A Data Management and Sharing Plan authored by Jonathan Juliano and harvested by ODUM. The plan describes the scientific data to be generated and/or used in research on predicting the spread of antimalarial drug resistance using deep learning surrogates. It outlines a strategy for managing and sharing project data.
Use Cases
- Designing data governance workflows for AI-driven health research based on the described management plan.
- Planning data sharing protocols for collaborative projects on infectious disease modeling.
- Structuring metadata and documentation for datasets used in deep learning surrogates for drug resistance.
Strengths
- Plan authored by a named researcher, Jonathan Juliano, providing accountability.
- Focuses on a specific and high-impact research topic: predicting antimalarial drug resistance spread.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
Provenance
- Source
- ODUM Harvested Dataverse
- Collection Method
- Harvested from a Dataverse repository.
- Time Range
- null
- Freshness
- Last updated 2026-04 27 03:11:14; freshness should be verified.
- Geography
- null