The pace of knowledge acquisition in science is frequently impeded by the difficulty researchers have in building on each other’s work. It often seems like we are retracing steps―re-inventing the wheel. In addition, “irreproducible results” can lead to misinformation and public distrust of science. Reproducibility and open science are closely related and share several needs in terms of both technological and software advances and changes in the expectations and culture of computational and data science.
As data-intensive scientific discovery becomes more common, our goal is to address these issues through software tools and practices that support the sharing, preservation, provenance, and reproducibility of data, software, and scientific workflows. We want to empower and encourage individual scientists and groups of collaborators to reproduce and build upon their own work as well as to later verify its correctness if necessary as well as work toward broader sharing that typically requires and facilitates reproducibility.
UC Berkeley, University of Washington, and New York University partners will share their expertise on existing tools and knowledge about the range of scientific problems at the three institutions to help guide future developments. In addition, we will hold workshops, tutorials, short courses, and office hours to assist scientists with using available reproducibility tools, discuss issues, and identify needs, and we will actively encourage faculty to incorporate more reproducibility/open science topics into existing and new curriculum. The group will also leverage the diverse range of expertise in science, software, and mathematics across the institutions to develop best practices for reproducibility that can be broadly applied across a range of disciplines.
