References

Papers

• Ueckermann, M., Bieszczad, J., Entekhabi, D., Shapiro, M. L., Callender, D., Sullivan, D. and Milloy, J., “PODPAC: Open-Source Python Software for Enabling Harmonized, Plug-and-Play Processing of Disparate Earth Observation Data Sets and Seamless Transition Onto the Serverless Cloud by Earth Scientists,” Earth Science Informatics, Vol. 13, No. 3, 2020, pp. 1507–1521.

Presentations

• Scipy 2020: Geospatial Analysis in the Cloud Using PODPAC and JupyterLab

• AMS 2020: PODPAC: The Easy Way to Analyze Earth Science Data in the Cloud

• AGU 2019: Building Web Browser Apps for On-Demand Retrieval and Processing of Cloud-Optimized Earth Science Data using the Open-Source WebESD Toolkit

• AMS 2018: A RESTful API for Python-Based Server-Side Analysis of High-Resolution Soil Moisture Downscaling Data

• AGU 2018: Use of the Open Source PODPAC Library for Remote, Cloud-Based Data Analysis, Visualization, and Collaboration in a Web Browser

Posters

• AMS 2023: Open Source Serverless Architecture for Sharing Earth Science Data

• AGU 2021: General Server for Rapid Publishing of OGC-Compliant Earth Science Data Products

• AMS 2020: PODPAC: The Easy Way to Analyze Earth Science Data in the Cloud

• AMS 2019: PODPAC: A Python Library for Automatic Geospatial Data Harmonization and Seamless Transition to Cloud-Based Processing

• AGU 2018: Seamless Transition of Data Analyses and Analytics from a Local Workstation to Scalable, Massively Distributed Processing on the Cloud Using the Open Source PODPAC Library