10.5880/GFZ.2.3.2022.002 eng dataset GFZ German Research Centre for Geosciences datapub@gfz-potsdam.de http://www.gfz-potsdam.de Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences pointOfContact 2024-01-08 urn:ogc:def:crs:EPSG:4326 GOCE ML-calibrated magnetic field data 2024-01-08 revision 2022-04-10 creation doi:10.5880/GFZ.2.3.2022.002 Styp-Rekowski, Kevin TU Berlin, Berlin, Germany author Michaelis, Ingo GFZ German Research Centre for Geosciences, Potsdam. Germany author Stolle, Claudia IAP - Leibniz Institute of Atmospheric Physics at the University of Rostock, Kühlungsborn, Germany author Baerenzung, Julien GFZ German Research Centre for Geosciences, Potsdam. Germany author Korte, Monika GFZ German Research Centre for Geosciences, Potsdam. Germany author Kao, Odej TU Berlin, Berlin, Germany author GFZ Data Services publisher The Gravity field and steady-state ocean circulation explorer (GOCE) satellite mission carries three platform magnetometers. After careful calibration, the data acquired through these can be used for scientific purposes by removing artificial disturbances from other satellite payload systems. This dataset is based on the dataset provided by Michaelis and Korte (2022) and uses a similar format. The platform magnetometer data has been calibrated against CHAOS7 magnetic field model predictions for core, crustal and large-scale magnetospheric field (Finlay et al., 2020) and is provided in the ‘chaos’ folder. The calibration results using a Machine Learning approach are provided in the ‘calcorr’ folder. Michaelis’ dataset can be used as an extension to this dataset for additional information, as they are connected using the same timestamps to match and relate the same data points. The exact approach based on Machine Learning is described in the referenced publication. The data is provided in NASA CDF format (https://cdf.gsfc.nasa.gov/) and accessible at: ftp://isdcftp.gfz-potsdam.de/platmag/MAGNETIC_FIELD/GOCE/ML/v0204/ and further described in a README. Styp-Rekowski TU Berlin, GFZ Potsdam Research Associate styp-rekowski@tu-berlin.de pointOfContact GOCE satellite machine learning platform magnetometers calibration Earth Observation Satellites > Earth Explorers > GOCE GCMD Platforms publication Earth Remote Sensing Instruments > Passive Remote Sensing > Magnetic Field/Electric Field Instruments > MAGNETOMETERS GCMD Instruments publication EARTH SCIENCE > SOLID EARTH > GEOMAGNETISM > MAGNETIC FIELD EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS NASA/GCMD Earth Science Keywords publication CC BY 4.0 CC BY 4.0 10.1186/s40623-022-01695-2 DOI IsSupplementTo 10.1186/s40623-020-01252-9 DOI Cites 10.5880/GFZ.2.3.2022.001 DOI Cites https://cdf.gsfc.nasa.gov/ URL References eng geoscientificInformation -180 180 -90 90 2009-11-01 2013-09-30 http://doi.org/10.5880/GFZ.2.3.2022.002 WWW:LINK-1.0-http--link Data Access - DOI Data Access - DOI information The data was recorded onboard the GOCE satellite mission with varying time intervals of the differ-ent subsystems measuring. The magnetometer measurements (16s intervals) were aligned to match the closest position measurement (1s intervals) and interpolated accordingly. All other avail-able data of different intervals was interpolated and aligned to the same timestamps. The data was calibrated using a Machine Learning approach involving Neural Networks, the whole method of calibration is described precisely in the referenced publication. The data was mainly processed for its calibration which yields a lower residual compared to a refer-ence model than the uncalibrated data, more details about the many steps involved can be found in the referenced publication.