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KIT Seasonal Forecast Task Force / PyCast S2S
MIT LicenseUpdated -
KIT KIAOS / KITcube / AtmoProKIT
European Union Public License 1.2Updated -
HZB / ResearchDataManagement / SEPIA / SEPIA testenv
Apache License 2.0Updated -
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HIFIS / HIFIS Software Services / Education and Training / Workshop Materials / Python - First Steps
Creative Commons Attribution 4.0 InternationalAn introductory course for programming with Python
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HZB / ResearchDataManagement / SEPIA / SEPIA Backend
Apache License 2.0Updated -
m-team / nfdi / nfdi-aai-doc
Creative Commons Attribution Share Alike 3.0 UnportedUpdated -
CAT4KIT / Cat4KIT Docker
MIT LicenseThis repository provides a Docker Compose setup for automatically deploying and running the entire Cat4KIT service on any server.
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Software Project Templates / BugBusters
MIT LicenseUpdated -
PINE / PIMAV
GNU General Public License v3.0 or laterUpdated -
RODARE / RODARE
GNU General Public License v3.0 onlyRossendorf Data Repository - https://rodare.hzdr.de
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HZB / Bluesky / qt_gui / images / minimal-qt-gui-image
GNU General Public License v2.0 or laterUpdated -
m-team / ai / ai4os-yolov8-torch
GNU Affero General Public License v3.0Ultralytics YOLOv8 with DEEPaaS API
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The Linear Meta-Model optimization (LiMMo) is a Python-tool designed for the objective tuning of regional climate models (RCM) and numerical weather prediction models (NWPM) to gridded observational datasets.
In this approach, the surface 2D output of the RCM/NWPM is approximated using regression method (linear, piecewise-linear, quadratic). A user-defined error norm, which quantifies the difference between the regression approximation and the observations, is then minimized using a gradient-based optimization method.
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Micromagnetic modeling / TetraX
GNU General Public License v3.0 onlyFinite-Element Micromagnetic-Modeling Package
Documentation: https://www.tetrax.software
Help and discussions: https://discussions.tetrax.software
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