Canada: Portage Announces Federated Research Data Repository (FRDR) Now in Full Production, New Features and Benefits Available
From a Portage Announcement:
Portage’s Federated Research Data Repository (FRDR) has officially launched into full production! Full production offers many new features and benefits:
- Publish research data in a Canadian-owned, bilingual national repository option
- 1 TB of repository storage available to all faculty members at Canadian post-secondary institutions – more storage may be available upon request
- Secure repository storage, distributed geographically across multiple Compute Canada Federation hosting sites
- Data curation support provided by Portage
- Ability to work with multiple collaborators on a single submission
- Your data will be discoverable alongside other Canadian collections in the FRDR Discovery Portal
FRDR is designed to address a longstanding gap in Canada’s research infrastructure by providing researchers with a robust repository option into which large research datasets can be ingested, curated, processed for preservation, discovered, cited, and shared.
FRDR is made possible through a collaboration between Portage, the Compute Canada Federation and the Canadian Association of Research Libraries, with development and infrastructure support from the University of Saskatchewan, Simon Fraser University, the University of Waterloo, and the University of Toronto.
Direct to FRDR Website
About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.