Issue 2.4, Fall 2020

From the Editor-in-Chief

Reproducibility, Replicability, and Reliability

by Xiao-Li Meng

Issue 2.4 / Fall 2020

Panorama

Overviews, Visions, and Debates

Special Theme: Reproducibility & Replicability

Guest Editor: Victoria Stodden

Theme Editor's Introduction to Reproducibility and Replicability in Science

by Victoria Stodden

Connections

Supplements (1):

National Academies of Sciences, Engineering, and Medicine

Highlights of the US National Academies Report on “Reproducibility and Replicability in Science”

by Harvey Fineberg, Victoria Stodden, and Xiao-Li Meng

An interview with Harvey Fineberg and Victoria Stodden by Xiao-Li Meng

Self-Correction by Design

by Marcia McNutt

Leveraging the National Academies’ Reproducibility and Replication in Science Report to Advance Reproducibility in Publishing

by Manish Parashar

Toward Reproducible and Extensible Research: From Values to Action

by Aleksandrina Goeva, Sara Stoudt, and Ana Trisovic

Reproducibility and Replicability in Economics

by Lars Vilhuber

Reproducibility in Science: A Metrology Perspective

by Anne Plant and Robert Hanisch

Perspectives on Data Reproducibility and Replicability in Paleoclimate and Climate Science

by Rosemary Bush, Andrea Dutton, Michael Evans, Rich Loft, and Gavin A. Schmidt

Learning Lessons on Reproducibility and Replicability in Large Scale Genome-Wide Association Studies

by Xihong Lin

Science Communication in the Context of Reproducibility and Replicability: How Nonscientists Navigate Scientific Uncertainty

by Emily L. Howell

Selective Inference: The Silent Killer of Replicability

by Yoav Benjamini

Trust but Verify: How to Leverage Policies, Workflows, and Infrastructure to Ensure Computational Reproducibility in Publication

by Craig Willis and Victoria Stodden

Reproducibility and Replication of Experimental Particle Physics Results

by Thomas Junk and Louis Lyons

Connections

Commentaries (1):

Andrew Fowlie

Cornucopia

Impact, Innovation, and Knowledge Transfer

Special Theme: 2020 Election

Guest Editors: Liberty Vittert, Ryan Enos, & Steve Ansolahebere

Predicting the 2020 Presidential Election

by Liberty Vittert, Ryan D. Enos, and Stephen Ansolabehere

An Updated Dynamic Bayesian Forecasting Model for the U.S. Presidential Election

by Merlin Heidemanns, Andrew Gelman, and G. Elliott Morris

Connections

Supplements (1):

Andrew Gelman & 3 others

Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens From 2016

by Michael Isakov and Shiro Kuriwaki

The Keys to the White House: Forecast for 2020

by Allan Lichtman

Connections

Supplements (1):

Allan Lichtman & 2 others

Forecasting the 2020 U.S. Elections With Decision Desk HQ: Methodology for Modern American Electoral Dynamics

by Mukul Ram, Matthew Shor, Kiel Williams, Sreevani Jarugula, Dan DeRemigi, Alex Alduncin, and Scott Tranter

Connections

Supplements (1):

Scott Tranter & 2 others

Stepping Stones

Learning, Teaching, and Communication

The Opportunities of Two-Year College Data Science

by Brian Kotz

Milestones and Millstones

Foundations, Theories, and Methods

Special Theme: 2020 Election

Guest Editors: Liberty Vittert, Ryan Enos, & Steve Ansolahebere

Reconsidering Lost Votes by Mail

by Charles H. Stewart III

Minding the Future

Building Pipelines for Data Science

Column Editor: Nicole Lazar

Introducing GAISE II: A Guideline for Precollege Statistics and Data Science Education

by Christine Franklin and Anna Bargagliotti

High School Data Science Review: Why Data Science Education Should Be Reformed

by Angelina Chen

Recreations in Randomness

Data Science for Leisure Activities

Column Editor: Mark Glickman

Recreating the Game: Using Player Tracking Data to Analyze Dynamics in Basketball and Football

by Brian Macdonald

From the Editor-in-Chief

Reproducibility, Replicability, and Reliability

Panorama

Theme Editor's Introduction to Reproducibility and Replicability in Science

Highlights of the US National Academies Report on “Reproducibility and Replicability in Science”

Self-Correction by Design

Leveraging the National Academies’ Reproducibility and Replication in Science Report to Advance Reproducibility in Publishing

Toward Reproducible and Extensible Research: From Values to Action

Reproducibility and Replicability in Economics

Reproducibility in Science: A Metrology Perspective

Perspectives on Data Reproducibility and Replicability in Paleoclimate and Climate Science

Learning Lessons on Reproducibility and Replicability in Large Scale Genome-Wide Association Studies

Science Communication in the Context of Reproducibility and Replicability: How Nonscientists Navigate Scientific Uncertainty

Selective Inference: The Silent Killer of Replicability

Trust but Verify: How to Leverage Policies, Workflows, and Infrastructure to Ensure Computational Reproducibility in Publication

Reproducibility and Replication of Experimental Particle Physics Results

Cornucopia

Predicting the 2020 Presidential Election

An Updated Dynamic Bayesian Forecasting Model for the U.S. Presidential Election

Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens From 2016

The Keys to the White House: Forecast for 2020

Forecasting the 2020 U.S. Elections With Decision Desk HQ: Methodology for Modern American Electoral Dynamics

Stepping Stones

The Opportunities of Two-Year College Data Science

Milestones and Millstones

Reconsidering Lost Votes by Mail

Columns

Minding the Future

Introducing GAISE II: A Guideline for Precollege Statistics and Data Science Education

High School Data Science Review: Why Data Science Education Should Be Reformed

Recreations in Randomness

Recreating the Game: Using Player Tracking Data to Analyze Dynamics in Basketball and Football