Abstract:
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 the Learning Tool for Python (LTPy), which is a training course (based on Jupyter
notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has
taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding
principles from different fields (mainly scientific computing and Jupyter notebook research) to make
the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow
the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements
to improve navigation and user experience, (iii) modularize functions to follow best practices for
scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim
for being reproducible. We see two areas for future developments: first, to collect feedback and
evaluate whether the instructional design elements proposed meet their objective; and second, to
develop tools that automatize the implementation of best practices.
