The imperative of scientific reproducibility offers researchers a rationale for inserting best practices early into the research lifecycle. We consider activities that ensure that statistical and analytic claims about given data can be reproduced with that data, curating for reproducibility (CURE).
This half day workshop is open to all researchers* and will teach participants practical strategies for publication-ready and independently understandable package of research materials for reproducibility. Given the replication crisis and requirements of most journals that data and code be submitted upon acceptance or made available upon request as a condition for publication, it is imperative that researchers provide high quality, worry-free, reproduction materials. Participants will learn about the basic components of the Curating for Reproducibility workflow using examples and hands-on activities including best practices for data management and curating code; preparing data availability statements, packaging reproduction materials, and common mistakes to watch out for and avoid to ensure transparent and reproducible research.
The workshop will be based on the data quality review, a framework for helping ensure that research data are usable, that code executes properly and reproduces analytic results, and that all digital scholarly objects are well documented. The workshop will introduce models for putting this framework into practice developed by the co-founders of the Curating for Reproducibility (CURE) consortium composed of the Institution for Social and Policy Studies (ISPS) at Yale University, the Cornell Institute for Social and Economic Research (CISER) at Cornell University, and the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill.
Presenters:
*Requirements: Participants must bring their laptop with Stata pre-installed and must be proficient in Stata. Exercises for the workshop will be in Stata.