Session

DescriptionData-driven research methods have become essential for many scientific domains, such as earth sciences, high-energy physics, material science, or biomedical engineering. Within these domains, data is collected with different tools, stored in different formats and systems, organized and accessed in typical workflows, and overly-tuned towards the domain requirements. Additionally, the data-management practices by which data is collected, prepared, processed, and shared vary a lot, ranging from highly centralized to small-scale and perhaps siloed approaches. We propose this minisymposium as a forum for experts to share and discuss practices, requirements and challenges for data management. Our aim is to highlight best practices and challenges across the breadth of scientific computing and establish a common understanding of scientific data management, to enable the co-design of HPC methods and tools for a diverse portfolio of data-driven, cross-domain scientific workflows and research infrastructure. This minisymposium will consist of three 25-minute presentations, followed by a Q&A session organized as a panel with the presenters. For all our invited speakers, we gave the same assignment: introduce your research and ideas through the lens of demand and offer for next generation data management for scientific computing. This will be the focus of the panel, as well.