EarthyScience
- 35 followers
- Jena, Germany
- https://www.bgc-jena.mpg.de/en/bgi/home
The EarthyScience organization at theMax Planck Institute for Biogeochemistry is committed to advancing our understanding of Earth's biogeochemical processes through cutting-edge research. Our scientific mission focuses on integrating long-term in-situ observations with Earth observations collected via aircraft and satellites, spanning multiple spatial scales. To analyze and interpret these complex datasets, we combine data-driven machine learning approaches with theory-driven mechanistic modeling.
This GitHub organization serves as a central hub for our software development efforts, hosting and linking to various tools and packages that support our research. Our repositories feature software designed to process, analyze, and visualize large, multidimensional environmental datasets. One prominent example isYAXArrays.jl, a Julia package optimized for handling large-scale, multidimensional datasets common in Earth system science.
Our interdisciplinary team, with expertise in environmental science, data science, and software engineering, is dedicated to developing advanced tools for Earth system research. By making our software open-source and accessible on this platform, we aim to foster collaboration within the scientific community and drive progress in biogeochemistry and related fields.
We invite contributions from researchers and developers with interests in Earth system science, big data analysis, and scientific software development. Our repositories reflect our commitment to open science and reproducible research, advancing the study of Earth's complex biogeochemical systems.
Popular repositoriesLoading
- EasyHybrid.jl
EasyHybrid.jl PublicEasyHybrid.jl provides a simple and flexible framework for hybrid modeling, enabling the integration of neural networks with process-based models.
- Bigleaf.jl
Bigleaf.jl Publicecosystem properties based on a 'big-leaf' representation of the vegetation
Repositories
- EasyHybrid.jl Public
EasyHybrid.jl provides a simple and flexible framework for hybrid modeling, enabling the integration of neural networks with process-based models.
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EarthyScience/RQADeforestation.jl’s past year of commit activity Uh oh!
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EarthyScience/RQADeforestation.py’s past year of commit activity - stacathome Public Forked fromMarkusZehner/stacathome
load satellite data from various sources into xarrays
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EarthyScience/stacathome’s past year of commit activity - HybridVariationalInference.jl Public
Extenting variation inference to hybrid, i.e process and machine learning, models
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EarthyScience/HybridSymbolics.jl’s past year of commit activity - DistributionFits.jl Public
Extension of Distributions.jl allows specifying distributions by aggregate statistics #1296
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EarthyScience/DistributionFits.jl’s past year of commit activity - UnpackSinTiles.jl Public
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EarthyScience/UnpackSinTiles.jl’s past year of commit activity
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