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2,214 results for “Strength”
DocumeNDT Uniaxial Compression Strength (UCS) tests dataset
<p>DocumeNDT Uniaxial Compression Strength (UCS) Tests dataset</p> <p>This repository contains the raw data obtained from the Uniaxial Compression Strength (UCS) tests performed on six stone masonry walls at the laboratory of the Eduardo Torroja Institute for Construction Sciences (IETCC), from the Spanish National Research Council (CSIC).</p> <p>The data set is structured in 6 folders corresponding to the 6 tested stone masonry walls (Walls 1-6). Each folder contains:<br> (1) Position of each LVDT during the test (plan view "PositionLVDTs.jpg)<br> (2) Elevations A and B (long sides), marked in plan<br> (3) Recorded force and displacement data (*.csv file)<br> (4) readme file with specific details about the test</p> <p>Raw data (force and displacement) is provided in .csv files, structured in columns. First column corresponds to the Force measured (in kN) and columns 2-5 correspond to the displacement measured by each LVDT (in mm).</p> <p>The frequency of acquisition is 1 samples/s.</p> <p>The detailed geometry of all walls (including the inner position of each stone) is publicly available and can be found in https://doi.org/10.5281/zenodo.7713700</p> <p>Please cite the following related publication:</p> <p>Ortega J, Meersman MFL, Aparicio S, Liébana JC, Anaya JJ, Gonzalez M. Capabilities of sonic tomography to assess historic masonry deformability properties, in situ stress level and damage evolution (2023)</p>
DocumeNDT Uniaxial Compression Strength (UCS) tests – tomographic inspections dataset
<p>DocumeNDT Uniaxial Compression Strength (UCS) Tests - tomographic inspections dataset</p> <p>This repository contains data from the sonic tomography inspections carried out during the cyclic Uniaxial Compression Strength (UCS) tests performed on six stone masonry walls at the laboratory of the Eduardo Torroja Institute for Construction Sciences (IETCC), from the Spanish National Research Council (CSIC). </p> <p>A first sonic tomography inspection was carried out before starting the test (C0) with no loading. Then, cycles of increasing loading were imposed to the wall. Two cycles are performed for each loading level. After reaching the maximum load in each cycle, the load is sustained and a sonic tomography inspection is carried out. </p> <p>The data set is structured in 3 levels of folders:<br> - At first level, the 6 folders correspond to the 6 tested stone masonry walls (Wall 1-6).<br> - At second level, for each wall, there are two folders and a readme file:<br> - The readme file contains specific details about the inspection, e.g., number of emission and reception points or maximum load corresponding to each cycle<br> - The folder 'Coordinates' contains two diagrams of the emission and reception locations in elevation<br> - The folder 'Loading Cycles' contains the sonic raw data recorded in each cycle<br> - At third level, for each loading cycle carried out during the UCS test, there is one folder. Each folder contains:<br> - The folder 'Coordinates' contains the exact coordinates of the emission and reception points in *.txt files<br> - The folder 'Emission raw signal' contains the recorded emission signal for each emission location<br> - The folder 'Reception raw signal' contains the recorded reception signal for each emission location</p> <p>Sonic data are presented in *.csv files, structured in columns. Each column correspond to a reception location. The values correspond to the voltage recorded. Specific details about the csv can be found in the readme file of each wall.</p> <p>The frequency of acquisition is 256000 samples/s.</p> <p>The detailed geometry of all walls (including the inner position of each stone) is publicly available and can be found in https://doi.org/10.5281/zenodo.7713700</p> <p>The experimental results of the UCS test are also publicly available and can be found in https://doi.org/10.5281/zenodo.8341725. </p> <p>Please cite the following related publication:</p> <p>Ortega J, Meersman MFL, Aparicio S, Liébana JC, Anaya JJ, Gonzalez M. Capabilities of sonic tomography to assess historic masonry deformability properties, in situ stress level and damage evolution (2023)</p>
Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
<ol> <li>Population size is a key metric for management and policy decisions, yet wildlife monitoring programs are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent.</li> <li>We present a case study demonstrating how data from the eBird citizen science program can be combined with regional monitoring efforts by the U.S. Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information to those based on aerial survey data alone.</li> <li>Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations.</li> <li>We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference, and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the U.S. Fish and Wildlife Service and inform permitting regulations (e.g., mortality/harm associated with wind energy development).</li> <li> <em>Policy implications</em>. Our results demonstrate the ability of citizen science data to complement targeted monitoring programs and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.</li> </ol>
Dataset: Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study
<p>This dataset accompanies the paper "Challenges, Strengths, and Strategies of People with ADHD in Software Engineering: A Case Study". It contains interview guides as well as anonymous interview transcripts for six interviewees who explicitly consented to the publication.</p> <p>Specifically:</p> <ul> <li>Interview transcripts are Excel files starting with "<strong>interview</strong>".</li> <li>The interview instruments used for the 19 interviews are named "<strong>interview_guide_professionals</strong>" followed by a version number. V1 was used for the first 3 interviews, V2 for the 9 following interviews, and V3 for the remaining 7.</li> <li><strong>manager_feedback_guide.pdf</strong> is the interview guide used for discussions with the 4 managers. The preliminary results we showed them are depicted in <strong>01_challenges_themes_relations_noStrat_v2</strong>, <strong>02_strengths_themes_relations_v1</strong> and <strong>02_strengths_themes_relations_v2</strong></li> <li><strong>consent_form.pdf</strong> is a PDF version of the online form used to handle consent and intake of interviewees.</li> </ul>
Regression Datasets: Strength as a Function of Liquid Water Content in Different Snow Types
<p>This repository contains two datasets used for determining the shear strength of different snow types as a function of liquid water content. It also contains one dataset with additional information pertaining to our experiment, including site and condition details. <strong>Please read the 'Read me.txt' file before using these datasets.</strong></p>
TRIple in Asthma hiGh strenGth vErsus Ics/Laba hs and tiotRopium (TRIGGER)
ClinicalTrials.gov study NCT02676089. IPD Sharing: YES. Countries: 17. Publications: 6.
Diabetes Strengths Study - Pilot of Provider-delivered Strengths-based Intervention
ClinicalTrials.gov study NCT02510664. IPD Sharing: YES. Countries: 1. Publications: 1.
Parenting Strength At Home- Parents Pilot
ClinicalTrials.gov study NCT03403153. IPD Sharing: NO. Countries: 1. Publications: 1.
Expiratory Muscle Strength Training and Phonation Resistance Training Exercises For Elderly Patients With Vocal Fold Atrophy
ClinicalTrials.gov study NCT03696576. IPD Sharing: YES. Countries: 1. Publications: 33.
TRIple in asthMA With uncontRolled pAtient on Medium streNgth of ICS + LABA
ClinicalTrials.gov study NCT02676076. IPD Sharing: YES. Countries: 1. Publications: 6.
Study Evaluating the Effects of Dietary Supplement on Hair Thickness and Hair Strength in Individuals With Self-Perceived Thinning Hair
ClinicalTrials.gov study NCT06605768. IPD Sharing: NO. Countries: 1. Publications: 2.
The relative strength of selection on modifiers of genetic architecture under migration load
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Facilitation strength across environmental and beneficiary trait gradients in stream communities
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Reprogramming feedback strength in gibberellin biosynthesis highlights conditional regulation by the circadian clock and carbon dioxide
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Feats of supercontractile strength: Functional convergence of supercontracting muscle properties among hyoid musculature in chameleons
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Data from: Strength of enemy release from parasitoids is context-dependent in the invasive African Fig Fly, Zaprionus indianus
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Reduced strength and increased variability of extinction selectivity during mass extinctions
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Data from: Effects of high dose aspartame-based sweetener on the gut microbiota and bone strength in young and aged mice
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Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
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Data from: Complementary strengths of spatially-explicit and multi-species distribution models
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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.