An empirical analysis of journal policy effectiveness for computational reproducibility

By Victoria Stodden, Jennifer Seiler, Zhaokun Ma. Proceedings of the National Academy of Sciences of the United States of America, 2018

321 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a7c2d6b4710ea409b14a716616a9caf466adb2f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Arvind Narayanan
2. Joseph Bonneau

In Information Systems and Management

1. Arvind Narayanan

In Statistics and Probability

1. Sayash Kapoor
2. Arvind Narayanan
3. Aaron Dotter

Citations in the corpus

• Leakage and the reproducibility crisis in machine-learning-based science (2023, background): “Other factors, such as the lack of available code and data, can also lead to irreproducibility, and there are several studies investigating these shortcomings.(50,51) We discuss our choice of terminology in detail in the supplemental experimental procedures (section S1).”
• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “In some cases, code and data may not be accessible at all even after contacting authors (Stodden et al., 2018).”
• Modules for Experiments in Stellar Astrophysics (MESA): Time-dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure (2022, background): “Provenance, as the term relates to software instruments (Van den Bussche & Vianu 2001; Carata et al. 2014; Stodden et al. 2018), is the ability to record the full history of a result.”