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The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform: Next-Generation Collaboration Tool for Scientific Algorithms and Datasets
  • +10
  • Laura Jewell,
  • George Chang,
  • Hook Hua,
  • Manil Maskey,
  • Rahul Ramachandran,
  • Kaylin Bugbee,
  • Laura Duncanson,
  • Marco Lavalle,
  • Aimee Barciauskas,
  • Chris Lynnes,
  • Clement Albinet,
  • Amanda Whitehurst,
  • Bjoern Frommknecht
Laura Jewell
Jet Propulsion Laboratory

Corresponding Author:laura.a.jewell@jpl.nasa.gov

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George Chang
Jet Propulsion Laboratory
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Hook Hua
Jet Propulsion Laboratory
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Manil Maskey
University of Alabama in Huntsville
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Rahul Ramachandran
NASA Marshall Space Flight Center
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Kaylin Bugbee
University of Alabama in Huntsville
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Laura Duncanson
University of Maryland
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Marco Lavalle
Jet Propulsion Laboratory
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Aimee Barciauskas
Development Seed
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Chris Lynnes
NASA Goddard Space Flight Center
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Clement Albinet
ESA
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Amanda Whitehurst
ASRC Federal Holding Company
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Bjoern Frommknecht
European Space Research Institute
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Abstract

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process within the platform, from the conception of a hypothesis to the creation of scientific results in a version-controlled and reproducible environment. Users can document their process, collaborate with other users, and share algorithms and datasets. We will conclude with an outlook on the features that the next phase of development will bring to the platform.