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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Presentation Title Galaxy: A Critical Framework for Large Data Volumes and Data-intensive Processing in the Synchrotron World
Author(s) Kelly E. Nygren, Werner Sun, Rolf Verberg, Keara Soloway, Valentin Y. Kuznetsov, Devin Bougie, Matthew P. Miller , Katherine S. Shanks
On-Site Speaker (Planned) Kelly E. Nygren
Abstract Scope Synchrotron techniques provide critical access to the microstructural and micromechanical response of materials across a range of relevant length scales and timescales. Because access to these techniques requires specialized training, it is important to release not only the research outcomes, but the data itself to the larger scientific community. However, as synchrotron technologies advance, data volumes and data complexity are growing at an unprecedented rate. The addition of data-intensive computing and cyber expertise to the ever-requisite x-ray and materials science expertise overburdens the userbase. To bring the focus back to the science and foster the curation, sharing, and reproducibility of workflows, datasets, and results, CHESS has adopted a science gateway built on the open-source Galaxy framework. In this talk we will present the science gateway, how it enables FAIR practices, and discuss the ripple effect it has had on creating a robust data and software infrastructure across the facility.
Proceedings Inclusion? Planned:
Keywords Characterization,


A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Advanced Mechanical Properties Prediction of Functionally Graded Materials through High-Throughput Characterization.
Advances in Atom Probe Crystallographic Analysis
Connectivity of Experimental Equipment and Interoperability of Experimental Data: Challenges and Opportunities
Data-driven Discovery of Dynamics from Time-resolved Coherent Scattering
Data Management in Additive Manufacturing – Lessons Learned and Opportunities
Data Management, Data Sharing and the Future of Federal Research Funding
Deep Learning-Driven Semantic Segmentation of large 4D Lab-Scale X-ray Tomography Data for Quantification of Microstructural Features
Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Enabling Uninterrupted In-situ X-ray Experiments through Rapid Data Feedback and On-the-fly Experiment Optimization
G-19: Accessing the Microstructure State Space
G-20: TESCAN TENSOR a 4D-STEM for Multimodal Characterization of Challenging and Interesting Specimens
Galaxy: A Critical Framework for Large Data Volumes and Data-intensive Processing in the Synchrotron World
Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
HPC+AI@Edge Enabled Real-Time Materials Characterization
Melt Pool Quantification from In Situ Radiography of Directed Energy Deposition of Nickel Superalloys
New Strong and Ductile Titanium-oxygen-iron Alloys Enabled by AM and Insights from Multiscale Microscopy
Probabilistic Orientation Analysis via Direct ODF Calculation from Far Field HEDM
Quantitative 2D and 3D Characterization of Precipitates Microstructure in the Additively Manufactured Titanium Alloy
Real-Time In-Situ Characterization with Web Technologies at Any Scale
Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Understanding Relaxation Dynamics Beyond Equilibrium Using AI-Informed X-ray Photon Correlation Spectroscopy
Using Video Games for Training Data on Microstructural Design
Utilizing Advanced Computer Vision Techniques Based on Machine Learning and Artificial Neural Networks to Process Micrographs of Ni-base Superalloys
Utilizing Deep Learning Techniques to Accelerate X-ray Absorption and Diffraction Contrast Imaging

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