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35
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ShareScore release 0.9.0
Dataset results
35 results for “remote testing”
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Towards a real-world technical test battery for remote microphone systems used with hearing prostheses
<p>Wav format audio files of both source and response for 5 different wireless remote microphone systems under various test conditions, as reported in the paper :</p> <p>Stone M.A., Lough M., Whiston H., Wilbraham K., Dillon H. (2023) Towards a real-world technical test battery for remote microphone systems used with hearing prostheses. Trends in Hearing DOI: 10.1177/23312165231182518</p> <p>Also includes the MATLAB script used to analyse the recordings.</p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
Testing State of the Art Remote Glucose Monitoring at Diabetes Camp
ClinicalTrials.gov study NCT01680653. IPD Sharing: NO. Countries: 1. Publications: 1.
Pre-Test Genetic Education and Remote Genetic Counseling in Communicating Tumor Profiling Results to Patients With Advanced Cancer
ClinicalTrials.gov study NCT02823652. IPD Sharing: Not stated. Countries: 1. Publications: 1.
eTest: Real-time, Remote Monitoring System for Home-based HIV Testing Among High-risk Men Who Have Sex With Men
ClinicalTrials.gov study NCT03654690. IPD Sharing: YES. Countries: 1. Publications: 1.
Test Write File - Remote
<p>Test Write File - Remote</p>
Pilot-trial Testing Remote Sleep Apnea Evaluation in Patients with Atrial Fibrillation
ClinicalTrials.gov study NCT06188247. IPD Sharing: NO. Countries: 1. Publications: 1.
Pilot Testing a Novel Remotely Delivered Intensive Outpatient Program for Individuals With OUD
ClinicalTrials.gov study NCT05817825. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Video Game Hearing Tests for Remote Monitoring of Ototoxicity
ClinicalTrials.gov study NCT05847556. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The 5 Repetitions Sit-to-stand Test, Carried Out Remotely Via Videoconference, in Patients With COPD: Is There a Learning Effect?
ClinicalTrials.gov study NCT05852821. IPD Sharing: NO. Countries: 1. Publications: 0.
ALT Routinely Recorded Remotely: A Comparator Study of Liver Function Tests Using the Tasso+ to Venipuncture.
ClinicalTrials.gov study NCT05259618. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Testing of a System for Remote Ischemic Conditioning in Cerebral Small Vessel Disease and Pre-hospital Stroke Care
ClinicalTrials.gov study NCT05967728. IPD Sharing: NO. Countries: 1. Publications: 1.
Remote Testing in Abbiategrasso (RTA)
ClinicalTrials.gov study NCT05135806. IPD Sharing: NO. Countries: 1. Publications: 9.
Efficacy of a Remotely Administered Functional Capacity Test on Return-to-work Outcomes
ClinicalTrials.gov study NCT05370872. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy and Safety of ICD Remote Monitored Exercise Testing to Improve Heart Failure Outcomes: REMOTE HF-ACTION
ClinicalTrials.gov study NCT04629066. IPD Sharing: NO. Countries: 1. Publications: 0.
Vdrive Evaluation of Remote Steering and Testing in Lasso Electrophysiology Procedures Study
ClinicalTrials.gov study NCT01656772. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Remote Pulmonary Function Testing and Nurse Coaching in ALS
ClinicalTrials.gov study NCT04490148. IPD Sharing: NO. Countries: 1. Publications: 0.
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.