Fernando Hoces de la Guardia, Project Scientist

Open (1) Accelerating Computational Reproducibility in Economics and other Social Sciences

Open. Apprentices needed for the spring semester. Enter your application on the web beginning January 11th. The deadline to apply is Monday, January 24th at 9 AM.

Computational reproducibility, or the ability to reproduce published results, tables, and other figures using the available data, code, and materials, through a process of reproduction, is necessary for ensuring that science is self-correcting. Reproducing published work can be used as a teaching tool to introduce students to scientific concepts, research methods, and fundamental scientific principles such as the Mertonian norms (Merton, 1973). In collaboration with Dr. Lars Vilhuber, the current American Economic Association’s Data Editor, the Berkeley Initiative for Transparency in the Social Sciences (BITSS) has developed an adaptable curricular module to teach reproducible research through reproductions of published work. The module includes two complementary teaching resources: (1)The Guide for Accelerating Computational Reproducibility includes detailed steps, definitions of fundamental concepts, and criteria for assessing and improving reproducibility. (2) The Social Science Reproduction Platform (SSRP) is an open-source platform that crowdsources and catalogs attempts to assess and improve the reproducibility of published social science research. SSRP allows users to upload the results of their reproductions using a standardized form, receive feedback from peers through a discussion forum, and contribute citable evidence on the reproducibility of research.

URAPs will be asked to conduct computational reproductions of published research and submit their results to the SSRP platform.

About BITSS:
Established by the Center for Effective Global Action (CEGA) in 2012, the Berkeley Initiative for Transparency in the Social Sciences (BITSS) works to strengthen the integrity of social science research and evidence used for policy-making. We seek to drive the evolution of scientific norms and change the practices of social scientists in ways that promote research transparency and reproducibility. Visit www.bitss.org and follow @UCBITSS on Twitter to learn more.

Students will become fluent in common tools and best practices for transparent and reproducible research, such as version control using Git, reproducible coding conventions, code, and data sharing, and results reporting. Students will be working closely with BITSS Project Scientist Fernando Hoces de la Guardia.

Day-to-day supervisor for this project: Fernando Hoces de la Guardia, Staff Researcher

Qualifications: All applicants should have at least a conceptual understanding of programming, preferably in R, Python, or Julia. Priority will be given to students who have successfully completed Data 8: Foundations of Data Science. Beyond this, preferred applicants should meet other desirable qualifications depending on the role for which they would like to be considered. We will also consider applications from highly motivated students who are willing to learn the skills specific to their role during their URAP engagement.

Weekly Hours: 9-11 hrs

Related website: https://www.bitss.org/opa/
Related website: https://www.socialsciencereproduction.org/