We believe that all researchers should share their analytic code. Open sharing provides an unambiguous record of the analytical methods used, aiding reproducibility and error spotting. It also permits efficient re-use by other researchers. This is why we share all source code for all our projects, including OpenPrescribing, OpenPathology, OpenSAFELY and FDAAA Trials tracker is available on our main GitHub account. In addition, every paper we publish has its code shared, linked to from the page, and usually in a Jupyter notebook, where you can see exactly how the code runs against the underlying data. This is all part of our open working ethos, and our other core value: combining best practice from the academic and software development communities.
We want to help others work in this way. We have a range of resources we use to onboard external researchers, and we are currently developing a course on Open Analytic Methods for Health Data Analysis. If you are interested in learning how to use Jupyter Notebooks, Github and other tools then please get in touch, we will add you to our contact list for the course.
Check out some of our main GitHub repositories below!
This is the repository for the OpenSAFELY job runner. A job runner is a service that encapsulates: the task of checking out an OpenSAFELY study repo; executing actions defined in its project.yaml configuration file when requested via a jobs queue; and storing its results in a particular locations.
The documentation is aimed at developers looking for an overview of how the system works. It also has some parts relevant for end users, particularly the project.yaml documentation.
This is the code for the OpenSAFELY job server designed for mediating jobs that can be run in an OpenSAFELY secure environment. The Django app provides a simple REST API which provides a channel for communicating between low-security environments (which can request that jobs be run) and high-security environments (where jobs are run).
Allowing developers to generate random data based on their study expectations. They can then use this as input data when developing analytic models.
Supporting downloading of codelist CSVs from the OpenSAFELY codelists repository, for incorporation into the study definition
This is the repository containing everything you need to recreate our analysis published in The Lancet assessing compliance with the Final Rule of The Food and Drug Administration Amendments Act (FDAAA) (2007). The code can also be easily adapted for future analyses of interest using ClinicalTrials.gov data.