Deliver Dataset RASI

Dataset Information

Dataset name:
RASI

Data source

Is the dataset in NASA's Common Metadata Repository (CMR)?

• Yes, concept ID is: ________________
• No
• I don't know

Where will the data be located?

• Amazon Public Data Program Bucket
• PO.DAAC AWS Bucket
• GES-DISC AWS Bucket
• LPDAAC AWS Bucket
• ORNL AWS Bucket
• ASF AWS Bucket
• VEDA SMCE AWS Bucket
• Other: ________________
• I don't know

Please provide any additional details required to discover all the files for this dataset.

Please provide a link to an example data file:

[Enter URL here - e.g., Google Drive Link, S3 URI]

Dataset Extent

How frequently is this dataset updated?

• Sub-daily (hourly or more frequent)
• Daily
• Weekly
• Monthly
• Annually
• Irregularly/As available
• Static (never updated)
• Other: ________________

What is the temporal extent of the data?

What is the spatial extent of the data?

Requested services

What portal is requesting this dataset?

• VEDA Earthdata
• MAAP
• GHG Center
• Disasters
• AIR4US
• Water Insights
• Earth Information Explorer (EIE)
• Other: ________________

What capabilities and ODD services are of interest for this dataset? (Select all that apply)

• Assessment - help me decide how to deliver this dataset optimally
• Map visualization
• Timeseries visualization
• Statistics over an area and/or time-frame of interest
• Python access in the VEDA Hub
• QGIS access in the VEDA Hub
• ArcGIS access via EGIS
• Virtualization (aka virtual Zarr store)
• Structural assessment (how should this dataset be chunked and stored)
• Other: ________________
• I don't know

What is the target deadline for integrating this dataset?

• Next 1-3 months
• 3-6 months
• 6-12 months
• No specific deadline
• Other: ________________

What is driving this timeline?

[Enter details about what's driving your deadline - e.g., publication, conference, stakeholder requirements]

Progress Tracking

Create Sub-issues

• 1. Access data and exploration

  • Identify data source/location
  • Verify access permissions
  • Initial data inspection

• 2. Integration

  • Define ingestion pipeline
  • Visualize data using one of the tiling services

• 3. Exploring ingested data (Jupyter Notebook)

  • Create notebook for validation
  • Visualize dataset
  • Document findings

• 4. Benchmarking original vs ingested (Jupyter Notebook)

  • Compare performance

• 5. Demo

  • Prepare demo use case
  • Generate visual outputs
  • Share/demo with stakeholders

Other relevant information

Please share any other relevant information about this dataset.