Helmholtz Institute Freiberg for Resource Technology
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1328 research outputs found
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surfpy is a Python package for computing surface integrals over smooth embedded manifolds.
No full textSurfpy is a Python package for computing surface integrals over smooth embedded manifolds using spectral differentiation. Surfpy rests on curved surface triangulations realised due to kth-order interpolation of the closest point projection, extending initial linear surface approximations. It achieves this by employing a novel technique called square-squeezing, which involves transforming the interpolation tasks of triangulated manifolds to the standard hypercube using a cube-to-simplex transformation that has been recently introduced
Simulation data for "Cylindrical compression of thin wires by irradiation with a Joule-class short pulse laser"
No full text2D PIC of laser interaction with wire
Flash simulations for the shock formation and propagatio
Heat flow data from the fungus Schizophyllum commune
No full textThe data set contains three typical heat flow curves recorded from the fungus Schizophyllum commune and exemplifies the evaluation of such data by the software tool metabolator (https://doi.org/10.14278/rodare.3049)
Data publication: DATIV - Remote enhancement of smart aerosol measurement system using Raspberry Pi based distributed sensors
No full textTwo data sets are provided: one contains measurements taken without ventilation, and the other contains measurements taken with ventilation (open window)
METABOLATOR: Analysis of Microcalorimetric Metabolic Data Using Monod's Equation
No full textCurve fitting automation for metabolic load of bacteria in solutions
Data publication: X-ray Thomson scattering absolute intensity from the f-sum rule in the imaginary-time domain
No full textThis repository contains the raw data for the relevant figures of the article "X-ray Thomson scattering absolute intensity from the f-sum rule in the imaginary-time domain". All data are standard gnuplot output
Data publication: Identifying magnetic phases in chemically ordered and disordered FeAl thin films
No full textSatz von AFM/MFM und SQUID Daten, wie im zugehörigen Paper beschrieben
Data and Code: Transforming martensite in NiTi within nanoseconds
No full textThis dataset belongs to the paper "Transforming martensite in NiTi within nanoseconds" and contains all raw data used for the paper. It includes raw data of reciprocal space maps taken at P23 Petra III DESY Synchrotron. It also contains the Python code used to analyze the raw data and the martensite intensities extracted from the raw data. Information about sample, measurement techniques and further data description can be found in README.txt
mlphys101 - Exploring the performance of Large-Language Models in multilingual undergraduate physics education
No full textLarge-Language Models such as ChatGPT have the potential to revo-
lutionize academic teaching in physics in a similar way the electronic calculator,
the home computer or the internet did. AI models are patient, produce answers
tailored to a student’s needs and are accessible whenever needed. Those involved
in academic teaching are facing a number of questions: Just how reliable are pub-
licly accessible models in answering, how does the question’s language affect the
models’ performance and how well do the models perform with more difficult tasks
beyond retrieval? To adress these questions, we benchmark a number of publicly
available models on the mlphys101 dataset, a new set of 823 university level MC5
questions and answers released alongside this work. While the original questions
are in English, we employ GPT-4 to translate them into various other languages,
followed by revision and refinement by native speakers. Our findings indicate that
state-of-the-art models perform well on questions involving the replication of facts,
definitions, and basic concepts, but struggle with multi-step quantitative reason-
ing. This aligns with existing literature that highlights the challenges LLMs face
in mathematical and logical reasoning tasks. We conclude that the most advanced
current LLMs are a valuable addition to the academic curriculum and LLM pow-
ered translations are a viable method to increase the accessibility of materials, but
their utility for more difficult quantitative tasks remains limited.
The dataset is available in English here only and will be removed, once the mlphys101 publication was accepted and released to the public.The dataset is available in English here only and will be removed, once the mlphys101 publication was accepted and released to the public
Collinstown Hyperspectral Drillcore Data
No full textThis hyperspectral drillcore dataset (shed) contains 10 drill holes, totalling 413 boxes that cumulatively contain 2845 meters of scanned cores. Hyperspectral data is stored in the widely used ENVI format (.dat and associated .hdr files), which can be opened using e.g., napari-hippo (GUI) and hylite (python). The whole directory structure is compatible with hycore, for easier out-of-core processing and visualisation.
These hyperspectral data and associated visualisations can also be viewed interactively here.
These cores intersect stratigraphic units of the Lower Carboniferous Irish Midlands, including the Lucan Formation (Upper Dark Limestones), the Feltrim Formation (Boulder Conglomerate), the Slane Castle Formation (Argillaceous Bioclastic Limestone), the Meath Formation (Shaley Pale Limestones unit), the Liscarton Formation (Mixed Beds unit), and Lower Paleozoic basement rocks.
Scanning was conducted on cores such that a variety of lithology, sedimentary facies, proximity to mineralization, alteration intensity, and dolomitization intensity were sampled.
These data were acquired as part of the Horizons Europe project Vector. Teck Ireland is acknowledged for providing access to core material and assisting with the hyperspectral scanning logistics