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1,742 results for “activity data”
Data and code used for 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Thigh-Worn Accelerometry: A Comparative Study of Two No-Code Classification Methods for Identifying Physical Activity Types'.</p> <p> </p> <h2><strong>File structure</strong></h2> <p><strong>- analysis</strong></p> <p>The analysis subfolder contains all R scripts used for the study:</p> <p>1. Sample size estimation<br>2. Synchronisation of the timestamps<br>3. Processing of the raw data<br>4. Calculating the interrater agreement<br>5. Calculating all performance metrics and producing the plots</p> <p>In addition, the two subfolders contain the plots and result tables produced when running the scripts.</p> <p> </p> <p><strong>- data</strong></p> <p>The data folder contains all raw data as well as the processed data. A subfolder for each subject contains the video annotations (.eaf), the raw acceleration data (.csv) and the SENS motion classification data (.csv).</p> <p>The ActiPASS subfolder contains the raw acceleration files in binary file format as well as the ActiPASS output.</p> <p>The shiny subfolder contains the R code used for running the shiny app during data collection as well as the logged data and timestamps.</p> <p> </p> <p><span><strong><span>- documents</span></strong></span></p> <p><span>This folder contains any additional documents used in the study.</span></p> <p> </p> <h2><strong><span>Requirements</span></strong></h2> <p>The data processing and analysis was performed in R (Version 4.3.2). To run the full analysis in R, the following packages need to be installed:</p> <ul> <li>tidyverse</li> <li>xml2</li> <li>lubridate</li> <li>here</li> <li>dygraphs</li> <li>hms</li> <li>irr</li> <li>yardstick</li> <li>cowplot</li> <li>gt</li> </ul>
Data of "Efficient and Device-Independent Active Quantum State Certification"
<p>Dataset and analysis code for the manuscript "Efficient and Device-Independent Active Quantum State Certification"</p>
flood modeling datas for Catastrophic outburst floods along the middle Yarlung Tsangpo River: responses to coupled fault and glacial activity on the southern Tibetan Plateau
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GPS data for 'Low-Latitude Ionospheric Density Irregularities and Associated Scintillations Investigated by Combining COSMIC RO and Ground-Based GPS Observations over a Solar Active Period' by Zhe Yang and Zhizhao Liu
<p>This dataset contains the final derived GPS data reported in the paper 'Low-Latitude Ionospheric Density Irregularities and Associated Scintillations Investigated by Combining COSMIC RO and Ground-Based GPS Observations over a Solar Active Period' by Zhe Yang and Zhizhao Liu.</p>
Probing the effect of cadence on the estimates of photospheric energy and helicity injections in eruptive active region NOAA AR 11158 – movies of the data series
<p>This dataset contains movies of all photospheric data series used in the article Lumme et al. (2019) “Probing the effect of cadence on the estimates of photospheric energy and helicity injections in eruptive active region NOAA AR 11158”, submitted. Movies track the evolution of several photospheric quantities in the NOAA active region 11158 from the emergence of the active region well beyond the time of the strongest eruptive activity in the region.</p> <p>The tracked quantities include: magnetic, plasma velocity and electric fields, as well as the vertical component of the Poynting vector and the relative helicity flux density. All quantities are given in several spatial and temporal resolutions: the cadences range from 2.25 minutes to 24 hours, and the spatial resolution is either the maximum resolution of the SDO/HMI instrument (0.03 deg in heliographic coordinates, 364 km on the Sun) or 15 times lower (0.45 deg, 5470 km). Since Lumme et al. (2019) employs three electric field inversion methods, PDFI, raw DAVE4VM and inductive DAVE4VM methods, there are three versions of the electric field maps as well as the derivative quantities Poynting and helicity fluxes for each cadence and spatial resolution.</p> <p>All series, except for the magnetic field and LOS plasma velocity series, are plotted only at the central strong field parts of the active region. The temporal extent of the series used in the paper is Feb 10 14:00 – Feb 17 00:00, 2011. However, some of the movies span only the the most interesting part of the evolution Feb 13 00:00 onward, after which the active region started to exhibit strong flux emergence and energy and helicity fluxes.</p> <p>All movie files in the dataset are given using the following naming convention: “{quantity}_{method}_{cadence}_{res_info}.avi”, where “quantity” specifies the plotted quantity (e.g. horizontal electric field “Eh”), “method” specifies the method used to derive the quantity (e.g., “PDFI” for electric field; if empty, no method is specified), “cadence” specifies the cadence of the data either in minutes or hours (e.g. “2.25_min” or “12_h”), and “res_info” specifies the spatial resolution (if empty then, the data is given in full resolution, otherwise “res_info” is “rebin_15x” corresponding to the 15 lower spatial resolution).</p> <p> Movies of the following data series are included for all cadences and spatial resolutions.:<br> - All three components of the photospheric magnetic field “(Bx,By,Bz)” in a Cartesian basis where the solar surface is approximated flat via Mercator projection, as well as the vertical component of the magnetic field “Bz” and the LOS component of the plasma velocity “Vlos” (i.e. Dopplergram velocity) in the same system (note that the unlike in the usual convention, the LOS plasma velocity is negative for motions away from the observer). These data series are given only for two cadences 2.25- and 12-minutes in full spatial resolution, since rest of the cadences are created by sampling the 2.25-minute data. Data in the 15 times lower spatial resolution are given only at a cadence of 2.025 hours, as it is the highest cadence for which the 15 times lower resolution was used (see Lumme et al., 2019 for details).<br> - Horizontal photospheric plasma velocity "Vh = (Vx,Vy)" estimates derived using two optical flow methods FLCT (Fourier Local Correlation Tracking) and DAVE4VM (Differential Affine Velocity Estimator For Vector Magnetograms) plotted as arrows above the Bz component of the magnetic field.<br> - Horizontal photospheric electric field “Eh = (Ex,Ey)” estimates derived using three methods, PDFI, raw DAVE4VM and the inductive DAVE4VM method, plotted as arrows above the Bz component of the magnetic field.<br> - Vertical component of the Poynting vector “Sz” derived for each electric field estimate.<br> - Photospheric relative helicity flux density (denoted by “dHR/dt”) derived for each electric field estimate.</p> <p>January 16, 2019<br> Erkka Lumme<br> Doctoral student, MSc<br> Department of Physics<br> erkka.lumme@helsinki.fi<br> P.O. Box 68<br> FI-00014 University of Helsinki</p>
Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
Neutron activation analysis data of pottery from Sicán, Peru
<p>Neutron activation analysis data of pottery from Sicán, Lambayeque Department, Peru.</p>
Neutron activation analysis data of pottery from Yanaorco, Peru
<p>Neutron activation analysis data of pottery from Yanaorco, Cajamarca Department, Peru.</p>
Neutron activation analysis data of pottery from La Tiza, Peru
<p>Neutron activation analysis data of pottery from La Tiza, Nasca region, Ica Department, Peru</p>
Neutron activation analysis data of pottery from Huaca Prieta, Peru
<p>Neutron activation analysis data of pottery from Huaca Prieta, La Libertad Department, Peru.</p>
Neutron activation analysis data of pottery from Potrero Mendieta, Ecuador
<p>Neutron activation analysis data of pottery from Potrero Mendieta, Oro Province, Ecuador.</p>
Supporting data and code for: Regional variation in active bottom contacting gear footprints.
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Neutron activation analysis data of pottery from La Puntilla, Peru
<p>Neutron activation analysis data of pottery from La Puntilla, Nasca District, Ica Department, Peru.</p>
Neutron activation analysis data of pottery from Zorropata, Peru
<p>Neutron activation analysis data of pottery from Zorropata, Nasca Region, Ica Department, Peru</p>
Neutron activation analysis data of pottery from Villa El Salvador, Peru
<p>Neutron activation analysis data of pottery from Villa El Salvador, Lima Province, Peru</p>
Code and data for "ActivityGen: Extracting Enabled Activities from Screenshots"
<p>The code and data for the paper "ActivityGen: Extracting Enabled Activities from Screenshots" is provided here. </p> <p><strong>Abstract</strong></p> <p>Many tasks in organizations are performed in a desktop environment. It is possible to record users' interactions in a desktop environment by taking screenshots when an action happens. The result is an interaction log. By considering the associated images of a record, it is possible to detect which activity was performed and which activities were enabled. This information can be extracted, resulting in a translucent event log. Such a translucent event log is valuable and can be used as input for dedicated process-mining techniques. The results can be used to analyze human-computer interactions or create bots for robotic process automation. However, current techniques for extracting information on enabled activities rely on template matching, which is rigid and sensitive to variations. To solve this issue, we present our modular framework, ActivityGen. ActivityGen detects and labels graphical user interface elements by also considering additional information. ActivityGen uses more advanced techniques to overcome the limitations of previous approaches and can extract information without a user's input. Furthermore, it can be adjusted to a user's needs. It detects graphical user interface elements more accurately than state-of-the-art techniques and labels them faster, more robust, and more domain-oriented than state-of-the-art techniques.</p> <p><strong>Data</strong></p> <p>ReDraw_CLS and ReDraw_ViSM are specified in the work. </p> <p>The basis for ReDraw_CLS is the ReDraw dataset. We focus on the following components: Button, CheckBox, EditText, Image, ImageButton (which we refer to as icon), RadioButton, and Switch. We noticed that the examples of ImageView and ImageButton are similar, primarily consisting of icon images. Therefore, we removed the ImageView class and introduced an Image class instead. The Image class contains images from the validation set of the Coco validation set 2017 and the YouTube Thumbnails dataset, enabling the detection of general website images.</p> <p>ReDraw_ViSM iterates add 6,000 synthetically created buttons to the former dataset by distributing them in the same ratio into train, test, and validation sets.</p> <p>lm_basic and lm_extended contain the text training for the language models. </p> <p><strong>Code</strong></p> <p>The code allows for the execution of ActivityGen. Moreover, we provide our evaluation scripts. However, the models do not have to be trained. The models' weights are provided in the model folder.</p>
fMRS data of prolonged motor activation acquired at 3 Tesla
<p>Supporting data for:</p> <p>Maria Morelli<span>, </span>Katarzyna Dudzikowska<span>, </span>Dinesh K. Deelchand<span>, </span>Andrew J. Quinn<span>, </span>Paul G. Mullins<span>, </span>Matthew A. J. Apps<span>, </span>Martin Wilson; Functional magnetic resonance spectroscopy of prolonged motor activation using conventional and spectral GLM analyses. <em>Imaging Neuroscience</em> 2025; 3 imag_a_00452. doi: <a href="https://doi.org/10.1162/imag_a_00452" target="_blank" rel="noopener">https://doi.org/10.1162/imag_a_00452</a></p>
CONTAM Project Files and Exposure Data for An In Silico Investigation of Activity-Related Physical, Chemical, and Biological Pollutant Exposure in Basements Using CONTAM
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Investigation of the influence of active cooling on battery cell testing - Measurement Data
<p>Measurement Data for Investigation of the influence of active cooling on battery cell testing.</p> <p>A detailled description of the tests and the arrangement of the numbered cells (see filenames) is given in the report, chapter II: Experimental.</p>
Data of active calibration near the surface
<p>Active calibration data acquired at varying distances to the surface</p>
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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.