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4,562 results for “correlation”
Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of two parts. The first part of the data set is measurements for six different specimens with wave type impact – short sweep signal with duration 0.05 s. The second part is the measurements during splice connection degradation of one of the specimens with short impulse. The degradation of a connection is presented by four different states of joints. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the second part of the data set is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Database of measurements for damage detection of steel beam splice connections by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated splice connection between two steel beams. The data set consists of measurements for six different specimens with two types of impact – sweep signal with duration 0.5 s and short impulse, during degradation of the splice connections realised by unbolting the bolts in the connections. In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>This database is a continuation of the database Kurtenoks, V., Buka-Vaivade, K., Serdjuks, D., Lapkovskis, V., Mironovs, V., & Podkoritovs, A. (2023). Database of measurements for damage detection of steel beam splice connection by Coaxial Correlation Method in 6-D space (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10077332<br>Suggested by authors data post-processing is described in Buka-Vaivade, K.; Kurtenoks, V.; Serdjuks, D. Non-Destructive Damage Detection of Structural Joint by Coaxial Correlation Method in 6D Space. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1151. https://doi.org/10.3390/buildings13051151</p>
Brain Correlates of Math Development
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Brain Correlates of Deductive Reasoning in Adults
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Brain Correlates of Math Processing in Adults
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Database of measurements for damage detection of T-type timber structural joint by Coaxial Correlation Method in 6-D space
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned on either side of the investigated joint between two timber beams connected at an angle of 90⁰. Presented data related to seven different states of joints, five load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p>
Database of measurements for damage detection of panel-to-panel moment joints in timber structures by Coaxial Correlation Method
<p>This database includes series of measurements of the structure's response taken in six-dimensional space using two 6D sensors, coaxially positioned in two different ways on either side of the investigated panel-to-panel connection. Presented data related to ten different states of joints, two load levels, and two type of input signal (short impulse and sweep signal with duration 0.5 seconds with frequency range from 10 Hz to 2000 Hz). In the "<strong>Read_me_first.pdf</strong>" is described the experiment, the format of .csv files names and files' structure.</p><p>Used materials, methods and results for the case of static load equal to 151.8 kg with sweep-type input signal, and T2 scheme of sensors placement is described in Kurtenoks, V.; Kurajevs, A.; Buka-Vaivade, K.; Serdjuks, D.; Lapkovskis, V.; Mironovs, V.; Podkoritovs, A.; Vilnitis, M. The Quality Assessment of Timber Structural Joints Using the Coaxial Correlation Method. <i>Buildings</i> <strong>2023</strong>, <i>13</i>, 1929. https://doi.org/10.3390/buildings13081929</p>
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions
<p><span>Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.</span></p>
Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis
<p>Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression.</p> <p>The data was saved with numpy 1.21 with <a href="https://numpy.org/doc/1.21/reference/generated/numpy.lib.format.html#format-version-1-0">NumPy format version 1.0</a> as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data.</p> <p>In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with <a href="https://imagej.nih.gov/ij/">ImageJ</a>.</p> <p> </p> <p>A <a href="https://github.com/Kitware/MetaIO">METAImage</a> header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,</p>
Neuroanatomical correlates of approach-avoidance conflict (fMRI)
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Neural correlates of the LSD experience revealed by multimodal neuroimaging
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Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </p>
Correlated order at the tipping point in the kagome metal CsV3Sb5
<p>Data deposite for the manuscript entitled "Correlated order at the tipping point in the kagome metal CsV3Sb5". The manuscript will soon appear online. </p>
Inter-Chemical Correlation results for the study: NHANES20172018 (NHANES Survey 2017-2018)
Title: NHANES Survey 2017-2018 <br>Species: Homo sapiens <br>Number of samples: 8433 <br>Number of named analytes: 86 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
Inter-Chemical Correlation results for the study: ECHOAKA02 (NA)
Title: cohort name is masked <br>Species: Homo sapiens <br>Number of samples: 112 <br>Number of named analytes: 12 <br>Datasource url: https://dash.nichd.nih.gov/study/424675 <br>
Inter-Chemical Correlation results for the study: ECHOAHA02 (NA)
Title: cohort name is masked <br>Species: Homo sapiens <br>Number of samples: 91 <br>Number of named analytes: 11 <br>Datasource url: https://dash.nichd.nih.gov/study/424673 <br>
Inter-Chemical Correlation results for the study: ECHOAHA01 (NA)
Title: cohort name is masked <br>Species: Homo sapiens <br>Number of samples: 538 <br>Number of named analytes: 173 <br>Datasource url: https://dash.nichd.nih.gov/study/424672 <br>
Inter-Chemical Correlation results for the study: ECHOAKA01 (NA)
Title: cohort name is masked <br>Species: Homo sapiens <br>Number of samples: 243 <br>Number of named analytes: 53 <br>Datasource url: https://dash.nichd.nih.gov/study/424674 <br>
Inter-Chemical Correlation results for the study: ECHOAGA01 (NA)
Title: cohort name is masked <br>Species: Homo sapiens <br>Number of samples: 1947 <br>Number of named analytes: 205 <br>Datasource url: https://dash.nichd.nih.gov/study/424671 <br>
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.