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Data Dana

The Data-Hungry Scientist

Role

Research Scientist / Data Analyst

Description

Data Dana is a mid-career scientist who analyzes Landsat and Sentinel-2 imagery to study environmental changes. She's comfortable writing Python code but frustrated by hours spent downloading, reformatting, and preprocessing data before actual analysis.

Key Needs

✓Cloud-optimized data formats (COG, Zarr) to eliminate download bottlenecks
✓STAC catalog for discovering and querying available datasets
✓JupyterHub environment with pre-configured libraries and scalable compute
✓Example Jupyter Notebooks to accelerate her learning curve
✓Help desk support when encountering technical issues

Pain Points

✗Time lost to data wrangling instead of science
✗Local computing resources insufficient for large-scale analysis
✗Dependency conflicts when setting up analysis environments

Researcher Accelerator

Eliminates data preparation bottlenecks so scientists can focus on analysis. Combines cloud-optimized data access, scalable computing environments, and expert support into a seamless research platform.

Bundled Core Services

Data Services →

• Cloud-Optimized GeoTIFFs, Zarr stores
• STAC catalog access
• Statistics API

Algorithm Development & Data Integration →

• Version-controlled environments
• Data harmonization
• Multi-mission integration

Cloud Computing →

• JupyterHub deployment
• Scalable storage/memory
• Library dependency resolution

Content Curation →

• Example Jupyter Notebooks
• Data access workflows

User Services →

• Help desk support
• Community forum access

📊 Publisher Pete

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