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The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences

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by Justin Kitzes, Daniel Turek, and Fatma Deniz, editors


2017 / 368 pages / Softcover / ISBN 978-0-520594-75-2 / List Price $30.00 / SIAM Member Price $21.00 / Order Code: UC01

The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research.

Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible.


Preface: Nullius in Verba

Part I: Practicing Reproducibility
Assessing Reproducibility
The Basic Reproducible Workflow Template
Case Studies in Reproducible Research
Lessons Learned
Building Toward a Future Where Reproducible, Open Science is the Norm

Part II: High-level Case Studies
Case Study 1: Processing of Airborne Laser Altimetry Data Using Cloud-Based Python and Relational Database Tools
Case Study 2: The Trade-Off between Reproducibility and Privacy in the Use of Social Media Data to Study Political behavior
Case Study 3: A Reproducible R Notebook Using Docker
Case Study 4: Estimating the Effect of Soldier Deaths on the Military Labor Supply
Case Study 5: Turning Simulations of Quantum Many-Body Systems into a Provenance-Rich Publication
Case Study 6: Validating Statistical Methods to Detect Data Fabrication
Case Study 7: Feature Extraction and Data Wrangling for Predictive Models of the Brain in Python
Case Study 8: Using Observational Data and Numerical Modeling to Make Scientific Discoveries in Climate Science
Case Study 9: Analyzing Bat Distributions in a Human-Dominated Landscape with Autonomous Acoustic Detectors and Machine Learning Models
Case Study 10: An Analysis of Household Location Choice in Major US Metropolitan Areas Using R
Case Study 11: Analyzing Cosponsorship Data to Detect Networking Patterns in Peruvian Legislators
Case Study 12: Using R and Related Tools for Reproducible Research in Archaeology
Case Study 13: Achieving Full Replication of Our Own Published CFD Results, with Four Different Codes
Case Study 14: Reproducible Applied Statistics: Is Tagging of Therapist-Patient Interaction Reliable?
Case Study 15: A Dissection of Computational methods Used in a Biogeographic Study
Case Study 16: A Statistical Analysis of Salt and Mortality at the Level of Nations
Case Study 17: Reproducible Workflows for Understanding Large-Scale Ecological Effects of Climate Change
Case Study 18: Reproducibility in Human Neuroimaging Research: A Practical Example from the Analysis of Diffusion MRI
Case Study 19: Reproducible Computational Science on High-Performance Computers: A View from Neutron Transport
Case Study 20: Detection and Classification of Cervical Cells
Case Study 21: Enabling Astronomy Image Processing with Cloud Computing Using Apache Spark

Part III: Low-Level Case Studies
Case Study 22: Software for Analysing Supernova Light Curve Data for Cosmology
Case Study 23: pyMooney: Generating a Database of Two-Tone Mooney Images
Case Study 24: Problem-Specific Analysis of Molecular Dynamics Trajectories for Biomolecules
Case Study 25: Developing an Open, Modular Simulation Framework for Nuclear Fuel Cycle Analysis
Case Study 26: Producing a Journal Article on Probabilistic Tsunami Hazard Assessment
Case Study 27: A Reproducible Neuroimaging Workflow Using the Automated Build Tool "Make"
Case Study 28: Generation of Uniform Data Products for AmeriFlux and FLUXNET
Case Study 29: Developing a Reproducible Workflow for Large-Scale Phenotyping
Case Study 30: Developing and Testing Stochastic Filtering methods for Tracking Objects in Videos
Case Study 31: Developing, Testing, and Deploying Efficient MCMC Algorithms for Hierarchical Models Using R

About the Editors
Justin Kitzes is Assistant Professor of Biology at the University of Pittsburgh.

Daniel Turek is Assistant Professor of Statistics at Williams College.

Fatma Deniz is Postdoctoral Scholar at the Helen Wills Neuroscience Institute and the International Computer Science Institute, and Data Science Fellow at the University of California, Berkeley.


ISBN 9780520594752

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