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Inconsistent and incomplete applications of metadata standards and unsatisfactory approaches to connecting repository holdings across the global research infrastructure inhibit data discovery and reusability. The Realities of Academic Data Sharing (RADS) Initiative has found that institutions and researchers create and have access to the most complete metadata, but that valuable metadata found in these local institutional repositories (IRs) are not making their way into global data infrastructure such as DataCite or Crossref. This panel examines the local to global spectrum of metadata completeness, including the challenges of obtaining quality metadata at a local level, specifically at Cornell University, and the loss of metadata during the transfer processes from IRs into global data infrastructure. The metadata completeness increases over time, as users reuse data and contribute to the metadata. As metadata improves and grows, users find and develop connections within data not previously visible to them. By feeding local IR metadata into the global data infrastructure, the global infrastructure starts giving back in the form of these connections. We believe that this information will be helpful in coordinating metadata better and more effectively across data repositories and creating more robust interoperability and reusability between and among IRs.
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Development of a Pilot Manufacturing Cyberinfrastructure With an Information Rich Mechanical CAD 3D Model Repository
Abstract Data driven advanced manufacturing research is dependent on access to large datasets made available from across the product lifecycle — from the concept design phase all the way down to end use and disposal. Despite such data being generated at a rapid pace, most product design data is archived in inaccessible silos. This is particularly acute in academic research laboratories and with data generated during product design and manufacturing courses. This project seeks to create an infrastructure that allow users (academia and the general public) to easily upload project data and related meta-data. Current manufacturing research must shift from siloed repositories of product manufacturing data to a federated, decentralized, open and inter-operable approach. In this regard, we build ‘FabWave’ a cyber-infrastructure tool designed to capture manufacturing data. In its first pilot implementation, we focused our attention to gathering information rich 3D Mechanical CAD data and related meta-data associated with them, with the intent to make it easier for users to upload and access product design data. We describe workflows that we have initially tested out within the two academic universities and under two different course structures. We have also developed automated workflows to gather license appropriate CAD assemblies from commercial repositories. Our intent is to create the only known largest available CAD model set within academia for enabling research in data-driven computational research in digital design, fabrication and quality control.
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- PAR ID:
- 10173545
- Date Published:
- Journal Name:
- ASME 2019 14th International Manufacturing Science and Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2019-2882
