The Incubator Lab fosters novel data science developments for ESS in dedicated focused projects. The objective of this task is to steer the exploration of new, potentially relevant building blocks to be included in NFDI4Earth and related NFDIs. Examples are tools for automatic metadata extraction and annotation, semantic mapping and harmonization, machine learning, data fusion, visualization, and interaction. The Incubator Lab also serves as a forum where novel requirements can be formulated and trends presented in terms of a user consultation process. In this way, scouting for new trends and opportunities is achieved. The forum will materialize in annual meetings of NFDI4Earth-Experiment, where both achievements will be presented (e.g. from Lab projects but also from Pilots) and demands will be formulated (e.g. from the participants) which will trigger new ideas and potential projects. The results of the projects as well as the consultation process will be continuously monitored, evaluated and updated, resulting in a living document that describes current and future trends and records their implementation. The measure lead must oversee and monitor that compliance rules concerning the software and infrastructural developments are fulfilled while at the same time innovative blue sky developments should also be encouraged.
Note: The first round of incubator projects is closed. There will be a new call launched in 2023. Stay tuned !
If you are interested in current or future incubator projects, please
contact the coordination office.
For contact
persons of specific projects see descriptions below.
Domain: Atmospheric Science, Oceanography and Climate Research
Contact: Marco Kulüke, Deutsches Klimarechenzentrum (DKRZ) 
Cooperators: Stephan Kindermann, DKRZ;
Tobias Kölling,
Max-Planck Institute for Meteorology.
Duration: 4 months
Making data FAIR requires not only trusted repositories but also trusted workflows between data providers and infrastructure providers. Limited data access, unintentional and unnoticed data changes or even (overlooked) data loss pose great challenges to those involved. This incubator project aims to mitigate these challenges by exploring an easy-to-use data management service for researchers based on the InterPlanetary File System (IPFS), an emerging distributed web technology, which ensures data authenticity and fault-tolerant remote access. Based on a transferable prototypical implementation to be built within the DKRZ infrastructure, the suitability of the IPFS for a distributed and secure "web" for research data is being examined.
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Domain: Mineralogy, Petrology and Geochemistry
Contact: Artem Leichter, Institute of Cartography and Geoinformatics, Leibniz University Hannover
Cooperators: Renat Almeev and Francois Holtz, Institut of Mineralogy, Leibniz University Hannover
Duration:
5 months
Creating training datasets for machine learning (ML) applications is always time consuming and costly. In domains where a high degree of expertise is required to generate the reference data, the corresponding costs are high and thus slow down the use of artificial intelligence (AI) systems. This proposal focusses on automated mineralogy and will provide tools to characterize the microscopic textural and mineralogical features of thin sections of rocks using back scattered electron images. Our goal is to address this problem with a data mining application where unsupervised methods in combination with expert users generate reference data without additional effort and cost for explicit labeling. The tools will be developed so that it can be used by scientists that have not a profound knowledge of ML.
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Domain: Atmospheric Science, Oceanography and Climate Research
Contact: Ankita Ravi Vaswani, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon
Cooperators: Klas Ove Möller, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon
Duration:
6 months
Advances in high-throughput in situ imaging offer unprecedented insights into aquatic ecosystems by observing organisms in their natural habitats. However, unlocking this potential requires new analysis tools that transcend species identification to reveal morphological, behavioral, physiological and life-history traits. We will develop, document and validate an image analysis pipeline for semi-automated functional trait annotation, apply it to zooplankton in a continuously monitored North Sea region, and train a neural network for full automation. We foresee that these tools will enable new avenues of investigation in aquatic research, ecosystem modelling and global biogeochemical flux estimations, revealing previously inaccessible relationships between species biodiversity, zooplankton traits and seasonal variations in environmental conditions.
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Domain: Geography
Contact: Benjamin Risse, Institute for GeoInformatics, University of Münster
Duration: 6 months
The field of artificial intelligence can roughly be categorized into Machine Learning-based and Knowledge-based approaches. In the proposed incubator project we want to combine the strengths of both approaches. As a first step for such hybrid systems we want to develop a tool for using knowledge graphs to annotate training data with environmental features for making machine learning more reliable and transparent.
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Domain: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
Contact: Hao Li, Chair of Big Geospatial Data Management, Department of Aerospace and Geodesy, Technical University of Munich
Cooperators: Martin Werner, Chair of Big Geospatial Data Management, Department of Aerospace and Geodesy, Technical University of Munich
Duration:
6 months
Humans rely on clean water for their health, well-being, and various socio-economic activities. To ensure an accurate, up-to-date map of surface water bodies, the often heterogeneous big geodata (remote sensing, GIS, and climate data) must be jointly explored in an efficient and effective manner. In this context, a cross-platform and rock-solid data representation system is key to support advanced water-related research using cutting-edge data science technologies, like deep learning (DL) and high-performance computing (HPC). In this incubator project, we will develop a novel data representation system based on Hierarchical Data Format (HDF), which supports the integration of heterogeneous water-related big geodata and the training of state-of-the-art DL methods. The project will deliver high-quality technical guidelines together with an example water-related data repository based on HDF5 with the support of the BGD group in TUM, with which the NFDI4Earth will consistently benefit from this incubator project since the solution can serve as a blueprint for many other research fields facing the same big data challenge.
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