Other language confidence: 0.5374757858258643
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).
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
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.
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.
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.
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.
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.
This publication contains software that can be used to pre-process data from the Globe at Night citizen science project, and then run an analysis to determine the rate of change in sky brightness. The software requires input data, which can be obtained directly from Globe at Night. The data used for our publication "Citizen scientists report global rapid reductions in the visibility of stars from 2011 to 2022" is published here, and can be used as input to the software. The process requires access to the World Atlas of Artificial Night Sky Brightness, which is also available from GFZ Data Services.
The success of scientific projects increasingly depends on using data analysis tools and data in distributed IT infrastructures. Scientists need to use appropriate data analysis tools and data, extract patterns from data using appropriate computational resources, and interpret the extracted patterns. Data analysis tools and data reside on different machines because the volume of the data often demands specific resources for their storage and processing, and data analysis tools usually require specific computational resources and run-time environments. The data analytics software framework DASF, developed at the GFZ German Research Centre for Geosciences (https://www.gfz-potsdam.de) and funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (https://www.digitalearth-hgf.de/), provides a framework for scientists to conduct data analysis in distributed environments. The data analytics software framework DASF supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others. DASF supports many programming languages and platforms since the implementation of the protocol uses WebSockets. It provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language. In order to share a python method or class, users add an annotation in front of it. In addition, users need to specify the connection parameters of the message broker. The central message broker approach allows the method and the client calling the method to actively establish a connection, which enables using methods deployed behind firewalls. DASF uses Apache Pulsar (https://pulsar.apache.org/) as its underlying message broker. The Typescript bindings are primarily used in conjunction with web frontend components, which are also included in the DASF-Web library. They are designed to attach directly to the data returned by the exposed RPC methods. This supports the development of highly exploratory data analysis tools. DASF also provides a progress reporting API that enables users to monitor long-running remote procedure calls. One application using the framework is the Digital Earth Flood Event Explorer (https://git.geomar.de/digital-earth/flood-event-explorer). The Digital Earth Flood Event Explorer integrates several exploratory data analysis tools and remote procedures deployed at various Helmholtz centers across Germany.
Monitoring Velocity Changes using Ambient Seismic Noise SeisMIC (Seismological Monitoring using Interferometric Concepts) is a python software that emerged from the miic library. SeisMIC provides functionality to apply some concepts of seismic interferometry to different data of elastic waves. Its main use case is the monitoring of temporal changes in a mediums Green's Function (i.e., monitoring of temporal velocity changes). SeisMIC will handle the whole workflow to create velocity-change time-series including: Downloading raw data, Adaptable preprocessing of the waveform data, Computating cross- and/or autocorrelation, Plotting tools for correlations, Database management of ambient seismic noise correlations, Adaptable postprocessing of correlations, Computation of velocity change (dv/v) time series, postprocessing of dv/v time series, plotting of dv/v time-series
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