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142 results for “zenodo”
Overview exoplanets for ChatGPT and Zenodo
<p> <strong>This Excel sheet is produced based on the data from the NASA Exoplanet Archive. It is Reference [14] in Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT <span><a href="../records/12683899">https://zenodo.org/records/12683899</a> </span> </strong> <br><br>You find the calculations for the Preferred Distances of star systems in column N from cell 7 down. The largest planets are likely found at that preferred distance, based on NASA data. Open the Excel sheet and scroll down to row 195 to view the original data from NASA.<br><a href="http://exoplanetarchive.ipac.caltech.edu">http://exoplanetarchive.ipac.caltech.edu</a> </p> <p>In <em>'Seeking Evidence for the Cosmic Influx Theory (CIT)</em> <em>Unveiling a Universal Ether-like Energy Field spanning the vast scales of exoplanets down to the minute details of dew and rime)</em>', we find surprising confirmations from ChatGPT that many natural phenomena exemplify an influx of energy, converting the Big Bang (BB) concept into Continuous Creation (CC) This perspective represents a significant shift in how we view familiar phenomena—from rain to stardust, and from volcanoes to the spreading ocean floor.<br>Citation: Loeffen, R. (2024). Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT<br><span><a href="../records/12683899">https://zenodo.org/records/12683899</a> </span> </p>
Qu-DOS/Dataset-Barcodes: Zenodo
[dataset] Barcode dataset used in arXiv:2409.01496
Yeast-MetaTwin-zenodo
<p>Dataset for the github repository containing the code for the manuscript "Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning".</p>
Demonstaration of the use of Zenodo website to upload data and link it to the PersonalizeAF project
<p>This video demonstarates the use of Zenodo website to upload data and link it to the PersonalizeAF project</p>
(re)Use Indications of High Energy Physics related Research Data and Software in Zenodo
<p>This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from Zenodo. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Overtopping events in breakwaters under climate change scenarios [Dataset]. Zenodo
<p>Reliable prediction of wave run-up/overtopping and structure damage is a key task in the design and safety assessment of coastal and harbor structures. Run-up/overtopping and damage must be below acceptable limits, both in extreme and in normal operating conditions, to guarantee the stability of the structure and the safety of people and assets on and behind the structure. The mean-sea-level rise caused by climate change and its effects on wave climate may increase the number and intensity of run-up/overtopping events and make the existing coastal/harbor structures more vulnerable to damage.</p> <p>Accurate estimates, through physical modelling, of the statistics of overtopping waves for a set of climate change conditions, are needed. The research project HYDRALAB+ (H2020-INFRAIA-2014-2015) gathers an advanced network of environmental hydraulic institutes in Europe, which provides access to a suite of environmental hydraulic facilities. They play a vital role in the development of climate change adaptation strategies, by allowing the direct testing of adaptation measures and by providing data for numerical model calibration and validation. The use of physical (scale) models allows the simulation of extreme events as they are now, and as they are projected to be under different climate change scenarios.</p> <p>The enclosed dataset refers to the experimental work developed at LNEC within HYDRALAB+ and considers 2D damage and overtopping tests for a rock armor slope, with four different approaches to represent storms. Data of free surface elevation, overtopping and damage is presented.</p>
First Zenodo
This is my first upload
OWI-Lab/py_fatigue: Zenodo registration
<p><strong>py-Fatigue toolbox for Fatigue assessment</strong></p> <p>It provides:</p> <ul> <li>a powerful cycle-counting implementation based on the ASTM E1049-85 rainflow method that retrieves the main class of the package: <code>CycleCount</code></li> <li>capability of storing the <code>CycleCount</code> results in a sparse format for storage and memory efficiency</li> <li>easy applicability of multiple mean stress effect correction models</li> <li>implementation of low-frequency fatigue recovery when "summing" multiple <code>CycleCount</code> instances</li> <li>fatigue analysis through the combination of SN curves and multiple damage accumulation models</li> <li>crack propagation analysis through the combination of the Paris' law and multiple crack geometries</li> <li>and more...</li> </ul> <p>Py-Fatigue is heavily based on <a href="https://numba.pydata.org/"><code>numba</code></a>, <a href="https://numpy.org/"><code>numpy</code></a> and <a href="https://pandas.pydata.org/"><code>pandas</code></a>, for the analytical part, and <a href="https://matplotlib.org/"><code>matplotlib</code></a> as well as <a href="https://plotly.com/python/"><code>plotly</code></a> for the plotting part.</p> <p>Therefore, it is highly recommended to have a look at the documentation of these packages as well.</p>
Demo data to evaluate zenodo-govdata integration
<p>This data will be used to investigate the feasibility of using Zenodo as a data repository for publishing BMWK data. In particular, the linking of Zenonod with GovData will be examined.</p>
Zenodo Collection: Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption
<p>This Zenodo collection contains figures and results from grid searches used to estimate point source parameters (force or moment tensor) for the main seismic subevent of the 2022 Hunga-Tonga submarine volcanic eruption. The collection includes misfit maps and waveform fits for the best-fitting force or moment tensor, as well as MTUQ weight files and a zipped version of the MTUQ code. These results were obtained using various software tools, including MTUQ, Axisem, Instaseis, Syngine, Obspy, GMT, and PyGMT. The collection was prepared for a manuscript in review for Geophysical Journal International.</p>
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
lpeyruchat/JHD-paper-zenodo: Zenodo version for submission
<p><strong># Transconductance quantization in a topological Josephson tunnel junction circuit</strong></p> <p>By Léo Peyruchat, Joël Griesmar, Jean-Damien Pillet, Çağlar Girit</p> <p> </p> <p>Python source code to generate data from https://arxiv.org/abs/2009.03291</p>
test zenodo jgilis (2)
<p>Test zenodo for later use part 2</p>
Zenodo Overview
<p>A short video introduction to Zenodo</p>
The Future of Stress Research: A Zenodo Collection of Datasets and Tools for Investigating the Stress-Mental Health Relationship
<p>This Zenodo collection offers a comprehensive dataset for researchers exploring the intricate relationship between stress and Mental Health. It provides a valuable resource for investigating how individual experiences influence mental well-being.</p><p><strong>Data Included:</strong></p><ul><li><strong>Participant Information:</strong> Name, age, gender, community (rural/urban), and occupation.</li><li><strong>Stress Level:</strong> Self-reported stress levels, identified stressors, and coping mechanisms.</li><li><strong>Mental Health Scores:</strong> Scores on standardized anxiety and depression scales.</li><li><strong>Mental Health State:</strong> Categorical diagnosis (Average, Poor, Excellent etc.. ).</li></ul><p> </p>
Zenodo package for Kayanoki et al. 2023, MNRAS
<p>The supplementary materials here contain SPEX data, command files to reproduce fitting results, and python3 notebooks to reproduce figures in the Mrk 6 paper (Kayanoki et al. 2023, MNRAS). </p>
Zenodo package for Zhou+2024, APJ --- WAs in RE J1034+396
<p>The supplementary package here contains the XMM-Newton data, SPEX scripts, and Python scripts to reproduce the spectral fitting results and the figures in the paper (Zhou+2024, APJ --- On the Connection between the Repeated X-ray Quasi-periodic Oscillation and Warm Absorber in the Active Galaxy RE J1034+396).</p>
Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development: Raw Diffraction Images (38 week soak). Zenodo
<p>The synchrotron raw diffraction images obtained at 38 weeks and wavelength 0.976 Å, illustrates the covalent coordination of the rhenium(I) tricarbonyl fragment to the His and Asp amino acid residues as well as movement along the solvent channels as described in the publication titled "Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development", written by Jacobs, Helliwell & Brink,<em> IUCrJ</em>, 2024, https://doi.org/10.1107/S2052252524002598.</p> <p>The raw diffraction images for the DLS data sets are made available at the Zenodo research data archive, as specified in the publication.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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