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619 results for “configuration”
Full information on the eORCA1 grid (mesh_mask) used in IPSL-CM6A-LR configuration
<p>eORCA1.2_mesh_mask.nc : This file contains all relevant information on the eORCA1 grid used in the NEMO_v3.6_STABLE configuration of the oceanic module of the IPSL-CM6A-LR climate model. See https://www.nemo-ocean.eu/wp-content/uploads/NEMO_book.pdf for more details on the grid.</p> <p>eORCA_R1_bathy_meter_v2.2.nc: This file contains the bathymetry of the eORCA1 configuration used in IPSL-CM6A-LR.</p>
Sensor Defect Detection Datasets with Configuration
<p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.csv) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario (data_scenario_2.csv) there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p> <p>Additionally attached is configuration data (Configurations.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</p>
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
<p>The datasets in this release support the results presented in the paper</p> <blockquote> <p>P. Jamshidi, G. Casale, "An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems", accepted for presentation at MASCOTS 2016.</p> </blockquote> <p>An open access to the paper is available at https://arxiv.org/abs/1606.06543</p> <blockquote> <p>Also open source code is available at https://github.com/dice-project/DICE-Configuration-BO4CO</p> </blockquote> <p>The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "<em>benchmark_application-dimensions-cluster_name</em>". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data! </p> <p>For more information about the data refer to the appendix of the paper: https://arxiv.org/abs/1606.06543. </p> <p>When referring to the dataset or code please cite the paper above.</p>
VALIANT Wing-Flap Configuration 4 - 50m/s
<p>VALIANT Wing-Flap Configuration 4 - 50m/s without turbulence grids</p>
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
Simulated Detection and Localization in LIGO's HL-Configuration for O4a Run, and LIGO/Virgo/KAGRA's HLVK-Configuration for O4b and O5 Runs (November 2023 edition)
<p>We have conducted a simulation of the <strong>HL</strong>-configuration deployed during the ongoing <strong>O4a</strong> run and the <strong>HLVK-</strong>configuration planned for <strong>O4b </strong>and O5, as outlined in the observation scenarios, which can also be accessed at the following <a href=" https://doi.org/10.5281/zenodo.7026209"> https://doi.org/10.5281/zenodo.7026209. </a></p>
Replication package for the paper "A configurational approach to job quality analysis: forms of inequalities at work in Europe"
<p>The following replication package is appended to the article <span><span><span><span>Étienne Penissat</span><span>, </span></span><span><span>Cécile Rodrigues</span><span> & </span></span><span><span>Alexis Spire</span></span></span></span> <span>(2024)</span> "<span>A configurational approach to job quality analysis: forms of inequalities at work in Europe",</span> <span>European Societies,</span> <span>DOI: <a href="https://doi.org/10.1080/14616696.2024.2312950">10.1080/14616696.2024.2312950</a></span></p> <p>The scripts to be run in the following order are:</p> <p>- 1_Penissat_EuropeanSocieties_2023_DataPreparation.R : Recoding, formatting and scope of data used</p> <p>- 2_Penissat_EuropeanSocieties_2023_DataAnalysis.Rmd : Analysis and statistical results</p> <p>The data used in the article comes from the EWCS (2015) - European Working Condition Survey - provided by the European foundation for the improvement of living and working conditions. The data is not available on free access but can be obtained on request. Information on the survey wave used can be found here : https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015</p> <p>- In the first "1_Penissat_EuropeanSocieties_DataPreparation.R" script, the file containing data named "ewcs_1991-2015.dta" is used. The file called "eseg2_trad.csv" contains english labels for the nomenclature of professional positions ESeG. As "ewcs_1991-2015.dta" is not freely available, it is not included in the package and "eseg2_trad.csv" is located in the "data" folder.</p> <p>- The first script creates the data file "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" in the "results" folder.</p> <p>- The second script called "2_Penissat_EuropeanSocieties_DataAnalysis.Rmd" uses the "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" data file and produces the "2_Penissat_EuropeanSocieties_DataAnalysis.html" file containing all code and results presented in the article.</p>
Regional Centromere Configuration in the Fungal Pathogens of Pneumocystis Genus
<p>Supplementary material </p>
Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations
<p>This dataset supplements the publication "Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations" by Wechselberger et al. (2025).</p> <p>The dataset contains primary and secondary data underlying the statistical analysis and reported methane emission factors. Emission factors were calculated as described in section 2.3 of the paper. </p> <p>Available files (UTF-8 encoded):</p> <ul> <li>Data.csv (dataset)</li> <li>Glossary.csv (column/variable descriptions of dataset)</li> </ul> <p>The dataset includes plant characteristics and whole-site methane losses of 135 anaerobic digestion plants, covering normal and various other-than-normal operating conditions (155 rows). For statistical analysis, only periods during normal operation and plants with information on the analyzed emission factors and plant characteristics were considered (cf. supplementary information C of the paper). Consequently, the final dataset contained 109 anaerobic digestion plants for statistical analysis on the methane emission factor (% of methane produced) and 28 plants when analyzing the wastewater-specific emission factor (kg methane per population equivalent and year). All but one facility continuously processed feedstock without any post-rotting stages. Plant DE-MH_WP5_1 of the secondary data implemented garage digesters.</p> <p>Data from three plants were collected only after completion of statistical analyses. These data were used to compare methane losses during normal and other-than-normal operating conditions. The respective rows are marked accordingly in the dataset (column “data_collected_after_statistical_analyses”).</p> <p>Version v2 contains the final reference to the publication Wechselberger et al. (2025). The data are the same as in version v1.</p>
Migrating birds real flight V-formation spatial configuration.
<p>Bird real flight V-formation dataset: Arbitrary (pixel) coordinates of migrating birds, probably Geese, flying in V-formation. Photo is taken in an angle so their formation data is only a cross-section in 3-D perspective but with entire pack. However, despite this limitation this V-formation configuration provides a quantitative data for the understanding for the spatial properties, i.e., V-shape characteristics. There are 95 birds in total including the lead bird. Lower V-arm is denoted with tags dXX has 51 birds and upper V-arm is denoted by tags uXX has 43 birds. Lead bird has two entries d00 and u00 for consistency. Annotated image provides boxes and labels. The data is given under bird_arbitrary_coordinates as pixel location on the plane with tags. In coordinate annotation head of the bird is taken as a refrence point.</p>
Sticky Pi -- Machine Learning Data, Configuration and Models
<p><strong>Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/)</strong></p> <p>Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier.</p> <p><strong>Universal Insect Detector:</strong></p> <p>`universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` – A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml `– The configuration file defining the hyperparameters to train the model</li> <li>`mask_rcnn_R_101_C4_3x.yaml` – the base configuration file from which config is derived</li> </ul> </li> </ul> <p> </p> <p><strong>Siamese Insect Matcher</strong></p> <p>`siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` – a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `<device>.<datetime_frame_1>.<datetime_frame_2>.svg`</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train</li> </ul> </li> </ul> <p><strong>Insect Tuboid Classifier:</strong></p> <p>`insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication.</p> <ul> <li>`data/` <ul> <li>`database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables.</li> <li>A directory tree of the form: `<series_id>/<tuboid_id>/`. Each terminal directory contains: <ul> <li> <ul> <li>`tuboid.jpg` – a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom – might be padded with empty images</li> <li>`metadata.txt` – a csv text file with columns: <ul> <li> <ul> <li>parrent_image_id – <device>.<UTC_datetime></li> <li>X – the X coordinates of the object centroid</li> <li>Y – the Y coordinates of the object centroid</li> </ul> </li> </ul> </li> <li>scale – The scaling factor applied between the original and image and the 224 x 224 tile (>1 => image was enlarged)</li> <li>`context.jpg` – a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes)</li> </ul> </li> </ul> </li> </ul> </li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>config/ <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li> </ul> </li> </ul>
CARBODIN Virtual Reality Configurator Tool
<p>Within WS8, CARBODIN built a software able to generate different layouts for interiors using a hybrid desktop and VR Headset tool, known as Virtual Reality Configuration Tool. The solution has been designed with scalability and modularity in mind so that it is possible to implement new features like real time global illumination, weight distribution heatmaps and anchor points investigation</p>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
H2020 ENODISE: SISW Numerical Database Configuration C
<p><span>This database contains the acoustic numerical results generated by Siemens Industry Software (SISW) for the propeller configuration C investigated in the framework of the European project ENODISE. </span></p> <p><span><span>This configuration consists of</span> two co-rotating co-axial Mejzlik propellers operating at 6000 rpm without flight stream. The effect of varying the clocking angle between the two propellers is studied as a possible noise mitigation strategy. </span></p> <p><span>The numerical simulations reproduce the conditions of the experimental campaign carried out at von Karman Institute for Fluid Dynamics, Belgium (see H2020 Enodise: Experimental dataset of configuration C with mitigation VKI, <a href="../records/10880751">https://zenodo.org/records/10880751</a>).</span></p>
Synthetic Shape Dataset for Numerosity: Exploring Six Configurations of Complexity
<p>The Synthetic Shape Dataset for Numerosity is a meticulously crafted collection designed to advance the understanding and training of machine learning models in the realm of numerosity perception, mirroring human neurocognitive abilities. Comprising six distinct configurations ranging from fundamental to intricate, this dataset offers a comprehensive exploration of shape complexity.</p> <p>Each configuration presents a unique array of synthetic images, where shapes are dynamically generated and randomly distributed against contrasting backgrounds. Configuration 1 serves as the foundation, featuring only white circles against a black background, with each circle sharing a uniform size. As complexity escalates through the configurations, additional elements are introduced, including variations in shape type, size, orientation, and pixel intensity.</p> <p>One notable feature of this dataset is that no shapes overlap or touch each other, ensuring clarity and precision in each image. The total dataset comprises 73,686 images, with each configuration meticulously crafted to offer distinct challenges for numerosity perception tasks.</p> <p>The shape generation process is divided into two types of shape sizes:</p> <ul> <li> <p><strong>Bounded:</strong> In this category, there is no correlation between the pixel count for shapes contained in an image and the target numerosity count. Each of the six configurations contains 9,212 images, totaling 55,272 images.</p> </li> <li> <p><strong>Unbounded:</strong> Here, there is a correlation between the pixel count for shapes in an image and the target numerosity count. Each configuration consists of 3,069 images, totaling 18,414 images.</p> </li> </ul> <p>The configurations are as follows:</p> <ol> <li> <p><strong>Configuration 1:</strong> Features only white circles against a black background, with uniform circle size.</p> </li> <li> <p><strong>Configuration 2:</strong> Similar to Configuration 1, but circles vary in size.</p> </li> <li> <p><strong>Configuration 3:</strong> Presents a black background with full white circles, triangles, squares, and pentagons. Shapes have a uniform orientation but do not share a uniform size.</p> </li> <li> <p><strong>Configuration 4:</strong> Similar to Configuration 3, but shapes do not have a uniform orientation.</p> </li> <li> <p><strong>Configuration 5:</strong> Similar to Configuration 4, but shapes also vary in pixel intensity, exhibiting different shades of grey.</p> </li> <li> <p><strong>Configuration 6:</strong> Features a white background with full black circles, triangles, squares, and pentagons. Shapes do not have a uniform orientation.</p> </li> </ol> <p>The dataset is split into training and test sets:</p> <ul> <li> <p><strong>Training Set:</strong> Contains 61,440 images, with each image containing 1-8 shapes, evenly distributed across target numerosity counts.</p> </li> <li> <p><strong>Test Set:</strong> Comprises 12,246 images, including a variety of numerosity counts ranging from 0 to 12 shapes per image.</p> </li> </ul> <p>Additionally, each image is accompanied by structured label information provided in a CSV file:</p> <ul> <li><strong>id:</strong> A unique number assigned to each image.</li> <li><strong>config:</strong> Indicates the configuration of the image.</li> <li><strong>target:</strong> Denotes the label for the image, representing the number of shapes contained within it.</li> <li><strong>shape:</strong> Specifies whether the image contains bounded or unbounded shapes. Options include 'bounded' or 'unbounded'.</li> <li><strong>numerosity_id:</strong> Identifies the image in the creation process (not crucial for end-users).</li> <li><strong>shape_id:</strong> Identifies the image in the creation process (not crucial for end-users).</li> <li><strong>split:</strong> Labels each image as belonging to either the training or test dataset. Options are 'train' or 'test'.</li> <li><strong>path:</strong> Provides the path to the image file.</li> </ul> <p>This dataset serves as a valuable resource for researchers seeking to explore and enhance machine learning models' ability to comprehend numerosity in various contexts.</p>
Fig. 2. Initial configuration with 15 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 2. Initial configuration with 15 landmarks (a) and analysis of absolute per-landmark imprecision (b, c). Figure 2b shows the profile plot for the summary statistics of per-landmark variance in the two digitizations. Figure 2c shows the scatter of landmarks purely due to digitization error (red landmarks mark the mean form, to which the differences between the first and second digitization were added).
Data for "The very-high resolution configuration of the EC-Earth global model for HighResMIP"
<p>Model data and plot scripts to reproduce the figures of the manuscript "<em>The very-high resolution configuration of the EC-Earth global model for HighResMIP</em>".</p> <p><strong>Authors</strong></p> <p>Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, Sergi Palomas</p> <p><strong>Abstract</strong></p> <p>We here present the very-high resolution version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ~16 km and an oceanic resolution of 1/12° (~8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ~100-km-grid EC-Earth3P-LR, and the ~25-km-grid EC-Earth3P-HR. The models' biases are evaluated against observations over the period 1980–2014. Compared to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, with improved stormtrack over the North Atlantic, yet worsened stormtrack over the Southern Ocean compared to the lower resolution model versions. Other biases persist with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, and the precipitation excess over the tropical South Atlantic and North Pacific. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at mid-latitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional/local spatial scales, in line with regional climate models.</p>
Fig. 12 in Diversity and evolution of Hunter-Schreger Band configuration in tooth enamel of perissodactyl mammals
Fig. 12. Cladogram summarizing relationships among major lineages of Perissodactyla and the evolution of various HSB configurations. Boxes on the right indicate HSB configurations in various perissodactyl taxa. Boxes on the tree itself indicate changes in HSB configuration, as inferred from the distribution of HSB configurations given this phylogeny. The phylogeny is a conservative estimate of perissodactyl relationships drawn from Hooker (1989, 1994), Froehlich (1999), and Holbrook (1999, 2009).
Fig. 8 in Diversity and evolution of Hunter-Schreger Band configuration in tooth enamel of perissodactyl mammals
Fig. 8. Compound HSB configuration in Hyrachyus minimus (Fischer, 1829) (KOE 4050); middle Eocene, Geiseltal, Germany. Tangential section of the protoconid of a lower molar in three sequential levels. A. Outer layer with vertical HSB. B. Middle level with a transitional orientation of the HSB. C. Inner layer with transverse HSB.
Fig. 11 in Diversity and evolution of Hunter-Schreger Band configuration in tooth enamel of perissodactyl mammals
Fig. 11 Curved HSB configuration in Moropus elatus. A. Buccal aspect of M2. B. detailed mapping of visible HSB in the paracone (modified from Koenigswald 1994). doi:10.4202/app.2010.0021
ScienceDex guides
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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.