<table class="table table-bordered"> <tr style="text-align: center; background: #ffedd1"> <td><span style="font-size: 16px;"><strong>   Buy the book on <a href="http://www.amazon.com/Implementing-Reproducible-Research-Chapman-Series/dp/1466561599/">Amazon</a>!  </strong></span></td> </tr> </table> <table> <tr> <td style="width: 192px"><img src="http://ecx.images-amazon.com/images/I/41S2lyCracL._SY300_.jpg"></td> <td>   </td> <td> <table> <tr> <td><span style="font-size: 22px"><strong>Implementing Reproducible Research</strong></span></td> </tr> <tr><td> </td></tr> <tr> <td><strong>Editors:</strong> Victoria Stodden, Friedrich Leisch, Roger D. Peng</td> </tr> <tr><td> </td></tr> <tr><td> </td></tr> <tr> <td>In many of today’s research fields, including biomedicine, computational tools are increasingly being used so that the results can be reproduced. Researchers are now encouraged to incorporate software, data, and code in their academic papers so that others can replicate their research results. Edited by three pioneers in this emerging area, this book is the first one to explore this groundbreaking topic. It presents various computational tools useful in research, including cloud computing, data repositories, virtual machines, R’s Sweave function, XML-based programming, and more. It also discusses legal issues and case studies.</td> </tr> </table> </td> </tr> </table> <div> <br><br> The chapters of <em>Implementing Reproducible Research</em> are available for download for free. Click the links below to view and download contents. <br><br> </div> <table> <tr> <td width="50%"> <table> <tr> <th><span style="font-size: 20px"><strong>Table of Contents</strong></span></th> </tr> <tr><td> </td></tr> <tr> <td><Strong>Introduction</strong></td> </tr> <tr><td> </td></tr> <tr> <td> <table> <tr><td><strong><a href="https://openscienceframework.org/project/w6fp4/wiki/home" style="font-size: 18px">Tools</a></strong></td></tr> <tr><td><a href="https://osf.io/haub8/">Yihui Xie</a></td></tr> <tr><td><a href="https://osf.io/c3kv6/">Juliana Freire</a></td></tr> <tr><td><a href="https://osf.io/xrbqv/">Andrew Davison</a></td></tr> <tr><td><a href="https://osf.io/3bqw7/">Philip Guo</a></td></tr> <tr><td><a href="https://osf.io/2skh7/">Peter Murray-Rust, Dave Murray-Rust</a>
</td></tr> <tr><td><a href="https://osf.io/ns2m3/">Tanu Malik, Quan Pham, and Ian Foster</a>
</td></tr> </table> </td> </tr> <tr><td> </td></tr> <tr> <td> <table> <tr><td><strong><a href="https://openscienceframework.org/project/pk46g/wiki/home" style="font-size: 18px">Practices and Guidelines</a></strong></td></tr> <tr><td><a href="https://osf.io/h9gsd/">Jarrod Millman / Fernando Perez</a></td></tr> <tr><td><a href="https://osf.io/zqbu2/">C. Titus Brown</a></td></tr> <tr><td><a href="https://osf.io/4wgma/">Holger Hoefling, Anthony Rossini</a></td></tr> <tr><td><a href="https://osf.io/emvbz/">Luis Ibanez</a></td></tr> <tr><td><a href="https://osf.io/sp2vg/">Bill Howe</a></td></tr> <tr><td><a href="https://osf.io/9h47z/">Open Science Collaboration</a></td></tr> </table> </td> </tr> <tr><td> </td></tr> <tr> <td> <table> <tr><td><strong><a href="https://openscienceframework.org/project/hym6x/wiki/home/" style="font-size: 18px">Platforms</a></strong></td> <tr><td><a href="https://osf.io/6m8w9/">Mikio Braun, Cheng Soon Ong</a></td></tr> <tr><td><a href="https://osf.io/39eq2/">Christophe Hurlin, Christophe Perignon, Victoria Stodden</a></td></tr> <tr><td><a href="https://osf.io/35s9d/">Iain Hrynaszkiewicz, Peter Li, and Scott Edmunds</a></td></tr> <tr><td><a href="https://osf.io/yi8k2/">Victoria Stodden</a></td></tr> </table> </td> </tr> </table> </td> <td width="50%"> <table> <tr><th><span style="font-size: 20px"><br><strong>About the Editors</strong></span></th></tr> <tr><td><strong>Victoria Stodden</strong> is an Assistant Professor of Statistics at Columbia University, and affiliated with the Columbia University Institute for Data Sciences and Engineering. Her research centers on the multifaceted problem of enabling reproducibility in computational science. This includes studying adequacy and robustness in replicated results, designing and implementing validation systems, developing standards of openness for data and code sharing, and resolving legal and policy barriers to disseminating reproducible research. She is the developer of the award winning "Reproducible Research Standard," a suite of open licensing recommendations for the dissemination of computational results.</td></tr> <tr><td><br><strong>Roger D. Peng</strong> is an Associate Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental health data. Dr. Peng is the the Associate Editor for Reproducibility for the journal <em>Biostatistics</em> and is the author of numerous R packages.</td></tr> <tr><td><br><strong>Friedrich Leisch</strong> is Head of the Institute of Applied Statistics and Computing at the University of Natural Resources and Life Sciences in Vienna. He is a member of the R Core Team the original creator of the Sweave system in R and has published extensively about tools for reproducible research. He is also a leading researcher in the area of high-dimensional data analysis.</td></tr> </table> </td> </tr> </table> <a rel="license" href="http://creativecommons.org/licenses/by/3.0/us/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by/3.0/us/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/3.0/us/">Creative Commons Attribution 3.0 United States License</a>.