Scholaris is a national shared repository service that provides hosted infrastructure and support for Canadian institutional repositories. The service is managed by Scholars Portal and developed by the Canadian Association of Research Libraries (CARL), the Ontario Council of University Libraries (OCUL), University of Toronto Libraries (UTL), regional consortia, expert groups and members of the Canadian institutional repository community.

The Ontario Council of University Libraries (OCUL) is pleased to call for member nominations for subcommittees and working groups, under the Information Resources Standing Committee and the Collaborative Futures Steering Committee.

Information resources (IR) and Collaborative Futures (CF) are key initiatives of OCUL and reflect a commitment to advancing research, teaching, and learning through the shared development and delivery of transformative services, resources, and digital research infrastructure for the province's universities. 

Every year, the online Collaborative Futures Mini Conference brings together experts from across Ontario to share their learnings and experiences working with collaborative library technologies.

For 2024, we’re thrilled to announce a program that explores analytics, workflows, accessible discovery and more!

The Ontario Council of University Libraries (OCUL) seeks library or information studies students and recent graduates (MLIS/MI) interested in providing academic reference assistance through our Scholars Portal virtual reference service, Ask a Librarian/Clavardez avec nos bibliothécaires.

Opportunities are available for bilingual (French/English) operators with Clavardez and English-language operators with Ask a Librarian. Interested candidates who meet the qualifications are welcome to apply for either position.

[La version française suit] 

The Ontario Council of University Libraries (OCUL) Task Force on Machine Learning and AI has released its interim report for feedback from the academic community.

The interim report describes use cases for machine learning in university libraries and recommends projects that incorporate machine learning technologies. It also considers key issues such as ethical concerns, technical capacity, available expertise, and infrastructure needs.

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