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453 results for “Dynamic dataset”
Datasets for "Dynamics and deposits of pyroclastic density currents in magmatic and phreatomagmatic eruptions revealed by a two-layer depth-averaged model"
<p>Dataset for the manuscript entitled "Dynamics and Deposits of Pyroclastic Density Currents in Magmatic and Phreatomagmatic Eruptions Revealed by a Two-Layer Depth-Averaged Model" by H. A. Shimizu, T. Koyaguchi, and Y. J. Suzuki for submission in Geophysical Research Letter. This contains datasets for each run.</p>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
Dynamic light scattering datasets used to assess the Raynals software
<p>Updated - 23/04/2023<br> This folder contains the associated data from Burastero et al., 'Raynals, an online tool for the analysis of dynamic light scattering'</p> <p>The experimental datasets can be found at ./experimentalData/<br> The artificially generated datasets at ./SimulatedDataDLS_case1 and ./SimulatedDataDLS_case2</p> <p>To produce the experimental datasets, we performed measurements on Carbonic Anhydrase, Bovine Serum Albumin, Gold Nanoparticles, and three in-house samples: A protein with a beta-Propeller domain, a coiled-coil like protein, and an intrinsically disordered protein.</p> <p>Additionally, you'll find R scripts to generate Fig. 3, Fig. 5, and Fig. S1 to S4.<br> </p>
Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>RHEED raw dataset and Gaussia fitting parameter dataset for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Dataset to reproduce firgures for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"
<p>Dataset provided to reproduce figures <br> for the paper "A unifying method to study Respiratory Sinus Arrhythmia dynamics implemented in a new toolbox"</p> <p>Jupyter notebooks are available here:<br> https://github.com/samuelgarcia/physio_benchmark</p> <p>Human dataset<br> =============</p> <p>Context: A research aimed to decipher the impact of respiration on brain oscillations</p> <p>Data collection methods: ECG and Respiration of 15 healthy adults subjects <br> (age : 30.9 +/- 9.5 yo).All participants gave informed consent to take part to the study, and all experiments<br> were approved by the national french committee (CPP number 4090). They were sitting quietly and instructed just<br> to relax. Recording lasted 5 minutes. Respiration signal was recorded from a nasal sensor<br> (Sensortechnics GmbH, Puchheim , Germany) at a sampling rate of 1000 Hz, amplified by actiCHamp<br> Plus amplifier (Brain Products GmbH, Gilching, Germany). ECG signal was recorded from 3 skin electrodes<br> (right forearm, left forearm, left iliac region), at a sampling rate of 1000 Hz (same amplifier).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respiratorysignal.<br> The sampling rate is 1000Hz</p> <p>Data manipulations: The original dataset has longer durationand and contain channels (EEG).<br> This sub-dataset was extracted from the original using the neo python package from the VHDR brain product format.<br> Signal tarces haven't been preprocessed they correspond to the "raw" signal.</p> <p><br> Data confidentiality and permissions: Experiments were approved by the national french committee (CPP number 4090)</p> <p><br> Animal dataset<br> ==============</p> <p>Context: The dataset was recorded to validate the device telemetric jacket from Etisense.</p> <p>Data collection methods: ECG and Respiration of 1 adult rat were recorded. Recording lasted 30 seconds during<br> freely behaving. Respiration signal and ECG were recorded from a thoraco-abdominal telemetric<br> jacket at which it was habituated before. Recorded were done at a sampling rate of 500 Hz, amplified by<br> Etisense acquisition unit (Etisense, MedTech company, Lyon, France).</p> <p>Structure of files: tabular separated values text files.<br> The first columns correspond to the ECG signal, the second is the respirator signal.<br> The sampling rate is 500Hz</p> <p><br> Data manipulations:<br> The dataset was extracted from the original HDF5 structure.<br> The ECG signal correcpond to the "raw" traces from the HDF5 files.<br> The respiratory signal was originaly sample at 200Hz on the device and resample with linear interpolation<br> to 500Hz to be easy aligned with the ECG signal.</p> <p>Data confidentiality and permissions: Experiments were carried according to the ethical guidelines of the<br> European Communities Council Directive of 24 November 1986 (86/609/EEC), as well as the approval 16979 of the<br> Lyon 1 University CEEA-55 ethical committee and of the Ministry of Higher Education, Research and Innovation.<br> </p>
A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment
<p>An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices.</p> <p>The dataset is described in detail in an accompanying data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923006716">A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Dataset for "Impact of surface anisotropy on the spin-wave dynamics in thin ferromagnetic film"
<p>The dataset consist of the data used to prepare the figures for the manusript entitled <em>Impact of surface anisotropy on the spin-wave dynamics in thin ferromagnetic film.</em> </p> <p>Please read README.txt file to see the description of the data in the files.</p>
Digitisation of Weather Records of Seungjeongwon Ilgi: A Historical Weather Dynamics Dataset of the Korean Peninsula (1623-1910)
<p><strong>Introduction</strong></p> <p>This study has exploited the daily weather records of Seungjeongwon Ilgi from the NIKH database (http://sjw.history.go.kr/main.do). Seungjeongwon Ilgi is a daily record of the Seungjeongwon, the Royal Secretariat of the Joseon Dynasty of Korea. These diaries span from 1623 to 1910 and generally involve daily weather records in the entry header. Their observational site would be located in Seoul (N37°35′, E126°59′). We have exploited the weather records from the NIKH database and classified the daily weather using text mining method. We have also converted the report dates from the traditional lunisolar calendar to the Gregorian calendar, to better contextualise our data into the contemporary daily measurements.</p> <p><strong>Data</strong></p> <p>We provide different formats (csv, xlsx, json) to facilitate the usage of data. The main contents of data are listed as below.</p> <ul> <li><strong>ID</strong>: The unique identifier of a specific record in the metadata, which can also serve as the identifier to merge with external data in the NIKH digital database.</li> <li><strong>Traditional calendar</strong>: The original lunar dates in the NIKH digital database, which are listed in data format "YYYY-MM-DD". More specifically, "L0" implies the leap year and "L1" implies the common year.</li> <li><strong>Leap</strong>: The identifier of a leap year.</li> <li><strong>Gregorian calendar</strong>: The Gregorian calendar date that converted by the traditional calendar date.</li> <li><strong>Weather Text</strong>: The text that describe the weather conditions. Specifically, multiple weather descriptions of the same day have been put together.</li> <li><strong>Flag</strong>: The computed value that indicates different combinations of weather conditions.</li> <li><strong>Volume</strong>: The volume of text in the original record.</li> <li><strong>Herbal Volume</strong>: The volume of text in the herbal record.</li> <li><strong>Sunny</strong>: A dummy variable that represents whether the weather description contains the expression of sunny.</li> <li><strong>Cloudy</strong>: A dummy variable that represents whether the weather description contains the expression of cloudy.</li> <li><strong>Rainy</strong>: A dummy variable that represents whether the weather description contains the expression of rainy.</li> <li><strong>Snow</strong>: A dummy variable that represents whether the weather description contains the expression of snow.</li> <li><strong>Wind</strong>: A dummy variable that represents whether the weather description contains the expression of wind.</li> </ul> <p><strong>Import Data</strong></p> <pre><code class="language-python"># Python # CSV file import pandas as pd data=pd.read_csv('~/SJWilgi_Seoul_Weather_YR1623_1910.csv',encoding="utf-8") # JSON file data=pd.read_json('~/SJWilgi_Seoul_Weather_YR1623_1910.json',encoding="utf-8") # Excel file data=pd.read_excel('~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx') # Excel file</code></pre> <pre><code class="language-bash"># R # CSV file library(readr) data<- read_csv("~/SJWilgi_Seoul_Weather_YR1623_1910.csv") # Excel file library(readxl) data <- read_excel("~/SJWilgi_Seoul_Weather_YR1623_1910.xlsx")</code></pre> <p> </p>
Dataset for "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases"
<p>This repository contains data and numerical simulation code for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases".</p> <p>The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environment such as python (h5py), matlab (`h5read`), and many others.</p> <p>Numerical simulation scripts consist of functions.c, MonteCarloSampling.cpp and phase12_den_sample_time.cpp. Please see the section for numerical simulation in the Supplementary Material.</p> <p>The description for each hdf5 file in this repository is provided in readme.txt.</p>
dataset of 'Electric control of optically-induced magnetization dynamics in a van der Waals ferromagnetic semiconductor'
<p>This .zip archive contains all raw data used for the paper 'Electric control of optically-induced magnetization dynamics in a van der Waals ferromagnetic semiconductor'. In addition, it contains (partially) processed data, and the data that is plotted in the figures of this paper.</p>
The GET insertase exhibits conformational plasticity and induces membrane thinning - The Molecular Dynamics Dataset
<p>The molecular dynamics simulation systems.</p> <p>List of files: </p> <ol> <li><strong>SimulationSystems.pdf</strong>: List of all simulation systems reported and their compositions</li> <li><strong>ProteinComplex.pdb</strong>: The initial model for the hsGet2ΔN-Get1/Get3 complex used in simulations was constructed based on the cryo-EM structure (PDB accession 6SO5). Missing residues (except the terminal ones) were modeled using Modeller.</li> <li><strong>prod.mdp</strong>: The GROMACS molecular dynamics parameters (mdp) file used for all simulations</li> <li><strong>toppar.zip</strong>: The Charmm36(m) force field parameter and topology set used for all simulations generated by CHARMM-GUI.</li> <li>Compressed (zip) files containing simulations inputs and trajectories</li> </ol> <p><strong>1-PC.zip<br> 2-1:4_PI:PC.zip<br> 3-1:4_PE:PC.zip<br> 4-1:4_PS:PC.zip<br> 5-1:4_CL:PC.zip<br> 6-1:4_chol:PC.zip<br> 7-1:1:1:1_PC:PI:PS:PE.zip<br> 8-1:1:1:1:1:1_PC:PDPC:PS:PI:PE:chol.zip</strong></p> <p>Each zip file contains the following files:</p> <ol> <li><strong>System_0ns.pdb</strong>: The initial configuration used for the simulations generated using CHARMM-GUI and equilibrated following the CHARMM-GUI equilibration protocol</li> <li><strong>index.ndx</strong>: GROMACS index file</li> <li><strong>topol.top</strong>: GROMACS topology file</li> <li><strong>prod0.tpr, prod1.tpr, prod2.tpr</strong>: GROMACS run topology files (tpr) for each repeat</li> <li><strong>prod0.gro, prod1.gro, prod2.gro</strong>: The final configuration after 3 μs production runs for each repeat</li> <li><strong>noW_0ns.pdb</strong>: The initial configuration without the water molecules.</li> <li><strong>noW_0.xtc, noW_1.xtc, noW_2.xtc</strong>: The 3 μs processed production trajectories. The water molecules were removed, and the trajectories were subsampled at 1 ns intervals.</li> </ol> <p> </p> <p> </p>
Zika virus prM protein contains cholesterol binding motifs required for virus entry and assembly - Molecular Dynamics Simulation Dataset
<p>The molecular dynamics (MD) simulation dataset. The contents:</p> <ul> <li><strong>5ire_BIOMT_expanded.pdb</strong>: The complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE) </li> <li><strong>5ire_Mprotein_BIOMT_expanded.pdb</strong>: The M proteins extracted from the complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE). The biological assembly shows the dimeric organization of M proteins.</li> <li><strong>0chol.zip, 10chol.zip, 20chol.zip, and 30chol.zip</strong> contain simulation input and output files for the simulated membrane compositions: 0:100, 10:90, 20:80, 30:70 (mol%:mol%) Cholesterol:POPC, respectively. <ul> <li>In each zip file, there are 5 directories: <strong>wt, R253L+F257A, R253L+F257S, K275L+Y278A, K275L+Y278S</strong> corresponding to each simulated M protein dimer variant: wild type, CARC 2-A, CARC 2-S, CARC 3-A, and CARC 3-S. In each directory, there are the following files: <ul> <li><strong>toppar</strong>: This directory contains all force field topologies and parameters</li> <li><strong>topol.top</strong>: GROMACS topology (top) file</li> <li><strong>index.ndx</strong>: GROMACS index (ndx) file</li> <li><strong>prod.mdp</strong>: GROMACS MD parameters (mdp) file</li> <li><strong>0, 1, 2, 3, 4, 5, 6, 7, 8, 9</strong>: These directories contain the simulation inputs and outputs for each simulation repeat. In each of these directories, there are the following files: <ul> <li><strong>t0.pdb</strong>: The pdb file of the starting coordinates</li> <li><strong>prod0.tpr</strong>: GROMACS binary run input (tpr) file </li> <li><strong>prod0.edr</strong>: GROMACS energy (edr) file</li> <li><strong>prod0.gro</strong>: GROMACS output coordinates and velocities after 1 microsecond of simulation</li> <li><strong>prod0.cpt</strong>: GROMACS checkpoint file after 1 microsecond of simulation</li> <li><strong>noW.pdb</strong>: The pdb file of the starting coordinates with all water molecules removed</li> <li><strong>noW.xtc</strong>: GROMACS compressed trajectory (xtc) file with all water molecules removed</li> </ul> </li> </ul> </li> </ul> </li> </ul>
The dataset for the research "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
In recent years, the topic of digitalisation and sustainability of supply chains has become increasingly important. In addition, as the environmental dynamism becomes more complex, it is essential to explore how technologies impacts on sustainability under the supply chain dynamism. Hence, there is a study to explore the relationship between technologies and sustainability under the supply chain dynamism in the energy supply chain. In this study, the author collects quantitative data from two Chinese companies, including China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd. This is a questionnaire survey and it has 24 questions, including 3 general questions, 5 technologies dimension questions, 12 sustainability dimension questions and 4 supply chain dynamism questions. The author collected data from 30 May 2024 to 6 June May 2024, and there are totally 316 answers.
Forest Community Dynamics in Hemlock Overlook, Virginia: A Ten-Year Student-Collected Forest Plot Dataset
During a ten-year observation period three 0.1 ha plots in Hemlock Overlook Regional Park, Fairfax County, VA, were regularly sampled. Student observers recorded and identified to species wherever possible, all trees with a DBH of 3cm or greater in these plots. In total there were 94 sampling events across the ten years with a total of 4,536 trees counted and measured. Of the recorded trees, the most frequently sampled trees were beech (Fagus grandifolia), with 1,681 records and the least frequently recorded trees were maple (Acer spp.) with 410 records. This dataset adds to the literature a longitudinal consistent sampling dataset that can be used for forestry and forest ecology research.
Dataset related to the publication "Nonlinear dynamics and chaos in an optomechanical beam", DOI: 10.1038/ncomms14965
<p>This folder contains the raw data from which the graphs in paper "Nonlinear dynamics and chaos in an optomechanical beam", DOI: 10.1038/ncomms14965, have been obtained</p>
Dataset used in the paper: "Scaling laws and dynamics of hashtags on Twitter"
<p>This dataset was used in the manuscript "Scaling laws and dynamics of hashtags on Twitter"..</p> <p>The Twitter data was obtained from a sample of 10% of all public tweets, provided by the Twitter streaming application programming interface. We extracted the hashtags from each tweet and counted how many times they were used in different time intervals. Time intervals of three different lengths were used: days, hours, and minutes. The tweets were published between November 1st 2015 and November 30th 2016, but not all time intervals between these dates are available.</p> <p>The <strong>four files</strong> in this dataset correspond each to one folder (collected using tar). Each folder contains compressed .csv files (compressed using gzip). The content of the .csv files in each folder are:</p> <p><em><strong>hashtags_frequency_day.tar</strong></em><br> Counts of hashtags in each day. The name of each file in the folder indicates the date (GMT). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>hashtags_frequency_hour.tar</strong></em><br> Counts of hashtags in each hour. The name of each file in the folder indicates the date (GMT). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>hashtags_frequency_minutes.tar</strong></em><br> Counts of hashtags in each minute. The name of each file in the folder indicates the date (GMT, only a fraction of all days is available). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>number_of_tweets.tar</strong></em><br> Counts of the number of tweets in each minute. The name of each file in the folder indicates the day. The entries in each file are the minute in the day (GMT) and count of tweets in our dataset.</p>
Data from: Dynamic reorganization of neuronal activity patterns in parietal cortex dataset
<p>Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.</p>
Dataset for "Seasonal dynamics of the COS and CO2 exchange of a managed temperate grassland"
<p>Data of measurements and model output of the publication "Seasonal dynamics of the COS and CO<sub>2</sub> exchange of a managed temperate grassland". https://doi.org/10.5194/bg-2020-27</p> <p>Data consists of micrometeorological data, COS and CO<sub>2</sub> flux measurements for a managed temperate mountain grassland in Austria.</p> <p>For additional information please contact: <a href="mailto:Georg.Wohlfahrt@uibk.ac.at">Georg.Wohlfahrt@uibk.ac.at</a></p> <p>changes in version 2: includes extrapolated COS mixing ratios & includes the ustar filter for the flux data</p> <p>changes in version 3: corrected every instance of mixing ratio with mole fraction </p>
Dataset to reproduce MD simulations described in " Insights on the dynamics of the human zinc transporter ZnT8 by MD simulations"
<p>Second version includes full length 6xpf-based models and corresponding MD simulations.</p>
Dataset of alternative process plan networks for dynamic integrated process planning and scheduling
<p>This dataset includes 3D models of representative manufacturing parts (jobs) as well as their features. For each of the manufacturing part, alternative process plan networks are given containing alternative machine tools, cutting tools, Tool Access Directions (TADs) and manufacturing times. The dataset also includes a detailed technical specification for all parts and calculated manufacturing times for all operations based on given alternative manufacturing resources.</p>
ScienceDex guides
Understand access before you commit
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.