2023 Workshop on Reproducibility
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Overview
CANSSI Ontario and the Data Sciences Institute at the University of Toronto are excited to host the Toronto Workshop on Reproducibility, February 23-24, 2022. This two-day workshop brings together academic and industry participants on the critical issue of reproducibility in applied statistics and related areas.
This hybrid workshop is free and open to all.
The Workshop has three broad focus areas:
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.
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?
Practices of Reproducibility
We need new tools and approaches that encourage us to think more deeply about reproducibility and integrate it into everyday practice.
Sponsors
2022 Workshop on Reproducibility
Wednesday, 23 February 2022
08:40-09:00 | Lisa Strug, University of Toronto: Introduction and welcome
09:00-09:30 | Benjamin Haibe-Kains, University Health Network: The (Not-So-)Hard Path To Transparency and Reproducibility in AI Research
09:30-10:00 | Colm-cille Caulfield, University of Cambridge: Reproducibility in an Uncertain World: How should academic data science researchers give advice?
10:00-10:30 | Stephen Eglen, University of Cambridge: Evaluating the reproducibility of computational results reported in scientific journals
10:30-11:00 | Valentin Danchev, University of Essex: Reproducibility and Replicability of Large Pre-trained Language Models
11:00-11:30 | Monica Alexander, University of Toronto: Reproducibility in Demography: where are we at and where can we go?
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12:00-12:30 | Ariel Mundo, University of Arkansas: Statistics and reproducibility in biomedical research: Why we need both
12:30-13:00 | Shilaan Alzahawi, Stanford University: Lay perceptions of scientific findings: Swayed by the crowd?
13:30-14:00 | Fernando Hoces de la Guardia, University of California, Berkeley: Social Sciences Reproducibility Platform
15:30-16:00 | Carl Laflamme, YCharOS: Antibody Characterization through Open Science (YCharOS)
16:00-16:30 | Robert Hanisch, National Institute of Standards and Technology and Research Data Alliance: Reproducibility: A Metrology Perspective
16:30-17:00 | Yann Joly, McGill University: Incentivizing open data sharing - what’s in it for me!?
Thursday, 24 February 2022
08:30-09:00 | Julien Chiquet, Université Paris-Saclay: Computo: a journal of the French Statistical Society promoting reproductibility
09:00-09:30 | Nick Radcliffe, Global Open Finance Centre at the University of Edinburgh: Gentest: Automatic Test Generation for Data Science
09:30-10:00 | Markus Fritsch, University of Passau: Towards reproducible GMM estimation
10:30-11:00 | Aneta Piekut, Sheffield Methods Institute, University of Sheffield: Integrating reproducibility into the curriculum of an undergraduate social sciences degree
12:30-13:00 | Jason Hattrick,-Simpers, University of Toronto: Towards Trust and Reproducibility in Materials AI
13:00-13:30 | Aya Mitani, University of Toronto: Reproducible, reliable, replicable? In-class exercise using peer-reviewed studies
13:30-14:00 | Shannon Ellis, UC San Diego: Structuring & Managing Group Projects in Large-Enrollment Undergraduate Data Science Courses
14:00-14:30 | Maria Tackett, Duke University: Knit, Commit, and Push: Teaching version control in undergraduate statistics courses
15:00-15:30 | Lars Vilhuber, Cornell University: Teaching for large-scale Reproducibility Verification
15:30-16:00 | Michael Geuenich, Lunenfeld Tanenbaum Research Institute and University of Toronto: With great data come great pipelines: creating flexible standardized pipelines for common biomedical analysis tasks using Snakemake
16:00-16:30 | Paraskevi Massara, University of Toronto: MOSS4Research: A maturity model to evaluate and improve reproducibility in research projects
16:30-17:00 | Chris Kenny, Harvard University: Reproducible Redistricting
17:00-17:30 | Dewi Amaliah, Monash University: Reproducible Practice in Taming the Wild Data
Friday, 25 February 2022
09:00-09:30 | Marco Prado, University of Western Ontario: Reproducibility for Behavior Experiments in Basic Science
09:30-10:00 | David Grubbs and Lara Spieker, CRC Press: On book publishing
10:00-11:00 | Joelle Pineau, McGill University & Meta (Facebook) AI Research: Improving Reproducibility in Machine Learning Research
11:00-11:30 | Debbie Yuster, Ramapo College of New Jersey: Infusing Reproducibility into Introductory Data Science
11:30-12:00 | Colin Rundel, Duke University: Teaching Statistical computing with Git and GitHub
12:00-12:30 | Mine Çetinkaya,-Rundel, Duke University and RStudio: Reproducible authoring with Quarto
12:30-13:00 | Erin Heerey, Western University: The Experimenter in the Room
13:00-13:30 | John McLevey, University of Waterloo: Reproducibility and Principled Data Processing in Python
14:00-14:30 | Kevin Wilson, Brown University and Jake Bowers, University of Illinois at Urbana-Champaign: Six Tips for Reproducible Field Experiments
14:30-15:00 | Abel Brodeur, University of Ottawa: Introducing the Institute for Replication
15:00-15:30 | Allison Koenecke, Cornell University and Microsoft Research: Reproducible Retrospective Analysis
15:30-16:30 Michael Hoffman, University Health Network and University of Toronto Reproducibility standards for machine learning in the life sciences