Communities of Practice
Community driven, interdisciplinary groups of scholars
DataLab supports groups that meet to learn and apply concepts, methods and software with an aim towards furthering their research and data capabilities.
Membership is open to the UC Davis community. Each group organizes autonomously with DataLab support, including expertise, space, and infrastructure.
Bioinformatics (POOH)
Peer Organized ‘Omics Help (POOH), a graduate student led working group, is a resource for computational biology research, facilitating bioinformatic learning and discussions.
Causal Inference
The Causal Inference Learning Cluster (CILC) is a study and support group for learning about causal inference, with emphasis on quasi-experimental methods for analyzing non-randomized experiments and observational data.
Computational Pedagogy
The Computational Pedagogy group focuses on sharing and advancing pedagogy for hands-on instruction in practical, applied computational thinking.
Cultural Analytics
The Cultural Analytics Community of Practice focuses on using cultural data to pose and investigate research questions in the humanities and social sciences.
Data Feminism
The Data Feminism group explores the interaction between systems of power and oppression within the development and application of the data sciences.
Julia Users
The UC Julia Users Group (UC JUG) is a virtual community for UC affiliates learning and using Julia in research computing (other use cases are also welcome).
Network Science
The Network Science Community of Practice connects researchers across domains who are using network data and conducting network analyses.
Python Users
The Davis Python Users Group (DPUG) is a community that meets to discuss their experiences, discoveries, and questions about using Python and research computing in general.
R Users
The Davis R Users Group (D-RUG) is a community that supports each other in using R for science and research.
Spatial Sciences (#maptimeDavis)
The Spatial Sciences Community of Practice is for people (undergraduate and graduate students, faculty, postdoctoral scholars, staff, etc.) at UC Davis and our immediate community who have an interest in working with spatial data.