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104
datasets available to search
ShareScore release 0.9.0
Dataset results
104 results for “modal data”
Data from: Sexual selection and population divergence II. divergence in different sexual traits and signal modalities in field crickets (Teleogryllus oceanicus)
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Data from: Behavioural mechanisms of sexual isolation involving multiple modalities and their inheritance
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Data from: Dissection of signaling modalities and courtship timing reveals a novel signal in Drosophila saltans courtship
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Data from: Cross-modal influence of mechanosensory input on gaze responses to visual motion in Drosophila
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K-State - ARPA-E SMARTFARM Grain Sorghum 2021 Kansas site comprehensive sensor modalities data set.
<p>Comprehensive Year 1 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Kansas site of the project. </p>
Anonymized Data Collection From the CS6BP and Other Modalities for the Purpose Measurement of Blood Pressure
ClinicalTrials.gov study NCT05370066. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Collection From the CardiacSense1 and Other Modalities for Developing a System for Monitoring of Respiratory Rate
ClinicalTrials.gov study NCT04580615. IPD Sharing: NO. Countries: 1. Publications: 0.
Deciphering shared molecular dysregulation across Parkinson's Disease variants using a multi-modal network-based data integration and analysis
GEO Series GSE276684. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Data from: Timescale- and sensory modality-dependency of the central tendency of time perception
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Data and analysis code for 'Does visual modality improve perceptual acquisition of L2+ English vowels?'
<p>R script used for cleaning data (not to be run)</p> <p>Cleaned data as CSV files</p> <p>R script for analysis and plots</p> <p>This version removes an infideliyt in the script that kept the data on my computer OK but had corrupted/overwritten a test data file.</p>
DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES
DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES KANISHKA BHADURI*, KAMALIKA DAS**, AND PETR VOTAVA*** Abstract. There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets ate physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.
VEGA1 DUST MASS SPECTROMETER MODAL DATA V1.0
The data from MPI for this dataset were received as text files each containing spectra of a single instrument mode (there were several files for most modes). These spectra were reformatted into binary tables, and all spectra from each mode were combined into a single file. The original order of the spectra has been preserved. Spacecraft time, relative to switch-on of the instrument is specified as 1 clock tick = 0.11852 seconds. The exact equation is:
Distributed Anomaly Detection Using Satellite Data From Multiple Modalities
There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.
VEGA2 PUMA DUST MASS SPECTROMETER MODAL DATA V1.0
The data from MPI for this dataset were received as text files each containing spectra of a single instrument mode (there were several files for most modes). These spectra were reformatted into binary tables, and all spectra from each mode were combined into a single file. The original order of the spectra has been preserved. Spacecraft time, relative to switch-on of the instrument is specified as 1 clock tick = 0.11852 seconds. The exact equation is:
Machining Simulated Data, Modal 0, 6 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 6 cutting teeth across forces from 600-800</p>
Machining Simulated Data, Modal 0, 3 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 3 cutting teeth acorss forces from 600-800</p>
Machining Simulated Data, Modal 0, 2 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 2 cutting teeth across forces from 600-800</p>
Machining Simulated Data, Modal 1, 4 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 1, with 4 cutting teeth across forces from 600-800</p>
Machining Simulated Data, Modal 1, 6 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 1, with 6 cutting teeth across forces from 600-800</p>
Machining Simulated Data, Modal 1, 3 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 1, with 3 cutting teeth across forces from 600-800</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.