Machine Learning and Open Science: Research Data Management Community of Practice

Our Research Data Management Community of Practice is back with a bang! September's session is all about Machine Learning and Open Science. With training data impacting research results, what's the best way to share ML-based science and ensure research is reproducible and valid? Join us Thursday, September 26 at 11 AM for a great discussion between researchers!

Dr. Alemu Gonsamo (Canada Research Chair in Remote Sensing of Terrestrial Ecosystems, Earth, Environment & Society) and Ricardo Barros Lourenco (Earth, Environment & Society) will unpack training algorithms with data from the Ministry of Natural Resources and then sharing data for a recent paper on peat depth and carbon storage.

Dr. Cynthia Lokker (Health Research Methods, Evidence & Impact) and Rick Parrish (Programmer/Analyst, Health Information Research Unit) will discuss sharing machine learning data for an article and their move from data available on request to a publicly findable restricted access dataset backed by a data sharing agreement. Rick will also share details on depositing an algorithm separately from a dataset. 

Here is the meeting link. Learn more and join the RDM community!