The UC Berkeley Research Data Management (RDM) Program is a campus-wide program led jointly by Research IT and the Library to help faculty, staff, and students manage research data throughout the research process. With our partners across campus, we offer a discipline-agnostic service that supports researchers in working with their data. The RDM Program also addresses current and emerging data management issues, compliance with policy requirements imposed by funders and by the University, and reduction of risk associated with the challenges of data stewardship.
The RDM Program supports researchers by consulting, delivering training/workshops, and developing documentation in the areas of:
Data management -- help researchers understand and comply with best practices related to handling data during research activity. This includes guidance complying with institutional, regulatory, and funder requirements, policies, and procedures. Emphasize leveraging campus storage and computing technologies to fulfill researcher responsibilities and obligations.
Data collection -- advise researchers on tools/systems for data collection available at UC Berkeley, particularly as it relates to:
- REDCap at UC Berkeley -- assist researchers with onboarding to and use of REDCap for their research projects. Manage the service side in collaboration with RTL Dev Ops (sys admin side)
Active data practices -- advise researchers on data management during active research. Includes providing guidance and recommendations on migration, storage, workflow (e.g., analysis pipelines, lab workflows, etc.), and organization.
- Data transfer -- provide assistance to researchers who need to move data, internally or externally, during the course of their research.
- Data storage & backup -- provide assistance with the storing and backup of active data during a research project (regular, preferably automated). Backup in this case is distinguished from "computer backups'' which include the whole machine, though that is recommended by consultants as well.
- Data sharing -- develop advanced workflows for data sharing during/post research in addition to advising on how security may affect data sharing during/post research.
Data classification & security -- advise researchers on the data classification level of their research data and associated (campus) systems and tools that support the determined data classification level.
- Data use agreements -- review data use agreements (DUAs) for data security requirements and assist researchers with identifying appropriately secure processes/systems to use to comply with requirements of DUAs.