Showing 22 results for Author: Sean R. Wilkinson
Aug, 2026
Conference Paper
Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publica…
Aug, 2026
Book Chapter
In the rapidly evolving landscape of scientific computing, the efficacy of workflow execution is paramount for driving groundbreaking discoveries. This chapter provides a critical examination of benchmarking and simulation methodologies for scientific workflows, highlighting the limitations of traditional benchmarks and showcasing recent innovations tailored to the unique…
Jun, 2026
Book
The past two decades have seen a steep increase in computational requirements for analyzing scientific data sets. The reasons are manifold: Typical data sets increased enormously in size, the growing complexity of scientific questions required more complex analysis methods, and the growth of methods based on machine learning and artificial intelligence called for additiona…
Jun, 2026
Book Chapter
Computational workflows represent major investments of effort and expertise. As first-class, publishable research objects of their own, they are key to sharing methodological know-how for reuse, reproducibility, and transparency. Thus, the application of the FAIR Principles to workflows is inevitable to enable them to be Findable, Accessible, Interoperable, and Reusable. M…
Mar, 2026
Conference Paper
High Performance Computing (HPC) centers provide resources to users who require greater scale to “get science done”. They deploy infrastructure with singular hardware architectures, cutting-edge software environments, and stricter security measures as compared with users’ own resources. As a result, users often create and configure digital artifacts in ways that are specia…
Aug, 2025
Book Chapter
Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over tim…
Jun, 2025
Journal
The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow…
Jun, 2025
Journal
The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unneces…
Mar, 2025
Journal
Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findabl…
Oct, 2024
ORNL Report
The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 109 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused on six key areas: time-sensitive workflows, convergence of AI and HPC workflows, multi-facility workflows, heterogeneous HPC environmen…