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8,877 results for “body”
body_finishing_13
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_04
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_12
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_09
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_06
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_08
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
body_finishing_15
This is the process of making a carafe Bontemps. The step depicted is called "body finishing"(from the Mingei project).
Between Minds and Bodies
<p>The audiovisual installation <em>Between Minds and Bodies</em> aimed to recreate the dancers' experience throughout an improvisational session. The installation was shown during <em>Off the Lip 2017</em> from 16 to 18 August and at the <em>Balance Unbalance 2017</em> conference from 21 to 23 August in Plymouth, UK.</p> <p>The video files contain the narrated experience of each of the four participating dancers using the video-stimulated recall method introduced in 'Dance Improvisational Cognition'. While each file contains all four audio tracks, one of the dancers' voice is selected as the default for each video file.</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Annual and 33-year water body frequency maps of the contiguous US from 1984 to 2016
<p>There are 50 5-by-5 degree tiles covering the entire CONUS. In each zipped tile folder, there are 33 annual water body frequency images (e.g. freq_2016_-070_040.tif), one 33-year water body frequency image (e.g. 33YearFreq_1984_2016_-070_040.tif), and one 33-year good observation image (e.g. 33YearGoodObs_1984_2016_-070_040.tif). </p> <p>The annual and 33-year water body frequency are defined as the ratio of water observations to total good observations in a year and in 1984-2016, respectively. The frequency stored in these image is compressed in 8 bits (1-255). To get the frequency in floating point (0-1.0), use the equation: f = (F-1)/254.0, where F is in 8 bits while f is in floating point. </p> <p>For each Landsat image, the CFmask band was used as a quality control band to remove the cloud, cloud shadow, and snow pixels. The solar azimuth and zenith angles of each image were used along with the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to simulate terrain shadows and remove them. The remaining pixels were considered as good observations that can be used for water body detection. The total good observation number during 1984-2016 is stored in the 33-year good observation images.</p> <p>Projection is WGS84, while spatial resolution is 0.000269494585236.</p> <p>For additional details, please go to our lab server (http://mangrove.rccc.ou.edu/eomfftp/conus_water_dataset/).<br> To use this data, please cite our articles: <br> Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R.B., Menarguez, M.A., Zhang, G., Wang, J. Divergent Trends of Open Surface Water Body Area in the Contiguous United States from 1984 to 2016, PNAS, doi: 10.1073/pnas.1719275115</p> <p>Zou, Z., J. Dong, M. A. Menarguez, X. Xiao, Y. Qin, R. B. Doughty, K. V. Hooker, and K. David Hambright (2017), Continued decrease of open surface water body area in Oklahoma during 1984-2015, Sci Total Environ, 595, 451-460, doi: 10.1016/j.scitotenv.2017.03.259.</p>
3D body shapes - INKREATE
<p>This dataset contains 56 3D complete human body shapes in STL format (file <strong>STL.zip</strong>).</p> <p>The table <strong>measurements.csv </strong>contains the code of the SLT files, gender and measurements. The measurements are explained in <strong>ibvtape_doc.pdf</strong></p> <p>The dataset was created for testing purposes in the project <a href="https://www.inkreate.eu/">INKREATE </a>: <em>Transfer the real 3D world to interactive creative endeavours in the apparel industry</em></p> <p>This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 731885</p>
Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.
<p>Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations. </p> <p><br>This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type.</p> <p>Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination.</p> <p><br>The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m<sup>2</sup>) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded.</p> <p>Description of columns found in the dataset:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Column heading</strong></p> </td> <td> <p><strong>Column description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Units</strong></p> </td> </tr> <tr> <td> <p>Fish _Catchment</p> </td> <td> <p>The river catchment/basin location of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_River</p> </td> <td> <p>The river/watercourse location of each fish site.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_SiteName</p> </td> <td> <p>The name of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_Latitude</p> </td> <td> <p>The latitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Longitude</p> </td> <td> <p>The longitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Area</p> </td> <td> <p>Area of fish site surveyed</p> </td> <td> <p>Integer</p> </td> <td> <p>m<sup>-2</sup></p> </td> </tr> <tr> <td> <p>Fish_SurveyDate</p> </td> <td> <p>Date fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>dd/mm/yyyy</p> </td> </tr> <tr> <td> <p>Fish_Year</p> </td> <td> <p>Year fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>yyyy</p> </td> </tr> <tr> <td> <p>Common_Name</p> </td> <td> <p>The common/vernacular name of each fish taxon recorded in the dataset.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Latin_Name</p> </td> <td> <p>The scientific name of each fish taxon recorded in the dataset</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Net_Number</p> </td> <td> <p>The net number the fish in a given survey were caught on</p> </td> <td> <p>Integer</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Length_mm</p> </td> <td> <p>Length of individual fish caught</p> </td> <td> <p>Integer</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>Count of fish caught accounting for sub- sampling</p> </td> <td> <p>Integer</p> </td> <td> <p>Number of fish</p> </td> </tr> </tbody> </table> <p> </p>
Files from TCGA-KIRC Study for Body Part Regression Tutorial
<p>The data here are in whole based upon data generated by the TCGA Research Network: <a href="https://cancergenome.nih.gov/">http://cancergenome.nih.gov/</a>.</p> <p><br> The DICOM files from the <a href="https://wiki.cancerimagingarchive.net/display/Public/TCGA-KIRC#580038695f8cd691bda43dda71b4093c69c7318">TCGA-KIRC </a>study were converted to nifti files. Moreover, the nifti files with greater size than 35 MB and smaller size than 5 MB were removed (to reduce the size of the dataset and to remove the files with few slices). Furthermore, the metadata from the DICOM files is saved in a separate excel-file.</p>
GWAS summary statistics for waist-to-hip ratio and body principal components
<p>This dataset contains genome-wide association summary statistics for waist-to-hip ratio (WHR), as well as those for body principal components (PCs). A subset of 387,139 unrelated, white British individuals were analyzed for WHR. PCs were combined from the summary statistics for WHR and 13 other anthropometric traits (body mass index, standing height, weight, hip circumference, waist circumference, arm lean mass (left), arm fat mass (left), leg lean mass (left), leg fat mass (left), trunk lean mass, trunk fat mass, body fat percentage, basal metabolic rate) provided by the Neale lab (http://www.nealelab.is/uk-biobank). All traits were inverse-rank normal transformed (by the Neale lab or ourselves for WHR).</p> <p>All effect sizes, including those for PCs, are standardized, i.e. they represent the effects on a trait with variance 1.</p> <p>The zip files contain the data to run the sample pipeline and the shiny app, both available from <a href="https://github.com/JonSulc/PCA_Cross-sex_MR">https://github.com/JonSulc/PCA_Cross-sex_MR</a>.</p>
Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial
<p>Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA.<br> The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling.</p> <p>This dataset was created for a Body Part Regression tutorial.</p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as:</p> <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> <p>The files named <strong>data_*.txt</strong> contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows:</p> <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
Sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic)
<p>A dataset of sampled and simulated benthic invertebrate body-mass data from the Porcupine Abyssal Plain Sustained Observatory (4850 m, 48.83° N 16.50 °W, NE Atlantic) has been produced. It includes data on macro- and megabenthos derived from a randomly sampled power law distribution, seabed core samples, large-scale seabed photography, and seabed trawls.</p>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Genome-wide association study of full body nevus count in the Brisbane Twin Nevus Study (BTNS)
<p>The project uses the Brisbane Twin Nevus Study (BTNS) (N=3863)) to compare nevus counts on different anatomical sites to assess which anatomical site serves as best proxy for counting nevi on the whole body.In the project, a GWAS of nevus count on the whole body and GWAS of nevus count on the outer arm are performed.Here is the GWAS of total nevus count.</p> <p>Sample: GWAS analysis only includes samples of European ancestry. total nevus count were counted by trained research nurse.</p> <p>Genotype: All genotypes were imputed to a human haplotype map (HapMap) reference panel. Genome-wide association analyses were performed using Genome-wide Efficient Mixed model Association (GEMMA), which can account for genetically related individuals such as twins and siblings. Sex, age, age2, sex*age, sex*age2, sunburn, BSA, sun exposed hours weighted by UV index and 5 PCs, additionally two batch effect variables; were included as covariates. SNP imputation quality filter retained SNP with an INFO > 0.3. minor allele frequency frequency filter was applied to retain SNP MAF > 0.1</p> <p>Columns include:</p> <p>CHR: Chromosome</p> <p>BP: Base pair</p> <p>SNP: rsID</p> <p>A1: Effect allele</p> <p>A2: Non-effect allele</p> <p>A1FQ: Effect allele frequency</p> <p>HWE: Hardy-Weinberg Equilibrium</p> <p>BETA: Effect estimate (of effect allele_</p> <p>SEB: Standard error of beta</p> <p>PRB: P value</p> <p>N: Per SNP sample size</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.