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prosa / yamdb
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Simone Vadilonga / wavepy
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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.
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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.
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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.
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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.
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A Django-App to manage a THREDDS Server
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A Django-App to manage a THREDDS Server
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A Python Ecosystem for Harvesting Datasets information from Thredds Data Server and Cultivating STAC-Metadata.
Please have a look at https://tds2stac.readthedocs.io for the documentation.
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Collects data from different sources and prepares it for further usage in statistical analysis. Mainly aimed to help with creating the relevant statistics for the yearly report but also extendable for different use-cases. Current status: POC/WIP
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CAT4KIT / DS2STAC / STA2STAC
European Union Public License 1.2A Python Ecosystem for Harvesting Time Series data information from SensorthingsAPI (STA) and Cultivating STAC-Metadata.
Please have a look at https://sta2stac.readthedocs.io/ for the documentation.
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This repository is designed to systematically archive SQL-based backups of two distinct databases: the pgSTAC production database and the Django REST Framework (DRF) PostgreSQL database. Each backup is compressed into a tar.gz format and managed using Git Large File Storage (LFS) to efficiently handle large files. The archival process is automated, with the server configured to periodically push the latest backup data to the repository, ensuring that the repository remains up-to-date with the most recent snapshots of the databases. This setup not only optimizes storage through compression but also leverages Git LFS for scalable management of binary data, providing a robust solution for maintaining critical database backups.
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