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10,554 results for “measurements”
Data used in: Low Velocity Zones in the Martian Upper Mantle Highlighted by Sound Velocity Measurements
<p>Data used in</p> <p> </p> <p><strong>Low Velocity Zones in the Martian Upper Mantle Highlighted by Sound Velocity Measurements</strong></p> <p>F. Xu<strong><sup>1,</sup></strong><sup>†<strong>,</strong>‡</sup><strong>,</strong>, N. C. Siersch<sup>1<strong>,</strong>‡</sup>, S. Gréaux<sup>2</sup>, A. Rivoldini<sup>3</sup>, H. Kuwahara<sup>2,4</sup>, N. Kondo<sup>2</sup>, N. Wehr<sup>5</sup>, N. Menguy<sup>1</sup>, Y. Kono<sup>2</sup>, Y. Higo<sup>6</sup>, A.-C. Plesa<sup>7</sup>, J. Badro<sup>5</sup>, D. Antonangeli<sup>1</sup></p> <p><sup>1</sup> Sorbonne Université, Muséum National d‘Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France</p> <p><sup>2</sup> Geodynamics Research Center, Ehime University, Matsuyama, Japan</p> <p><sup>3</sup> Royal Observatory of Belgium, Brussels, Belgium</p> <p><sup>4</sup> Institute for Planetary Materials, Okayama University, Misasa, Tottori, Japan</p> <p><sup>5</sup> Université de Paris, Institut de physique du globe de Paris, CNRS, Paris, France</p> <p><sup>6</sup> Japan Synchrotron Radiation Research Institute, SPring-8, Hyogo, Japan</p> <p><sup>7</sup> DLR Institute of Planetary Research, Berlin, Germany</p> <p> </p> <p>Corresponding author: Daniele Antonangeli (<a href="mailto:email@address.edu)">daniele.antonangeli@upmc.fr)</a></p> <p><sup>†</sup> Current address: Department of Earth Sciences, University College London, London, United Kingdom</p> <p><sup>‡</sup> Equal contributing authors</p> <p> </p> <p> </p>
Accurate measurement of microsatellite length by disrupting its tandem repeat structure
<p>Read, first copy, and template tables for microsatellite data pertaining to the manuscript: "Accurate measurement of microsatellite length by disrupting its tandem repeat structure". </p>
Supporting dataset for manuscript "Direct viscosity measurement of peridotite melt under lower-mantle conditions supports a fractional magma ocean solidification at top lower mantle conditions"
<p>Supporting material for manuscript "<strong>Direct viscosity measurement of peridotite melt under lower-mantle conditions supports a fractional magma ocean solidification at top lower mantle conditions"</strong></p>
Real Operatiοn Data from PV, WT, Battery (measurements from pilot site)
<p>Real Operatiοn Data from PV, WT, Battery (measurements from pilot site)</p>
Dataset to: The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba
<p>The file contains data used in the paper mentioned aboved.</p> <p>Abbreviations of variables, species etc. please see Table 1 and Appendix S5 in the publication, respectively.</p> <p>The variables dbh and ele (elevation) are already transformed.</p> <p>The sheets "Ordination_Epiphytes_spec" and "Ordination_Epiphytes_env" were also used for detecting significant associations of species to the tree types (HT vs. AT).</p> <p> </p>
Data of "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?"
<p>Data for article "Poor repeatability of cortisol responses to adrenocorticotropic hormone (ACTH) in beef heifers: is the ACTH challenge a suitable measure for stress research in cattle?" Dataset of 64 crossbred beef heifers which were subjected to three ACTH challenges. Both experimental independent variables (animal id, horn status, replicate, time of day of the ACTH challenge, ACTH challenge number) and post-ACTH salivary cortisol concentrations (at the seven sampling timepoints and area under the curve values) are presented.</p>
Measurements of male rhyssine wasps (Hymenoptera: Ichneumonidae: Rhyssinae)
<p>Measurements of the mesosoma and tergite three of 114 male rhyssine wasps. Also the R code used to analyse these measurements, and the images used when measuring.</p> <p>These measurements were used in the associated paper (link to come) to deduce how the five of the seven wasp species likely mate. The two other species (<em>Megarhyssa nortoni</em> and <em>Rhyssa persasoria</em>) had too low sample sizes.</p> <p>The CSV file contains data on the:</p> <ul> <li>species</li> <li>specimen identifier (FinBif / Kotka format, all data on the specimen available by opening the url)</li> <li>length of tergite 3 (mm)</li> <li>width of tergite 3 (mm)</li> <li>length / width of tergite 3</li> <li>length of mesosoma (mm)</li> <li>length of forewing (mm)</li> </ul> <p>The images are in two different formats:</p> <ul> <li>jpg files with the measurements visible in the image</li> <li>jpg files with the measurements and other metadata (microscope zoom etc) as a separate layer; these can be viewed in QuickPHOTO MICRO 3.1</li> </ul>
Dental measurement and diet data for mammals
<p>Because teeth are the most easily preserved part of the vertebrate skeleton and are particularly morphologically variable in mammals, studies of fossil mammals rely heavily on dental morphology. Dental morphology is used both for systematics and phylogeny as well as for inferences about paleoecology, diet in particular. We analyze the influence of evolutionary history on our ability to reconstruct diet from dental morphology in the mammalian order Carnivora, and we find that much of our understanding of diet in carnivorans is dependent on the phylogenetic constraints on diet in this clade. Substantial error in estimating diet from dental morphology is present regardless of the morphological data used to make the inference, although more extensive morphological datasets are more accurate in predicting diet than more limited character sets. Unfortunately, including phylogeny in making dietary inferences actually decreases the accuracy of these predictions, showing that dietary predictions from morphology are substantially dependent on the evolutionary constraints on carnivore diet and tooth shape. The "evolutionary ratchet" that drives lineages of carnivorans to evolve greater degrees of hypercarnivory through time actually plays a role in allowing dietary inference from tooth shape, but consequently requires caution in interpreting dietary inference from the teeth fossil carnivores. These difficulties are another reminder of the differences in evolutionary tempo and mode between morphology and ecology.</p>
Snow hyperspectral measurements in Terra Nova Bay (Antarctica) and supplementary materials
<p>SISpec is a database containing spectroradiometric, snow and ancillary (environmental and meteorological) data acquired in polar environments. The project is the result of the co-operation of different expertise, and its main objective is to contribute to the knowledge of the interaction between microphysics characteristic of the snow cover and its reflection properties of the solar incident radiation and to study glacial environment and particularly to monitor the snow/ice covers by multispectral remote sensing data. Field surveys were performed in Antarctica, in the region where the Italian research station of Terra Nova Bay is located, the climatic characteristics and the low human impact allow to study snow/ice surfaces without impurities and with different characteristics with respect to those of the Arctic and the Alpine regions, where seasonal melting of the snow cover occur.</p>
Data release for the "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280"
<p>This data release is associated with the publication "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280". It is currently available on arXiv and in JHEP:</p> <p><a href="https://arxiv.org/abs/2002.11986">arXiv:2002.11986 [hep-ex]</a> and <a href="https://doi.org/10.1007/JHEP10(2020)114">J. High Energ. Phys. 10, 114 (2020)</a></p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p><em>The full author list and acknowledgements for the T2K collaboration are described in the article.</em></p> <p>The data release contains:</p> <ul> <li>cross-section measurements with NEUT 5.3.2 (fraction and total with covariances)</li> <li>cross-section measurements with GENIE 2.8.0 (fraction and total with covariances)</li> <li>smearing matrices for selected electron/positron momentum</li> </ul> <p><strong>Description:</strong></p> <p>The cross-section measurements are provided in the form of text files and a PDF summary. The detailed method and results are presented in the paper (especially section 8).</p> <p>The smearing matrices are provided as one ROOT file with two 2D histograms showing the electron/positron smearing matrices for momentum and angle, obtained using the selection from the ND280 nue CC inclusive analysis. It is similar to the figure 10 of the paper, but with more statistics and finer binning. They are accompanied with a README file presenting how to use these matrices and the related caveats. <strong>Please read it carefully.</strong></p> <p><strong>We strongly encourage any users of the matrices to present these caveats alongside any public comparison to T2K data.</strong></p> <p> </p> <p><strong>Full abstract:</strong></p> <p>The electron (anti-)neutrino component of the T2K neutrino beam constitutes the largest background in the measurement of electron (anti-)neutrino appearance at the far detector. The electron neutrino scattering is measured directly with the T2K off-axis near detector, ND280. The selection of the electron (anti-)neutrino events in the plastic scintillator target from both neutrino and anti-neutrino mode beams is discussed in this paper. The flux integrated single differential charged-current inclusive electron (anti-)neutrino cross-sections, dσ/dp and dσ/dcos(θ), and the total cross-sections in a limited phase-space in momentum and scattering angle (p>300 MeV/c and θ≤45<sup>∘</sup>) are measured using a binned maximum likelihood fit and compared to the neutrino Monte Carlo generator predictions, resulting in good agreement.</p>
HCl measurement data using WMS in H2 and N2
<p>In the article 'Trace level analysis of reactive ISO 14687 impurities in hydrogen fuel using laser-based spectroscopic detection methods' (<a href="https://doi.org/10.1016/j.ijhydene.2020.09.046">https://doi.org/10.1016/j.ijhydene.2020.09.046</a>) measurements of various reactive compounds are presented in hydrogen and nitrogen. The dataset contain data for HCl. </p>
Supplementary Files: Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow using Proper Orthogonal Decomposition
<p>Gappy POD reconstruction of the line-of-sight velocities from a nacelle-mounted scanning SpinnerLidar with 30% missing data for two LES cases.</p>
Measurement-Based Spatially Explicit Methane Emission Inventory (EI-ME)
<p>Accurate and comprehensive assessment of methane emissions, a powerful climate warming pollutant, is a key first step in reducing these emissions, while supporting the ability to track progress toward such reductions over time. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, adn their reliance on generic emission factors produces underestimations when compared with measurement-based inventories.</p> <p>In this work, we compile and analyze previous peer-reviewed measurement-based data on facility-level methane emissions in the US oil and gas sector and use these data to develop statistically robust emissions models from which we estimate total methane emissions for the population of major US oil and gas facilities.</p> <p>This dataset (EI_ME_v1.0.gpkg) aggregates the results of this measurement-based methane emission inventory (EI-ME), which is focused on oil and gas methane emissions in the US onshore production regions. The emissions estimates are spatially resolved at 0.1 x 0.1 degree spatial scales.</p> <p>The data layers in the GeoPackage are:</p> <ul> <li><em>EI-ME_gridded_ch4_emissions:</em> estimated methane emissions, spatially resolved at 0.1x0.1 degree spatial grids</li> <li><em>EI-ME_US_oil_gas_basins:</em> major US oil and gas basin boundaries, based on <a href="https://www.eia.gov/maps/maps.php">EIA</a> basin boundary definitions.</li> <li><em>EI-ME_facility_ch4_measurements_data: </em>A compilation of previous peer-reviewed facility-level measurement-based data for oil and gas methane emissions in the US.</li> </ul> <p>We also provide a netcdf version ("EI_ME_2021_inventory_CONUS_point1_degrees_v1.nc") which includes estimated mean oil and gas methane emissions over the contiguous US (excludes Alaska) aggregated over a slightly offset spatial grid compared to the full domain in the above .gpkg.</p> <p>Complete details of the emissions model development and dataset creation can be found in the following manuscript:</p> <ul> <li><strong>How to cite: </strong>Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>UPDATE (10/10/2025):</p> <p>The spatially explicit measurement-based oil and gas methane emissions inventory (EI-ME) is developed by MethaneSAT, a wholly owned subsidiary of Environmental Defense Fund, to support comprehensive oil and gas methane assessment, methane source attribution, and mitigation. The inventory combines ground-based measurement-based data with statistically robust methane emissions modeling to provide representative estimates of total methane emissions for key facility categories in the contiguous US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. It is spatially resolved at 0.1x.0.1 degree spatial scales.</p> <p> Version 1 of the EI-ME inventory for the contiguous United States was published in 2024 and provided an estimate of the 2021 oil and gas methane emissions and uncertainties that are spatially resolved at 0.1x0.1 degree spatial scales.</p> <p> Here, we provide an update to the EI-ME inventory for the years 2023 and 2024. In this update, we follow the same methodology and use the same input emissions datasets as described in detail in Omara et al. (2024), https://doi.org/10.5194/essd-16-3973-2024. We incorporate the latest available oil and gas activity data for the years 2023 and 2024 based on data from Enverus Prism (<a href="http://www.eneverus.com/">www.eneverus.com</a>), supplemented with additional information from the Oil and Gas Infrastructure Mapping database (OGIM v2.7, <a href="https://doi.org/10.5281/zenodo.15103476">https://doi.org/10.5281/zenodo.15103476</a>) and global annual gas flaring data from VIIRS (Visible Infraed Imagin Radiometer Suite), available from the Earth Observation Group (<a href="https://eogdata.mines.edu/products/vnf/global_gas_flare.html">https://eogdata.mines.edu/products/vnf/global_gas_flare.html</a>).</p> <p>---</p> <p>Contact at Environmental Defense Fund: Mark Omara (momara@edf.org), Anthony Himmelberger (ahimmelberger@methanesat.org)</p> <p>---</p>
Dataset for "Moon-originating Ions Measured by Kaguya at Various Altitudes in the Magnetotail Lobes"
<p>Datasets for figures in the manuscript.</p>
Supplementary material to "A dynamic flight model for Slocum gliders and implications for turbulence microstructure measurements" by Merckelbach et al.
<p>Supplementary information to the publication "A dynamic flight model for Slocum gliders and implications for turbulence microstructure measurements" by Lucas Merckelbach, Anja Berger, Gerd Krahmann, Marcus Dengler and Jeffrey R. Carpenter, Journal of Atmospheric and Oceanic Technology, 2019, 36(2), DOI: 10.1175/JTECH-D-18-0168.1. The supplementary information consists of the set of glider data and DVL data as used in the manuscript.</p> <p> </p>
Data in support to the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024)
<p>The files contain data presented and discussed in the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024).</p> <div> <div>Gianessi, Stefano, Matteo Polo, Luca Stevanato, Marcello Lunardon, Till Francke, Sascha E. Oswald, Hami Said Ahmed, et al. “Testing a Novel Sensor Design to Jointly Measure Cosmic-Ray Neutrons, Muons and Gamma Rays for Non-Invasive Soil Moisture Estimation.” <em>Geoscientific Instrumentation, Methods and Data Systems</em> 13, no. 1 (January 16, 2024): 9–25. <a href="https://doi.org/10.5194/gi-13-9-2024">https://doi.org/10.5194/gi-13-9-2024</a>.</div> </div> <p> </p>
Supplemental data for "Applications and Limitations of Portable Density Meter Measurements Of Na-Ca-Mg-K-Cl-SO4 Brines"
<p>Geochemistry, brine density measurements, environmental observations and geochemical modeling of the Bonneville Salt Flats and surrounding area. </p>
Samples in Westergren tubes for automated measurements of ESR
<p>Image sequence of blood samples in Westergren tubes, used to perform automated measurements of Erythocyte Sedimentation Rate (ESR). (Methodological publication with open-access code is under review.)</p>
Data from: Agroecological measures in meadows promote honey bee colony development and winter survival
<p><span>The homogenization of agricultural landscapes has led to a decrease in pollinator diversity and abundance. In response to this decline, farmers have implemented agroecological measures, which, in meadows, aim at providing more floral resources. These measures are the availability of unmown floral strips, delayed mowing, and discouraging the use of the conditioner, a device known to harm insects. The aim of our study was to investigate the cascade of effects of these agroecological measures on honey bee colony development and winter survival. We (i) determined the effect of these measures on colony size during the nectar and pollen collecting season in spring and summer, (ii) evaluated the effect of spring and summer colony sizes on autumn size, and (iii) described the effect of colony size in autumn on winter mortality. In this study, 300 honey bee colonies were monitored over three years </span><span>in three cantons of Switzerland. Colony size was defined by the number of brood cells and adult workers, Honey bee colony size in summer and autumn were improved by agroecological measures on meadows and likely contributed to the increased overwintering success.</span> <span>This study is a first step towards the targeted identification of viable agroecological measures on temporary meadows that can be implemented to promote honey bee colonies' health in the agricultural landscape.</span></p>
Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes
<p>This is the data release associated with <strong>Vitale et al <a href="https://arxiv.org/abs/2209.06978">2209.06978</a></strong></p> <p><strong>Samples.zip: </strong>Contains all of the hyper posterior samples for the runs listed in Tables G.1.</p> <p>The files are in json format. Bilby offers a dedicated routine to read them in</p> <p> </p> <blockquote> <p>import bilby<br> data= bilby.core.result.read_in_result(path_to_json)</p> </blockquote> <p> </p> <p>See the <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby documentation </a>for what is contained in the result object. </p> <p>For each run, we report the posterior hyper samples for the mass model, reshift model, spin magnitude model, spin tilt model and merger rate [Gpc^-3 yr^-1]</p> <p>Here the name used to store and a short description of each parameter (Follow the references in the Method section of the paper for a description of each sub-model):</p> <ol> <li>Primary mass model (Power Law + Peak for all runs) <ol> <li>power_law_slope_m1, slope of the primary mass power law component</li> <li>minmass_m1, minimum BH mass</li> <li>maxmass_m1, maximum BH mass</li> <li>low_end_smoothing_m1, smoothing at the low-mass end</li> <li>peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)</li> <li>peak_mean_m1, mean of the Gaussian peak</li> <li>peak_sigma_m1, sigma of the Gaussian peak </li> </ol> </li> <li>Mass ratio model (power law for all runs) <ol> <li>power_law_slope_mass_ratio, slope of the mass ratio </li> </ol> </li> <li>Redshift (power law for all runs) <ol> <li>power_law_slope_redshift, slope of the redshift</li> </ol> </li> <li>Spin magnitude (IID beta distributions for all runs) <ol> <li>alpha_chi, first argument of beta distribution</li> <li>beta_chi, second argument of beta distribution</li> </ol> </li> <li>Cosine of tilt angle <ol> <li>Gaussian models <ol> <li>mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian</li> <li>sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian</li> <li>mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian</li> <li>sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian</li> <li>mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean</li> <li>mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean</li> <li>sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma</li> <li>sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma</li> </ol> </li> <li>Beta models <ol> <li>alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution</li> <li>alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution</li> <li>beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution</li> <li>beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution</li> </ol> </li> <li>Tukey models: <ol> <li>tukey_x0, the center of the Tukey as defined in appendix E of the paper</li> <li>tukey_k, Tk as defined in appendix E of the paper</li> <li>tukey_r, Tk as defined in appendix E of the paper</li> </ol> </li> <li>Branching ratios: <ol> <li>spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component</li> <li>spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component; for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong></li> </ol> </li> </ol> </li> <li>Merger rate <ol> <li>rates, merger rate per unit Gpc cubed per unit year</li> </ol> </li> </ol> <p>Note that some of the parameters for the tilt models might not be used, but still stored (and fixed to - usually - zero). This can be checked by verifying what priors were used for each parameter. For example the <em>Isotropic</em> run was obtained from the <em>Isotropic + Gaussian </em>model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) </p> <blockquote> <p>> data['prior']<br> [...]<br> <strong> 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> </p> </blockquote> <p> </p> <p><strong>Figures.zip:</strong> Contains PDFs for all figures in the paper, plus individual figures for p(costau) and dR/dcostau for each model.</p> <p>Drop me (Salvatore Vitale) an email if anything doesn't work, is missing, or if you spot issues. Thanks! </p> <p> </p>
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.