The software is provided as an executable python module. The software automatically analyzes the files present in the data publication. The results are saved in the form of the images presented in the main publication. Each figure is implemented as a dedicated function that first loads the necessary data, then does some processing steps, such as curve fitting, and then plots the outputs in the desired layout. A 'main' function calls all figure functions sequentially. However, the packages is modular so that each individual plot has a standalone function which could be used with other, similarly structured data. Several submodules provide additional data for plotting, e.g. the 'groups' submodule that contains naming schemes and the densities for all samples.
Physical samples (or specimen or artefacts) represent the origin of research results in many scientific disciplines. Assigning persistent identifier (PID) to samples is a fundamental step to make them discoverable and traceable in unambiguous way over the Web. The International Generic Sample Number (IGSN) is a PID for physical samples and connecting these with their online description following a dedicated metadata schema.
Sample descriptions of samples are available in various formats and detail. In order to publish them in a standardized manner and to automate and standardize the preparation and processing, the software product SAMIRA (Sample IGSN Registration Automation) was created as part of the Project FAIR WISH, funded by the Helmholtz Metadata Collaboration (HMC).
SAMIRA aims to automate the generation of Metadata XML-Files for the Registration of PIDs from different input sources (e.g. the FAIR Samples Template, Wiezcorek et al., 2023). This first version of SAMIRA implements the creation of IGSN metadata and Datacite metadata and the respective registration.
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.