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11,506 results for “Dynamics”
FIGURE 1 in A host, a parasite, and a predator: the dynamics of successive invasions in the eastern Mediterranean
FIGURE 1. Carapace width of Charybdis longicollis collected off Ashdod, Israel, at 40 m depth. Legend: stripes—unparasitized crabs; black dots—internally parasitized crabs; white dots—externa-bearing crabs; males on the left, females on the right.
Exploring the structural dynamics of the vesicle priming machinery
<p>Here are the pdb files for the simulations with only 6 central SNAREs and 6 central and 6 peripheral SNAREs with C2B of Syt1 and residue 51-75 of Cpx1 shown in figure 3.</p> <p>File for figure 3a: ic_6c_snares.pdb is the initial condition for simulations with only 6 central SNAREs, and the tcl file figure_3a.tcl is to show this initial condition.</p> <p>File for figure 3b: ic_6c6p_snares_c2b.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a vesicle, and the tcl file figure_3b_top_view.tcl and figure_3b_side_view.tcl are to show this initial condition.</p> <p>File for figure 3c: ic_6c6p_snares_c2b_nd.pdb is the initial condition for simulations with 6 central and 6 peripheral SNAREs with a nanodisc and ic_6c6p_snares_c2b_nd.xtc file is the corresponding trajectory of this simulation. The tcl file movie_for_figure_3c.tcl is to show the movie of this simulation. If you want to look at all proteins in this simulation, the tcl file movie_for_figure_3c_all.tcl can do that.</p> <p>To view these snapshots by VMD, load the corresponding .tcl file or run "vmd -e name.tcl" in your terminal.</p>
Data and Code for the manuscript "Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space"
<p>This repository includes the data and code to reproduce the results for the study "<em>Confounding effects of leaf angle dynamics on vegetation indices - implications for monitoring vegetation from space</em>". The study shows that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, it is demonstrated that these effects are not random but tightly linked to abiotic environmental conditions. These findings demonstrate both challenges and opportunities for using VIs for vegetation monitoring.</p>
code for: The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity
<p>MATLAB codes for data analyses performed in "The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity".</p>
Epigenetic-gene-variant-dynamics-analysis
<p>The dataset and analysis pipeline provided here are part of the study titled "Mapping Epigenetic Gene Variant Dynamics: Comparative Analysis of Frequency, Functional Impact, and Trait Associations in African and European Populations." This study investigates the frequency and functional impact of genetic variants in epigenetic genes across different populations, focusing on African and European ancestries. The files included are essential for replicating the analysis described in the manuscript. Key files include data from the GWAS Catalog and phenotype from the Pan-UK Biobank study and the UK Biobank, facilitating the investigation of trait associations with epigenetic variants. The complete code and description of additional data files necessary for the analysis can be accessed on GitHub at<span> </span><a href="https://github.com/smsinks/epigenetic-gene-variant-dynamics-analysis" target="_new">https://github.com/smsinks/epigenetic-gene-variant-dynamics-analysis</a>.</p>
Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective.
<p>The data for the study in the PRE, paper named 'Exploring water accumulation dynamics in the Pearl River estuary from a Lagrangian perspective'.</p>
Dynamical decoding of the competition between charge density waves in a kagome superconductor
<p>P1, P2, P3 denote the peaks at (0 -1.5 2.5), (-0.5 -1 2), (0 -1.5, 3), respectively.</p>
Dataset Dynamics of Clostridium perfringens in BSF production process
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Dataset Staphylococcus aureus screening and dynamics in BSF larvae rearing
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Data Sets for: Spiers Memorial Lecture: From Cold to Hot, The Structure and Structural Dynamics of Dense Ionic Fluids
<p>Data Sets for Section 2.1 of Article: Spiers Memorial Lecture: From Cold to Hot, The Structure and Structural Dynamics of Dense Ionic Fluids</p>
PyRAMD scheme: a protocol for computing the infrared spectra of polyatomic molecules using ab initio molecular dynamics
<p>Here are illustrative examples of how to compute molecular-dynamics-based vibrational infrared spectra of polyatomic molecules using PyRAMD software.</p> <p>The dataset includes the version of the software used (PyRAMD from https://gitlab.desy.de/denis.tikhonov/pyramd, obtained July 15, 2024) for running molecular dynamics (MD) and various examples of MD simulations that illustrate various analysis issues. The large-time-step correction applicability is shown with methane (CH4) as an example. The applicability of the regularized least-squares spectral analysis is demonstrated in the case of carbon dioxide (CO2). The scale factors for the MD are obtained using water, ammonia, methane, ethane, methylamine, and methanol as the training set of molecules. The scale factors were obtained at the BLYP-D3(BJ)/6-31G, PBE-D3(BJ)/6-31G, and PBEh-3c levels of theory. The whole procedure was demonstrated with protonated methane as a test case.</p>
Supplementary materials of The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia
<p>Supplementary materials of <em><strong>The Dynamics of The Relationship between Religious Identity and Fundamentalism in Predicting Muslim Prejudice against Christian in Indonesia</strong> </em>published in <em><strong>Islamic Guidance and Counseling Journal (IGCJ) Vol. 7, No. 2, 2024</strong></em></p>
Data Supplement for: "Gradient dynamics model for drops of volatile liquid on a porous substrate"
<p>This dataset contains supplementary data for the following preprint:</p> <p>Hartmann, S. & Thiele, U.<br>Gradient dynamics model for drops of volatile liquid on a porous substrate.<br>(submitted 2024)</p> <p>We provide the data and sources necessary to generate all figures in the paper.</p> <p>The figures are built either with LaTeX/TikZ (Figure 1) or Python/Matplotlib (all other figures).<br>Each subfolder contains the full source code and data for one figure each.</p>
2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Open-Ended Dynamics of Coevolutionary Games Datasets
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ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.