Bay Area Open Science Group
A virtual community for Stanford, UCSF, UC Berkeley and beyond
The Bay Area Open Science Group is a growing community for Bay Area academics and researchers interested in incorporating open science into their research, teaching, and learning. Targeting students, faculty, and staff at UCSF, Berkeley, and Stanford, the goal of the community is to increase awareness of and engagement with all things open science, including open access articles, open research data, open source software, and open educational resources. Through this work the group hopes to connect researchers with tools they can use to make the products and process of science more equitable and reproducible.
Every month the Bay Area Open Science group hosts a virtual meetup with a featured speaker from one of the three campuses who shares a project related to open science. In addition to meetups, members can ask questions and share tips, tools, and best practices via the community slack channel.
Our goal is to build an inclusive and welcoming community for everyone, regardless of their familiarity with open science. Learn more in our group participation guidelines.
The Bay Area Open Science Group meets on the 4th Tuesday of the month from 2-3 Pacific Time via Zoom.
In April we will be joined by Tim Glinin from UCSF, who will present a new framework for data sharing designed to address a central challenge in open science: how to incentivize researchers to share high-quality datasets while ensuring appropriate credit and continued scientific involvement. His work introduces an “author-curated data reuse” model and a Collaboration Requirement License (CRL), combining open access to datasets with a structured mechanism for collaboration between data creators and data users. The discussion will explore why only a limited number of well-annotated, high-quality datasets are available in open repositories, what barriers prevent researchers from sharing data in a form that supports meaningful reuse, and how new models may increase dataset publication, improve data quality, and support reproducibility, while positioning datasets as independent research outputs.
The session will be followed by an open discussion, and participants will be invited to join a working group focused on testing and implementing this model. As demand for large, well-annotated datasets continues to grow—driven by advances in AI and data-intensive research—developing effective incentives for data sharing is becoming increasingly critical. Approaches such as author-curated data reuse may also help address aspects of the replication crisis by improving data interpretation, documentation, and collaborative validation of results.
We welcome all participants to our events. If you need a reasonable accommodation to participate in this event because of a disability, please contact Kristen Greenland at kgreenland@stanford.edu as soon as possible
Interested in joining the group or learning about future events?
Join the discussion on Slack or email Kristen Greenland to be added to the mailing list.