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Wednesday February 23, 2022

08:30-08:40

Housekeeping

  • With: Rohan Alexander

08:40-09:00

Welcome

  • With: Lisa Strug

Session 1

9:00-9:30

The (Not-So-)Hard Path To Transparency and Reproducibility in AI Research

  • Benjamin Haibe-Kains
  • University Health Network

Practices

09:30-10:00

Reproducibility in an Uncertain World:
How should academic data science researchers give advice?

  • Colm-cille Caulfield
  • University of Cambridge

Institutions

10:00-10:30

Evaluating the reproducibility of computational results reported in scientific journals

  • Stephen Eglen
  • University of Cambridge

Evaluating

10:30-11:00

Reproducibility and Replicability of Large Pre-trained Language Models

  • Valentin Danchev
  • University of Essex

Practices

11:00-11:30

Reproducibility in Demography: where are we at and where can we go?

  • Monica Alexander
  • University of Toronto

Evaluating

11:30-12:00

Break

Session 2

12:00-12:30

Statistics and reproducibility in biomedical research: Why we need both

  • Ariel Mundo
  • University of Arkansas

Evaluating

12:30-13:00

Lay perceptions of scientific findings: Swayed by the crowd?

  • Shilaan Alzahawi
  • Stanford University

Evaluating

13:00-13:30

Break

13:30-14:00

Social Sciences Reproducibility Platform

  • Fernando Hoces de la Guardia
  • University of California, Berkeley

Evaluating

14:00-15:30

Break

Session 3

15:30-16:00

Antibody Characterization through Open Science (YCharOS)

  • Carl Laflamme
  • YCharOS

Institutions

16:00-16:30

On the Research Data Alliance

  • Robert Hanisch
  • National Institute of Standards and Technology and Research Data Alliance

Institutions

16:30-17:00

Incentivizing open data sharing – what’s in it for me!?

  • Yann Joly
  • McGill University

Institutions

Thursday February 24, 2022

Session 4

08:30-09:00

Computo: a journal of the French Statistical Society promoting reproductibility

  • Julien Chiquet
  • Université Paris-Saclay

Practices

09:00-09:30

Gentest: Automatic Test Generation for Data Science

  • Nick Radcliffe
  • Global Open Finance Centre at the University of Edinburgh

Practices

09:00-10:00

TBD

  • Marco Gorelli
  • Gousto

10:00-10:30

Towards reproducible GMM estimation

  • Markus Fritsch
  • University of Passau

Practices

10:30-11:00

Integrating reproducibility into the curriculum of an undergraduate social sciences degree

  • Aneta Piekut
  • Sheffield Methods Institute, University of Sheffield

Teaching

11:00-12:30

Break

12:30-13:00

Towards Trust and Reproducibility in Materials AI

  • Jason Hattrick-Simpers
  • University of Toronto

Practices

Session 5

13:00-13:30

Reproducible, reliable, replicable? In-class exercise using peer-reviewed studies

  • Aya Mitani
  • University of Toronto

Teaching

13:30-14:00

Structuring & Managing Group Projects in Large-Enrollment Undergraduate Data Science Courses

  • Shannon Ellis
  • UC San Diego

Teaching

14:00-14:30

Knit, Commit, and Push: Teaching version control in undergraduate statistics courses

  • Maria Tackett
  • Duke University

Teaching

14:30-15:00

Break

15:00-15:30

Teaching for large-scale Reproducibility Verification

  • Lars Vilhuber
  • Cornell University

Teaching

Session 6

15:30-16:00

With great data come great pipelines: creating flexible standardized pipelines for common biomedical analysis tasks using Snakemake

  • Michael Geuenich
  • Lunenfeld Tanenbaum Research Institute and University of Toronto

Practices

16:00-16:30

MOSS4Research: A maturity model to evaluate and improve reproducibility in research projects.

  • Paraskevi Massara
  • University of Toronto

Practices

16:30-17:00

Reproducible Redistricting

  • Chris Kenny
  • Harvard University

Practices

17:00-17:30

Reproducible Practice in Taming the Wild Data

  • Dewi Amaliah
  • Monash University

Practices

Friday February 25, 2022

Session 7

09:00-09:30

TBD

  • Marco Prado
  • University of Western Ontario

09:30-10:00

On book publishing

  • David Grubbs
    Lara Spieker
  • CRC Press

10:00-11:00

Improving Reproducibility in Machine Learning Research

  • Joelle Pineau
  • McGill University

Session 8

11:00-11:30

Infusing Reproducibility into Introductory Data Science

  • Debbie Yuster
  • Ramapo College of New Jersey

Teaching

11:30-12:00

Teaching Statistical computing with Git and GitHub

  • Colin Rundel
  • Duke University

12:00-12:30

Reproducible authoring with Quarto

  • Mine Çetinkaya-Rundel
  • Duke University and RStudio

Practices

12:30-13:00

TBD

  • Erin Heerey
  • Western University

Practices

13:00-13:30

Reproducibility and Principled Data Processing in Python

  • John McLevey, Pierson Browne, and Tyler Crick.
  • University of Waterloo

13:30-14:00

Break

Session 9

14:00-14:30

Six Tips for Reproducible Field Experiments in Public Policy

  • Jake Bowers and Kevin Wilson
  • University of Illinois at Urbana-Champaign and Brown University

14:30-15:00

Introducing the Institute for Replication

  • Abel Brodeur
  • University of Ottawa

Institutions

15:00-15:30

Reproducible Retrospective Analysis

  • Allison Koenecke
  • Cornell University and Microsoft Research

Practices

15:30-16:30

Reproducibility standards for machine learning in the life sciences

  • Michael Hoffman
  • University Health Network and University of Toronto

Practices

16:30-16:45

Concluding Remarks

  • Rohan Alexander
  • University of Toronto

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