SFU Library and KEY, SFU's Big Data Initiative present an all-day Research Data Management Symposium on Friday October 5th, 2018, hosted at SFU's Big Data Hub.
Join us for a day of workshops on tips for research data security, archiving and describing your data to make it more discoverable, and managing the disclosure risk of your sensitive dataset using data de-identification methods.
Find Data / Share Data
Are you interested in increasing the visibility of your research? This workshop will help you describe your data for long term access and findability. As a bonus, we'll also show you how to find data relevant to your research. Some of the tools we'll be looking at are DataCite, the Abacus Dataverse Network, ICPSR, Radar (SFU's Research Data Repository), and the new Google Dataset Search.
Protect your research data by following strong security practices. This workshop will cover topics including:
- cloud storage,
- full disk encryption,
- threat modelling,
- communication and file sharing,
- account security,
- two-factor authentication (2FA), and
- digital preservation.
Learn how to keep data secure with SFU resources.
De-identification is the process of removing or masking information from a dataset that could be used to personally identify an individual. This process is fundamental in enabling the sharing and re-use of data for secondary research purposes. The possibility of individual identification from given data is determined by disclosure risk, and this risk is an important consideration when collecting, analysing, and sharing research data. De-identification can balance the risk of disclosure with the increased research value of a shared dataset.
This workshop will touch on issues related to sharing sensitive data and offers practical suggestions on how such data can be made ready for re-use. Topics include how to assess disclosure risk, direct and indirect identifiers, risk thresholds and measurement, and how to reduce disclosure risk in various academic disciplines with techniques such as generalization, suppression, and subsampling. Examples will be used to illustrate disclosure risk and protection methods.