NFDI4Earth Educational Pilots

We fund the Earth system science community to create educational content through annual calls for educational pilots. The outcomes will be available through the NFDI4Earth educational portal for the public. The submitted proposals are evaluated by NFDI4Earth co-applicants according to the following criteria (read the full guideline here):

a.‎ ​Relevance to NFDI4Earth ‎
b.‎ State of the art content
c.‎ Novelty (addressing the gaps in existing OERs in ESS)‎
d.‎ Use of active teaching methods ‎
e.‎ Relevance to RDM in ESS
f.‎ Potential for integration into NFDI4Earth curricula

Here's a peek at the educational pilots that have made the cut year after year, each adding something special to our understanding of Earth system sciences:

Image pre-processing,‎ feature generation and ‎classification in remote sensing

The increasing availability of satellite data in recent years opens up new applications in many areas of ‎environmental science. The processing of large amounts of data, especially satellite data, is one of the ‎most important pillars of environmental monitoring. However, they also require extensive knowledge and ‎appropriately trained personnel. Educational institutions such as universities have the task of adapting to ‎these requirements. This adaptation must include all steps of the complex process chain for processing ‎satellite data. In this context, it is important not only to train technical skills, but also that methodological ‎competencies enable students to critically evaluate their own work steps. To reduce the complexity, a ‎modular structure is used, which also makes it possible to take into account existing skills in the data ‎processing chain.‎
At the latest due to the restrictions by the Corona pandemic digital teaching and learning opportunities ‎experienced an enormous boost. Experience has shown that flexible content can be an essential element ‎in motivating learning. The growing importance of MOOCs impressively underlines this development. These ‎developments and effects represent an opportunity to transform the necessary content of satellite data ‎processing into teaching and self-learning materials.‎
The objective is to develop Jupyter notebooks as self-learning material which provide a processing chain ‎of common classification task with remote sensing data.‎
The project comprises three main work stages, whereby the technical implementation of the ‎modules is the central element, namely methodological development of learning modules on the ‎process flow of processing satellite data, technical implementation of the modules in Jupyter ‎notebooks with example datasets, and testing and assessment of the modules with MSc students ‎in Environmental Sciences at TU Dresden.‎

AquaFortR: Streamlining Atmospheric Science, ‎Oceanography, Climate, and Water Research with ‎Fortran-accelerated R

Generally, simulation and modelling of the environmental processes are accomplished on the grid ‎level in which the investigation region is discretized to numerous grid points in the three ‎dimensions of space plus time. Consequently, these simulations produce enormous data sets and ‎processing this data extends beyond the current average Personal Computer capacity. However, ‎only some people have access to high-performance computing centers. Additionally, the ‎possibility of speeding up calculations and modelling exists in each PC through compiled ‎programming languages such as Fortran. This solution speeds up computations and can reduce ‎the CO2 footprint drastically.‎
R is one of the languages widely used in data analysis, visualization, and presentation, and it has a ‎wide supporting community and thousands of packages. Nevertheless, Fortran is one of the ‎fastest-performing languages -if not the fastest in number crunching- and one of the oldest. Due ‎to the latter, interest in Fortran is constantly low. Considering all the above, the need for ‎educational material that links R and Fortran is essential.‎
This project aims to provide one OER platform that will be a one-stop for all R users looking for ‎speed in general and users from Environmental Science disciplines in particular.‎
Many developers have made efforts to speed up R using C++; however, integration of Fortran ‎and R in such a package has yet to exist, to the best of the authors' knowledge. Filling this gap is ‎important because Fortran is well-suited for numerical and scientific computations due to its array ‎processing capabilities, performance, and efficiency. Commonly, computationally demanding ‎models are written in Fortran; thus, integrating Fortran and R will allow environmental modelers ‎and researchers to minimize changes between different programming languages.‎

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