DataLad-Registry is a service that maintains up-to-date information on over ten thousand datasets, with the collection expanding as more datasets are added.
Finding a compromise between researchers’ needs, their skills in data management, data access restrictions, and limited funding for RDM is a complex but highly relevant and timely challenge.
In this lightning talk, I will share my experience using DataLad, git-annex and ReproMan to run software pipelines on hundreds of fMRI datasets on an HPC cluster.
In the complex realm of network engineering design, optimisation methods have been instrumental, using a range of components across different systems and scenarios.
Scientific computing workflows have become increasingly complex, often comprising of numerous interdependent tasks executed on distributed computing resources.
We present an ecosystem consisting of NeuroBagel, a distributed and scalable approach based on semantic web technologies for harmonizing and sharing phenotypic and neuroimaging variables with a DataLad backend, and NiPoppy, a specification for MRI processings to integrate derived data and curation information.