and widespread challenge for the scientific community, funding bodies and publishers. Although a definitive solution is still elusive, the problem is recognized to affect all disciplines and lead to a critical system inefficiency. Here, we propose a blockchain-based approach to enhance scientific reproducibility, with a focus on life science studies and precision medicine. While the interest of encoding permanently into an immutable ledger all the study key information-including endpoints, data and metadata, protocols, analytical methods and all findings-has been already highlighted, here we apply the blockchain approach to solve the issue of rewarding time and expertise of scientists that commit to verify reproducibility. Our mechanism builds a trustless ecosystem of researchers, funding bodies and publishers cooperating to guarantee digital and permanent access to information and reproducible results. As a natural byproduct, a procedure to quantify scientists' and institutions' reputation for ranking purposes is obtained.