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Dataset results
89 results for “linear model”
AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.
<p>Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article.<br> <br> Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.</p>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Datasets for the paper "A linear parameter-varying modelling approach for dielectric elastomer loudspeakers"
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "A linear parameter-varying modelling approach for dielectric elastomer loudspeakers" by G. Moretti et al., In <em>IFAC-PapersOnLine Volume 55, Issue 20, 2022</em> (<a href="https://doi.org/10.1016/j.ifacol.2022.09.150">https://doi.org/10.1016/j.ifacol.2022.09.150</a>).</p> <p>Please refer to the readme file for information on the files structure and content. </p>
VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic grids for interpolation
<p>VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic interpolation grids in ASCII format. The generation of these grids is described in http://doi.org/10.1007/s00190-015-0871-8</p>
Sample FITS file with non-linear wavelength solution using a Chebyshev model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Chebyshev model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a cubic spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a cubic spline model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a Legendre model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Legendre model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a linear spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a linear spline model.</p> <p>This data is in its original shape.</p>
Data from: Calculating global annual methane increases from satellite data using an ensemble dynamic linear model approach
<p><em><strong>NOTE: This is no official S5P/TROPOMI WFMD XCH4 L3-Dataset.</strong></em></p> <p>This data is used and created by the example code provided in <a href="http://www.doi.org/10.5281/zenodo.8178927">10.5281/zenodo.8178927</a>, which is a supplement to the manuscript <em>'Zonal variability of methane trends derived from satellite data' </em>(Hachmeister et al., 2024 ; 10.5194/acp-24-577-2024). This data can be downloaded to skip the gridding step in the mentioned example code, to avoid downloading the complete input data.</p>
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Linear Time Varying System Examples for Model Order Reduction
<p>Three linear time varying system benchmarks implemented in MATLAB. </p> <p>1.) a time varying version of the Oberwolfach Steel Cooling Benchmark</p> <p>2.) a one dimensional heat equation with a moving point heat source</p> <p>3.) a linearized Burgers equation</p> <p>Model 1 comes in the same 5 resolutions as the original time-invariant version. the other two are freely scalable.</p> <p> </p>
Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations
<p>The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA). </p> <p> </p> <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol> <p> </p>
data sets of "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"
<p>data sets from "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland"</p> <p>Monthly means ozone profiles data sets of MCH homogenized Dobson D051 and of Brewer B040 used in the article entitled: "Dynamic Linear Modeling estimates of long-term ozone trends from homogenized Dobson Umkehr profiles at Arosa, Switzerland" by Eliane Maillard Barras, Alexander Haefele, René Stübi, Achille Jouberton, Herbert Schill, Irina Petropavlovskikh, Koji Miyagawa, Martin Stanek, and Lucien Froidevaux.</p> <p><a href="https://doi.org/10.5194/acp-2022-344">https://doi.org/10.5194/acp-2022-344</a></p>
Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach
<p>Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation processes may partially buffer species from the impacts of these changes, but currently the knowledge about the temporal dynamics of acclimatisation remains limited. Acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterize the natural environment. Through a Distributed Lag Non-linear Model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short term responses. Our results also indicate that individuals may de-acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments. </p>
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Fig. 2. Linear regression models showing the relationship between Aphis citricola and Harmonia axyridis abundance. A in Behavioral responses of Aphis citricola (Hemiptera: Aphididae) and its natural enemy Harmonia axyridis (Coleoptera: Coccinellidae) to non-host plant volatiles
Fig. 2. Linear regression models showing the relationship between Aphis citricola and Harmonia axyridis abundance. A: Catnip (Nepeta cataria) + French marigold (Tagetes patula), B: ageratum (Ageratum houstonianum) + French marigold, C: catnip + ageratum, and D: native vegetation.
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.