Front Cover
Contents
Foreword
Acknowledgments
About the Authors
Abbreviations
Introduction
How to read this book
The DIME Wiki: A complementary resource
Standardizing data work
Standardizing coding practices
The team behind this book
Looking ahead
References
Chapter 1 Conducting reproducible, transparent, and credible research
Developing a credible research project
Conducting research transparently
Analyzing data reproducibly and preparing a reproducibility package
Looking ahead
References
Chapter 2 Setting the stage for effective and efficient collaboration
Preparing a collaborative work environment
Organizing code and data for replicable research
Preparing to handle confidential data ethically
Looking ahead
References
Chapter 3 Establishing a measurement framework
Documenting data needs
Translating research design to data needs
Creating research design variables by randomization
Looking ahead
References
Chapter 4 Acquiring development data
Acquiring data ethically and reproducibly
Collecting high-quality data using electronic surveys
Handling data securely
Looking ahead
References
Chapter 5 Cleaning and processing research data
Making data "tidy"
Implementing data quality checks
Processing confidential data
Preparing data for analysis
Looking ahead
References
Chapter 6 Constructing and analyzing research data
Creating analysis data sets
Writing analysis code
Creating reproducible tables and graphs
Increasing efficiency of analysis with dynamic documents
Looking ahead
References
Chapter 7 Publishing reproducible research outputs
Publishing research papers and reports
Preparing research data for publication
Publishing a reproducible research package
Looking ahead
References.

Chapter 8 Conclusion
Bringing it all together
Where to go from here
Appendix A: The DIME Analytics Coding Guide
Appendix B: DIME Analytics resource directory
Appendix C: Research design for impact evaluation
Boxes
Box I.1 The Demand for Safe Spaces case study
Box 1.1 Summary: Conducting reproducible, transparent, and credible research
Box 1.2 Registering studies: A case study from the Demand for Safe Spaces project
Box 1.3 Writing preanalysis plans: A case study from the Demand for Safe Spaces project
Box 1.4 Preparing a reproducibility package: A case study from the Demand for Safe Spaces project
Box 2.1 Summary: Setting the stage for effective and efficient collaboration
Box 2.2 Preparing a collaborative work environment: A case study from the Demand for Safe Spaces project
Box 2.3 Organizing files and folders: A case study from the Demand for Safe Spaces project
Box 2.4 DIME master do-file template
Box 2.5 Writing code that others can read: A case study from the Demand for Safe Spaces project
Box 2.6 Writing code that others can run: A case study from the Demand for Safe Spaces project
Box 2.7 Seeking ethical approval: An example from the Demand for Safe Spaces project
Box 2.8 Obtaining informed consent: A case study from the Demand for Safe Spaces project
Box 2.9 Ensuring the privacy of research subjects: An example from the Demand for Safe Spaces project
Box 3.1 Summary: Establishing a measurement framework
Box 3.2 Developing a data linkage table: An example from the Demand for Safe Spaces project
Box 3.3 Creating data flowcharts: An example from the Demand for Safe Spaces project
Box 3.4 An example of uniform-probability random sampling
Box 3.5 An example of randomized assignment with multiple treatment arms
Box 3.6 An example of reproducible randomization.

Box 4.1 Summary: Acquiring development data
Box 4.2 Determining data ownership: A case study from the Demand for Safe Spaces project
Box 4.3 Piloting survey instruments: A case study from the Demand for Safe Spaces project
Box 4.4 Checking data quality in real time: A case study from the Demand for Safe Spaces project
Box 5.1 Summary: Cleaning and processing research data
Box 5.2 Establishing a unique identifier: A case study from the Demand for Safe Spaces project
Box 5.3 Tidying data: A case study from the Demand for Safe Spaces project
Box 5.4 Assuring data quality: A case study from the Demand for Safe Spaces project
Box 5.5 Implementing de-identification: A case study from the Demand for Safe Spaces project
Box 5.6 Correcting data points: A case study from the Demand for Safe Spaces project
Box 5.7 Recoding and annotating data: A case study from the Demand for Safe Spaces project
Box 6.1 Summary: Constructing and analyzing research data
Box 6.2 Integrating multiple data sources: A case study from the Demand for Safe Spaces project
Box 6.3 Creating analysis variables: A case study from the Demand for Safe Spaces project
Box 6.4 Documenting variable construction: A case study from the Demand for Safe Spaces project
Box 6.5 Writing analysis code: A case study from the Demand for Safe Spaces project
Box 6.6 Organizing analysis code: A case study from the Demand for Safe Spaces project
Box 6.7 Visualizing data: A case study from the Demand for Safe Spaces project
Box 6.8 Managing outputs: A case study from the Demand for Safe Spaces project
Box 7.1 Summary: Publishing reproducible research outputs
Box 7.2 Publishing research papers and reports: A case study from the Demand for Safe Spaces project
Box 7.3 Publishing research data sets: A case study from the Demand for Safe Spaces project.

Box 7.4 Releasing a reproducibility package: A case study from the Demand for Safe Spaces project
Figures
Figure I.1 Overview of the tasks involved in development research data work
Figure B2.3.1 Folder structure of the Demand for Safe Spaces data work
Figure B3.3.1 Flowchart of a project data map
Figure B4.4.1 A sample dashboard of indicators of progress
Figure 4.1 Data acquisition tasks and outputs
Figure 5.1 Data-cleaning tasks and outputs
Figure 6.1 Data analysis tasks and outputs
Figure 7.1 Publication tasks and outputs
Figure 8.1 Research data work outputs.