Additive Manufacturing Benchmarks 2022 (AM-Bench 2022): AM Bench 2022 Data Management I
Program Organizers: Brandon Lane, National Institute of Standards and Technology; Lyle Levine, National Institute of Standards and Technology
Monday 3:30 PM
August 15, 2022
Room: Old Georgetown Room
Location: Hyatt Regency Bethesda
Session Chair: Lyle Levine, National Institute of Standards and Technology
3:30 PM Invited
Data Management for AM Bench 2022: Gretchen Greene1; Lyle Levine2; Chandler Becker2; Gerard Lemson3; Jai Won Kim3; Arik Mitschang3; Ben Long2; Kevin Brady2; Brandon Lane2; Andrew Reid2; 1National Institute of Standards and Technology ; 2National Institute of Standards and Technology; 3Johns Hopkins University
Delivery of Additive Manufacturing Benchmark (AMBench) data to the broader community draws upon emerging areas in data science. We have structured the AMBench 2022 data systems for curation and public access to data and highly descriptive metadata to align with FAIR (Findable Accessible Interoperable Reusable) data principles. Cloud collaboration tools are key to a designed workflow to collect data and instantiate a syntactic data model. Processed datasets are ingested into a rich discipline metadata repository and the NIST Public Data Repository (PDR), a standards-based trusted file repository. The PDR features data citation, metrics, natural language processing aided search and semantically linked data assets. The repository systems collectively form a basis for computation and also provide machine readability through multiple application programming interfaces. A mirror of the NIST PDR AMBench dataset in the Johns Hopkins University SciServer serves as a platform for computation and community-contributed results tofurther innovation using the AMBench 2022 data collection.
4:00 PM Invited
SciServer Analysis on AM-Bench Data: Chandler Becker1; Gretchen Greene1; Miyu Mudalamane2; Jordan Raddick3; Gerard Lemson3; Lyle Levine1; 1National Institute of Standards and Technology; 2University of Delaware; 3Johns Hopkins University
This presentation will focus on the application of SciServer to access AM-Bench data in a way that can readily be used for analysis and modeling within the collaborative computing environment. An overview will be given of how to access the data with appropriate distribution-level metadata. This will be followed by a demonstration of using SciServer to perform basic analysis using image data.
4:30 PM Question and Answer Period