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Dataset results
12 results for “Digit Data Set”
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History
<p>Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the <em>Poses of People in Art</em> data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19<sup>th</sup> century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.</p>
Data set from "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data"
<p>This data set contains the complete digitalized individual patient data that are used in the manuscript "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data", which is accepted from "Acta Diabetologica".<br> The data file is in CSV format and contains the five variables "OutcomeType" (with values "AllcauseMortality" or "PrimaryOutcome"), "Study" (denoting the respective cardiovascular outcome trial), "Treatment" (denoting the respective study treatment or placebo), "Event" (denoting if the respective has been observed (Event=1) or not (Event=0)), and "SurvivalTimeMonths" (denoting the respective time to the event or censoring in months).</p>
Data: set for "Low-voltage 2D materials-based printed field-effect transistors for integrated digital and analog electronics on paper"
<p>The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.</p>
Data Set Literature Review Digital Forensic and Forensic Sciences
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>Digital Forensic</em>" dan "<em>Forensic Sciences</em>"</p>
Digital Commensality data-set
<p>The Digital Commensality Data-set consists of facial activity data of 11 pairs of persons sharing a meal online through a videoconferencing software and self-reported qualitative and qualitative measures of their commensal experience (Computer-Mediated Communication questionnaire and Digital Commensality questionnaire). Facial activity data is extracted using the OpenFace tool.</p> <p>If you use this data-set for the uses allowed in the license, e.g., research purposes, please add the following citation:</p> <p>Ceccaldi, E., Niewiadomski, R., Mancini, M., & Volpe, G. (2022). What's on your plate? Collecting multimodal data to understand commensal behavior. <em>Frontiers in Psychology</em>, <em>13</em>.</p> <p>https://doi.org/10.3389/fpsyg.2022.911000</p>
Attitudes of Health Care Professionals Toward Older Adults' Abilities to Use Digital Technology: Open Access Data Sets
<p>Open access dataset related to 2 studies published under:</p> <p>Mannheim I, Wouters EJM, van Boekel LC, van Zaalen Y<br> Attitudes of Health Care Professionals Toward Older Adults’ Abilities to Use Digital Technology: Questionnaire Study<br> J Med Internet Res 2021;23(4):e26232<br> URL: https://www.jmir.org/2021/4/e26232<br> doi: 10.2196/26232</p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Data set for Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation
<p>The dataset contains over-the-air measurements on a 64 active antenna array (Anokiwave AWMF-0129) operating at 28 GHz carrier frequency and transmitting a 200 MHz OFDM waveform with FFT size of 4096, 3168 active subcarriers, subcarrier spacing of 60 kHz and 5 times oversampling w.r.t the critical sampling rate. The dataset contains the I/Q samples of the TX waveform as well as the corresponding over-the-air received signals when the electrical beam is steered toward different beamforming directions. This dataset allows to observe and study the so-called beam-dependent load modulation, which is the phenomenon that causes the nonlinear characteristics of the antenna array to change with the beamforming direction. A Matlab script for data visualization is also provided.</p> <p>For further details please refer to the following papers:</p> <p>A. Brihuega <em>et al</em>., "Piecewise Digital Predistortion for mmWave Active Antenna Arrays: Algorithms and Measurements," in <em>IEEE Transactions on Microwave Theory and Techniques</em>, doi: 10.1109/TMTT.2020.2994311.</p> <p>A. Brihuega <em>et al</em>., “Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation,” in <em>IEEE Microwave and Wireless Components Letters, </em>doi: 10.1109/LMWC.2020.3004003</p>
Data set for article''Digital health intervention in patients undergoing cardiac rehabilitation: systematic review and meta-analysis''
Open the record for dataset details and reuse information.
Data Collection Using Eko Digital Devices in a Clinical Setting
ClinicalTrials.gov study NCT07227376. IPD Sharing: NO. Countries: 1. Publications: 0.
Data Collection For Adventitious Lung Sounds Algorithm Using Eko Digital Devices in a Clinical Setting
ClinicalTrials.gov study NCT07360522. IPD Sharing: NO. Countries: 1. Publications: 0.
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.