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Docker-based setup for an THREDDS deployment

This repository provides a Docker Compose setup for automatically deploying and running the entire Cat4KIT service on any server.

This repository contains docker compose deployment files for the whole software stack behind the Helmholtz knowledge graph, work from the unHIDE initiative. Deployment includes containers for the harvesters and utility, the API, SOLR, Virtuoso, Web Frontend as well as for nginx and letsencrypt. More information on the unHIDE initiative, which was launched by the Helmholtz Metadata Collaboration (HMC), you can find under https://docs.unhide.helmholtz-metadaten.de.

Viable North Sea (ViNoS) is an Agent-based Model (ABM) of the German Small-scale Fisheries. As a Social-Ecological Systems (SES) model it focusses on the adaptive behaviour of fishers facing regulatory, economic, and resource changes. Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry. These fisheries are typically family-run operations that use smaller boats and traditional fishing methods to catch a variety of bottom-dwelling species, including plaice, sole, and brown shrimp.

Fishers in the North Sea face area competition with other uses of the sea---long practiced ones like shipping, gas exploration and sand extractions, and currently increasing ones like marine protection and offshore wind farming (OWF). German authorities have just released a new maritime spatial plan implementing the need for 30% of protection areas demanded by the United Nations High Seas Treaty and aiming at up to 70 GW of offshore wind power generation by 2045. Fisheries in the North Sea also have to adjust to the northward migration of their established resources following the climate heating of the water. And they have to re-evaluate their economic balance by figuring in the foreseeable rise in oil price and the need for re-investing into their aged fleet.

Rossendorf Data Repository - https://rodare.hzdr.de

Copy of gsi-geoff zweiter versuch from sep 17 to test rebasing

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.