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ShareScore release 0.7.1
Dataset results
233 results for “kernel”
Accurate resampling of radial dose point kernels to a Cartesian matrix for voxelwise dose calculation
<p>Note: The version 1.1 dataset has been corrected to include only accurate kernels. Errors were found in Version 1.0 kernels with the following sizes: 1.953, 2.21, 2.4, 3.9, 4, 4.42, 4.8, and 6.8 mm</p> <p>Cartesian matrix dose point kernels, generated by resampling of prior radial dose point kernels. See accompanying paper for additional details: "<strong>Accurate resampling of radial dose point kernels to a Cartesian matrix for voxelwise dose calculation"</strong></p> <p>Refer to the upload titled "<em>Kernel_Format_README.xlsx</em>" for details regarding kernel format and modification for use.</p> <p>Source (radial) kernel generation is described in the Medical Physics article titled "Dose point kernels for 2,174 radionuclides." - https://doi.org/10.1002/mp.13789</p> <p>Source (radial) kernels can be downloaded here: https://doi.org/10.5281/zenodo.2564036</p>
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"
<p>Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.</p>
Memory kernel extraction and mean first-passage time for fast-folding proteins (Q - trajectories)
<p>Fraction of native contacts reaction coordinate (Q) trajectories for the 8 proteins that appear in the Dalton et. al. PNAS 2023. For the original all-atom data from which the Q(t) were calculated, contact the group of David E. Shaw at Shaw Research (see the paper - K. Lindorff-Larsen, S. Piana, R. O. Dror, D. E. Shaw, How fast-folding proteins fold. Science 334, 517–520 (2011))</p> <p>Data contains:<br> - Q(t) trajectories for 8 proteins <br> - Corresponding free energy profiles<br> <br> Example analysis codes (written in C++) are included for:<br> - Free energy calculation<br> - Mean first-passage times<br> - Velocity-velocity correlation function and position-force correlation function calculations, needed for memory kernel extraction<br> - Memory kernel extraction (see Ayaz et. al. PNAS 2021)</p>
Supplementary information for the manuscript 'Mutation in SHAGGY-like kinase AsGSK2.1 causes a short kernel phenotype in oat (Avena sativa)'
<p>This is the supplementary information of the manuscript 'Mutation in SHAGGY-like kinase AsGSK2.1 causes a short kernel phenotype in oat (<em>Avena sativa</em>)' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p> <p>SI.pdf contains supplementary figures 1-3.</p> <p>Supplementary figure 4.html contains supplementary figure 4.</p> <p>Supplementary tables.xlsx contains supplementary tables 1-5.</p>
Larval and adult traits coevolve in response to asymmetric coastal currents to shape marine dispersal kernels
<p>Dispersal emerges as an outcome of organismal traits and external forcings. However, it remains unclear how the emergent dispersal kernel evolves as a by-product of selection on the underlying traits. This question is particularly compelling in coastal marine systems where dispersal is tied to development and reproduction, and where directional currents bias larval dispersal downstream causing selection for retention. We modelled the dynamics of a metapopulation along a finite coastline using an integral projection model and adaptive dynamics to understand how asymmetric coastal currents influence the evolution of larval (pelagic larval duration) and adult (spawning frequency) life history traits, which indirectly shape the evolution of marine dispersal kernels. Selection induced by alongshore currents favors the release of larvae over multiple time periods, allowing long pelagic larval durations and long-distance dispersal to be maintained in marine life cycles in situations where they were previously predicted to be selected against. Two evolutionary stable strategies emerged: one with a long pelagic larval duration and many spawning events resulting in a dispersal kernel with a larger mean and variance, and another with a short pelagic larval duration and few spawning events resulting in a dispersal kernel with a smaller mean and variance. Our theory shows how coastal ocean flows are important agents of selection that can generate multiple, often co-occurring, evolutionary outcomes for marine life history traits that affect dispersal.</p>
Larval and adult traits coevolve in response to asymmetric coastal currents to shape marine dispersal kernels
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Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
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Life-cycle greenhouse gas emissions in power generation using palm kernel shell
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Spectral kernel machines with electrically tunable photodetectors
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Individual variation in marine larval-fish swimming speed and the emergence of dispersal kernels
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Evolution of interspecific variation in marine larval dispersal kernels: The role of larval navigation ability
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Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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A monthly shortwave radiative forcing kernel for surface albedo change using CERES satellite data
We present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balance and Filled (EBAF) Edition 4.0 products based on a 16-year climatology (2001-2016). Both monthly temporally-explicit and monthly climatological mean CERES albedo change kernels (CACK) are provided with their respective uncertainty layers. Octave script files for generating monthly CACK from CERES EBAF data and demonstrating the application of CACK with user-specified temporal and spatial extents are also included.
Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
1. Agriculture benefits from ecosystem services provided by mobile agents, such as biological pest control by natural enemies and pollination by bees. However, methods that can generate spatially explicit predictions and maps of these ecosystem services based on empirical data are still scarce. 2. Here we propose a generic statistical model to derive kernel functions to characterize the spatial distribution of ecosystem services provided by mobile agents. The model is similar in spirit to a generalized linear model, and uses data of landscape composition and ecosystem services assessed at target sites to estimate parameters of the kernel. The approach is tested in a simulation study and illustrated by an empirical case study on parasitism rates of the diamondback moth Plutella xylostella. 3. The simulation study shows that the scale parameter of the exponential power kernel can be estimated with limited bias, whereas estimation of the shape parameter is difficult. For the case study the model provides biologically relevant estimates for the kernel associated with parasitism of Plutella xylostella. These estimates can be used to generate ecosystem service maps for existing or planned landscapes. The case study reveals that predictions can be sensitive to the parameter values for the width and shape of the kernel, and to the link function used in the statistical model. 4. In the last two decades numerous empirical studies assessed ecosystem services at target sites and related these to the surrounding landscape. Our method can take advantage of these data by estimating underlying kernels that can be used to map the spatial distribution of ecosystem services. However, empirical data that can discriminate between alternative kernel shapes remain critical.
Data from: Modelling unbiased dispersal kernels over continuous space by accounting for spatial heterogeneity in marking and observation efforts
1. Although a key demographic trait determining the spatial dynamics of wild populations, dispersal is notoriously difficult to estimate in the field. Indeed, dispersal distances obtained from the monitoring of marked individuals typically lead to biased estimations of dispersal kernels as a consequence of i) restricted spatial scale of the study areas compared to species potential dispersal and ii) heterogeneity in marking and observation efforts and therfore in detection probability across space. 2. Here we propose a novel method to circumvent these issues that does not require data on observation effort per se, to correct for the variability in detection of marked individuals across space. Observed dispersal events were weighted by the distribution of departure points and an eroded spatial window approach was applied so as to deal with border effect. We conducted a set of simulations which indicated that our method was successful in correcting the effect of spatially heterogeneous detectability and produce unbiased dispersal kernels. 3. We applied this method to a real dataset on Montagu's harrier (>5000 chicks tagged), providing ca. 6000 resightings collected in entire France by a network of 1200 volunteers within a citizen-science program. The median dispersal distance observed was 32 km (range: 0.1-627 km). Once corrected for spatial heterogeneity in marking and observation efforts and border effect, the modelled dispersal kernel indicated a median dispersal distance of 78-123 km depending on the spatial scale considered (constrained within French borders or not, respectively). 4. Synthesis and applications: The current rise of citizen-science programs is likely to stretch our estimate of the ecologically-relevant spatial scale at which dispersal takes place for many taxa. Our method is particularly suited for such large scale data that typically suffer from high spatial heterogeneity in marking and observation efforts and offers the possibility to derive unbiased dispersal kernels, a key component for modelling population dynamics and species distribution in a context of environmental change. Currently, our method assumes homogeneity in both habitat and dispersal behaviour across individuals. We discuss however how to relax these hypotheses to further investigate the effect of e.g. local conspecific density or habitat quality on dispersal propensity.
Artifact from "A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing"
<p>Artifact from "A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing"</p>
How Configurable is the Linux Kernel? Analyzing Two Decades of Feature-Model History
<p>Reproduction package for the TOSEM'25 paper "How Configurable is the Linux Kernel? Analyzing Two Decades of Feature-Model History"</p>
Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework
<p>This repository contains code and data to re-create the numerical results shown in</p> <p>"Error analysis of kernel EDMD for prediction and control in the Koopman framework"</p> <p> <a href="http://arxiv.org/abs/2312.10460">http://arxiv.org/abs/2312.10460</a></p> <p>Please see the README file for detailed description of the codes in this repository.</p>
Reproduction package for the paper "Analyzing long-term maintenance releases of the Linux kernel"
<p>This is the reproduction package of the paper "Analyzing long-term maintenance releases of the Linux kernel". This package includes the datasets and the software for methods presented in the paper.</p>
The artifact for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.
<p>This artifact is for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.</p> <p>More detail and update please refer to https://github.com/ZHYfeng/Dependency.</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.