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11,506 results for “Dynamics”
Datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate"
<p>These are datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate". The thirteen cases of WRF domain 2 simulation in historical and PGW experiments were uploaded, respectively. </p>
Dataset for "Evolutionary Dynamics and Mechanisms of Chain-style Landslide Dam Failure Hazard: Insights from Experimental Field Research"
<p>Yang et al. (2024) Dataset for "Evolutionary Dynamics and Mechanisms of Chain-style Landslide Dam Failure Hazard: Insights from Experimental Field Research", Journal of Geophysical Research-Earth Surface.</p> <p>The data that support the findings of this study are available on request from the corresponding author upon reasonable request.</p>
Data supplement for "Gradient dynamics approach to reactive thin-film hydrodynamics"
<p>This archive contains the data and code supplement to the article `Gradient dynamics approach to reactive thin-film hydrodynamics' available at https://doi.org/10.1007/s10665-024-10402-x (J. Eng. Math. (2024) 149, 2)</p> <p>Structure:<br> The directories are named according to the related figure in the main text (e.g. `Fig2'). Each directory contains a data folder (e.g. `data' or `data_ab') and codes to produce the corresponding figure. Gnuplot scripts are `.plt' files, python scripts are `.py' files.<br>Executing the codes:<br> To execute the gnuplot scripts, start gnuplot and load the script, for example via `load "Fig3ab.plt"'. For the python scripts, use the command `python "Fig3cdef.py"' or `python3 "Fig3cdef.py"' from the terminal. Both gnuplot and python scripts require a latex kernel.<br>Requirements:<br> The codes should run on most Linux-based systems, we have used:</p> <p> Ubuntu 20.04.2<br> gnuplot 5.2.8<br> Python 3.8.10<br> matplotlib 3.7.5<br> numpy 1.20.3<br> MiKTeX-pdfTeX 4.17 (MiKTeX 23.12) </p>
Evolution of the conformational dynamics of the molecular chaperone Hsp90
<p>MD snapshots from: "Evolution of the conformational dynamics of the molecular chaperone Hsp90"</p> <p> </p>
Cilia structure and intraflagellar transport differentially regulate sensory response dynamics within and between C. elegans chemosensory neurons
<p><u>List of data with corresponding files</u></p> <p><strong>Figure 1</strong></p> <p>A) <em>File name:</em> Figure 1A- ASH_IFT_mutants_length_copy.pzfx</p> <p>B) <em>File names: </em>(Folder) ASH_1Mglycerol_IFT_mutants <em>and </em>(Folder) ASH_10-2IAA_IFT_mutants</p> <p>C) <em>File names: </em>(Folder) ASH_1Mglycerol_IFT_mutants > Peak_responses <em>and </em>(Folder) ASH_10-2IAA_IFT_mutants > Peak_responses - first pulse</p> <p>D) <em>File names: </em>Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 and 30 degree graph <em>and </em>Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 degrees</p> <p>E) <em>File name: </em>Figure 1E – ASH IFT Quantification.xlsx</p> <p>F) <em>File name: </em>(Folder) ASH_1Mglycerol_osm3ts</p> <p>G) <em>File names: </em>Figure 1 – Glycerol_Assays_Data.pzfx <em>and </em>Figure 1 – Glycerol_Assays_Data.xlsx</p> <p>H) <em>File name: </em>Figure 1H – ALL ASH Marianas and Cher Lengths che-3(nx159ts)</p> <p>I) <em>File names: </em>Figure 1- ASH IFT PY12007 v AP457 <em>and </em>Figure 1- ASH IFT PY12007 v AP457.xlsx</p> <p>J) <em>File name:</em> (Folder) ASH_1Mglycerol_che3ts</p> <p>K) <em>File names: </em>Figure 1 – Glycerol_Assays_Data.pzfx <em>and </em>Figure 1 – Glycerol_Assays_Data.xlsx</p> <p><strong>Figure 2</strong></p> <p>B) <em>File name: </em>(Folder) 10-7Diacetyl_Calcium_IFT_mutants</p> <p>C) <em>File names: </em>(Folder) 10-6Pyrazine_Calcium_IFT_mutants <em>and </em>(Folder) 10-7Pyrazine_Calcium_IFT_mutants</p> <p>D) <em> File names: </em>(Folder) Microfluidics > (Folder) 10-6pyrazine <em>and </em>(Folder) 10-7diacetyl</p> <p>E) <em>File names: </em>(Folder) Microfluidics > Diacetyl_Chemotaxis_index_osm6_osm3kap1.pzfx <em>and</em><em> </em>Pyrazine_Chemotaxis_index_pyrazine_osm3kap1_osm6.pzfx</p> <p>G) <em>File name: </em>(Folder) 10-6Pyrazine_Calcium_misexpression</p> <p><strong>Figure 3</strong></p> <p>A) <em>File name: </em>Figure 3A- AWA IFT AP303 v AP379.xlsx</p> <p>C) <em>File names: </em>(Folder) AWA_Calcium_Diacetyl <em>and </em>(Folder) AWA_Calcium_Pyrazine</p> <p>D) <em>File names: </em>(Folder) AWA_Calcium_Diacetyl <em>and </em>(Folder) AWA_Calcium_Pyrazine</p> <p>E) <em>File name: </em>Figure 3E- SRX64 and ODR10 within dendritic branches.pzfx</p> <p><strong>Figure 4</strong></p> <p>A) <em>File name: </em>(Folder) AWA_Calcium_Diacetyl_grk2</p> <p>B) <em>File name: </em>(Folder) AWA_Calcium_Pyrazine_grk2</p> <p>C) <em>File name: </em>(Folder) Microfluidics_diacetyl</p> <p>D) <em>File name: </em>(Folder) Microfluidics_pyrazine</p> <p>E) <em>File name: </em>Figure 4E- GRK2tRFP_kap1osm3ts.pzfx</p> <p><strong>Figure 5</strong></p> <p>A) <em>File names: </em>(Folder) AWA_Diacetyl_Adaptation_osm3ts <em>and </em>(Folder) AWA_Pyrazine_Adaptation_osm3ts</p> <p>B) <em>File name: </em>Figure 5B and S7B- FRAP.pzfx</p> <p>C) <em>File names: </em>Figure 5C- SRX64_bbs7jhu590.pzfx <em>and </em>Figure 5C- SRX64_bbs7ok1351.pzfx <em>and </em>Figure 5C- oy158_bbs7jhu590.pzfx <em>and </em>Figure 5C- oy158_bbs7ok1351.pzfx</p> <p>D) <em>File names: </em>(Folder) AWA_Diacetyl_Adaptation_bbs7 <em>and </em>(Folder) AWA_Pyrazine_Adaptation_bbs7</p> <p>E) <em>File name: </em>Figure 5E- Marianas_EV_ODR10_SRX64_osm3tskap1.pzfx</p> <p><strong>Figure S1</strong></p> <p>A) <em>File names: </em>(Folder) ASH_IFTmutants_0.5and2Mglycerol > (Folder) 0.5Mglycerol <em>and </em>(Folder) 2Mglycerol</p> <p>B) <em>File names: </em>(Folder) ASH_IFTmutants_quinine > (Folder) 5mM_quinine <em>and </em>(Folder) 10mM_quinine</p> <p>C) <em>File name: </em>ASH_IAA_t1/2</p> <p>D) <em>File names: </em>(Folder) ASH_IFTmutants_heptanol > (Folder) 10-3_heptanol <em>and </em>(Folder) 10-4_heptanol</p> <p>E) <em>File names: </em>(Folder) ASH_IFTmutants_2Mglycerol_GABA <em>and </em>(Folder) ASH_IFTmutants_2Mglycerol_bicuculline</p> <p><strong>Figure S2</strong></p> <p>B) <em>File name: </em>Figure S2B- Dye-filling_data</p> <p>C) <em>File name: </em>Figure S2C- ASH_IFT_mutants_length.pzfx</p> <p>D) <em>File names: </em>Figure S2D- ASH IFT PY6345 v PY12007 <em>and </em>Figure S2D- ASH IFT Quantification</p> <p><strong>Figure S3</strong></p> <p>A) <em>File name: </em>(Folder) AWA_10-7Diacetyl_IFTmutants</p> <p>B) <em>File names:</em> (Folder) AWA_Calcium_heptanone > (Folder) 10-4_heptanone <em>and </em>(Folder) 10-5_heptanone</p> <p>C) <em>File name: </em>(Folder) AWA_10-5Pyrazine_IFTmutants</p> <p>D) <em>File names: </em>(Folder) AWA_Calcium_methylpyrazine > (Folder) 10-8_methylpyrazine <em>and </em>(Folder) 10-9_methylpyrazine</p> <p>E) <em>File name:</em> Figure S3- Chemotaxis IFT_mutants.pzfx</p> <p><strong>Figure S4</strong></p> <p>A) <em>File name: </em>Figure S4A – AP419_pyrazine_Chemotaxis.pzfx</p> <p>C) <em> File name: </em>Figure S4C – srx64SL2gfp_osm5.pzfx</p> <p><strong>Figure S6</strong></p> <p>A) <em>File names: </em>Diacetyl_prism_decay_grk2 <em>and </em>Pyrazine_prism_decay_grk2</p> <p><strong>Figure S7</strong></p> <p> A) <em>File name: </em>(Folder) AWA_Calcium_Adaptation_diacetyl_IFT_mutant</p> <p> B) <em>File name: </em>Figure 5B and S7B- FRAP</p> <p> C) <em>File name: </em>Figure S7C- oy158_grk2_rescue.pzfx</p> <p> F) <em>File names: </em>(Folder) AWA_Calcium_Adaptation_diacetyl_grk2 > (Folder) grk2 <em>and </em>(Folder) grk2bbs7double</p> <p><strong>Additional materials</strong></p> <p> All code is listed under Code from Github > MF.matR-master <em>and </em>Additional Methods</p>
Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction
<p>The dataset includes additional data for the same paper, <em>"Annual dynamics and metagenomics of marine vesicles: a layer of complexity in the dissolved organic fraction."</em> The data were uploaded separately due to the 50.00 GB limit on available upload space.</p> <p>The gene sequences contained within EVs were annotated using the DRAM program within the Kbase platform, enabling us to identify the specific types and functions of genes packaged within EV fractions. This annotation process provided a comprehensive overview of the genetic cargo potentially encapsulated in EV fractions 25% and 20%.</p> <p>The provided Excel files represent the output from DRAM annotation and contain the detected genes in EV fractions 25% and 20%. Although the gene names in the files are labeled as 30%, 25%, and 20%, this is a naming error. The file labeled "annotation for EVs 25%" contains genes detected in the 25% EV fraction, while the file labeled "annotation for EVs 20%" corresponds to genes from the 20% EV fraction.</p> <p>There are two replicates for the 20% EV fraction, denoted as rep1 and rep2, and both samples were analyzed. The gene abundance was calculated as the mean percentage of the two replicates, corrected with standard error.</p> <p> </p>
Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics
<p>Raw data for the article "Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics" by N. Chapuis and L.Bocquet.</p>
Groundwater dynamics beneath a marine ice sheet – supplementary animations
<p>Supplementary animations for the article "Groundwater dynamics beneath a marine ice sheet" by Gabriel J. Cairns, Graham P. Benham and Ian J. Hewitt. </p>
Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma
<p><span>Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography mass spectrometry data of sera, we identified the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB)</span> <span>algorithm. Also, we located the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still had no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data was successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provided a new insight to locate the pre-metastasis status of lung cancer and primarily examined its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.</span></p>
Dataset for "Advances in Docking Protocols for PPIs: Insights from AlphaFold2 and Molecular Dynamics Refinement"
<p>Dataset files used in 'Advances in Docking Protocols for PPIs: Insights from AlphaFold2 and Molecular Dynamics Refinement' (https://github.com/SysBioUAB/docking_benchmark)</p>
Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex
<p># Data and srcipts used to perform analyses presented in the manuscript: "Spring vegetation dynamics drive sex-specific migration propensity and fine-scale green-up tracking in Alpine ibex"</p>
Dataset for: Dynamic transition and Galilean relativity of current-driven skyrmions
<p>This data repository contains the experimental data utilised to produce the figures shown in the article "Dynamic transition and Galilean relativity of current-driven skyrmions" by Max Birch, Ilya Belopolski, Yukako Fujishiro, Minoru Kawamura, Akiko Kikkawa, Yasujiro Taguchi, Max Hirschberger, Naoto Nagaosa and Yoshinori Tokura.</p> <p>All data are in the typical .dat file format from a Quantum Design PPMS. The data are organised according to each main/extended/supplementary figure in the article. This means that same data files may appear in more than one figure folder. Files are labelled according to which device was measured, with additional information on the sample environment (temperature/field/current etc) included where relevant.</p>
Supplementary_Gene ontology defines pre-post- hatch energy dynamics in the complexus muscle of broiler chickens
Open the record for dataset details and reuse information.
Evolutive Dynamics in Early Modern European Drama: Reproduction Materials
<p>The ZIP archive contains all materials necessary to reproduce/replicate workflows and results from this work:</p> <blockquote> <p>Luca Giovannini, <em>Evolutive Dynamics in Early Modern European Drama: A Computational Approach</em>, PhD dissertation, Universität Potsdam and Università di Padova, 2024.</p> </blockquote> <p>The archive is a copy of <a href="https://github.com/lucagiovannini7/emdracor" target="_blank" rel="noopener">this GitHub repository</a> and contains the following folders:</p> <ul> <li><code>tei</code>: 150 early modern European plays, encoded in XML-TEI, composing the EmDraCor corpus. This folder, together with the <code>corpus.xml</code> file, can be used to (re)build the corpus on a dockerised version of DraCor (Börner et al., 2023b) through the <a href="https://github.com/dracor-org/stabledracor" target="_blank" rel="noopener">StableDraCor</a> Python library.</li> <li><code>scripts</code>: several commented Python scripts for corpus building, metadata enrichment, and analysis.</li> <li><code>metadata</code>: metadata tables, retrieved from the API and enriched through scripts, and other useful files. Most computations carried out in the dissertation can be performed directly on these tables, without need for dockerisation.</li> <li><code>legacy</code>: deprecated XML files.</li> </ul>
Data Supporting The Paper 'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection'
<p>The dataset includes raw images, source code, raw vectors saved from MATLAB, and curated data used to generate figures and analyses in the paper <strong>'Fine-tuned spatiotemporal dynamics of DNA replication during phage lambda infection'</strong> by Z. Yu, et al.</p> <p>Additional information about the experiments will be available upon request. </p>
Towards Dynamical Annual To Decadal Climate Prediction Using the IAP-CAS Model
<p>We execute a set of Annual to Decadal (A2D) hindcast experiments using the IAP-CAS model (FGOALS-f2), which integrates 129 months of each prediction initialized from 1981 to 2015 annually, and evaluate the results of this experiment. The IAP-CAS model output associated with this work is stored here.</p>
Molecular dynamics simulation: The process of phosphate transfer from fructose-1,6-bisphosphate to phosphoglycerate mutase 1
<p><span>Molecular dynamics (MD) simulations were performed to investigate the dynamic process of the binding and phosphorylation of PGAM1 by FBP. </span></p>
Dataset for "Constructing many-body dissipative particle dynamics models of fluids from bottom-up coarse-graining"
<ul> <li>The trajectory files used for processing time correlation functions, as reported in the original paper, are provided here.</li> <li>The analysis tools can be found in the following GitHub repository: <a href="https://github.com/jaehyeokjin/ManyBodyDPD/tree/main/Time-Correlation" target="_new" rel="noopener">ManyBodyDPD/Time-Correlation</a>. These tools are designed to work with the two trajectory files included in this repository.</li> </ul>
DFT data from article "Oxide Ion Mobility in V- and P-doped Bi2O3-Based Solid Electrolytes: Combining Quasielastic Neutron Scattering with Ab Initio Molecular Dynamics"
<p>DFT data from article: "Oxide Ion Mobility in V- and P-doped Bi2O3-Based Solid Electrolytes: Combining Quasielastic Neutron Scattering with Ab Initio Molecular Dynamics" (<span><a href="https://pubs.acs.org/doi/full/10.1021/acs.chemmater.2c03103">https://pubs.acs.org/doi/full/10.1021/acs.chemmater.2c03103</a>). Published by 'creators' listed above. </span></p>
Collection of data and Matlab-codes for analyzing water dynamic at the Zugspitze.
<p>This collection includes the raw data and the Matlab codes used for the analyses of the article: "Decadal in-situ hydrological observations and empirical modeling of pressure head in a high-alpine, fractured calcareous rock slope" by Scandroglio et al. 2024 [Link].<br>Explanations are included in the file METADATA.docx. See the paper for further details.</p> <p>Documentation and code by Riccardo Scandroglio (<a href="mailto:johannes.leinauer@tum.de">r.scandroglio@tum.de</a>)<br>Technische Universität München, Arcisstr. 21, 80333 München, Germany</p> <p>For updating the analysis: <br>- Water discharges are collected by the Environmental Research Station Schneefernerhaus (contact: Dr. Till Rehm).<br>- Data from the German Weather Service can be downloaded from the official server: <a href="https://cdc.dwd.de/portal/">https://cdc.dwd.de/portal/</a>.<br>- Snow data for SNOWPACK can be obtained from the Bavarian Avalanche Center (contact: Dr. Thomas Feistl).</p> <p>This study was supported by the AlpSenseRely project, funded by the Bavarian State Ministry of the Environment and Consumer Protection (TUS01UFS-76976), and by the Hydro-PF project, funded by the TUM International Graduate School of Science and Technology IGSSE (Team 12.9). </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.