Development Research in Practice

Page 171

Chapter 7 Publishing reproducible research outputs

Publishing research typically involves preparing many iterations of data, code, and code output files, with inputs from multiple collaborators. This process can quickly become unwieldy. It is in nobody’s interest for a skilled and busy researcher to spend days renumbering figures, tables, or references when a reasonable amount of up-front effort can automate the task. Similarly, simultaneous collaboration should not involve the repetitive and error-prone task of manually resolving sets of track-change documents with conflicting edits. Furthermore, for most development research projects, a working paper or a policy brief is not the end of the publication stage. Academic journals and research consumers increasingly require reproducibility packages containing the data, code, and supporting materials needed to recreate the results. All working papers resulting from DIME projects are required to produce a reproducibility package and pass a reproducibility check. Replication materials make an intellectual contribution in their own right, because they enable others to learn from the process and better understand the results obtained. If the analysis process is organized according to the general principles outlined in earlier chapters, publication will not require substantial reorganization of the work already done. Hence, publication is the culmination of the system of transparent, reproducible, and credible research introduced in the first chapter of this book. This chapter recommends tools and workflows for efficiently ­managing collaboration on research and policy outputs and ensuring reproducible results. The first section discusses how to use dynamic ­documents to collaborate on writing. The second section covers how to prepare and publish original data, an important research contribution in its own right. The third section provides guidelines for preparing functional and informative reproducibility packages. In all cases, technology is evolving rapidly, and the specific tools noted here may not remain cuttingedge, but the core principles involved in publication and transparency will endure. Box 7.1 summarizes the main points, lists the responsibilities of different members of the research team, and supplies a list of key tools and resources for implementing the recommended practices.

CHAPTER 7: PUBLISHING REPRODUCIBLE RESEARCH OUTPUTS

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Appendix C: Research design for impact evaluation

33min
pages 215-231

Appendix A: The DIME Analytics Coding Guide

24min
pages 195-210

Appendix B: DIME Analytics resource directory

3min
pages 211-214

8.1 Research data work outputs

6min
pages 190-194

Chapter 8: Conclusion

1min
page 189

7.4 Releasing a reproducibility package: A case study from the Demand for Safe Spaces project

3min
pages 184-186

7.1 Summary: Publishing reproducible research outputs

8min
pages 172-175

7.3 Publishing research data sets: A case study from the Demand for Safe Spaces project

10min
pages 180-183

7.2 Publishing research papers and reports: A case study from the Demand for Safe Spaces project

8min
pages 176-179

Chapter 7: Publishing reproducible research outputs

1min
page 171

6.1 Data analysis tasks and outputs

3min
pages 168-170

6.8 Managing outputs: A case study from the Demand for Safe Spaces project

10min
pages 163-167

6.7 Visualizing data: A case study from the Demand for Safe Spaces project

4min
pages 161-162

6.6 Organizing analysis code: A case study from the Demand for Safe Spaces project

4min
pages 159-160

6.5 Writing analysis code: A case study from the Demand for Safe Spaces project

3min
pages 157-158

6.4 Documenting variable construction: A case study from the Demand for Safe Spaces project

4min
pages 155-156

6.3 Creating analysis variables: A case study from the Demand for Safe Spaces project

1min
page 154

6.2 Integrating multiple data sources: A case study from the Demand for Safe Spaces project

9min
pages 150-153

6.1 Summary: Constructing and analyzing research data

10min
pages 146-149

Chapter 6: Constructing and analyzing research data

1min
page 145

5.7 Recoding and annotating data: A case study from the Demand for Safe Spaces project

3min
pages 140-141

5.6 Correcting data points: A case study from the Demand for Safe Spaces project

4min
pages 138-139

5.5 Implementing de-identification: A case study from the Demand for Safe Spaces project

9min
pages 134-137

5.1 Summary: Cleaning and processing research data

7min
pages 122-124

5.4 Assuring data quality: A case study from the Demand for Safe Spaces project

7min
pages 131-133

5.3 Tidying data: A case study from the Demand for Safe Spaces project

7min
pages 128-130

5.2 Establishing a unique identifier: A case study from the Demand for Safe Spaces project

7min
pages 125-127

Chapter 5: Cleaning and processing research data

1min
page 121

B4.4.1 A sample dashboard of indicators of progress

12min
pages 113-117

4.4 Checking data quality in real time: A case study from the Demand for Safe Spaces project

2min
page 112

4.3 Piloting survey instruments: A case study from the Demand for Safe Spaces project

14min
pages 106-111

4.2 Determining data ownership: A case study from the Demand for Safe Spaces project

16min
pages 100-105

B3.3.1 Flowchart of a project data map

37min
pages 81-96

B2.3.1 Folder structure of the Demand for Safe Spaces data work

36min
pages 55-72

Chapter 4: Acquiring development data

5min
pages 97-99

Chapter 3: Establishing a measurement framework

18min
pages 73-80

Chapter 1: Conducting reproducible, transparent, and credible research

35min
pages 31-46

Chapter 2: Setting the stage for effective and efficient collaboration

18min
pages 47-54

I.1 Overview of the tasks involved in development research data work

18min
pages 22-30

Introduction

2min
page 21
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