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Dataset results
2,214 results for “strengths”
IODP Expedition 356 Compressional strength (penetrometer)
Compressional strength was measured on section halves using a Humbolt Manufacturing Pocket Penetrometer. The penetrometer is only suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength can be calculated from the measured compressional strength. Compressional strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes compressional strength results, penetration direction, and adapter foot information.
IODP Expedition 356 Vane shear strength (Torvane)
Sediment strength was measured on section halves using a Humbolt Manufacturing Torvane handheld instrument. The Torvane is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment).
IODP Expedition 353 Vane shear strength (AVS)
Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.
IODP Expedition 359 Vane shear strength (Torvane)
Sediment strength was measured on section halves using a Humbolt Manufacturing Torvane handheld instrument. The Torvane is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment).
root tensile strength
<p>datasets containing information on the root tensile strength for the following plant species: </p> <p>- Ash</p> <p>- Goat willow</p> <p>- Basket willow</p> <p>- Sycamore/maple</p> <p>- Silene dioica</p> <p>- Erigeron acris</p> <p>Root tensile strength was measured with a universal testing machine. Root tensile strength is a useful variable for estimating the degree of soil reinforcement provided by plant roots </p>
Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases
<p>These data were used for the development of the paper "<strong>Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases</strong>". Especifically, we added ecological and environmental data that were used for modeling.</p>
Dynamic strength characterisation of wet granular ice layers
<p>Dataset for the research paper "Dynamic strength characterisation of wet granular ice layers" submitted to the International Journal of Impact Engineering.</p>
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
IODP Expedition 366 Vane shear strength (AVS)
<p>Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.</p>
Data from: Complementary strengths of spatially-explicit and multi-species distribution models
<p><span><span><span><span><span><span><span><span><span><span><span> Species distribution models (SDMs) project the outcome of community assembly processes - dispersal, the abiotic environment, and biotic interactions - onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock (<i>Tsuga canadensis</i>L.) and five additional co-occurring overstory tree species in 35,569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span> Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species-environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.</span></span></span></span></span></span></span></span></span></span></p>
2017 Solar Eclipse Doppler and signal strengths
<p>K3KO 30.210666N,81.603833W Jacksonville FL. No .wav file. Antenna Vee pointed north.K3 rx. CHU 14.670 MHz and WWV 15 MHz measured.</p> <p>Rb locked reference oscillators. Methodology described in *. doc file upload</p>
Data and script for: A marker of biological age explains individual variation in the strength of the adult stress response
<p>Data and script for: A marker of biological age explains individual variation in the strength of the adult stress response.</p> <p>Comprises 2 CSV data files and one R script.</p>
THOR - the rock strength database
<blockquote> <p><em>Thor </em>is an extensive and (nearly) exhaustive rock strength compilation based on publications from 1980 - 2024 (<a href="https://www.scopus.com/results/results.uri?sort=plf-f&src=s&st1=Schmidt+hammer&sid=434f0cbd07555c3c2ad338640bcd4488&sot=b&sdt=cl&sl=29&s=TITLE-ABS-KEY%28Schmidt+hammer%29&origin=resultslist&editSaveSearch=&sessionSearchId=434f0cbd07555c3c2ad338640bcd4488&limit=10&cluster=scosubjabbr%2C%22ENGI%22%2Ct%2C%22EART%22%2Ct%2C%22MATE%22%2Ct%2C%22ENVI%22%2Ct%2Bscosubtype%2C%22ar%22%2Ct" target="_blank" rel="noopener">Scopus</a>). Data reported in <em>Thor</em> includes:</p> </blockquote> <ul> <li><strong>Search tool </strong>(<a href="https://zenodo.org/records/15465793/files/Search%20tool.xlsx?download=1">download here</a>) -<strong> </strong>enables users to select specific lithologies and generate summary statistics. This tool also calculates watershed erodibility by accounting for lithologies and their respective proportions in a watershed.</li> <li><strong>R<sub>N</sub></strong> - rebound values (all converted to Schmidt Hammer model N), grouped by lithology.</li> <li><strong>σ<sub>UCS </sub></strong> - uniaxial compressive strength (σ<sub>UCS</sub>), grouped by lithology.</li> <li><strong><em>L<sub>E</sub></em></strong><strong> </strong>- lithologic erodibility index (L<sub>E</sub>), grouped by lithology.</li> <li>Authors list - a list of all publications/references used in this compilation.</li> </ul> <blockquote> <p><strong>Contributions Welcome! </strong></p> <p>Researchers are encouraged to contribute data to expand the database. Please send your data submissions to: <strong>mbhaag@mit.edu </strong>(<a href="https://www.researchgate.net/profile/Mauricio-Haag" target="_blank" rel="noopener">ResearchGate</a>)</p> </blockquote> <p>Please cite as: Haag, M.B., Schoenbohm, L.M., 2025. <em>Thor</em>: a rock strength database for investigating lithologic controls in landscape evolution. <em>Earth and Planetary Science Letters</em> 660, 119364. <a href="https://doi.org/10.1016/j.epsl.2025.119364" target="_blank" rel="noopener">https://doi.org/10.1016/j.epsl.2025.119364</a></p> <p><strong>New Feature</strong>: Map featuring all sampling locations, detailed site information, and direct links to the original studies: <a href="https://www.google.com/maps/d/edit?mid=1XBvZ8Ns2J-Vqc1vtrBtKYGKIJp-NvyI&usp=sharing" target="_blank" rel="noopener">map link</a>.</p>
Testing strengths, limitations and biases of current Pulsar Timing Arrays detection analyses on realistic data
<p>In this project, we carried out an extensive investigation of the performance of current Pulsar Timing Arrays (PTA) analyses on simulated PTA datasets where we modeled the gravitational waves (GW) signal as the incoherent superposition of sinusoidal signals from a cosmic population of super-massive black hole binaries (SMBHBs). Here we publish the dataset referred to in the paper as the <em><strong>SMBHB_set</strong></em>: 100 realisations of PTA datasets with 25 pulsars and a GWB signal of nominal amplitude 2.4e-15. For each realisation, listed from 1001 to 1100, this repository contains the pulsars .par and .tim files, the mcmc chain (sampling over 66 parameters: 60 for pulsars intrinsic noise parameters and log amplitude and slope of a common red noise process), and the SMBHB details for that specific realisation of the GWB. The columns of the <em>MBHB_list.dat</em> files contain (from left to right): log10 MBH chirp mass (in solar masses in the source frame), mass ratio, source redshift, log10 of GW fundamental mode (n=2) in the observer frame, phi and theta angles in the sky, source inclination angle, source polarisation angle, source initial orbital phase, source initial direction of periastron and source initial eccentricity.</p> <p>If you make use of any of this data, please cite:<br><br></p> <div>@article{ refId0,</div> <div> author = {{Valtolina, Serena} and {Shaifullah, Golam} and {Samajdar, Anuradha} and {Sesana, Alberto}},</div> <div> title = {Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data},</div> <div> DOI= "10.1051/0004-6361/202348084",</div> <div> url= "https://doi.org/10.1051/0004-6361/202348084",</div> <div> journal = {A&A},</div> <div> year = 2024,</div> <div> volume = 683,</div> <div> pages = "A201",</div> <div>}</div>
AI4ER GTC: ECCO Streamfunction, MOC Strength, and Surface Variables
<p><strong>Overview</strong></p> <p>This repository contains all <a href="https://ecco-group.org/" target="_blank" rel="noopener">NASA ECCO</a> data used for our AI4ER Guided Team Challenge project. Data was downloaded from <a href="https://podaac.jpl.nasa.gov/" target="_blank" rel="noopener">PO.DAAC</a>. Download and preprocessing scripts can be found in the corresponding GitHub repository (see the <a href="https://github.com/ai4er-cdt/OTP/tree/main" target="_blank" rel="noopener">repository</a> or the <a href="../doi/10.5281/zenodo.10817670" target="_blank" rel="noopener">archived version</a>).</p> <p>Use and redistribution of this data is in line with <a href="https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy" target="_blank" rel="noopener">NASA EarthData's Data and Information Policy</a> and <a href="https://podaac.jpl.nasa.gov/CitingPODAAC" target="_blank" rel="noopener">NASA PO.DAAC's Data Use and Citation Policy</a>.</p> <p><strong>Data Structure</strong></p> <p>Please see the included <code>README.md</code> file for full details on the dataset.</p> <p><strong>Authors</strong></p> <ul> <li><a href="mailto:el590@cam.ac.uk">Emilio Luz-Ricca</a></li> <li><a href="mailto:sm2783@cam.ac.uk">Sharan Maiya</a></li> <li><a href="mailto:av656@cam.ac.uk">Aline Van Driessche</a></li> <li><a href="mailto:ngb34@cam.ac.uk">Nina Baranduin</a></li> <li><a href="mailto:tc656@cam.ac.uk">Thomas Cowperthwaite</a></li> </ul>
Dataset for publication:Dataset for publication: "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys"
<p>The dataset contains a set of experimental data used in preparation for the manuscript "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys." The dataset contains results of fatigue tests, residual stress measurements, nanoindentation measurements, and surface roughness data.</p>
Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data
<p>These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).</p>
Novel polarimetric technique to constrain the magnetic field structure and strength of Gamma-ray burst jets
<p>Gamma-ray bursts (GRBs) are extremely energetic events of cosmological origin. Observed GRBs have high luminosity and rapid variability that requires ultra-relativistic motion in the production mechanism which drive the synchrotron radiation associated with the relativistic jets and their shocked interactions with the local ambient medium. They are broadly divided into two types based on the gamma-ray duration; long GRBs (>2 seconds), and short GRBs (<2 seconds). Long GRBs are thought to be originated from explosions of very massive stars and short GRBs are thought to be produced by the merger of compact binaries. Several key open questions about our understanding of GRB physics remain: What is the driving mechanism of GRB jets? What is the origin and role of magnetic fields in driving the explosion? Since these events happen at cosmological distances, they can not be resolved using traditional astronomical techniques. However, polarimetric observations of GRBs have allowed us to start the exploration of the structure and magnetic field configurations of their relativistic jets. Generally, polarization is measured via the ratio of fluxes by taking consecutive exposures, however for rapidly varying objects such as GRBs, it is not an effective way to observe polarization. Liverpool Telescope (LT) has utilized rapidly rotating polaroids to overcome this problem and created a series of polarimeters that have successfully detected early-time optical polarimetry of various GRBs. I will present photometric and polarimetric results of various GRBs observed by RINGO3. 10 GRBs were bright enough to perform analysis and we were able to perform polarimetric analysis for 7 GRBs. I will discuss how polarimetric detection for a long GRB 191016A along with photometric data constraint the energy injection mechanism for the central engine. In addition, I will present how polarization depends on various properties of GRBs such as photometric decay index, isotropic energy of GRBs, redshift etc.</p>
Influence of the imperfection direction on the strength of steel and stainless steel frames
<p>This file includes finite element simulation data (ultimate load factor) carried out on 60 steel and stainless steel frames, including regular and irregular frame configurations with different section sizes.</p>
IODP Expedition 372A Compressional strength (penetrometer)
<p>Compressional strength was measured on section halves using a Humbolt Manufacturing Pocket Penetrometer. The penetrometer is only suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength can be calculated from the measured compressional strength. Compressional strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes compressional strength results, penetration direction, and adapter foot information.</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.