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Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present t...

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Autoren: Wagemann, Julia, Fierli, Federico, Mantovani, Simone, Siemen, Stephan, Seeger, Bernhard, Bendix, Jörg
Formato: Artigo
Idioma:inglés
Publicado: Philipps-Universität Marburg 2023
Schlagworte:
training
Jupyter
atmospheric composition
Earth Observation data
Geografie, Reisen
computational notebooks
Python
Geography & travel
Acceso en liña:Texto completo PDF
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Número de Clasificación: urn:nbn:de:hebis:04-es2023-00377
Data de Publicación: 2023-04-03
Fonte: Erstveröffentlichung: Wagemann, J.; Fierli, F.; Mantovani, S.; Siemen, S.; Seeger, B.; Bendix, J. Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education. Remote Sens. 2022, 14, 3359. https://doi.org/ 10.3390/rs14143359
Downloads: 28 (2025), 95 (2024), 59 (2023)
Lizenz: https://creativecommons.org/licenses/by/4.0
URL de acceso: https://archiv.ub.uni-marburg.de/es/2023/0037
https://doi.org/10.3390/rs14143359

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