Rigor and Reproducibility Resource Page

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Rigor and Reproducibility Resources 


“Two of the cornerstones of science advancement are rigor in designing and performing scientific research and the ability to reproduce biomedical research findings. The application of rigor ensures robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of results. When a result can be reproduced by multiple scientists, it validates the original results and readiness to progress to the next phase of research. This is especially important for clinical trials in humans, which are built on studies that have demonstrated a particular effect or outcome.” –NIH


Upcoming Events:

Save the Date: Scientific Data Integrity Workshop, July 24

One in the series of the GCC Rigor and Reproducibility (RR) Program targeted modules, this workshop is designed to cover fundamental elements necessary to help assure the quality and integrity of data derived from research studies.  The workshop will review best practices for documentation of research activities, data capture, data (and document) management, and introduce risk mitigation strategies to enhance study reproducibility. A combination of mini lectures, case studies, and group exercises will comprise the activities. Knowledge gained will allow attendees to implement lessons learned within their research environment as elements of a quality system or internal to an individual research project. BioScience Research Collaborative, Room 1003, 6500 Main St  10 am -2:30 pm. Limited to the first 50 registrants. Registration  
If it has reached capacity, please choose to be placed on the waitlist from the registration page.

Save the Date: Reproducible Research (RR) with R and RStudio Workshop, July 30

First in the series of the GCC Rigor and Reproducibility (RR) Program targeted modules, this workshop will: 1) discuss examples motivating the shift to RR; 2) survey the simple nature of the most common problems; 3) discuss organizing data as projects; 4) use Rstudio, knitr, and rmarkdown to illustrate the use of literate programming to interleave text describing the analyses with the code producing the results; 5) use Rstudio, devtools, and roxygen2 to construct a basic R package; 6) survey other commonly used tools and give pointers to how they might be used and where to   learn more. This course will take place from presumes some working knowledge of R. Attendees are requested to bring laptops with recent versions of R and Rstudio installed, as well as the R packages knitr, rmarkdown, devtools, roxygen2, and RTools (this last is for Windows PCs; it’s required to compile R packages). fun. BioScience Research Collaborative, 6500 Main St, Room 1003, 9 am – 3 pm. Limited to the first 25 registrants.  Registration
If it has reached capacity, please choose to be placed on the waitlist from the registration page.


For Workshop Slides and Videos:

Click Here


Case Studies Video:


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