Toronto Data Workshop on Reproducibility

When and Where

Thursday, February 25, 2021 9:00 am to Friday, February 26, 2021 6:00 pm


Rohan Alexander, University of Toronto


The Faculty of Information and the Department of Statistical Sciences at the University of Toronto are excited to host a two-day conference bringing together academic and industry participants on the critical issue of reproducibility in applied statistics and related areas. The conference is free and will be hosted online on Thursday and Friday 25-26 February 2021. Everyone is welcome, you don’t need to be affiliated with a university.

The conference has three broad areas of focus:

  • Evaluating reproducibility: Systematically looking at the extent of reproducibility of a paper or even in a whole field is important to understand where weaknesses exist. Does, say, economics fall flat while demography shines? How should we approach these reproductions? What aspects contribute to the extent of reproducibility.
  • Practices of reproducibility: We need new tools and approaches that encourage us to think more deeply about reproducibility and integrate it into everyday practice.
  • Teaching reproducibility: While it is probably too late for most of us, how can we ensure that today’s students don’t repeat our mistakes? What are some case studies that show promise? How can we ensure this doesn’t happen again?

We intend to record the presentations and will add links here after the conference. Again, the conference is free and online via Zoom, everyone is welcome - you don’t need to be affiliated with a university.

Please join the event.

For more information, please visit the event page.

Our keynotes are:

  • Eva Vivalt, University of Toronto
  • Mine Çetinkaya-Rundel, University of Edinburgh
  • Riana Minocher, Max Planck Institute for Evolutionary Anthropology


Invited speakers include:

  • Amber Simpson, Queens University
  • Andrés Cruz, Pontificia Universidad Católica de Chile
  • Annie Collins, University of Toronto
  • Danielle Smalls-Perkins, Google
  • Emily Riederer, Capital One
  • Fiona Fidler, University of Melbourne
  • Florencia D’Andrea, National Institute of Agricultural Technology
  • Garret Christensen, US FDIC
  • Heidi Seibold, Helmholtz AI Cooperation Unit
  • Jake Bowers, University of Illinois & The Policy Lab
  • John Blischak, Freelance scientific software developer
  • John McLevey, University of Waterloo
  • Julia Schulte-Cloos, LMU Munich
  • Lauren Kennedy, Monash University
  • Mauricio Vargas, Catholic University of Chile
  • Monica Alexander, University of Toronto
  • Nancy Reid, University of Toronto
  • Nick Radcliffe, Global Open Finance Centre of Excellence & University of Edinburgh
  • Nicolas Didier Arizona State University
  • Ryan Briggs, University of Guelph
  • Sharla Gelfand, Freelance R Developer
  • Shemra Rizzo, Genentech
  • Shiro Kuriwaki, Harvard University
  • Simeon Carstens, Tweag/IO
  • Tania Allard, Quansight labs
  • Tiffany Timbers, University of British Columbia
  • Tom Barton, Royal Holloway, University of London
  • Tyler Girard, University of Western Ontario
  • Wijdan Tariq, University of Toronto
  • Yanbo Tang, University of Toronto