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.
233
datasets available to search
ShareScore release 0.7.1
Dataset results
233 results for “kernel”
Dose point kernels for 2,174 radionuclides
<p>Dose point kernels for 2,174 radionuclides generated using MCNP v6.2. See accompanying article in Medical Physics for further details. </p>
Linux Kernel 4.21 Call Graphs
<p>This is the<strong> Linux Kernel 4.21 Call Graphs</strong> created using <a href="http://github.com/dspinellis/cscout">CScout</a> containing the following graphs:</p> <ol> <li>File include graph (fgraph_I.txt) </li> <li>Compile Time Dependency Graph (fgraph_C.txt)</li> <li>Control Dependency Graph (through function calls) (fgraph_F_D.txt)</li> <li>Data Dependency Graph (through global variables) (fgraph_G.txt)</li> <li>Function and Macro Call Graph (cgraph.txt)</li> </ol> <p>Files are of the form</p> <p>foo.c boo.c</p> <p>which indicate a directed edge foo.c -> boo.c.</p> <p>The call graphs refer to <strong>all </strong>(ending with _all.txt) files or only the <strong>writable files.</strong> </p> <p>These graphs were produced by processing the Linux Kernel Codebase consisting of 20.3 million lines of source code. </p> <p>The results were produced on an Intel(R) Xeon(R) CPU E5-1410 0 @ 2.80GHz server with 64GB of RAM.</p> <p><strong>References: </strong></p> <p>1. Papachristou, Marios. "Software clusterings with vector semantics and the call graph." <em>Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering</em>. 2019.</p>
TOA and surface cloud radiative kernels calculated with RRTM
<p> These cloud radiative kernels (CRK) are calculated with RRTM, with meteorological variables from ERA-interim as inputs. The file format is netcdf4, and was created by python. To read these files, any software supporting netcdf4 can be used. </p> <p> Longwave cloud radiative effect at surface is primarily decided by cloud base properties, while top-of-atmosphere (TOA) cloud radiative effect is primarily decided by cloud top properties, so the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month,τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, we created a set of surface CRK on CTP-τ cloud fraction histograms using the statistical relationship between CTP, CBP and τ from collocated CloudSat-MODIS observations.</p> <p> There are five individual files. "SFC_CRK_CBP2.nc" is for surface CRK on CBP-τ histograms, "SFC_CRK_CTP2.nc" is for surface CRK on CTP-τ histograms, and "TOA_CRK_CTP2.nc" is for TOA CRK on CTP-τ histograms. The atmospheric CRK can be calculated as the difference between "TOA_CRK_CTP2.nc" and "SFC_CRK_CTP2.nc". The other two files denote separate CRK for ice and liquid clouds. </p> <p> </p> <p> Notes: (1) this version is used in the submitted draft for publication, and it might be renewed in the future.</p> <p> (2) To avoid NAN values, the value of CBP/CTP is set to be the lowest level near surface for cloud bins with CBP/CTP greater than surface air temperature (cloud base or top is below surface). </p> <p> </p>
Figure Sets Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry II: Demographis of Nearby Brown Dwarfs"
<p>Images for Figure Sets 2, 3, 4, 6, and 9 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Demographis of Nearby Brown Dwarfs" (Currently accepted and in press.). Figure captions and file names are described in their associated README files (inside the bundles).</p> <p>Figure 2 shows survey sensitivity, Figure 3 shows the posterior distributions of our population models, Figure 4 compares our sensitivity and model distributions to the observed population, Figure 6 shows the marginalized population as a function of mass ratio, and Figure 9 shows the results of injecting an additional artificial detection.</p>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
The shape of kernels and cracks, in a nutshell
Open the record for dataset details and reuse information.
Connectivity from a different perspective: comparing seed dispersal kernels in connected vs. unfragmented landscapes.
Habitat fragmentation can create significant impediments to dispersal. A technique to increase dispersal between otherwise isolated fragments is the use of corridors. Although previous studies have compared dispersal between connected fragments to dispersal between unconnected fragments, it remains unknown how dispersal between fragments connected by a corridor compares to dispersal in unfragmented landscapes. To assess the extent to which corridors can restore dispersal in fragmented landscapes to levels observed in unfragmented landscapes, we employed a stable-isotope marking technique to track seeds within four unfragmented landscapes and eight experimental landscapes with fragments connected by corridors. We studied two wind- and two bird-dispersed plant species, because previous community-based research showed that dispersal mode explains how connectivity effects vary among species. We constructed dispersal kernels for these species in unfragmented landscapes and connected fragments by marking seeds in the center of each landscape with 15N and then recovering marked seeds in seed traps at distances up to 200 m. For the two wind-dispersed plants, seed dispersal kernels were similar in unfragmented landscapes and connected fragments. In contrast, dispersal kernels of bird-dispersed seeds were both affected by fragmentation and differed in the direction of the impact: Morella cerifera experienced more and Rhus copallina experienced less long-distance dispersal in unfragmented than in connected landscapes. These results show that corridors can facilitate dispersal probabilities comparable to those observed in unfragmented landscapes. Although dispersal mode may provide useful broad predictions, we acknowledge that similar species may respond uniquely due to factors such as seasonality and disperser behavior. Our results further indicate that prior work has likely underestimated dispersal distances of wind-dispersed plants and that factors altering long-distance dis
Linux kernels v2.6.14 v3.18.129 v6.14.2 processed by CScout
<p>This dataset contains three SQLite databases containing a full image of the <a href="https://www.kernel.org/">Linux kernel</a> in relational form, as generated by the <a href="https://www.spinellis.gr/cscout/">CScout</a> refactoring browser (version 03023d - 2024-08-30 and version afdd54 - 2025-04-19) piping its output to SQLite (version 3.46.0 2024-05-23.</p> <p>The datasets can be used to perform empirical research on the Linux kernel's source code, especially identifier semantics, which they represent in the form of equivalence classes. They form part of the replication package of the paper titled “You Are not Expected to Understand this: The Usage of the C Preprocessor in the Linux Kernel”.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
A dataset of Linux Kernel commits
<p>Dataset with metadata about more than 1,200,000 changes (commits) of the Linux kernel, corresponding to a period since 2005 to 2023, which<br>can be easily ingested in data analytics systems.<br><br>It also includes a list of more than 90,000 pairs of bug fixing changes and their corresponding bug introducing changes, labeled by developers of the Linux Kernel.</p>
ClimKern Kernel & Data Repository
<h1>ClimKern Kernel and Data Repository</h1> <h2>New in v1.2:</h2> <ul> <li>An error was discovered in the HadGEM2 clear-sky surface albedo kernel. Please use v1.2 or later for that specific kernel.</li> </ul> <h2>What's stored here?</h2> <div>This Zenodo repository contains two types of data to be used by the ClimKern</div> <div> Python package. The subdirectory <code>/kernels/</code> contains 12 radiative kernels generously</div> <div> contributed by various research groups. The other directory <code>/tutorial_data/</code> contains</div> <div> sample Community Earth System Model v1 output for testing purposes.</div> <h2> Where are the kernels from?</h2> <table> <tbody> <tr> <td><strong>Kernel name</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>BMRC</td> <td><a href="https://doi.org/10.1175/2007JCLI2110.1" target="_blank" rel="noopener">Soden et al. (2008)</a></td> </tr> <tr> <td>CAM3</td> <td><a href="https://doi.org/10.1175/2007JCLI2044.1" target="_blank" rel="noopener">Shell et al. (2008)</a></td> </tr> <tr> <td>CAM5</td> <td><a href="https://doi.org/10.5194/essd-10-317-2018" target="_blank" rel="noopener">Pendergrass et al. (2018)</a></td> </tr> <tr> <td>CERES</td> <td><a href="https://doi.org/10.1175/JCLI-D-18-0045.1" target="_blank" rel="noopener">Thorsen et al. (2018)</a></td> </tr> <tr> <td>CloudSat</td> <td><a href="https://doi.org/10.1029/2018JD029021" target="_blank" rel="noopener">Kramer et al. (2019)</a></td> </tr> <tr> <td>ECHAM5</td> <td><a href="https://doi.org/10.1088/1748-9326/5/2/025211" target="_blank" rel="noopener">Previdi (2010)</a></td> </tr> <tr> <td>ECHAM6</td> <td><a href="https://doi.org/10.1002/jame.20041" target="_blank" rel="noopener">Block & Mauritsen (2013)</a></td> </tr> <tr> <td>ECMWF-RRTM</td> <td><a href="https://doi.org/10.1002/2017JD027221" target="_blank" rel="noopener">Huang et al. (2017)</a></td> </tr> <tr> <td>ERA5</td> <td><a href="https://doi.org/10.5194/essd-15-3001-2023" target="_blank" rel="noopener">Huang & Huang (2023)</a></td> </tr> <tr> <td>GFDL</td> <td><a href="https://doi.org/10.1175/2007JCLI2110.1">Soden et al. (2008)</a></td> </tr> <tr> <td>HadGEM2</td> <td><a href="https://doi.org/10.1029/2018GL079826" target="_blank" rel="noopener">Smith et al. (2018)</a></td> </tr> <tr> <td>HadGEM3-GA7.1</td> <td><a href="https://doi.org/10.5194/essd-12-2157-2020" target="_blank" rel="noopener">Smith et al. (2020)</a></td> </tr> </tbody> </table> <div> </div> <h2>How do I use this data with the ClimKern package?</h2> <p> </p> <div>Visit the <a href="https://github.com/tyfolino/climkern">ClimKern GitHub</a> for installation and use instrucitons.</div> <p> </p> <h2>How do I cite this?</h2> <div>Please cite <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2561/">Janoski et al. (2024)</a> and this Zenodo repository with the DOI corresponding to the version of the data you used. We also encourage you to cite the paper(s) documenting the kernel(s) you use.</div>
Individual variation in marine larval-fish swimming speed and the emergence of dispersal kernels
<p>Dispersal emerges as a consequence of how an individual's phenotype interacts with the environment. Not all dispersing individuals have the same phenotype, and variation among individuals can generate complex variation in the distribution of dispersal distances and directions. While active locomotion performance is an obvious candidate for a dispersal phenotype, its effects on dispersal are difficult to measure or predict, especially in small organisms dispersing in wind or currents. Therefore, we analyzed the effects of larval swimming on dispersal and settlement of coral-reef fish larvae using a high-resolution biophysical model. The model is, to date, the only biophysical model of marine larval dispersal that has been statistically validated against genetic parentage estimates of larval origin and destination, and incorporates empirically-estimated larval behaviors and their ontogeny. Larval swimming, in combination with depth, orientation, and navigation behaviors, actually reduced dispersal distances compared to those of passive larvae. Swimming had no consistent effects on long distance dispersal, but increased the spread of settlement locations. Swimming speed, in contrast, did not consistently affect median dispersal distances, but faster swimming larvae had greater mean and maximum dispersal distances than slower swimming larvae. Finally, faster larval swimming speeds consistently increased the probability of settlement. Our analysis shows how larval swimming differentially affects multiple properties of dispersal kernels. In doing so, it indicates how selection could favor faster larval swimming to increase settlement, which may actually result in longer dispersal distances as a by-product of larvae trying to locate habitat rather than to disperse greater distances.</p>
Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
<p>Studies characterizing the effect of a century of plant breeding on physiological traits are highly needed to identify candidate traits for future improvement in maize (<em>Zea mays</em> L.). A global evaluation of kernel weight progress over time requires the assembly of large and reliable data documenting genetic improvements in this trait across commercial breeding programs in different regions. We compiled a global dataset of yield and yield components from 34 published and unpublished studies comparing two or more maize cultivars from different decades of commercial release under field conditions. The dataset includes 750 entries of kernel weight data (requirement to be included in the systematic review), of which 642 and 666 include data entries of grain yield and kernel number, respectively. We also extracted the metadata describing experimental site information, agronomic management practices, and genotypic information. This dataset can be useful to identify trends of yield improvement across management conditions, with proper consideration of the trade-off between kernel number and kernel weight in maize.</p>
Life-cycle greenhouse gas emissions in power generation using palm kernel shell
<p>Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO<sub>2</sub> emissions from PKS combustion were also calculated by assuming that carbon neutrality was lost because of cultivation abandonment. The GHG emissions in one scenario, where the plantations were replanted and continuously managed and no biogas capture implemented in oil extraction, exhibited an average of 0.134 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Kyushu District, and 0.043 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Shikoku District for liquid natural gas-fired steam power generation, respectively. More than 65% of life-cycle GHG emissions originate from biogas generated during oil extraction; thus, biogas capture is an effective strategy to reduce current emissions. In contrast, in the case of accompanying land-use change or collapse of carbon neutrality, the emissions considerably exceeded those of fossil fuels. These findings indicated that the FIT fails to consider the risk of increased emissions or further substantial emission reductions. Therefore, the feasibility of FIT application to PKS needs to be re-established by evaluating the entire PKS life-cycle. </p>
A Multi-level Dataset of Linux Kernel Patchwork
<p>The dataset, source code, application and document of a MSR 2018 data showcase paper.</p> <p>Yulin Xu and Minghui Zhou. 2018. A Multi-level Dataset of Linux Kernel Patchwork. In MSR '18: MSR '18: 15th International Conference on Mining Software Repositories , May 28–29, 2018, Gothenburg, Sweden. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3196398.3196475</p> <p>Description of each files:</p> <p>The level-0 data is stored in the file `level-0.zip'.</p> <p>The level-1 and level-2 data are stored in the file `level-1 and level-2.7z'.</p> <p>The tool is the file `application.7z'.</p> <p>The source code of the tool is the file `source code.7z'.</p> <p>The document of the tool is the file `Usage of Application.pdf'.</p> <p>The document of the dataset is the file `Description of Tables.pdf'.</p>
Kernelized rank learning for personalized drug recommendation (training and evaluation datasets)
<p>Training and evaluation datasets for our "Kernelized rank learning for personalized drug recommendation" paper:</p> <p>He* X, Folkman* L & Borgwardt K (2018), <em>Bioinformatics</em> <strong>34</strong>(16), 2808–2816, <a href="https://doi.org/10.1093/bioinformatics/bty132">https://doi.org/10.1093/bioinformatics/bty132</a><br> *equal contributions</p> <p>For the source code, please visit the GitHub repository: <a href="https://github.com/BorgwardtLab/Kernelized-Rank-Learning">https://github.com/BorgwardtLab/Kernelized-Rank-Learning</a></p> <p> </p>
A Dataset of Multiple Types of Linux Kernel Patches
<p>We share a dataset of a nine-year history of patches (666,550 patches produced from December 2008 to December 2017) and related discussion recorded by the LKML project on the Linux kernel patchwork. To better help future studies, we analyzed the review results of these patches, and marked three types: accepted patches, rejected patches, and patches that are rejected because of the communication problem.</p>
Data of the Paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel"
<p>This repository includes the data of the paper "How to Communicate when Submitting Patches: an Empirical Study of the Linux Kernel".</p> <p>It contains three parts:</p> <ol> <li>Related online documents.</li> <li>Original questionnaire of the survey.</li> <li>Survey results.</li> </ol> <p> </p>
Dataset for "Kernel Plus method for quantifying wind turbine upgrades"
<p>This is the dataset used in the paper, Lee, Ding, Xie, and Genton, 2015, “Kernel Plus method for quantifying wind turbine upgrades,” <em>Wind Energy</em>, Vol. 18, pp. 1207-1219.</p>
Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification
<p>These are companion data and models of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science.</p> <p>The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. </p> <p>The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. </p> <p>The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. </p> <p>The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models.</p> <p>The trained models can be found in Trained_models.zip. There are four zip files:<br> - "simclr_pretrained_bb.zip" contains the selected pretrained backbone trained via SimCLR.<br> - "nnclr_pretrained_bb.zip" contains the selected pretrained backbone trained via NNCLR.<br> - "finetuned_classification" contains classification models which have undergone end-to-end finetuning, split into supervised and self-supervision-pretrained models.<br> - "segmentation" contains image segmentation models, where the names refer to the pretraining method.</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.