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.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “Multivariate regression”
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</p>
Reliable phylogenetic regressions for multivariate comparative data: illustration with the MANOVA and application to the effect of diet on mandible morphology in Phyllostomid bats
Open the record for dataset details and reuse information.
Multivariate Ordinary Least Squares (OLS) regression-based Seismic Hazard Model Data
<p>This dataset includes earthquake parameters, slab geometry, gravity anomalies, and fault proximities used for seismic hazard modeling in the Makran Subduction Zone (MSZ). Supplementary Table S1 contains earthquake data (location, depth, magnitude), slab properties (depth, dip, thickness, strike), and distances to key faults. Supplementary Table S2 provides intraslab seismicity, slab geometry, trench distances, and gravity data. The data are sourced from the USGS Earthquake Catalog, IRIS, Slab-2 model, GMRT, and other geophysical models.</p>
North Atlantic Ocean Drifter Dataset for Multivariate Probabilistic Regression with Natural Gradient Boosting
<p>Dataset created for Multivariate Probabilistic Regression with Natural Gradient Boosting application section. Supplied for replication. Variables in the dataset:</p> <ul> <li>Tx, Ty, Wx, Wy: Wind Stress (Pa) (T), Wind Speed (m/s) (W) in the longitudinal (x) and latitudinal (y) directions.</li> <li>u_av, v_av: surface geostrophic sea water velocity (m/s) longitudinal and latitudinal respectively.</li> <li>lon,lat: The longitude (-180,180) and latitude (-90,90) of the drifters.</li> <li>t: day of year (0,366).</li> <li>u, v: The velocities of the drifters (m/s).</li> </ul> <p>Note u, v, Tx, Ty, Wx, Wy are all filtered as described in the papers supplemental information.</p> <p>If used the following must be acknowledged or cited appropriately. You must follows the terms of the use of the following sources:</p> <p>The Global Drifter Program</p> <p>https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php</p> <p>(lon, lat, u, v)</p> <p>CMEMS datasets which are interpolated to the drifter locations:</p> <p>1. GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE) https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057/INFORMATION<br> (u_av, v_av)</p> <p>2. Global Ocean Wind L4 Reprocessed 6 hourly Observations https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L4_REP_OBSERVATIONS_012_006/INFORMATION (Wx, Wy, Tx, Ty)<br> <br> <br> <strong>Note</strong> this dataset will not be regularly updated. Any changes in the source products will not be brought into this dataset.</p>
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Article Multivariate Adaptive Regression Splines enhances Genomic Prediction of non-additive traits
Open the record for dataset details and reuse information.
A Scalable Local Algorithm for Distributed Multivariate Regression
This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the quality of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims.
An Efficient Local Algorithm for Distributed Multivariate Regression
This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the “quality ” of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims.
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.