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This webinar, given by James Doiron (UofA) on 2020/09/16, uses a research data lifecycle approach to provide attendees with an overview of research data management (RDM) best practices, including both guidance and resources to help them manage their own research data. Topics covered will include such things as overarching RDM principles (e.g., FAIR, CARE, OCAP), file management and version control, file naming conventions, data storage and backup strategies, data access and sharing, and data deposit. Specific support platforms that can be leveraged to help with RDM needs will additionally be touched upon including the Portage DMP Assistant, Dataverse, and Compute Canada’s Rapid Access Service.
This DMP-focused webinar, given by James Doiron (UofA) on 2021/03/25, will be of interest to researchers, graduate students, librarians, and research support stakeholders, and will provide foundational information on developing DMPs. Topics covered will include the importance and benefits of DMPs, how they support research excellence, and what makes a ‘good’ DMP, as well as a detailed look at their standard content. Resources to help with the development of DMPs – including bilingual training materials, guidance documents and Exemplar DMPs – will be presented, as well as an update on the activities of the Portage DMP Expert Group, including forthcoming resources. A brief overview of the DMP Assistant platform will be provided, while a second separate session will deliver an in-depth look at the latest version of this platform, including its key features.
This was a series of webinars, hosted by Kishaadigeh: The Manitoba Network Environments for Indigenous Health Research. Expert speakers shared their knowledge in developing and enhancing Indigenous data management plans, including skills and best practices in Indigenous data storage, metadata, preservation, and access. These webinars are intended for both Indigenous communities/organizations and researchers working with Indigenous data.
This webinar is intended to provide an accessible and practical introduction to the theory and concepts behind data anonymization, looking specifically at anonymization of survey microdata. Topics covered will include an overview of identifiers and quasi-identifiers, an introduction to k-anonymity, a look at some cases where k-anonymity breaks down, and anonymization hierarchies. The presenter will describe a method to assess a survey dataset for anonymization using standard statistical software. Much of the academic material looking at data anonymization is quite abstract and aimed at computer scientists, while material aimed at data curators does not always consider recent developments. This webinar is intended to help bridge the gap.