Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
239
datasets available to search
ShareScore release 0.9.0
Dataset results
239 results for “Data-driven”
AR12473 Time-Dependent Data-Driven Magnetofrictional Simulation
<p>This dataset is an excerpt of the time-dependent data-driven magnetofrictional modelling carried out in Price et al. (2020). This data is provided with a 6-hour cadence. For greater temporal resolution, please contact the authors. If you use this data, please cite the following publication:</p> <ul> <li>Price, D. J., Pomoell, J., & Kilpua, E. K. J. 2020, A&A, 644, A28 (doi: <a href="https://doi.org/10.1051/0004-6361/202038925">10.1051/0004-6361/202038925</a>)</li> </ul> <p>A compatible data-loader can be found, for example, in the <a href="https://github.com/pricedj/mafiat">MAFIAT repository</a> (Magnetic Field Analysis Tools, Price et al. 2022).</p>
Data-driven surrogate model for wind turbine damage equivalent load
<p>There are four zip files in this data set:</p> <ul> <li>PythonCode_OpenFAST: The code used to generate 32768 OpenFAST fst files to build the database.</li> <li>ML_TrainingCode: The code that used to train the TCN-FCNN and FCNN models for both free stream and wake</li> <li>Trained_Models: All the trained models are saved in Keras format. The models with max in their filenames were trained on maximum values. The models with XY in their naming were trained on wind in the X and Y directions.</li> <li>data: It includes all the CSV files for training and testing.</li> </ul>
Data-driven discovery of photoactive quaternary oxides: dataset
<p>Dataset required for the analysis at <a href="https://github.com/WMD-group/Solar_oxides_data">this repository</a>. The link to the original publication pertaining to this dataset is available in the repository. The dataset consists of:</p> <ul> <li>Quaternary oxide compositions generated by SMACT and featurized using the Matminer package (All_oxide_comps_dataframe_featurized.csv)</li> <li>Quaternary oxide compositions predicted to have a bandgap within the target window of 1.75 +/- 0.02 eV by a gradient boosting regression model, as used in the original publication. (Useful_BGs_published.csv)</li> <li>Crystal structures upon which first-principles (DFT) calculations were carried out in the original publication (structures_published.json)</li> <li>VASP files for compounds upon which first-principles (hybrid DFT) calculations were carried out in the original publication</li> </ul>
Resolution limit of data-driven coarse-grained models spanning chemical space
<p>This repository contains all databases referenced in the supporting information of the paper titled "Resolution limit of data-driven coarse-grained models spanning chemical space" by Kiran H. Kanekal and Tristan Bereau.</p>
Python functions -- cross-validation methods from a data-driven perspective
<p>This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), dissimilarity quantification by adversarial validation (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.</p>
Data-driven Discovery of Snow Cover Parameterization
<p>All data were derived from SNOTEL, Version 1, and were preprocessed for training symbolic regression models in convenience. Train and test data are range from water years of 2001-2009, and of 2010-2018. All NaN value were removed from data and shape as [Sample, feat]. The dataset contains preprocessed dimensonless features:</p> <p>The name and unit of each feature were listed in sequence as follows:</p> <p> 1. Snow depth (mm)<br> 2. Snow water equivalent (mm)<br> 3. Standard deviation of sub-grid topography (m)<br> 4. Air temperature (K)<br> 5. Precipitation (mm/day)<br> 6. 1/snow density (mm/mm)<br> 7. 1/Standard deviation of sub-grid topography (m^-1)</p> <p>The name and unit of target were listed as follows:</p> <p> 1. Snow cover fraction [%]</p> <p>Some own defined constant:</p> <ol> <li>surface roughness (0.1 m)</li> <li>0 degree of temperature (273.16 K)</li> <li>own defined std threshold (200 m)</li> <li>mean SWE (122.3 mm)</li> </ol>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 1
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 6.20% to 43.82% with approximately 2% intervals. In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p>
Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization
<p><span>Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named </span><span>Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models.</span> <span>Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.<br><br>This project provide the source dataset used to train and evaluate the overall model.<br></span></p>
Data-driven Soil Moisture Sensing with mmWave Radar
<p>This is a mmWave radar soil moisture dataset, which includes two different environments, specifically two conference rooms with different layouts. In Environment 1, soil with 20 different moisture levels was collected, ranging from 6.20% to 43.82%, with an interval of approximately 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 6.20%, 8.30%, 10.80%, 12.60%, 14.44%, 16.14%, 18.96%, 19.70%, 23.34%, 25.24%, 26.24%, 28.90%, 31.10%, 32.46%, 33.80%, 36.44%, 38.20%, 40.56%, 42.68%, and 43.82%, respectively.In the bin file names, the second number after "data" represents the height of the radar development board above the soil surface, measured in centimeters, specifically 16 cm, 20 cm, and 24 cm. The data was collected using the IWR1843 radar board, with 1 transmitting antenna and 4 receiving antennas. Each bin file contains 128 radar frames, with each frame consisting of 32 chirps, and each chirp containing 384 sampling points.</p> <p>In Environment 2, soil with 20 different moisture levels was collected, ranging from10.0% to 48.0%, with an interval of 2%.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
Data-driven Soil Moisture Sensing with mmWave Radar In Environment 2
<p>This is a millimeter-wave radar soil moisture dataset, which includes soil samples with 20 different moisture levels, ranging from 10.0% to 48.0% with approximately 2% intervals.In the bin file names, the first number after "data" represents the soil moisture level, labeled as 1 to 20, with moisture levels of 10.0%, 12.0%, 14.0%, 16.0%, 18.0%, 20.0%, 22.0%, 24.0%, 26.0%, 28.0%, 30.0%, 32.0%, 34.0%, 36.0%, 38.0%, 40.0%, 42.0%, 44.0%, 46.0%, and 48.0%, respectively.In the bin file name, the second number after 'data' indicates the environment: 0 represents a static environment, and 1 represents a dynamic environment, where there is human movement.In the bin file name, 'train' and 'test' after 'data' represent the training and testing data, which were collected in two separate sessions.The data was collected using the IWR1843 radar board, which has 3 transmitting antennas and 4 receiving antennas. Each bin file contains 64 radar frames, with each frame consisting of 96 chirps, and each chirp containing 384 sampling points.</p>
Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion
<p>Data from: Data-driven analysis of oscillations in Hall thruster simulations</p> <p> </p> <p>- Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino</p> <p>- Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a></p> <p>- Date: 2022-03-24</p> <p>- Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a></p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a></p> <p> </p> <p>Abstract</p> <p> </p> <p>This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication:</p> <p>Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 <em>Plasma Sources Sci. Technol.</em> 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>.</p> <p>Additionally, the raw simulation data is also employed in the following journal publication:</p> <p>Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 <em>Mach. Learn.: Sci. Technol.</em> 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a></p> <p> </p> <p>Dataset description</p> <p> </p> <p>The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>.</p> <p>Please refer to the relative article for further details regarding any of the parameters and/or configurations.</p> <p> </p> <p>Data files</p> <p> </p> <p>The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended.</p> <p>The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as:</p> <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> <p>In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables.</p> <p>The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.</p> <p> </p> <p>Citation</p> <p> </p> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p>The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.</p> <p>Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.</p> <p> </p> <p>Acknowledgments</p> <p> </p> <p>This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.</p>
Accompanying data for the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification"
<p>Experimental Data accompanying the paper "Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ijimpeng.2024.105083" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijimpeng.2024.105083</a></p> <p>One (holed and double notched) specimen is dynamically loaded via an hydraulic tensile test machine (MTS-819, 20 kN) at 5 m/s. The specimen were cut from a 0.8 mm-thick DC04 (XES French standards) sheet in the rolling direction provided by ONERA. Reference image is captured using a high definition camera (29 Mpix, Prosilica GT from Stemmer) combined with the same objective lens than the one used for experiments. Deformed sample images are captured using the rotating mirror Ultra-high speed (HR-UHS) Cordin camera Model 580 at 68 kfps with a resolution of 3296 x 2472 pixels. The field of view is 35.8mm x 47.9mm leading to a pixel size of 14.49um. Ufreckles <a href="https://doi.org/10.5281/zenodo.1433776">10.5281/zenodo.1433775</a> is used to perform FE-based DIC using T3P1 linear triangular elements and a Tikhonov regularisation (over 3 elements). Eventually, kinematic data and load measurement are used to identify stress fields via Data-Driven stress Identification (DDI) method.</p> <p>Are provided:</p> <ul> <li>raw images, camera distortion modes and parameters, load net force and timeline</li> <li>kinematic fields obtained from Digital Image Correlation</li> <li>Stress fields identified using Data-Driven stress Identification</li> </ul> <p>Matlab Codes to produce results (working with Ufreckles)</p> <ul> <li>MultiSensor_DIC_script.m: in /Codes/ is the main script to run DIC</li> <li>Shape functions: in /Codes/shape_functions/ containing Zernike polynomial shape functions and deconvolution algorithm to get effective displacement from total displacement knowing camera distortions</li> </ul> <p> </p>
Complementary dataset of the paper "The viewing angle in AGN SED models, a data-driven analysis"
<p>In this repository, you can find the SED data from the X-CIGALE estimates from the article: "<a href="https://academic.oup.com/mnras/article/510/1/687/6448487">The viewing angle in AGN SED models: a data-driven analysis</a>"</p>
Data-driven predictions of summertime visits to lakes across 17 US states
<p>Using a dataset of more than 51,000 US lakes, we estimated the relationship between summertime lake visits, lake water quality, landscape features, and other amenities, where visitation was estimated using counts of geolocated photographs. Given the size and complexity of our dataset, we used a combination of machine learning techniques, imputation techniques, and a Poisson count model to estimate these relationships. We found that every additional meter of average summer-time Secchi depth was associated with at least 7% more summer-time lake visits, all else equal. Second, we found that lake amenities, such as beaches, boat launches, and public toilets, were more powerful predictors of visits than water quality. Third, we found that visits to a lake were strongly influenced by the lake's accessibility and its distance to nearby lakes and the amenities the nearby lakes offered. Our research highlights the need for 1) a better understanding of how representative social media data are of actual recreational behavior, 2) the development of best practices to account for non-random patterns in missing natural feature data, and 3) a better understanding of the potential reverse causality in the lake visit-water quality relationships. </p>
Research data supporting: "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields"
<p>This repository contains the data used in the paper of Capelli <em>et al. </em>"A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields", published on Journal of Physical Chemistry B (DOI: 0.1021/acs.jpcb.1c02503).<br> <br> The archive traj_processed.tar.gz contains the trajectories converted in xyz format with the dimensions of the box.</p> <p>The archive trajectories_xtc.tar.gz contains the raw trajectories (of the membranes without solvent) in gromacs xtc format with a .tpr binary file. <br> </p>
Data-driven modeling of beam loss in the LHC
<p>LHC diagnostics datasets from runs 2017 & 2018 used for training of beam loss machine-learning models for research paper "Data-driven modeling of beam loss in the LHC".</p>
Supplementary data to: Soil Moisture to Runoff (SM2R) A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics
<p>This data archive includes simulated monthly runoff anomaly using the Soil Moisture to Runoff model during 1981‒2020 over the representative drainage basin in each water tower. Please see Readme for more data information. For calculation details please see the publication.</p>
Data from: A real data-driven simulation strategy to select an imputation method for mixed-type trait data
<p>Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly completed information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.</p>
Supplementary files for "Data-driven estimation of nitric oxide emissions from global soils based on dominant vegetation covers"
<p>In-situ observations collected from publications, data-driven model codes, DNDC simulation files, and the supporting data for all figures of this study are uploaded. In-situ observations included 1,356 observations of soil nitric oxide (NO) emissions from 192 sites, including 1,032 for cropland soils from 70 sites, 114 for grassland soils from 36 sites, and 208 for forest soils from 86 sites. Data-driven models provided three machine learning methods, including random forest (RF), generalized boosted regression model (GBM), and radial basis function (RBF). The DNDC simulation files included simulation files of 51 selected sites.</p>
Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics
<p><strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong></p> <p>Processed ACF level data can be provided by contacting me.</p> <p><strong>DOI of the publication:</strong></p> <p><strong>README file:</strong></p> <p>PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5</p> <p>HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit)</p> <p>ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation.</p> <p>The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)</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.