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Model files for the Neural network-based model of Electron density in the Topside ionosphere (NET)

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Deep neural network enhanced global tropospheric zenith delay model

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The Brazilian gravimetric geoid: MAPGEO2015

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Electron density derived with the Neural-network-based Upper-hybrid Resonance Determination algorithm from the Van Allen Probes EMFISIS measurements

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AIMS: Automatisierte Erkennung und Kennzeichnung von Mikrobenpopulationen

Das Projekt "AIMS: Automatisierte Erkennung und Kennzeichnung von Mikrobenpopulationen" wird vom Umweltbundesamt gefördert und von Stiftung Alfred-Wegener-Institut für Polar- und Meeresforschung e.V. (AWI) durchgeführt. The projekts overall aim is to develop, test and apply analytical procedures for identifying and characterising phytoplankton and heterotrophic bacterial populations using analytical flow cytometry (AFC). By identification, we mean the use of objective procedures for differentiating amongst populations of phytoplankton and bacteria within complex natural microbial communities. This involves classifying a community of heterogeneous organisms into its component populations. Identification will be based on optical properties of single cells measured by AFC and verified using molecular biology. By characterisation, we mean the determination of cell abundance together with an analysis of the intrinsic optical and chemical properties of these cells. These intrinsic properties include cell size, cell light scattering cross-section, cell carbon content, and additionally for phytoplankton, the light absorption cross-section and cell chlorophyll a content. The output of the identification and characterisation procedures will be a data matrix summarising the intrinsic cell properties of objectively defined populations. The products of this research will have application in large scale initiatives such as remote sensing and modelling basin scale and global oceanographic processes.

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