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Reproducibility and Sustainability for Neuroimaging Data Analysis Infrastructures adsa


Term:

Fall

Department:

20: Biological Engineering

Faculty Supervisor:

Alan Jasanoff

Faculty email:

jasanoff@mit.edu

Apply by:

2020-10-10

Contact:

horea@mit.edu

Project Description

Our lab develops high-level, transparent, and automated data analysis workflows for neuroimaging research and psychopharmacological applications. As part of this project you have the opportunity to get hands-on experience with a data analysis pipeline used in production across multiple labs, and acquire software engineering skills which are vital to scientific reproducibility. But wait, there's more! Simply ensuring reproducibility for the top-level interface is not enough for sustainable workflow development. To support the unit tests of our package, we automatically create software testing environments, so that reliable results can be obtained at the very cutting edge of biomedical software development. For this we employ technologies which are both lean and transparent, particularly the excellent Portage package Manager of Gentoo Linux. Your work will consist in familiarizing yourself with the environment building infrastructure ( https://github.com/IBT-FMI/gebuilder ), running and administering an image building server, and enabling automatic testing via the popular GitHub social coding platform and the TravisCI integration testing service.

Pre-requisites

To adequately tackle the challenges of this project you should have: • a keen interest in software transparency and research reproducibility • significant prior experience with Linux • confidence in working with the command line and strong prior experience with Bash Though not mandatory or assumed, the following would be a significant plus: • prior experience with Gentoo Linux or the Portage package manager • prior experience with Python • prior experience with Git