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Airborne Wind and Eddy Covariance Dataset - Recorded with the ASK-16 EC Platform between 2017 – 2022

This data publication contains airborne wind and eddy covariance data files, that were recorded with the ASK-16, a motorized glider owned by the FU Berlin, Germany. These data files include a large range of meteorological variables (wind speed, direction, temperature, humidity, etc.), positioning information, but also information on atmospheric chemistry (mainly methane concentration, carbon dioxide concentration, water vapor concentration) and turbulent matter (CH4 and CO2) and energy fluxes (latent heat flux) is available. Measurements were recorded between 2017 and 2022 to: (1) obtain three-dimensional wind vectors in within the atmospheric boundary layer (2) calibrate of wind measurements (3) record turbulent energy and matter fluxes A lot of these data files have been used in the publication “The ASK-16 Motorized Glider: An Airborne Eddy Covariance Platform to measure Turbulence, Energy and Matter Fluxes (to be published in atmospheric measurement techniques)” by Wiekenkamp et al., 2024a. This publication also provides a lot of additional details on the measurement system, the data handling and processing.

gravitools - A collection of tools to analyze gravimeter data

This Python package is a collaborative effort by the gravity Metrology group at the German Federal Agency for Carthography and Geoesy (BKG) and the Hydrology section at GFZ Helmholtz Centre for Geosciences. It comprises functionalities and features around the respectively new instrument type of a Quantum Gravimeter (here AQG). New (standardized) instrument data format additional to new measurement and processing concepts lead to the first collection of scripts and now complete python package for a fully-featured analysis of AQG data. This encompasses live-monitoring while the instrument is actually measuring (with enhanced functionality than what is provided by the manufacturer), data processing, visualizations as well as archiving data, fulfilling the idea of reproducible data within FAIR principles. Many of these functionalities and concepts also apply to other gravimeter types. It is thus planned to include also access and processing of data for these other devices (starting in the near future with CG-6 relative gravimeters). This package is actively maintained and developed. If you are interested in contributing, please do not hesitate to contact us. Please find instructions for its installation and usage in the documentation or git repository, linked in the left panel. gravitools is listed in the python standard repository database "PyPi". Some highlight features, available in the first official stable release are: • Read and process raw data of the Exail Absolute Quantum Gravimeter (AQG) • Apply standardized or customized AQG data processing and outlier detection • Read and write processed datasets with metadata to .nc-files in NETCDF4-format • Handle Earth orientation parameters (EOP) from iers.org for polar motion correction • Visualize data with matplotlib • CLI for standard processing of AQG raw data to .nc-file • Dashboard for real-time processing and visualization during measurements (on AQG laptop) • Dashboard includes a proposed standard template for a measurement protocol • Standardized, easy-to-read and modify config files for processing options and reproducible data handling • Generation of PDF reports from individual measurements

A Database of Centrifuge Analogue Models Testing the Influence of Pre-Existing Weak Zones During Continental Compression

This dataset presents the raw data of an experimental series of centrifuge models performed to test the influence of pre-existing weak zones in the lower crust (herein after referred to as Weak Lower Crust –WLC) during continental compression. We varied the width of the WLC, the dip of the interfaces bounding the WLC and the frictional properties at the WLC-LC interface by using lubricant (vaseline). In this dataset, we provide four different types of data, that can serve as supporting material and can be used for further analysis: 1) The top-view photos, taken at different stages and showing the deformation process of each model; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models; 3) Line-drawing of fault and fracture patterns to be used for fault statistical quantification; 4) A Python script to draw swath profiles (outputs) of the analogue models. Further details on the modelling strategy can be found in the publication associated with this dataset and in Milazzo et al. (2021), using a similar setup for achieving compression in the centrifuge. Materials used for these analogue models were described in Corti (2012), Montanari et al. (2017), Del Ventisette et al. (2019), Zou et al. (2024) and Wan et al. (2025).

Datasets of analog modeling results for the V-shaped opening of the South China Sea: 3D DEMs and PIV results

This dataset compiles quantitative outputs from eight sandbox experiments conducted under different boundary conditions (differential extension, strong blocks, and a weak zone). It contains 3-D scanning–derived digital elevation models (DEMs) from the final stage of experiments simulating the V-shaped opening of the South China Sea. In addition, it includes particle image velocimetry (PIV) products at four extension states (25 mm, 50 mm, 75 mm, and 100 mm), together with the plotting codes used to generate the figures.

geogravL3 - a Python Package for Processing Earth Gravity Field Data

This package processes Earth gravity field data—provided as spherical harmonic coefficients—into gridded, domain-specific datasets. It also includes uncertainty estimation and the generation of regional mean time series.

PyWingpod

In the last years, a whole series of codes has been developed to process airborne wind data. Initially, the PyWingpod package was mainly build to handle data from the Wingpod of the ASK-16 motorized glider of the FU Berlin. However, due to the modular buildup of the package, functions within the different libraries can also be used to process data from other airborne platforms. Functions and scripts within PyWingpod have been developed to: a. load and process airborne five hole probe and meteo data, this includes (1) 5 hole probe pressure sensor data (static pressure, dynamic pressure and the differential alpha and beta pressure), (2) INS-GNSS data, (3) Temperature and humidity data and (4) any auxillary data that you want to add to the time series/ data frame. b. calibrate pressure sensor data from the five hole probe (mainly to correct for any effect of aircraft movement) c. calculate a reliable wind vector based on the available data that are specified in a. and the calibration parameters, which are obtained in step b.

FlotteKarte - a Python library for quick and versatile cartography based on PROJ4-string syntax and using Matplotlib, NumPy, and PyPROJ under the hood

FlotteKarte is a low-overhead plotting routine using Matplotlib, NumPy, and PyPROJ under the hood. The conceptual idea behind this package is that a map is fully defined through the 2D cartesian coordinates that result from applying the map projection to different geographical data. For displaying data on a two-dimensional canvas, Matplotlib is a powerful tool. Conversion between geographic and projected coordinates can easily be done using PyProj. The gap between these two powerful tools and a polished map lies in potential difficulties when translating spherical line topology to 2D cartesian space, and by introducing typical map decorations such as grids or ticks. FlotteKarte aims to fill this gap with a simple interface. FlotteKarte's philosophy is to work completely within the 2D projected coordinates, that is, very close to the projected data. If projected coordinates of data can be obtained, the data can be drawn directly on the underlying Matplotlib Axes. The Map class can then be used to add typical map decoration to that axes using information that it derives from the numerics of the PROJ projection.

Assetmaster and Modelprop: web services to serve building exposure models and fragility functions for physical vulnerability to natural-hazards

Assetmaster and Modelprop are WPS (Web Processing Services) software components written in Python 3. They are implementing two of the several steps of a multi-hazard scenario-based decentralized risk assessment for the RIESGOS project. The reader can find more details in https://github.com/riesgos. Assetmaster provides as output a structural exposure model defined in terms of risk-oriented building classes (for a reference geographical region) in GeoJSON format. The simple service is based on an underlying exposure model in GeoPackage format (.gpkg). Modelprop provides as output for each defined building class the correspondent fragility function. The python code implementing the service can also be run locally in your computer to assess the physical vulnerability of a given building portfolio computing the direct financial losses associated to hazard and multi-hazard scenarios making use of the DEUS program. It is available in: https://github.com/gfzriesgos/deus/.

Rheometric Analysis of Viscous Material Mixtures Used in the Tectonic Laboratory (TecLab) at Utrecht University, Netherlands

This dataset contains measurements of viscous and viscoelastic materials that are used for analogue modelling. Proper density and viscosity scaling of ductile layers in the crust and lithosphere, requires materials like Polydimethylsiloxane (PDMS), to be mixed with fillers and low viscoity silicone oils. Changing the filler content and filler material, the density, viscosity and power-law coefficient can be tuned according to the requirements. All materials contain a large amount of PDMS and all but one a small amount of an additional silicone oil. Adding plasticine or barium sulfate lead to shear thinning rheologies with power-law exponents of p<0.95. Adding corundum powder only has a minor effect on the power-law exponent. Some mixtures also have an apparent yield point but all are in the liquid state in the tested range. In general, the rheologies of the materials are very complex and in some cases strongly temperature dependent. However, in the narrow range of relevant strain rates, the behaviour is well defined by a power-law relation and thus found suitable for simulating ductile layers in crust and lithosphere.

DAS Convert - Convert distributed acoustic sensing data

Convert and downsample distribute acoustic sensing (DAS) data acquired by Silixa iDAS or ASN OptoDAS to seismological data formats. Main purpose is to quickly convert and downsample massive amounts of high-resolution DAS data to MiniSEED and other seismological data formats. To handle the massive amount of data generated by DAS interrogators, the conversion tool is leveraging parallel I/O and multi-threaded signal-processing. A high throughput can be archived while converting and downsampling data in parallel threads. The tool can interact with tape storage systems and messaging bots to monitor the conversion process. The signal processing routines are based on Pyrocko, a mature and well tested seismological framework.

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