Sustainable data-sharing through open standards and tools for data management and discovery

Brisbane Convention & Exhibition Centre 

As datasets grow larger in size, so does “data gravity”, meaning that more energy is required to store, transfer, and process them. To reduce unnecessary duplication of these tasks, FAIR (findable, accessible, interoperable, and reusable) practices must be adopted for efficient sharing and reuse of raw and derived neuroimaging data. In this module, we will present open standards and tools that can help curate FAIR datasets and enable sustainable data-sharing. Standardized data organization and processing is critical for making data interoperable. We will introduce the Brain Imaging Data Structure (BIDS) community standard and the rich ecosystem of portable (containerized) open-source image processing pipelines that natively understand BIDS data (BIDS Apps). We will show how these standards facilitate data pooling and sharing and distribute compute-heavy processing to clusters with a smaller carbon footprint. The sharing, discovery, and reuse of processed data derivatives requires thorough documentation (and metadata) about the data processing methods. We will demonstrate existing tools and frameworks, including – DataLad, BABS, Boutiques, Nipoppy, and Neurobagel – that help with provenance tracking, (meta)data annotation, and data discovery. We will discuss how these tools can help generate a variety of interoperable research objects and share them with the larger community with fully specified underlying reproducible workflows. Alignment with the FAIR principles is essential for data-sharing to be meaningful and sustainable. This module will introduce attendees to decentralized neuroinformatics infrastructure for data processing, annotation and discovery, which can help reduce data gravity and make neuroimaging more sustainable.