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Dataset results
13 results for “sensor platform”
Preliminary data collected by 2 prototype Surface Velocity Platform drifters with Barometer and Reference Sensor for Temperature (SVP-BRST)
<p>The SVP-BRST drifter was developed to serve calibration and validation of Sentinel satellite SST retrievals. Two prototypes were deployed in the Mediterranean Sea end of April 2018. Preliminary data collected then until 11 June 2018 are published in this dataset. The drifters were developed and deployed under funding from the European Union's Copernicus Programme. The data are transmitted from the buoy to shore using data format #091 (see References).</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
<pre>- 1_Motion_Simulator/ - IMU_results/ - 20211028101756.csv - 20220114101543.csv - 20220117000000.csv - Rotary_Table/ - 15/ - rover_20220105.nav - rover_20220105.obs - solution_20220105_CAS.log - 360/ - rover_20211221.nav - rover_20211221.obs - solution_20211221_CAS.log - 360-15/ - rover_20220202_CAS.nav - rover_20220202_CAS.obs - solution_20220202_CAS.log - Static_Tests/ - solution_SSRA00CAS0 - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/ - platformmov_C1.xtd - platformmov_C2.xtd - platformmov_C3.xtd - TestBetaNoneMov_C1 - TestBetaNoneMov_C1.nav - TestBetaNoneMov_C1.obs - TestBetaNoneMov_C1.ubx - TestBetaNoneMov_C2 - TestBetaNoneMov_C2.nav - TestBetaNoneMov_C2.obs - TestBetaNoneMov_C2.ubx - TestBetaNoneMov_C3 - TestBetaNoneMov_C3.nav - TestBetaNoneMov_C3.obs - TestBetaNoneMov_C3.ubx - 3_Test_Sea/ - 20220503000000.xlsx - solution_28.nav - solution_28.obs - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>
REIP: a Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping - use case dataset
<p>Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We show the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.</p> <p>This dataset was created for the case study in Section 5 of the paper.</p>
Sling load mechanical design - Sensor platform (CAD)
<p>These files are the mechanical design of the sensor platform, i.e. the lower part of the sling load platform. </p>
Artificial Intelligence Mobile Health Trial Of A Digital Platform To Optimize GDMT Using Wearable Sensors
ClinicalTrials.gov study NCT04191330. IPD Sharing: NO. Countries: 1. Publications: 1.
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Robosense BPearl LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with multiple sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Acquired from a Robosense Bpearl.</li> <li><strong>IMU Data:</strong> Available through the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Captured using a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, available at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>, along with the necessary <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a>.</p> <p><strong>Note:</strong> The transform for the LiDAR sensor is not included in the rosbag. To include it, manually add the following static transform to your ROS setup:<br><node pkg="tf" type="static_transform_publisher" name="rslidar_broadcaster" args="0.15 0 0.13 0 0.309017 0 base rslidar 0.1" /><br><br><strong>Important:</strong> There was a camera malfunction midway through the dataset collection.</p>
Multimodal Sensor Dataset from a Forest Environment in Adões using a Unitree Go1 Platform equipped with a Velarray M1600 LiDAR
<p><strong>Dataset Description:</strong></p> <p>This dataset was collected using a Unitree Go1 robot equipped with various sensors:</p> <ul> <li><strong>LiDAR Data:</strong> Captured with a Velodyne Velarray M1600.</li> <li><strong>IMU Data:</strong> Available via the <code>/high_state</code> topic published by the Unitree Go1.</li> <li><strong>RGB Images:</strong> Streamed from a Realsense D435i camera.</li> </ul> <p>To process this dataset, you will need the <code>Unitree_transformer.py</code> Python script, which can be found at the provided <a href="https://github.com/pedrotomas27/python_executables/tree/252ecf0feb9c3e8652daa2e6def7f4c94667207e">URL</a>. Ensure that the <a href="https://github.com/unitreerobotics/unitree_legged_sdk">Unitree package</a> is also installed.</p> <p><strong>Note:</strong> The LiDAR transform is not included in the rosbag file. To incorporate this transform, manually add the following static transform to your ROS setup:<br><br><node pkg="tf" type="static_transform_publisher" name="lidar_0_broadcaster" args="0.15 0 0.13 0 0 0 base lidar_0 0.1" /><br><br></p>
Non-invasive TB Triage and Patient Mapping Platform Using Breath Via Low-Cost Titanium Dioxide Nanotube Sensor
ClinicalTrials.gov study NCT02681445. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Acceptability, Feasibility, and Development of an AI Driven, Multi-sensor, Non-invasive Digital Mental Health Platform in Children With ADHD and Emotional Symptoms
ClinicalTrials.gov study NCT06311370. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study on Building a Voice Cohort for the Development of a Non-face-to-face Machine Learning Diagnostic and Monitoring Platform Using Voice Analysis and Various Sensors in Patients With Dysphagia.
ClinicalTrials.gov study NCT05439447. IPD Sharing: NO. Countries: 1. Publications: 0.
A microRNA-inducible CRISPR-Cas9 platform serves as microRNA sensors and cell type specific genome regulation tools
GEO Series GSE122683. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
AI-Assisted Saliva Diagnostics Using an Electrochemical Sensor Platform for Periodontitis Detection (SALIENCE)
ClinicalTrials.gov study NCT07254039. IPD Sharing: NO. Countries: 0. 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.