{"id":10116,"date":"2025-02-27T15:35:37","date_gmt":"2025-02-27T15:35:37","guid":{"rendered":"https:\/\/ciqss.org\/event\/research-reproducibility-ensuring-the-transparency-and-credibility-of-your-work\/"},"modified":"2026-04-20T20:03:22","modified_gmt":"2026-04-20T20:03:22","slug":"research-reproducibility-ensuring-the-transparency-and-credibility-of-your-work","status":"publish","type":"tribe_events","link":"https:\/\/ciqss.org\/en\/event\/research-reproducibility-ensuring-the-transparency-and-credibility-of-your-work\/","title":{"rendered":"Reproducibility in research – Guaranteeing the transparency and credibility of your work"},"content":{"rendered":"

Bilingual<\/strong>, in person training. This intensive course explores the fundamental principles of reproducibility, a pillar of open science that ensures the transparency and credibility of scientific research. You’ll discover best practices for making your work reproducible, from data management and code documentation to version control and dissemination of results.<\/p>\n

Prerequisites<\/strong>: Previous knowledge of at least one statistical software package (STATA, R, Python, SPSS or SAS). Participants should bring their laptops with statistical software installed.<\/p>\n

Course outline – 2025 version<\/a>. (An updated course outline will be posted later over the winter)<\/p>\n

—<\/p>\n

Mobility grants<\/strong><\/p>\n

Up to five $500 CDN scholarships are available to students from outside Montreal who must travel to attend the course, thanks to funding from the Alfred P. Sloan Foundation at Cornell University.<\/p>\n

Submit your application by clicking on the following link (on the Cornell website)<\/a><\/p>\n

Application deadline: April 10, 2026<\/strong>.<\/p>\n

**Bursaries will be paid out to selected candidates once the course has been completed.<\/p>\n","protected":false},"excerpt":{"rendered":"

Bilingual, in person training. This intensive course explores the fundamental principles of reproducibility, a pillar of open science that ensures the transparency and credibility of scientific research. 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