FAIRly big: A framework for computationally reproducible processing of large-scale data.

ABSTRACT: Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original

SUBMITTER: Wagner AS 

PROVIDER: S-EPMC8917149 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature