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.

Register for this event via Zoom