| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Data Management for AM Bench 2022 |
| Author(s) |
Gretchen Greene, Lyle E. Levine, Chandler A. Becker, Gerard Lemson, Jai Won Kim, Arik Mitschang, Ben Long, Kevin Brady, Brandon Lane, Andrew C.E. Reid |
| On-Site Speaker (Planned) |
Gretchen Greene |
| Abstract Scope |
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. |
| Proceedings Inclusion? |
Undecided |