Data science for Space Weather services in Argentina
Space Weather services rely heavily on the data. Challenges include multiple data sources, multiple formats (not always structured data), raw data (direct from the instruments), different data resolutions (in time and in space), poor metadata, data missing (instrument failure, connectivity issues, e...
Guardado en:
Autores principales: | , , , , , , , , |
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Formato: | Objeto de conferencia |
Lenguaje: | Inglés |
Publicado: |
2021
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Materias: | |
Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/140164 http://50jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-17.pdf |
Aporte de: |
Sumario: | Space Weather services rely heavily on the data. Challenges include multiple data sources, multiple formats (not always structured data), raw data (direct from the instruments), different data resolutions (in time and in space), poor metadata, data missing (instrument failure, connectivity issues, etc.), bad calibrated data, among many other issues. Bearing in mind the above considerations, we present in this work the main data pipeline design and implementation details for the Tucumán Space Weather Center - TSWC (https://spaceweather.facet.unt.edu.ar/), Universidad Nacional de Tucumán in Argentina as a new web-based system for Space Weather services. |
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