The pace of knowledge acquisition in science is impeded by a lack of sharing of the research process in its entirety in addition to all of its outputs. This makes it difficult for researchers to build upon each other’s work and undoubtedly results in some “reinvention of the wheel.” Further, there is increasing scrutiny of publications reporting on “irreproducible results,” which can lead to misinformation and public distrust of science. As we transition to data-intensive scientific discovery, we have the opportunity to address these issues through software tools and practices that support the sharing, preservation, provenance tracking, and reproducibility of data, software, and scientific workflows.
As data-intensive scientific discovery becomes more common, reproducibility and open science becomes both more challenging and more important. Work around this theme includes identifying and promoting repositories for sharing data and workflows, developing techniques to query and analyze shared data and workflows that will facilitate reuse, and creating software tools to better support sharing and reproducibility.
For more information, please see our Reproducible page on Github.