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.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
Data from: The mediating effect of perceived Coach's emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model
<p>This data set is from the study titled, "The mediating effect of perceived Coach’s emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model."</p>
Code and data from : Using a spatially explicit population model to evaluate cost-effective management scenarios for an invasive deer population
<p>This record contains the following:</p> <p>-"WoJ SEPM.Rmd": Script used to build our spatially explicit population model, run simulations for our scenarios and calculate population summary statistics</p> <p>-"WoJ cpue.Rmd": Script used to run our catch-per-unit-effort model that estimates relationship between deer density and number of deer shot per hour</p> <p>-"Cost estimates.Rmd": Script used to calculate costs for each scenario</p> <p>-"Costs functions.R": Functions that are called in the "Cost estimates" script.</p> <p> </p> <p>In addition, all datafiles required to run the scrips are included here.</p>
Dataset of the paper "Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models"
<p>The Excel file contains the dataset whose analysis has been presented in the manuscript entitled <em>"Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models" </em>and published in J Neuroeng Rehabil (doi: 10.1186/s12984-024-01339-4; PMID: 38566189).</p> <p>This data set cannot be publicly available because of sensitive information. Please send your request to: a.caronni@auxologico.it.</p>
Yerrida Basin Geophysical Modeling - Input data and inverted models.
<p>This companion datasets relates to the manuscript "<strong>Integration of geological uncertainty into geophysical inversion by means of local gradient regularization</strong>", by J. Giraud, M. Lindsay, V. Ogarko, M. Jessell, R. Martin and E. Pakyuz-Charrier, submitted to Solid Earth. The archive contains the input and output geophysical data, starting and inverted models, probabilistic geological model and conditioning volume derived from the calculation of Shannon's entropy. </p>
Modelling of NBI ion wall loads in the W7-X stellarator: Wall load data
<p>Wendelstein 7-X stellarator neutral beam injected ion wall load calculation with the ASCOT code.</p> <p>Simulation result data from the Nuclear Fusion article <a href="https://doi.org/10.1088/1741-4326/aac4e5">https://doi.org/10.1088/1741-4326/aac4e5</a>.</p>
Salinity Yield Modeling Data - Upper Colorado River Basin, Nauman et al
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., 2019, Salinity yield modeling spatial data for the Upper Colorado River Basin, USA: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P9QSFDJN">https://doi.org/10.5066/P9QSFDJN</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., Ely, C. P., Miller, M. P., and Duniway, M. C., 2019, Salinity Yield Modeling of the Upper Colorado River Basin Using 30-m Resolution Soil Maps and Random Forests: Water Resources Research, v. 55, no. 6, p. 4954-4973. <a href="https://doi.org/10.1029/2018WR024054">https://doi.org/10.1029/2018WR024054</a></p> <p>This data set was developed for salinity yield modeling in the Upper Colorado River Basin in preparation of the paper "Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and machine learning" that is to be submitted to Water Resources Research. A github repository was also prepared to document the use of this data in the paper, and is available at https://github.com/naumi421/UCRB_Salinity.</p> <p>Please see the included file "README_SalinityYieldModel_documentation_initial_table_ReviewData.docx" for detailed descriptions of all included files.</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p>
X-ray models from "An XMM-Newton spectral survey of 12 µm selected galaxies - I. X-ray data"
<p>Copied table models that used to be hosted at http://astro.ic.ac.uk/mbrightman/home</p> <p>from publication: https://ui.adsabs.harvard.edu/abs/2011MNRAS.413.1206B</p>
ROAD CONGESTION PREVENTION MODELS BASED ON DATA FROM DIFFERENT SOURCES.
<p><em><span>This article analyzes data from various sources on road congestion prevention models. Approaches to reduce congestion include modeling traffic flow, using intelligent transportation systems, improving transportation infrastructure, developing public transportation, and providing information to drivers. The article explains how these models can be used together to improve the efficiency of modern transport systems and ensure road safety. As a result, it identifies the key factors needed to develop innovative solutions and strategies in traffic prevention.</span></em></p>
Case data: modeling spatial determinants of sugarcane abandonment in Rio de Janeiro
<p>This repository encompasses datasets for modeling spatial determinants of sugarcane abandonment in Rio de Janeiro, Brazil. Data includes previously published datasets and other publicly available data. Sugarcane mapping datasets should be referred to as outcomes from publication: https://doi.org/10.1016/j.rse.2022.113194 and might be used freely. The other datasets come from secondary sources and might be used for reproducibility. Further uses depend on the original data source policy. </p> <p>MSWEP data is released under the Creative Commons Attribution-NonCommercial 4.0 International (<a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a>) license. Please get in touch with the authors if you are affiliated with a commercial entity and want to try MSWEP. If you do not have a commercial affiliation and you intend to use the product for non-commercial purposes, please send the authors a request using the form on MSEP webpage: <a href="http://www.gloh2o.org/mswep/">http://www.gloh2o.org/mswep/</a></p>
Data Set: In-session dropout prediction model
<p>In-session dropout prediction model</p> <p>This project describes an in-session prediction model that predicts student early dropout from online learning exercises.<br> Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in<br> the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates,<br> school homework and assignments are supposed to be completed by all learners. In the pandemic, online<br> learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to<br> transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting<br> learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the<br> online language learning platform orthografietrainer.net. We calculated time-progressive machine learning<br> models that predict dropout after each step (completed sentence) in the assignment using learning process<br> data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention<br> strategy that distinguishes between motivational and subject-specific interventions. <br> A random state is not set, thus, results might differ marginally.</p> <p>Whole project described in: <br> N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart<br> Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios<br> Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,<br> SciTePress, 2022, ISBN 978-989-758-562-3 </p> <p> </p> <p> </p>
LAPSO PM2.5 in Scotland (training data and the model)
<p>Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO</p> <p>Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.</p>
ABS-rich model waste characterization for different sampling strategies – ATR FTIR data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw ATR-FTIR data of the model waste from the different sampling approaches. The content is:</p> <ul> <li>One Excel file containing ATR-FTIR data of the model waste, wherein the approach was based on extrusion and measurement protocol</li> <li>One Excel file containing ATR-FTIR data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</li> <li>One Readme file containing further information about the methodology and nomenclature </li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p> <p> </p> <p> </p>
ABS-rich model waste characterisation for different sampling strategies – MFR data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw MFR data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing MFR data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing MFR data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Word file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
ABS-rich model waste characterisation for different sampling strategies – TGA data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw TGA data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing TGA data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing TGA data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Word file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
ABS-rich model waste characterisation for different sampling strategies – DSC data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw DSC data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing DSC data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing DSC data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Word file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon European project (101058670)</p>
ABS-rich model waste characterization for different sampling strategies - XRF data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains XRF data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing XRF data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing XRF data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Readme file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling
<p>Milk and fungi dataset (already split in calibration and test set) in .mat format + MATLAB workspaces for i) variable selection (for milk and fungi); ii) PLSR on broadband spectra (for milk and fungi) iii) PLSR on single wavelengths (for milk and fungi); iv) PLSR on tuned wavelengths (for milk and fungi); v) PLSR test for fungi on milk selected wavelengths (FM_).</p>
PriOmics: integration of high_throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors
GEO Series GSE253910. Homo sapiens. 360 samples. Type: Expression profiling by high throughput sequencing.
Expression data from Imiquimod-induced psoriasis mouse model
GEO Series GSE104603. Mus musculus. 12 samples. Type: Expression profiling by array.
Expression data from a human primary syncytiotrophoblast model in response to double-stranded RNA
GEO Series GSE224785. Homo sapiens. 2 samples. Type: Expression profiling by array.
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.