I am an independent consultant, educator and community-builder who works across strategy, user engagement and training of Big Earth Data and Emerging Technologies. My work is in the intersection between data providers and users aiming to make large volumes of Earth data better accessible and used. For the past four years, I have trained more than 2000 Earth Observation and climate practitioners.
I have been working with Jupyter notebooks since 2014 and have developed more than 100 educational workflows on topics such as access and analysis of large volumes of Earth data and Machine Learning.
Previously, I worked for the European Space Agency and the European Centre for Medium-Range Weather Forecasts.
Jupyter notebooks are a popular choice for training and teaching data-intensive science, such as training users of large volumes of Earth Observation data. Computational notebooks, including Jupyter, are in particular valued for facilitating reproducibility and collaboration. However, quantitative analyses and empirical research have identified unique challenges when it comes to using them. Critics claim that notebooks foster bad coding practices due to the possibility of an out-of-order execution of cells, that only a small percentage of notebooks hosted on Github are in fact reproducible and that annotations are not evenly distributed and do not reach the objective of well-described computational narratives.
These findings are the motivation to develop and share best practices to make Jupyter notebooks more educational and reusable. During the development of a Jupyter-based training course on Earth Observation data (Learning Tool for Python (LTPy)), we defined and applied five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make these 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.
In this talk, we will share with you five guiding principles to make Jupyter notebooks educational and reusable and for each principle we share a practical example of how it can be applied and implemented. The guiding principles have also been published in the Journal of Remote Sensing.
Copernicus, as the European Union’s Earth Observation program ; is providing an unprecedented amount of data and added-value information on any aspect of terrestrial environment and climate, including data from new generation satellites, reanalysis, historical datasets and forecasts. This is enabling new science, services, operational applications and businesses in the fields of meteorology, climate, air quality, oceanography and, at large, environmental monitoring.
TheJupyter ecosystem is becoming central to support users in discovering, using and analysing this data.
The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is an implementer of the Copernicus programme and this talk will show how Jupyter is used to provide know-how and access toCopernicus Earth Observation data on the following aspects:
Training and awareness - jupyter-based platforms used in events to self-paced approach over a wide range of domains, including atmosphere, climate change, and marine. For example in 2022 have been held more than 50 international events making use of Jupyter environment with more than 5000 users
Data ready - using jupyter notebooks to showcase applications and grant a seamless access to data - providing notebooks and access to hubs through advanced and open platforms .
Among these, the European Weather Cloud and the WEkEO Copernicus Data and Information Access Services (DIAS) as collaboration among key international organisations and agencies (ECMWF, EUMETSAT, EEA and Mercator Ocean International)
Communications and outreach- make use of Jupyter to engage with a large public scientifically literate. This includes competition of jupyter notebooks applied to innovative data analysis and access and practical sessions using Jupyter Notebooks in largely attended massive on-line courses (MOOCs)