When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? - David L. Donoho; Victoria Stodden (NIPS, 2003)

Multiscale Representations for Manifold-Valued Data. - Inam Ur Rahman; Iddo Drori; Victoria Stodden; David L. Donoho; Peter Schröder (Multiscale Model. Simul., 2005)

Breakdown Point of Model Selection When the Number of Variables Exceeds the Number of Observations. - David L. Donoho; Victoria Stodden (IJCNN, 2006)

A global empirical evaluation of new communication technology use and democratic tendency. - Victoria Stodden; Patrick Meier (ICTD, 2009)

Reproducible Research in Computational Harmonic Analysis. - David L. Donoho; Arian Maleki; Inam Ur Rahman; Morteza Shahram; Victoria Stodden (Comput. Sci. Eng., 2009)

The Legal Framework for Reproducible Scientific Research: Licensing and Copyright. - Victoria Stodden (Comput. Sci. Eng., 2009)

Reproducibility of Computational Results: Opening Code and Data. - Victoria Stodden (IASSIST, 2010)

RunMyCode.org: A novel dissemination and collaboration platform for executing published computational results. - Victoria Stodden; Christophe Hurlin; Christophe Pérignon (eScience, 2012)

Reproducible Research for Scientific Computing: Tools and Strategies for Changing the Culture. - Victoria Stodden; Randall J. LeVeque; Ian Mitchell 001 (Comput. Sci. Eng., 2012)

Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE. - Doug James; Nancy Wilkins-Diehr; Victoria Stodden; Dirk Colbry; Carlos Rosales; Mark R. Fahey; Justin Shi; Rafael Ferreira da Silva; Kyo Lee; Ralph Roskies; Laurence Loewe; Susan Lindsey; et al. (CoRR, 2014)

ResearchCompendia.org: Cyberinfrastructure for Reproducibility and Collaboration in Computational Science. - Victoria Stodden; Sheila Miguez; Jennifer Seiler (Comput. Sci. Eng., 2015)

Making massive computational experiments painless. - Hatef Monajemi; David L. Donoho; Victoria Stodden (IEEE BigData, 2016)

Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments. - Bertram Ludäscher; Kyle Chard; Niall Gaffney; Matthew B. Jones; Jaroslaw Nabrzyski; Victoria Stodden; Matthew J. Turk (CoRR, 2016)

Four simple recommendations to encourage best practices in research software. - Rafael C. Jiménez; Mateusz Kuzak; Monther Alhamdoosh; Michelle Barker; Bérénice Batut; Mikael Borg; Salvador Capella-Gutiérrez; Neil P. Chue Hong; Martin Cook; Manuel Corpas; Madison Flannery; Leyla J. García; et al. (F1000Research, 2017)

AIM: An Abstraction for Improving Machine Learning Prediction. - Victoria Stodden; Xiaomian Wu; Vanessa V. Sochat (DSW, 2018)

Reproducibility in computational and data-enabled science. - Victoria Stodden (HPDC, 2018)

Realizing the potential of data science. - Francine Berman; Rob A. Rutenbar; Brent Hailpern; Henrik I. Christensen; Susan B. Davidson; Deborah Estrin; Michael J. Franklin; Margaret Martonosi; Padma Raghavan; Victoria Stodden; Alexander S. Szalay (Commun. ACM, 2018)

An empirical analysis of journal policy effectiveness for computational reproducibility. - Victoria Stodden; Jennifer Seiler; Zhaokun Ma (Proc. Natl. Acad. Sci. USA, 2018)

Open access to research artifacts: Implementing the next generation data management plan. - Victoria Stodden; Vicki Ferrini; Margaret J. Gabanyi; Kerstin A. Lehnert; John Morton; Helen M. Berman (ASIST, 2019)

Application of BagIt-Serialized Research Object Bundles for Packaging and Re-Execution of Computational Analyses. - Kyle Chard; Thomas Thelen; Matthew J. Turk; Craig Willis; Niall Gaffney; Matthew B. Jones; Kacper Kowalik; Bertram Ludäscher; Timothy M. McPhillips; Jarek Nabrzyski; Victoria Stodden; Ian J. Taylor (eScience, 2019)

Implementing Computational Reproducibility in the Whole Tale Environment. - Kyle Chard; Niall Gaffney; Matthew B. Jones; Kacper Kowalik; Bertram Ludäscher; Jarek Nabrzyski; Victoria Stodden; Ian J. Taylor; Matthew J. Turk; Craig Willis (P-RECS@HPDC, 2019)

Initial Thoughts on Cybersecurity And Reproducibility. - Ewa Deelman; Victoria Stodden; Michela Taufer; Von Welch (P-RECS@HPDC, 2019)

Scientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software Context. - Matthew Krafczyk; August Shi; Adhithya Bhaskar; Darko Marinov; Victoria Stodden (P-RECS@HPDC, 2019)

Toward Enabling Reproducibility for Data-Intensive Research Using the Whole Tale Platform. - Kyle Chard; Niall Gaffney; Mihael Hategan; Kacper Kowalik; Bertram Ludäscher; Timothy M. McPhillips; Jarek Nabrzyski; Victoria Stodden; Ian J. Taylor; Thomas Thelen; Matthew J. Turk; Craig Willis (PARCO, 2019)

Ambitious Data Science Can Be Painless. - Hatef Monajemi; Riccardo Murri; Eric Jonas; Percy Liang; Victoria Stodden; David L. Donoho (CoRR, 2019)

Computing environments for reproducibility: Capturing the "Whole Tale". - Adam Brinckman; Kyle Chard; Niall Gaffney; Mihael Hategan; Matthew B. Jones; Kacper Kowalik; Sivakumar Kulasekaran; Bertram Ludäscher; Bryce D. Mecum; Jarek Nabrzyski; Victoria Stodden; Ian J. Taylor; et al. (Future Gener. Comput. Syst., 2019)

Beyond Open Data: A Model for Linking Digital Artifacts to Enable Reproducibility of Scientific Claims. - Victoria Stodden (P-RECS@HPDC, 2020)

Understanding Reproducibility and Characteristics of Flaky Tests Through Test Reruns in Java Projects. - Wing Lam; Stefan Winter 001; Angello Astorga; Victoria Stodden; Darko Marinov (ISSRE, 2020)

The data science life cycle: a disciplined approach to advancing data science as a science. - Victoria Stodden (Commun. ACM, 2020)

Building a Vision for Reproducibility in the Cyberinfrastructure Ecosystem: Leveraging Community Efforts. - Dylan Chapp; Victoria Stodden; Michela Taufer (Supercomput. Front. Innov., 2020)

Domain-Specific Fixes for Flaky Tests with Wrong Assumptions on Underdetermined Specifications. - Peilun Zhang; Yanjie Jiang; Anjiang Wei; Victoria Stodden; Darko Marinov; August Shi (ICSE, 2021)

Research Reproducibility. - Manish Parashar; Michael A. Heroux; Victoria Stodden (Computer, 2022)

Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines. - Adhithya Bhaskar; Victoria Stodden (ACM-REP, 2024)

The Idealized Machine Learning Pipeline (IMLP) for Advancing Reproducibility in Machine Learning. - Yantong Zheng; Victoria Stodden (ACM-REP, 2024)