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Institutions have made significant investments to support public access to research data requirements; yet have little comparative data about these services, infrastructure, and costs. To address this need, the research team undertook a mixed-methods approach to understand the institution-wide expenses for research data management and sharing and began to draft an expense model for data management and sharing. This model is further useful for institutions that provide research data management and sharing.
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Dataset for Deep Domain Adaptation Eliminates Costly Data Required for Task-Agnostic Wearable Robotic Control
Associated validation data and code for the publication: Deep Domain Adaptation Eliminates Costly Data Required for Task-Agnostic Wearable Robotic Control
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- Award ID(s):
- 2328050
- PAR ID:
- 10689290
- Publisher / Repository:
- Georgia Tech Library
- Date Published:
- Format(s):
- Medium: X
- Location:
- https://repository.gatech.edu/entities/publication/d6798aa7-541e-4f6e-980e-4855cdd3f629
- Sponsoring Org:
- National Science Foundation
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