Reproducibility and Open Science

Working Group

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.

Working Group Members


Philip B. Stark

Statistics; Statistical Computing Facility
Co-I for Moore/Sloan Data Science Environment

Fernando Perez

Co-I for Moore/Sloan Data Science Environment

Erik Mitchell

University Libraries
Co-I for Moore/Sloan Data Science Environment

Karthik Ram

BIDS, Berkeley Initiative in Global Change Biology
Research Scientist

Kathryn D. Huff

Nuclear Engineering
Data Science Fellow

Fatma Deniz (née Imamoglu)

Helen Wills Neuroscience Institute
International Computer Science Institute

Nick Adams

Berkeley Institute for Data Science
Research Fellow—Social Science

Kyle Barbary

Berkeley Center for Cosmological Physics

Daniel Turek

Statistics & ESPM
Data Science Fellow

Beth Reid

Berkeley Center for Cosmological Physics
Data Science Fellow

Justin Kitzes

Energy & Resources Group
BIDS Data Science Fellow

Laura Waller

Electrical Engineering and Computer Sciences

Rachel Slaybaugh

Nuclear Engineering

Sandrine Dudoit

Biostatistics; Statistics

Carl Boettiger

Environmental Science, Policy, & Management

David Ackerly

Integrative Biology

Garret Christensen

Berkeley Initiative for Transparency in the Social Sciences

Jasmine Nirody

Data Science Fellow

Kellie Ottoboni