Lars Vilhuber 🇺🇦#CRRESS December 13, 2022 webinar "Reproducibility and confidential or proprietary data: can it be done?" w/ Paulo Guimarães (Banco de Portugal BPLIM), @[email protected] (@[email protected]), myself (@[email protected] @[email protected] @[email protected]). Aleks Michuda @[email protected] moderating! 12:15 ET
Register to join the session on Zoom. https://cornell.zoom.us/webinar/register/WN_FBXngdCaSN67UEiZCKZU5Q
Welcome! You are invited to join a webinar: CRRESS - Session 4: Reproducibility and confidential or proprietary data: can it be done?. After registering, you will receive a confirmation email about joining the webinar.
Panelists: Lars Vilhuber (Cornell University, AEA Data Editor) Paulo Guimarães (Banco de Portugal, BPLIM) John Horton (MIT) Moderator: Aleksandr Michuda (Cornell University, Data Science) What happens to reproducibility when data are confidential or proprietary? Many journals can only ask that detailed access procedures be provided in a ReadMe file, but what mechanims could be used to conduct computational reproducibility checks on such data? Should authors temporarily share their data with the journal for the purposes of reproducibility verification, even if they are not part of the public data replication package? Is it feasible to use a network of "insiders" to run code provided as part of a data replication package to assess reproducibility? Could a "certified run" be used? For more information on this session and the rest of the webinar series, see https://labordynamicsinstitute.github.io/crress/
What happens to reproducibility when data are confidential or proprietary? Many journals can only ask that detailed access procedures be provided in a ReadMe file, but what mechanisms could be used to conduct computational reproducibility checks on such data?
Should authors temporarily share their data with the journal for the purposes of reproducibility verification, even if they are not part of the public data replication package?
Is it feasible to use a network of "insiders" to run code provided as part of a data replication package to assess reproducibility? Could a "certified run" be used?
Learn more at https://labordynamicsinstitute.github.io/crress/ #CRRESS
Conference on Reproducibility and Replicability in Economics and the Social Sciences
The Conference on Reproducibility and Replicability in Economics and the Social Sciences is a series of virtual and in-person panels on the topics of reproducibility, replicability, and transparency in the social sciences. The purpose of scientific publishing is the dissemination of robust research findings, exposing them to the scrutiny of peers and other interested parties. Scientific articles should accurately and completely provide information on the origin and provenance of data and on the analytical and computational methods used. Yet in recent years, doubts about the adequacy of the information provided in scientific articles and their addenda have been voiced. The conferences will address the following topics: the initiation of research, the conduct of research, the preparation of research for publication, and the scrutiny after publication. Undergraduates, graduate students, and career researchers will be able to learn about best practices for transparent, reproducible, and scientifically sound research in the social sciences.
Aaron SojournerPerhaps it should become standard practice for journals to insist that researchers insist that DSAs include a clause giving a data editor at any eventual journal circumscribed access for reproducibility check.