Cite as:
Wagemann, J.; Fierli, F.; Mantovani, S.; Siemen, S.; Seeger, B. &amp; Bendix, J. (2022): <b>Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education</b>. <i>Remote Sensing</i> <b>14</b>(14), 3359.

Resource Description

Title: Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education
FOR816dw ID: 493
Publication Date: 2022-10-10
License and Usage Rights:
Resource Owner(s):
Individual: Julia Wagemann
Individual: Federico Fierli
Individual: Simone Mantovani
Individual: Stephan Siemen
Individual: Bernhard Seeger
Individual: Joerg Bendix
There is a growing demand to train Earth Observation (EO) data users in how to access<br/> and use existing and upcoming data. A promising tool for data-related training is computational<br/> notebooks, which are interactive web applications that combine text, code and computational output.<br/> Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter<br/> notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has<br/> taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding<br/> principles from different fields (mainly scientific computing and Jupyter notebook research) to make<br/> the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow<br/> the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements<br/> to improve navigation and user experience, (iii) modularize functions to follow best practices for<br/> scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim<br/> for being reproducible. We see two areas for future developments: first, to collect feedback and<br/> evaluate whether the instructional design elements proposed meet their objective; and second, to<br/> develop tools that automatize the implementation of best practices.
| Big Earth data | Jupyter Notebooks |
Literature type specific fields:
Journal: Remote Sensing
Volume: 14
Issue: 14
Page Range: 3359
Metadata Provider:
Individual: Jörg Bendix
Online Distribution:
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