My Google Scholar Page.
"Facilitating Reproducibility in Scientific Computing: Principles and Practice," David H. Bailey, Jonathan M. Borwein and Victoria Stodden, in Harald Atmanspacher and Sabine Maasen, eds, Reproducibility: Principles, Problems, Practices, John Wiley and Sons, New York, 2016.
"Reproducible Research in the Mathematical Sciences," with Donoho, The Princeton Companion to Applied Mathematics, Edited by Nicholas J. Higham, 2015. Draft available here.
"Self-correction in Science at Work," with co-authors, Science, 26 June 2015: Vol. 348 no. 6242 pp. 1420-1422 DOI: 10.1126/science.aab3847.
"ResearchCompendia.org: Cyberinfrastructure for Reproducibility and Collaboration in Computational Science," with S. Miguez and J. Seiler, Computing in Science and Engineering, Jan/Feb 2015. Also available here.
"Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE, An XSEDE14 Workshop," principal editors: Doug James, Nancy Wilkins-Diehr, Victoria Stodden, Dirk Colbry, and Carlos Rosales. Dec 17, 2014.
"Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research," with S. Miguez, Journal of Open Research Software 2(1), 2014. http://dx.doi.org/10.5334/jors.ay, also available here.
"The Reproducible Research Movement in Statistics," Statistical Journal of the IAOS, Volume 30 (2014). DOI 10.3233/SJI-140818
"Provisioning Reproducible Computational Science Information," with S. Miguez, reproducibility@XSEDE: An XSEDE14 Workshop, July 2014.
"Enabling Reproducibility in Big Data Research: Balancing Confidentiality and Scientific Transparency," chapter in Lane, J., Stodden, V., Bender, S., and Nissenbaum, H. (eds). 2014. Privacy, Big Data, and the Public Good: Frameworks for Engagement. Cambridge University Press.
Privacy, Big Data, and the Public Good: Frameworks for Engagement, Lane, J., Stodden, V., Bender, S., and Nissenbaum, H. (eds). 2014.
"What Computational Scientists Need to Know About Intellectual Property Law: A Primer," chapter in Stodden, V., Leisch, F., and Peng, R. (eds). 2014. Implementing Reproducible Computational Research. Boca Raton: Chapman & Hall/CRC), also available here.
"RunMyCode.org: A Research-Reproducibility Tool for Computational Sciences," with C. Hurlin and C. Perignon, chapter in Stodden, V., Leisch, F., and Peng, R. (eds). 2014. Implementing Reproducible Computational Research. Boca Raton: Chapman & Hall/CRC).
Implementing Reproducible Computational Research, Stodden, V., Leisch, F., and Peng, R. (eds). 2014.
"What? Me Worry? What to Do About Privacy, Big Data, and Statistical Research," with J. Lane, Amstat News, Dec 1, 2013.
"Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research," with S. Miguez, WSSSPE, Nov 2013. (and on SSRN)
"Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals," with P. Guo and Z. Ma, PLoS ONE, June 21, 2013.
"'Setting the Default to Reproducible' in Computational Science Research," with D. Bailey and J. Borwein, SIAM News, June 2013.
"Set the Default to 'Open'," with D. Bailey and J. Borwein, Notices of the AMS, June 2013.
Testified at the House Committee on Science, Space and Technology for the March 5, 2013 hearing on Scientific Integrity & Transparency (video on the hearing website or here (~600MB). Summary document here)
"Setting the Default to Reproducible: Reproducibility in Computational and Experimental Mathematics," ICERM Workshop report, with D. Bailey, J. Borwein, R. LeVeque, W. Rider, and W. Stein.
"What Computational Scientists Need to Know About Intellectual Property Law: A Primer," S. Bartling, S. Friesike (eds). (2014). Opening Science: The Evolving Guide on How the Web is Changing Research, Collaboration and Scholarly Publishing. Springer).
"Best Practices For Researchers Publishing Computational Results: An Open Source Community Resource," the wiki: http://wiki.stodden.net/
"Software Patents as a Barrier to Scientific Transparency: An Unexpected Consequence of Bayh-Dole," With I. Reich, The Seventh Annual Conference on Empirical Legal Studies (CELS 2012), Stanford, CA. Nov, 2012.
"RunMyCode.org: a novel dissemination and collaboration platform for executing published computational results," with C. Hurlin and C. Perignon. Analyzing and Improving Collaborative eScience with Social Networks (eSoN 12); Workshop with IEEE e-Science 2012; Monday, 8 October 2012, Chicago, IL, USA. Also available on IEEE Xplore.
"How Journals are Adopting Open Data and Code Policies," with P. Guo and Z. Ma, The First Global Thematic IASC Conference on the Knowledge Commons: Governing Pooled Knowledge Resources, Louvain-la-Neuve, Belgium, Sept 12, 2012. Draft version available here.
Guest editor introduction to a special issue on Reproducible Research: "Reproducible Research: Tools and Strategies for Scientific Computing," IEEE Computing in Science and Engineering, 14(4), July/Aug 2012, pp. 11-12. Also available for noncommercial purposes here.
"Reproducible Research for Scientific Computing: Tools and Strategies for Changing the Culture," with Ian Mitchell and Randall LeVeque, IEEE Computing in Science and Engineering, 14(4), July/Aug 2012, pp. 13-17. Also available for noncommercial purposes here.
"Scientists, Share Secrets or Lose Funding," with Sam Arbesman, Bloomberg View, Jan 10, 2012.
with participants, "Changing the Conduct of Science: Summary Report of Workshop Held on November 12, 2010," at the National Science Foundation Workshop Changing the Conduct of Science in the Information Age, June, 2011.
"Innovation and Growth through Open Access to Scientific Research: Three Ideas for High-Impact Rule Changes," in Rules for Growth: Promoting Innovation and Growth Through Legal Reform, edited by The Kauffman Task Force on Law, Innovation, and Growth. February, 2011.
"Cyber Science and Engineering: A Report of the NSF Advisory Committee on Cyberinfrastructure," Task Force on Grand Challenges, Nov 2010.
Remarks presented before The National Academies Committee on The Impact of Copyright Policy on Innovation in the Digital Era, Washington DC, Oct 15, 2010.
"Reproducible Research: Addressing the Need for Data and Code Sharing in Computational Science," with Yale Roundtable Participants, IEEE Computing in Science and Engineering, 12(5), pp. 8-13, Sep/Oct 2010, doi:10.1109/MCSE.2010.113
2009"Prepublication Data Sharing", with the Toronto International Data Release Workshop Authors, Nature, Vol 461, Issue 10, September 2009, p. 168-70.
"A Global Empirical Evaluation of New Communication Technology Use and Democratic Tendency", with Meier, nominated for best paper, 3rd IEEE/ACM International Conference on Information and Communication Technologies and Development, Doha, Qatar, April 2009.
"Reproducible Research in Computational Harmonic Analysis", with Donoho et al. IEEE Computing in Science and Engineering, 11(1), January 2009, p.8-18. (also download here with an earlier version here).
"The Legal Framework for Reproducible Research in the Sciences: Licensing and Copyright", IEEE Computing in Science and Engineering, 11(1), January 2009, p.35-40. (also download here with an earlier version here).
"Virtual Northern Analysis of the Human Genome", with Hurowitz, Drori, Donoho, and Brown. PLoS ONE, May 23, 2(5), 2007.
"Model Selection When the Number of Variables Exceeds the Number of Observations" Doctoral Dissertation, Department of Statistics, Stanford University, 2006.
Release of SparseLab, a collaborative library of MATLAB routines for finding sparse solutions to underdetermined systems. Code and data from 13 papers by 12 authors are included in this platform, making the results in these papers reproducible.
"Fast l1 Minimization for Genome-wide Analysis of mRNA Lengths", with Hurowitz and Drori. IEEE International Workshop on Genomic Signal Processing and Statistics, 2006.
"Breakdown Point of Model Selection when the Number of Variables Exceeds the Number of Observations", with Donoho. Proceedings of the International Joint Conference on Neural Networks, 2006. This paper is part of the library of MATLAB scripts SparseLab.
"Fast l1 Minimization for Genomewide Analysis of mRNA Lengths", with Hurowitz, Drori. Proceedings of the IEEE International Workshop on Genomic Signal Processing and Statistics, 2006.
2005 and earlier
"Multiscale Representations for Manifold-Valued Data", with Donoho, Drori, Schroeder, and Ur Rahman. Multiscale Modeling and Simulation, 4(4), 2005. This paper is part of the library of MATLAB scripts SymmLab.
"When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?", with Donoho. Proceedings of Neural Information Processing Systems, 2003. [`Swimmer' dataset]