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453 results for “Dynamic dataset”
Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022
<p>Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy".</p> <p>NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417)</p> <p>STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417)</p> <p>TPD.zip: Temperature programmed desorption, Figure 1a.</p> <p>UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.</p>
Binding site plasticity regulation of the FimH catch-bond mechanism: Molecular Dynamics dataset
<p>Dataset of Molecular Dynamics simulations and analysis scripts used in the article "Binding site plasticity regulation of the FimH catch-bond mechanism" [<a href="https://doi.org/10.1016/j.bpj.2023.05.029">paper</a>][<a href="https://doi.org/10.1101/2022.11.15.516604">bioRxiv</a>].</p> <p>Contains:</p> <ul> <li>Replica Exchange with Solute Scaling (REST2) simulations of the FimH protein lectin domain in its two main allosteric states (Associated and Separated), in presence and absence of its synthetic ligand heptyl α-ᴅ-mannose (input files and trajectories of the unscaled replicas)</li> <li>Replica Exchange Umbrella Sampling (REUS) simulations of the liganted systems along a collective variable (CV) describing binding site opening (input files and trajectories)</li> <li>REUS simulations in presence of a pulling force on the protein-ligand complex.</li> </ul> <p>See the article for more details.</p>
KeyRecs: Keystroke Dynamics Dataset
<p>KeyRecs is a keystroke dynamics dataset that can be used to train, validate, and test machine learning models for anomaly detection and robust typing pattern recognition, as well as the clustering and classification of users that present a similar behavior. It contains fixed-text and free-text samples of user typing behavior, obtained in a study with 100 participants of 20 different nationalities performing password retype and transcription exercises.</p> <p>The samples consist of inter-key latencies computed by measuring the time between each key press and release during an exercise, following a digraph model. Additionally, the participants were also asked to provide their demographic information regarding age, gender, handedness, and nationality.</p> <p>KeyRecs can be valuable to enhance the recognition of authorized users and prevent illegal logins in biometric authentication software, and can be combined with additional data recordings to create more extensive datasets and improve the generalization of machine learning models.</p> <p>If you use this dataset, please cite the primary data article: <a href="https://doi.org/10.1016/j.dib.2023.109509">https://doi.org/10.1016/j.dib.2023.109509</a></p> <p> </p>
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
Dataset for 'Comment on "Glassy Dynamics in Chiral Fluids" '
<p>Data sets from which figures of Dataset for 'Comment on "Glassy Dynamics in Chiral Fluids" ' were obtained, along with python code for produing the figures (in file 'correlations_matrix.ipynb').</p>
TauBench: A Dynamic Benchmark for Graphics Rendering (Dataset Reference Frames)
<p>TauBench is a dynamic graphics rendering benchmark dataset, targeted especially towards rendering methods relying on the reuse of temporal data. The dataset is available at <a href="https://doi.org/10.5281/zenodo.5729573">https://doi.org/10.5281/zenodo.5729573</a>, and this upload provides path traced reference frames for it in PNG format. The images are rendered with <a href="https://github.com/vga-group/tauray">Tauray</a> at 16384 samples per pixel (spp), at both 1080p and 2160p resolutions. Frame indices start from 0 and are <em>not</em> padded with leading zeroes.</p> <p>More information about TauBench is also available at <a href="https://webpages.tuni.fi/vga/taubench">https://webpages.tuni.fi/vga/taubench</a>.</p>
Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.)
<p>This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data".</p> <p><strong>Directories</strong></p> <p>Please untar the sciCSR-data-files.tar.gz archive.</p> <p><em><strong>Folder "Simulated_data"</strong></em></p> <ul> <li>'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis.</li> <li>'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes.</li> </ul> <p><em><strong>Folder "Seurat_objects"</strong></em></p> <ul> <li>'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below).</li> <li>'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below)</li> <li>'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775))</li> <li>'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6))</li> <li>'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas.</li> <li>* 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas.</li> <li>'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine.</li> <li>'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data.</li> <li>'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data.</li> <li>'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work.</li> </ul>
Dataset of Molecular Dynamics Simulations for the Upregulated Biomarker PSMB8: 3UNF and its G210V Mutant in Experimental Autoimmune Encephalomyelitis
<p>This dataset contains molecular dynamics simulations data generated using GROMACS for the upregulated biomarker 3UNF and its G210V mutant in the context of Experimental Autoimmune Encephalomyelitis (EAE). EAE is a widely studied animal model for multiple sclerosis, and investigating the behavior of biomarkers in this model is crucial for understanding disease progression and potential therapeutic interventions.</p> <p>The dataset includes trajectory files, coordinate files, and relevant parameters used in the simulations. These simulations provide valuable insights into the structural dynamics, conformational changes, and interactions of the PSMB8 biomarker 3UNF and its G210V mutant within the EAE system. The data offers researchers an opportunity to analyze and explore the behavior of these biomarkers at the atomic level, aiding in the identification of potential binding partners, functional sites, and mechanisms associated with disease progression.</p> <p>By sharing this dataset, we aim to contribute to the scientific community by providing a valuable resource for further analysis, validation, and comparison of the molecular behavior of the upregulated biomarker 3UNF and its G210V mutant in Experimental Autoimmune Encephalomyelitis.</p>
Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations"
<p>Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations", containing the most relevant simulation output trajectories ran with GROMACS 2021:</p> <p>1) apo simulations, including wildtype, Y28F, and H228A;<br> 2) holo simulations, including the two tested protonation states for the antibiotic;<br> 3) mutant simulations, including Y18F, H228A, E268Q, and E284Q.</p> <p>All folders contain the topology file (.top), restraint files (.itp), the initial coordinates file (.gro), and the coordinates after the first minimization (em1.gro). The output trajectories of all replicas (per system) have been concatenated in a single compressed file (.xtc).</p>
ASPLOS20-AE Artifact Dataset for 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'
<p>The empirical model database and dataset for the ASPLOS 2020 Paper 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'</p>
Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"
<p>This record contains raw data of a 3-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
Structural dynamics of DNA depending on methylation pattern: Simulation dataset
<p>Dataset of molecular dynamics simulations of double-stranded DNA<br> (repeats of CpG dinucleotides with regular patterns of methylation).</p> <p>- Input: Parameters and initial structures<br> - Output: Trajectories</p> <p>NAMD 2.13 (multi-core with CUDA) was used for the simulations.</p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
Holocene regional population dynamics and climatic trends in the Near East: a first comparison using archaeo-demographic proxies (dataset and R scripts)
<p>The present digital archive is the outcome of the paper: <strong>Palmisano, A., Lawrence, D., de Gruchy, M.W., Bevan, A., and Shennan, S., 2021. <a href="https://doi.org/10.1016/j.quascirev.2020.106739">Holocene regional population dynamics and climatic trends in the Near East: a first comparison using archaeo-demographic proxies</a>. <em>Quaternary Science Reviews</em>, <em>252</em></strong>.</p> <p>The dataset included here provides a collection of <strong>10,606</strong> radiocarbon dates, <strong>1884</strong> sites from archaeological surveys (1336 from Central Anatolia and 478 from Upper Mesopotamia) and <strong>16</strong> palaeoclimatic records for a period spanning between 14,000 and 2500 BP. In addition, the digital archive related to this paper provides reproducible analyses in the form of four scripts written in R statistical computing language.</p> <p>List of versions:</p> <ul> <li><strong>2.0.</strong> 15 December 2020 — Includes a few minor error corrections (the files 'References.txt' within the folder csv and the script 'radiocarbon.R').</li> <li><strong>1.0</strong> 28 November 2020 — First public release of the dataset on Zenodo.</li> </ul>
DEVILS: a tool for the visualization of large datasets with a high dynamic range
<p>This repository accompanying the article “DEVILS: a tool for the visualization of large datasets with a high dynamic range” contains the following:</p> <ul> <li>Extended Material of the article</li> <li>An example raw dataset corresponding to the images shown in Fig. 3</li> <li>A workflow description that demonstrates the use of the DEVILS workflow with BigStitcher.</li> <li>Two scripts (“CLAHE_Parameters_test.ijm” and a “DEVILS_Parallel_tests.groovy”) used for Figure S2, S3 and S4.</li> </ul>
Dataset for article: Perakakis P., Idrissi S., Vila J., Ivanov Ch.P. (2012). Dynamical patterns of human postural responses to emotional stimuli. Psychophysiology, 49 (9), pp. 1225–1229
<p>Dataset for article: Perakakis P., Idrissi S., Vila J., Ivanov Ch.P. (2012). Dynamical patterns of human postural responses to emotional stimuli. Psychophysiology, 49 (9), pp. 1225–1229</p>
Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885
<p>Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885</p>
2nd_dataset-Minimal radius of magnetic skyrmions: statics and dynamics
<p>In a broad range of applied magnetic fields and material parameters isolated magnetic skyrmions<br> condense into skyrmion lattices. While the geometry of isolated skyrmions and their lattice<br> counterparts strongly depend on field and Dzyaloshinski–Moriya interaction, this issue has not been<br> adequately addressed in previous studies. Meanwhile, this information is extremely important for<br> applications, because the skyrmion size and the interskyrmion distance have to be tuned for skyrmion<br> based memory and logic devices. In this investigation we elucidate the size and density-dependent<br> phase diagram showing traditional phases in field versus material parameters space by means of<br> Monte-Carlo simulations on a discrete lattice. The obtained diagram permits us to establish that, in<br> contrast to the continuum limit, skyrmions on a discrete lattice cannot be smaller than some critical<br> size and have a very specific shape. These minimal skyrmions correspond to the micromagnetic<br> configuration at the energy barrier between the ferromagnetic and the skyrmionic states.<br> Furthermore, we use atomistic Landau–Lifshitz–Gilbert simulations to study dynamics of the<br> skyrmion annihilation. It is shown that this procees consists of two stages: the continuous skyrmion<br> contraction and its discontinuous annihilation. The detailed analysis of this dynamical process is<br> given.</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </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.