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453 results for “Dynamic dataset”
Native MS dataset for: "Insights into the pathogenesis of primary hyperoxaluria type I from the structural dynamics of alanine:glyoxylate aminotransferase variants"
<p>Native mass spectrometry dataset used in: <strong>Insights into the pathogenesis of primary hyperoxaluria type I from the structural dynamics of alanine:glyoxylate aminotransferase variants.</strong> Pavla Vankova, Juan Luis Pacheco-Garcia, Dmitry S. Loginov, Atanasio Gómez-Mulas, Alan Kádek, José Manuel Martín-Garcia, Eduardo Salido, Petr Man and Angel L. Pey. FEBS Letters (2024)</p> <p><strong>Description:</strong></p> <p>Native mass spectrometry (MS) analysis verifying the oligomeric state of alanine:glyoxylate aminotransferase (AGT) protein and its P11L and I340M (LM) polymorphism and LM G170R mutation variants in primary hyperoxaluria type I.</p> <p><strong>Sample processing:</strong></p> <p>AGT protein as well as its LM and LM G130R mutants were buffer exchanged into 150 mM aqueous ammonium acetate solution (pH 7.5, MS-grade, Sigma-Aldrich) through six cycles of tenfold dilution and re-concentration using centrifugal concentrators Vivaspin 500 (30 kDa cut-off, <em>Sartorius</em>). Desalted proteins were introduced into a Synapt G2Si mass spectrometer (Waters) via static nanoelectrospray ionization from in-house prepared gold-coated borosilicate glass capillaries Kwik-Fil 1B120F-4 (<em>World Precision Instruments</em>). Protein concentration in samples was determined by 280 nm absorbance measurements using DeNovix DS-11 spectrophotometer. Samples were diluted in ammonium acetate and electrosprayed at 1 and 2 µM concentration. The mass spectrometer was carefully tuned for best signal quality and intensity, while keeping ion activation and unfolding minimal. Namely, electrospray voltage was kept at 1.3 kV, source desolvation temperature 80°C, sampling cone 80 V and 10 V collision voltage with 6 ml/min flow of argon in the trap region for thermalization of ions. Quadrupole was operated in a broadband transmission mode up to 8000 m/z while the spectra were acquired in mass range 500 – 20000 m/z. Spectra were externally mass recalibrated using known masses of caesium iodide clusters.</p> <p><strong>Data processing:</strong></p> <p>Raw mass spectra were averaged over 75 scans and further processed in Waters MassLynx 4.1. The averaged spectra were exported for ZENODO deposition as plain in plain m/z vs intensity .txt files as well uploaded as part of the .raw file format of the whole analysis (including initial metadata) with scan descriptions and parameter changes described in a stand-alone .txt descriptor file.</p>
Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing - Dataset
<p>Dataset for "<strong>Analysis of bacteria, inflammation, and exudation in epidermal suction blister wounds reveals dynamic changes during wound healing</strong>". Data were generated from samples collected to a biobank from suction blister wounds at various timepoints during a clinical trial (<a href="https://clinicaltrials.gov/study/NCT05378997?term=NCT05378997&rank=1"><strong>NCT05378997</strong></a>). Data include concentration of bacteria in swab and dressing fluid samples, neutrophil proteins (HNE, MPO, and HBP), cytokines (IFN-gamma, interleukin IL-1beta, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-alpha) and total protein measured in dressing fluid samples, and bacterial species identified by MALDI-TOF in swab and dressing fluid samples.</p>
Dataset underlying the study "Enhanced Susceptibility to Tomato Chlorosis Virus (ToCV) in Hsp90- and Sgt1-Silenced Plants: Insights from Gene Expression Dynamics"
<p>This dataset is underlying the scientific publication titled "Enhanced Susceptibility to Tomato Chlorosis Virus (ToCV) in Hsp90- and Sgt1-Silenced Plants: Insights from Gene Expression Dynamics", published in the <a href="https://www.mdpi.com/1999-4915/15/12/2370">Viruses</a> journal. </p><p>The dataset includes a time-course transcriptome analysis using RNA-seq of naïve (no whitefly and no virus), mock (non-viruliferous whiteflies) and ToCV (ToCV_viruliferous whiteflies)-treated tomato samples at 2, 7, and 14 days post-infection (dpi) and viral small RNAs derived from Tomato plants infected with ToCV at 14 dpi. The dataset provided here has been deposited in full by the authors in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB67704 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB67704"><strong>https://www.ebi.ac.uk/ena/browser/view/PRJEB67704</strong></a><br><br>The provided information in the dataset are further discussed and interpreted in detail, as well as their subsequent results, in the scientific publication.</p><p>This research was conducted within the VIRTIGATION project, which is part of the EU Open Research Data pilot. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101000570.</p>
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Dataset for the manuscript: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
<p>These are the data files used to generate the figures in the paper: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. DOI: 10.1101/2023.06.27.546748</p>
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>
IMU-Based Tip-Over Dataset for Space Exploration Rover Dynamics and Stability Analysis
<p>This dataset provides a comprehensive collection of Inertial Measurement Unit (IMU) sensor data captured from a space exploration rover under varying conditions of terrain, speed, and inclination. The primary goal of this dataset is to enable the study of dynamic stability, specifically the detection and analysis of tip-over events, which are critical for safe and efficient operation of autonomous rovers in extraterrestrial environments.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 50RA2124</em></p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
PubMed-Temporal: A dynamic graph dataset with node-level features
Open the record for dataset details and reuse information.
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
Dataset for publication "Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications"
<p>This is dataset related to paper published in 2020 IEEE I2MTC Conference proceedings:</p> <p>Crotti G., Giordano D., Letizia P. S., Delle Femine A., Gallo D., Landi C., Luiso M., Barbieri L., Mazza P., Pallidini D., (2020, October 29). Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications. https://doi.org/10.5281/zenodo.4154617</p> <p>Excel file contains the data for Figure 10 and following parameter evaluation.</p>
Dataset for Extreme Flooding and Nitrogen Dynamics of a Blackwater River
<p>Data used for analysis in the manuscript titled: Extreme Flooding and Nitrogen Dynamics of a Blackwater River.</p> <p> </p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks - Dataset
<p>This is the dataset of "Grid-graph modeling of emergent neuromorphic dynamics and heterosynaptic plasticity in memristive nanonetworks"</p>
Dataset accompanying manuscript "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope"
<p>Raw images and microscope calibration metadata for reconstruction of datasets presented in Fig. 1 and Fig. 3 of "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope". Notebooks demonstrating steps in the label-free and fluorescence anisotropy reconstruction pipelines can be found at https://github.com/mehta-lab/miPolScope.</p>
Dataset: Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model
<p>Dataset</p> <p>Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model</p> <p>Ryui Kaneko, Ippei Danshita</p>
Datasets for Dynamical Habitability of an Earthlike Planet with Two Giant Companions
<p>Two tables are included in this dataset:</p> <ul> <li>"Stability Results" table has the predicted stability outcomes for 80 Earthlike planets for a grid of parameters associated with two giant companions.</li> <li>"Relative Habitablity" has the calculated relative habitability for each configuration of giant planets. The relative habitability quantifies the predicted habitability for an Earthlike planet in a system with two giant planets compared to a system where the Earthlike planet is the only planet.</li> </ul> <p>See the accompanying readme files for more details on the columns of each table and the paper Bailey & Fabrycky 2022 for more information on how these datasets were created.</p>
KEasyLogger: Keystroke Dynamics Dataset
<p>KEasyLogger is a keystroke dynamics dataset containing labeled data of users typing free text on a desktop computer. The main characteristics of the dataset are:</p> <ul> <li>17 unique different users.</li> <li>379 sessions. Each session last one day.</li> <li>2,726,203 total digraphs.</li> <li>2,553,494 total trigraphs.</li> <li>Type of keystroke is free text.</li> <li>Scenario is desktop personal computer.</li> <li>Uncontrolled environment.</li> </ul> <p>The dataset is in MySQL format and consists of two tables, one for digraphs and one for trigraphs. Each row of the digraphs and trigraph table has the following attributes:</p> <ul> <li>Id: unique row identifiation number.</li> <li>KeyFrom: the first key of thediagraph.</li> <li>KeyTo: the second key of the digraph.</li> <li>Time: microseconds between keys</li> <li>idUser: unique user identifiation number</li> <li>idlogfile: unique session identification number. </li> </ul>
Dataset for Dynamic Analysis of Quantum Annealing Programs
<p>Quantum software engineering is emerging as a relevant field as it deals with the challenges of producing the new quantum software, whose adoption is increasing progressively. One of those challenges is how quantum software is migrated, how it operates in combination with classical software, or how it should be maintained. In this context this research focuses on reverse engineering of quantum annealing software to facilitates its integration in hybrid software systems. Quantum annealing software has gained a certain market penetration, demonstrating a good performance for optimization problems. While there are some preliminary reverse engineering techniques for gate-based quantum software, there is no reverse engineering techniques to discover the underlying optimization problem definitions (Hamiltonians functions to be minimized). Problem definitions are, in turn, dynamically defined through classical software, and can evolve over time, which make it difficult its accurate comprehension and abstract representation. Thereby, this paper presents a dynamic analysis technique for D-Wave (python) programs for reversing Hamiltonians expressions, that are additionally represented according to the Knowledge Discovery Metamodel. Due to the usage of this standard, the reversed Hamiltonians can be represented in combination with other parts of classical-quantum software systems. In order to facilitates its adoption, the proposed technique has been empirically validated through a case study with 27 D-Wave programs that demonstrates the effectiveness and efficiency. This dataset includes measures derived from that case study.</p>
Dataset for Surrogate Model Benchmarking for Dynamic Climate Impact Models
<p>The data represents time series of seasonal weather forecasts for rainfall and temperature. The dataset contains 10 forecasts of 6-month horizon from, two per year, from 2017 to 2021; start dates January 1 and July 1, respectively. Each forecast comprises 50 ensemble members. In total, this sums up to 91300 data points, each containing daily average rainfall, temperature.</p> <p>Each sample (row) comprises following features (columns):</p> <ul> <li><strong>datetime</strong>: Date of the forecast sample.</li> <li><strong>forecast</strong>: Identifier of the ensemble member, i.e. integer between 1 and total number of ensemblemembers.</li> <li><strong>precip</strong>: Averaged daily rainfall forecast in millimeters.</li> <li><strong>temp</strong>: Averaged daily temperature forecast in degree Celsius.</li> </ul> <p>Dataset created by The Weather Company, an IBM business. This service is based on data and products of the European Center for Medium-range Weather Forecasts (ECMWF-Archive and ECMWF-RT). Generated using Copernicus Climate Change Service information [2019 and ongoing]. ECMWF Archive data published under a Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/<br> Disclaimer: Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.</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.