Advanced Photon Source

An Office of Science National User Facility

APS Scientific Computation Seminar Series

Sessions will normally be held on the 3rd Monday of the month at 1:00 p.m. and last approximately one hour.

This seminar series focuses on scientific computation for APS experiments. The series focuses on advanced software and computing infrastructure for analysis, reduction, reconstruction, and simulation. It provides an opportunity to learn about state-of-the-art computational techniques and tools and how they are being applied to science at the APS. It will start with talks from Argonne staff who are working on projects in collaboration or in support of APS science.

Next Seminar:
Title: Materials Data Facility - Streamlined and Automated Data Sharing, Discovery, Access, and Analysis
Ben Blaiszik, Computation Institute, The University of Chicago
Date: April 17, 2017
Time: 1:00 p.m.
Location: 401/A1100

The Materials Data Facility (MDF), a NIST-funded Materials Genome Initiative project, operates two cloud-hosted production services, data publication and data discovery. These MDF services are built to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific) and automatically-extracted metadata in a registry while the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. The MDF services empower individual researchers, research projects, and institutions to publish research datasets, regardless of size, from distributed storage; and interact with and discover published and indexed data and metadata via REST APIs to facilitate automation, and analysis.

This talk will include live demonstrations of the services including a look at the new data discovery interface (Web UI and API) to find materials data indexed from over 15 databases and repositories and combine disparate materials datasets from distributed locations to train a state-of-the-art machine learning model on the JetStream National Science and Engineering Cloud.

Previous Seminars:

2017 | 2016 | 2015