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11,506 results for “Dynamics”
Lightweight Dynamic Build Batching Algorithms for Continuous Integration
<p>Replication package for Journal publication: "Lightweight Dynamic Build Batching Algorithms for Continuous Integration"</p>
Accompanying dataset for the paper "An explicit dynamics framework suited to highly non-smooth interface behaviors"
<h2>Contributions</h2> <ul> <li>Author #1 carried out most of the study, performed numerical simulations, and drafted the manuscript</li> <li>Author #2 helped with implementation and numerical issues</li> <li>All authors developed the methodology, conceived the study, and participated in its design, coordination, and critical review of the manuscript. All authors read and approved the final manuscript.</li> </ul> <h2>Funding sources</h2> <ul> <li>We gratefully acknowledge the French National Association for Research and Technology (ANRT, CIFRE grant number 2021/0957).</li> <li>This work was supported by the "Manufacture Française de Pneumatiques Michelin"</li> </ul> <h2>Data structure and information</h2> <ul> <li><code>data</code> -- folder for raw data<ul> <li><code>Peeling3Dv7.dgibi</code> -- <a href="https://www-cast3m.cea.fr">CAST3M</a> input file to produce the mesh (Gibiane language)</li> <li><code>Peeling3Dv7.inp</code> -- input FE data file (ASCII AVS UCD format)</li> <li><code>Peeling3Dv7.m</code> -- main matlab/Octave source file uses the open-source library matlabEF for reading input data and producing the FE required operators, which is available at <a href="https://github.com/dureisse/matlabEF.git">https://github.com/dureisse/matlabEF.git</a> and <a href="https://hal.science/hal-04647638">https://hal.science/hal-04647638</a>.</li> </ul> </li> <li><code>workflows</code><ul> <li><code>install.sh</code> -- script to reinstall the dependencies</li> <li><code>reproduce.sh</code> -- script for re-running the study</li> </ul> </li> </ul> <h2>Paper Description</h2> <p>Dynamic systems, and in particular mechanical structures, may be subjected to non-smooth loadings such as impacts or shocks. Moreover, their behavior itself may exhibit more or less non-smooth evolutions, as when fracture occurs. Therefore, robust simulation models are of interest to capture such behaviors. A particular focus is made herein on time-stepping explicit dynamics schemes to allow efficient simulations, and non-smoothness is embedded within the discrete resolution model, so that robust simulations can be obtained, with a minimum number of numerical parameters. The original contributions of this article lie in the way the non-smooth behavior is formulated to be embedded in an explicit dynamics framework. This study focuses on the solver for dynamics with non-smooth interface behavior, rather than on the behavior models themselves. The applications concern non-smooth interface behaviors at macroscopic scale, between displacement jump on the 2D interface surface with no thickness, and interfacial force distributions acting on the bodies apart the interface. The proposed test cases which can serve as benchmarks for simulation codes, concern in a first step contact and perfectly plastic interface behavior (for illustrative purpose, on a 0D example). The last numerical test deals with contact, friction, fracture and adhesion for an extrinsic perfectly brittle interface behavior, to exemplify the feasibility on a full 3D finite element model.</p>
Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"
<p>Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831<br><br></p> <p>There are 2 files in this upload:</p> <p>1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon.</p> <p>2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".</p>
Datasets and scripts for: Single-Molecule Dynamic Structural Biology with Vertically Arranged DNA on a Fluorescence Microscope
<p>The folder contains raw datasets (.ptu files) together with scripts and relevant results to reproduce the figures' plots of: "Single-Molecule Dynamic Structural Biology with Vertically Arranged DNA on a Fluorescence Microscope". </p>
Colony forming unit (CFU) data accompanying publication: Bacterial microbiome dynamics in commercial integrated aquaculture systems growing Ulva in abalone effluent water.
<p>Excel sheet with colony forming unit (CFU) data from an abalone farm growing the green seaweed <em>Ulva </em>in abalone effluent water. This dataset characterises the bacterial communities isolated from abalone effluent water entering and leaving the <em>Ulva </em>raceways, as well as from <em>Ulva </em>itself. Dataset is accompanied by two SigmaPlot files showing statistical analyses (statistical outcomes also available in publication).</p>
Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor
<p>Molecular dynamics simulations of AF1c region of human glucocorticoid receptor and its phosphovariants as described in the below paper: </p> <p>Phosphorylation regulated conformational diversity and topological dynamics of an intrinsically disordered nuclear receptor</p> <p>Vasily Akulov, Alba Jiménez Panizo, Eva Estébanez-Perpiñá, John van Noort, Alireza Mashaghi</p> <p> </p> <p>The data related to this project has been deposited in two repositories. This repository contains the second part of the data; the first part can be found at DOI: 10.5281/zenodo.13820169</p>
Companion Dataset for Manuscript "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device"
<p>This dataset provides the data necessary to reproduce the figures in the manuscript titled "A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device". Combined with the cQED4ChemDyn code, provided through Github and Zenodo, it regenerates the figures in the manuscript.</p> <p>The project itself provides an implementation that connects chemical kinetics of elementary reactivity models with the framework of the Kerr-Cat circuit quantum electrodynamics (cQED), using the Hamiltonian describing the physics of the hardware and a Lindbladian open quantum dynamics formalism for the time-evolution of the system. For more information, check the existing citation for the publication; any usage of the code/data should cite the preprints (and publications) once available.</p>
Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon
<p>We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories.</p> <p>The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.</p>
Automated discovery of reprogrammable nonlinear dynamic metamaterials — Data
<p>This dataset includes optimization and experimental data complementing the paper:</p> <p><a href="https://doi.org/10.1038/s41563-024-02008-6" target="_blank" rel="noopener">G. Bordiga, E. Medina, S. Jafarzadeh, C. Boesch, R. P. Adams, V. Tournat, K. Bertoldi. Automated discovery of reprogrammable nonlinear dynamic metamaterials. <em>Nature Materials.</em> (2024)</a>.</p> <p>Optimization and post-processing data in this dataset were generated using the code <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> developed for the paper. This dataset can be loaded and visualized using <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a> with the following steps:</p> <ul> <li>Install <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Download <code>data.zip</code> from this dataset.</li> <li>Extract <code>data.zip</code> and place its content in a <code>data</code> folder in the root of <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">DifFlexMM</a>.</li> <li>Load the data associated with the design problems shown in the paper using the <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/notebooks" target="_blank" rel="noopener">notebooks</a>.</li> </ul> <p>For more information on each problem, please refer to the <a href="https://github.com/bertoldi-collab/DifFlexMM" target="_blank" rel="noopener">README</a>.</p> <p>Videos illustrating the solved design problems can be viewed at <a href="https://github.com/bertoldi-collab/DifFlexMM/tree/main/videos" target="_blank" rel="noopener">DifFlexMM/videos</a>.</p>
Social-ecological dynamics of quarry restoration: a Flickr data analysis
<div> <div> <div> <p><span>With increasing urbanization and demand for construction materials, quarries have become central to the recovery of degraded landscapes into spaces that offer ecological, but also social benefits. While ecological restoration has long been investigated, integrated social-ecological restoration of post-mining landscapes remains underexplored. The overall aim of this study is to assess the perceptions of cultural ecosystem services and landscape features expressed in social media posts about quarries in Germany, Denmark, and the Czech Republic. We focus on concepts of cultural ecosystem services and landscape features to investigate the interactions between humans and restored ecosystems. Using a mixed-methods approach, we analyzed 1,660 geotagged photographs from 50 quarries across three regions: Berlin, Roskilde, and the Czech Karst. Flickr social media images were analyzed to elicit the richness of cultural ecosystem services (CES) and landscape features (LF), highlighting popular quarries and their social-ecological significance. Our results indicate that rehabilitated quarries exhibit higher CES richness than abandoned or operational ones, and that accessibility significantly influences public engagement. Our study demonstrates that once primarily industrial sites, quarries can evolve into vibrant social-ecological systems that provide diverse landscape features and cultural ecosystem services. It also points to the potential of social media data for designing restoration efforts from a social-ecological perspective. Such an approach provides insights into public perceptions of restored landscapes and may inform future restoration strategies. </span></p> <p> This dataset includes: (1) the review protocol, (2) a list of place names used for data collection on Flickr when posts were not geolocated, (3) data on landscape features and cultural ecosystem services identified in Flickr posts from 50 study quarries, (4) characteristics of the quarries, and (5) a shapefile of the quarry polygons.</p> <p> </p> <p> </p> </div> </div> </div>
Replication Data For: Spiking patterns in the globus pallidus highlight convergent neural dynamics across diverse genetic dystonia syndromes
<div><strong>Human Globus Pallidum Single-Unit Activity Dataset in Genetic Dystonia Patients</strong></div> <div> </div> <div>This dataset consists of tabular data encompassing diverse neural features extracted from spiking trains of stable single-unit activity. These units were isolated from raw microelectrode recordings obtained from the globus pallidum of genetic dystonia patients who underwent globus pallidus internal (GPi) deep brain stimulation (DBS) surgery. The dataset includes anonymized patient IDs, details about the patient's genetic dystonia mutation, as well as information on the hemisphere and depth of microelectrode recordings (MER). Additionally, it features neural properties such as firing rate, spiking regularity, neural bursts, oscillations, and pause characteristics of isolated single-unit activities (SUAs).</div> <div> </div> <div>To process the raw MER, we applied a semi-parametric offline spike sorting algorithm to isolate SUAs. The SUAs were analyzed both in the temporal and frequency domains to derive a comprehensive set of features related to spiking patterns.</div> <div> </div> <div>For those interested in replicating or understanding the feature extraction process, the MATLAB source code is available in the <a href="github.com/ahmetofficial/Spike-Feature-Generator">Github repository</a>.</div>
Data used in the study: Highly dynamic gamma-ray emissions are common in tropical thunderclouds
<p>This repository includes the data presented in the study: Highly dynamic gamma-ray emissions are common in tropical thunderclouds.</p> <p>Detailed description is given in the uploaded pdf document: Data_description.pdf</p>
Assessing Nonadiabatic Dynamics Methods in Long Timescales
<p>In this study, we employ the multiconfiguration time-dependent Hartree (MCTDH) and its multi-layer variants (ML-MCTDH), ab initio multiple spawning (AIMS), and fewest-switches surface hopping (FSSH) methodologies to simulate the excited-state dynamics of a weakly-coupled multi-dimensional (10 dimensional) Spin-Boson model Hamiltonian designed for a long timescale decay behavior. </p> <p>The pre-print of the article can be found at <a href="https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a">https://chemrxiv.org/engage/chemrxiv/article-details/66f68454cec5d6c142647b0a</a></p> <p>MUKHERJEE S, Lassmann Y, Mattos RS, Demoulin B, Curchod BFE, Barbatti M. Assessing Nonadiabatic Dynamics Methods in Long Timescales. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-j7xxl </p> <p> </p> <p><strong>The dataset contains</strong></p> <ul> <li>MCTDH.tar.bz2 - 10 sets of text files for MCTDH simulations </li> <li>ML-MCTDH.tar.vz2 - 10 sets of text files for ML-MCTDH simulations</li> <li>DC-FSSH.tar.bz2 - HDF5 output files for 2000 independent decoherence-corrected FSSH trajectories and a Python script for Wigner Sampling initial conditions </li> <li>CSS-AIMS.tar.gz - output text files for 44 independent cannibalistic stochastic selection approach of AIMS trajectories</li> </ul>
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</p>
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
Molecular Dynamics Trajectories Exploring the Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution
<h3>Primary data for the publication "Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution" by de Bruyn, Dorn, Rossetti, Fernandez, Outeiro, Schulz and Carloni. Submitted to the Journal of Chemical Information and Modeling.</h3> <p>Included are all GROMACS input files, parameterised topologies, starting and final configurations, and trajectories for the lowest temperature replica (at 300 K, lowest of 32 replicas between 300-500 K exchanging according to the REST2 algorithm (Wang et al. 2011)). The data is split into three archives:</p> <ol> <li><strong>all_atom_trajectories.zip</strong> <ul> <li>contains all input files and all atom trajectories including solvent</li> <li>trajectories written at 100 ps intervals</li> </ul> </li> <li><strong>protein+ion_trajectories.zip</strong> <ul> <li>contains configuration/non-parameterised topologies and trajectories excluding solvent, but including ions</li> <li>trajectories written at 10 ps intervals</li> </ul> </li> <li><strong>additional_simulations.zip</strong> <ul> <li>contains the all atom trajectories and input files, and</li> <li>solvent-free trajectories above,</li> <li>for the additional simulations in the Supplemental Information of the article: <ul> <li>includes the DES-Amber-based simulations with 64 replicas between 300-600 K, and</li> <li>a99SB-<em>disp</em>-based simulations</li> </ul> </li> </ul> </li> </ol> <p> </p> <p>Folders are named according to the following top level scheme:</p> <ul> <li><strong>DES-Amber simulations/</strong> Simulations created using the DES-Amber force field (Tucker et al. 2022)</li> <li><strong>a99SB-<em>disp</em> simulations/</strong> SImulations created using the a99SB-<em>disp</em> force field for Intrinsically Disordered Proteins (IDPs) (Robustelli et al. 2018)</li> </ul> <p>Sub-folders follow the following scheme:</p> <ul> <li><strong>AS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>unphosphorylated</li> </ul> </li> <li><strong>pAS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>phosphorylated at S129</li> <li>with double negative charge</li> </ul> </li> <li><strong>pASH/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein (a99SB-<em>disp</em> simulations only) <ul> <li>phosphorylated at S129</li> <li>with a single negative charge (monoprotonated)</li> </ul> </li> </ul> <p> </p>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
Dataset: Hybrid Polarizing Solids with Extended Pore Diameters for Dissolution Dynamic Nuclear Polarization
<p>This dataset contains raw NMR, EPR, and relaxometry data, N2 adsorption-desorption isotherms for best HYPSOs, and all of the codes, used for data processing and figures in the article.</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.