Laboratory for
Climatology and Remote Sensing

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.

Abstract:

The broad dissemination of open data policies for Big Earth data leads to a diversification of users. The literacy in data and data handling differs for each user group, resulting in different needs and requirements. In addition, the development of cloud-based data systems challenges traditional workflows of all users. In order to tailor new cloud-based data systems for Earth data users, it is of utmost importance to obtain a better understanding of users in terms of the type of data they explore, the applications they need the data for, the way they access and process data, and the challenges they face. This is an indispensable prerequisite to ensure an increased uptake of Big Earth data in the future. In order to get a better insight in the requirements and challenges of users coping with Big Earth data, we run a comprehensive web-based user survey. Our results, inclined to users of Big Earth data in Europe and the North American continent, reveal that a majority of survey respondents still download copies of data onto their local machine and handle and process data locally with a combination of programming and desktop-based software. However, survey respondents are facing severe problems related to the growing data volumes, the data heterogeneity and the limited processing capacities for their demanding applications. Thus, they show a specific interest in using cloud-based data services in the near future but express the need for an easier data discovery and the interoperability of data systems. Based on the survey findings, we draw a set of recommendations to make Big Earth data more FAIR (findable, accessible, interoperable and re-useable).