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Dataset results
849 results for “linear”
Reference solutions for "A validated non-linear Kelvin-Helmholtz benchmark for numerical hydrodynamics"
<p>This dataset contains hdf5 files with reference solutions to the validated non-linear Kelvin-Helmholtz benchmark problem described in <a href="https://doi.org/10.1093/mnras/stv2564">Lecoanet et al. (2016)</a>. The hdf5 files include snapshots at t=2, 4, 6, 8 for the Re=10^5 D4096 simulation with Δρ/ρ0=1 (5_4096_2_*.h5), the Re=10^5 D2048 simulation with Δρ/ρ0=0 (5_2048_1_*.h5), and the Re=10^6 D2048 simulation with Δρ/ρ0=0 (6_2048_1_*.h5). The velocity, density, temperature, and dye field are contained in the tasks group. The exact output time and the grid are contained in the scales group.</p>
DLOFTBs - Deformable Linear Objects Tracking with B-splines - videos
<p>Videos of the experiments described in the paper "DLOFTBs – Fast Tracking of Deformable Linear Objects with B-splines".</p> <p>Includes 2 videos:</p> <ul> <li>3d_real.mp4 - 3D tracking of real-life DLO</li> <li>3d_art.mp4 - 3D tracking of artificially generated DLO</li> </ul>
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter. in Termite coprolites (Blattodea: Isoptera) from the Early Cretaceous of eastern Inner Mongolia, Northeast China
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter.
Supplementary material 1 from: Fernandes N, Ferreira EM, Pita R, Mira A, Santos SM (2022) The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 177-196. https://doi.org/10.3897/natureconservation.47.71864
The effect of habitat encroachment by roads on space use and movement patterns of an endangered vole
Dataset and Additional Information for the paper A LINEAR-ALGEBRAIC MODEL FOR ESTIMATING ANTI-LEARNING WHEN A DECISION TREE SOLVES THE PARITY BIT PROBLEM, by ALEXEI LISITSA and ALEXEI VERNITSKI (submitted)
<p>This upload contains a dataset and additional information for the paper A LINEAR-ALGEBRAIC MODEL FOR ESTIMATING<br> ANTI-LEARNING WHEN A DECISION TREE SOLVES THE PARITY BIT PROBLEM, by ALEXEI LISITSA and ALEXEI VERNITSKI (submitted) </p>
Addiitional Files: The diagrams of population structure, highly divergent regions, GC content and Nanopore reads depth, SNP number and Nanopore reads depth, and analyses of co-linearity against Nipponbare reference genome in 251 accessions.
<p>Additional Files for " <strong>A Super Pan-Genomic Landscape of Rice".</strong></p> <p>Addtional File1: Supplementary File1.Population structure of 251 rice accessions inferred by ADMIXTURE from K=6 to K=15.</p> <p>Additional File2: Supplementary File2.The diagram of co-linearity for assembled genome against Nipponbare refercne genome in 251 rice accessions.</p> <p>Additional File3: Supplementary File3. Highly divergent regions based on SV.</p> <p>Additional File4: Supplementary File4. The diagram of SNP number and Nanopore reads depth per 100kb windows in 251 rice accessions.</p> <p>Additonal File5:Supplementary File5. The diagram of GC content and the Nanopore reads depth per 10kb windows in 251 rice accessions.</p> <p> </p>
Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition - Data Sets
<p>Data Sets for the Paper: "Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition".</p>
Data for Cell Reports paper "Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice"
<p>Datasets for Cell Reports paper "Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice", including mouse cortical neural activity recording (preprocessed), behavioral recording and other analysis results during intermedia computation steps.</p>
Lpnet: Reconstructing phylogenetic networks from distances using integer linear programming
<p>We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute optimal circular orderings that agree with all tree splits. This approach achieves an improved approximation of the input distance for the clear majority of experiments that we have run for simulated and real data. We release an implementation in R that can handle up to 94 taxa and usually needs about one minute on a standard computer for 80 taxa. For larger taxa sets, we include a top-down heuristic which also tends to perform better than Neighbor-net.</p>
Data for "Anthropogenic linear features exhibit greater mammal activity relative to surrounding game trails in a woody savanna"
<p>Code and data investigating mammal use of anthropogenic linear features relative to game trails in South Africa. </p> <p>Article is titled "Anthropogenic linear features exhibit greater mammal activity relative to surrounding game trails in a woody savanna".</p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (non-perturbed case)
<p><span>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the absence of external disturbance.</span> </p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (perturbed case)
<p><span>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the presence of an external disturbance.</span></p>
Virtual Furuta pendulum: linear, nonlinear and AI-based controllers implementation (perturbed case)
<p>This video shows simulations of the control of the virtual prototype of the Furuta pendulum in a MATLAB/Simulink environment controlled by linear, nonlinear, and AI-based controllers in the presence of an external disturbance.</p>
Raw data for 'Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning'
<p>Raw data from AIMS simulations and scripts for image generation for the paper "Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning": J. Chem. Phys. 2024.</p>
Conformational changes in insulin receptor illuminated by live-cell linear dichroism imaging and a FLIP biosensor
<p>Response of GP2(1)-eGFP-CD59, a FLIP biosensor co transfected with insulin receptor against anatgonists and agonists of IR was measured and qunatified using polarization-resolved fluorescence microscopy. Linear dichoirism values were measured by analyinzing cells using already published imageJ macros (Bondar, Rybakova et al. 2021). This data set contains both raw images obtained from polarization micrscopy and analyzed data.</p>
Linear Transport (only datapoints)
Open the record for dataset details and reuse information.
Long Simulation of Global Sea Surface Temperature using Linear Inverse Model
<p>The content of this dataset includes (a) observed product, which is the averaged product of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST), the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), and the Centennial in situ Observation-Based Estimates (COBE). This is called "global_sst_1958to2017.mat", i.e., 60 yrs of monthly SST over 1958-2017; it also contains the trend, the seasonal climatology, the anomaly field after subtracting trend and seasonal climatology, longitude, latitude, time, and land-sea mask. (b) 20 realizations of long SST simulation generated by a Linear Inverse Model (LIM). Each realization contains 6000 months (or 500 yrs) of SST. The file is named "stochastic_simulation_lim_sst_mem*.mat". </p> <p>To cite dataset if plot or extracted used in any publication, use the reference below:</p> <p>Xu, T., et al. (2022). "An increase in marine heatwaves without significant changes in surface ocean temperature variability." <span>Nature Communications</span> <strong>13</strong>(1): 7396.</p>
Stalization of the Furuta pendulum: Linear, nonlinear and AI based controllers (nominal case)
<p><br>Stabilization control of the Furuta pendulum in Matlab/Simulink Simscape environmet in nominla case.</p> <p><br>Linear controllers</p> <ul> <li>LQR</li> <li>PID</li> </ul> <p>Nonlinear controllers</p> <ul> <li>Feedback Linearization</li> <li>SMC</li> </ul> <p>AI-based controllers</p> <ul> <li>Feedback Linearization with adaptive nerual networks</li> <li>Reiforcement Learning</li> <li>Feedback Linearization with Reinforcement Learning compensation</li> </ul> <p> </p>
Stalization of the Furuta pendulum: Linear, nonlinear and AI-based controllers (perturbed case)
<p>Stabilization control of the Furuta pendulum in Matlab/Simulink Simscape environmet in perturbed case.</p> <p>Linear controllers</p> <ul> <li>LQR</li> <li>PID</li> </ul> <p>Nonlinear controllers</p> <ul> <li>Feedback Linearization</li> <li>SMC</li> </ul> <p>AI-based controllers</p> <ul> <li>Feedback Linearization with adaptive nerual networks</li> <li>Reiforcement Learning</li> <li>Feedback Linearization with Reinforcement Learning compensation</li> </ul>
Data of Rosenbrock method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Rosenbrock method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/rosenbrock/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution at the previous time step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=200</code>: maximum number of iterations</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>nsteps=45|150</code>: number of Rosenbrock steps</li> <li><code>tspan=(4500.0, 0.0)</code>: global time span of DRE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["rosenbrock_metrics"]</code>: data frame containing execution metrics of Rosenbrock iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Rosenbrock iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed (always <code>false</code>) </li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~22h and consumed ~2.72kWh of electricity.</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.