Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
42
datasets available to search
ShareScore release 0.9.0
Dataset results
42 results for “low-cost sensors”
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data
<p>WRF-Chem simulated daily mean PM2.5 concentrations for:</p> <p>1) with fires </p> <p>2) without fires</p> <p>simulations. </p>
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Using a low-cost 2D LiDAR Sensor to capture 3D Data - Raw Data
<p>Raw Data for an upcoming publication in the MDPI Journal of Sensors, titled: "Using a low-cost 2D LiDAR Sensor to capture 3D Data"</p>
Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi
Open the record for dataset details and reuse information.
Seasonally optimized calibrations improve low-cost sensor performance: Long-term field evaluation of PurpleAir sensors in urban and rural India
Open the record for dataset details and reuse information.
Indoor low-cost sensor system data
<p>Data used for a comparison of the Matterport Pro2 3D, Ricoh Theta V, and Leica BLK360 in two indoor settings using the Leica RTC360 as reference. The test sites are lecture hall 101 at the Aalto University Department of Machine Engineering in Espoo, Finland, and the Tetra Conference Hall at the Hanaholmen Swedish-Finnish Cultural Centre in Espoo, Finland.</p> <p>The data are divided into four sets - one room geometry with the furniture removed and one detailed segment for both test sites - with four registered point clouds and three registered meshes being provided for each, as well as the reference. The point clouds stem from data obtained with each of the three sensor systems and processed with Matterport's processing system, with the Leica BLK360 data also being processed with Leica's proprietary processing system for a fourth point cloud. For the meshes, the Matterport processing system has been used to produce one mesh from each sensor system.</p>
[data]Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization
<p>The data used in the case study(Cases-S1,S2,S3)in manuscript "Pollution source detection with low-cost low-accuracy sensors through coupling forward data assimilation and inverse optimization"</p>
Data products from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence"
<p>GNSS, GNSS-IR, and mass balance data from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence". Contains the following folders and files</p> <ul> <li>GNSS <ul> <li><em>Precise point positioning solution (.pos, etc) using the CSRS-PPP tool for each GNSS system</em></li> </ul> </li> <li>GNSS_basefix <ul> <li><em>Precise point positioning solution (.pos) with base station observations using the Emlid Studio desktop application for GNSS systems AB floating and AB fixed</em></li> </ul> </li> <li>GNSSIR<br> <ul> <li><em>Reflector height solutions for site AB floating, AB fixed, and D floating (see Fig. 7). The filename is the day of year 2023.</em></li> </ul> </li> <li>Monitored Ablation Stake<br> <ul> <li><em>Processed daily and seasonal climatic mass balance height changes (see Fig. 5)</em></li> </ul> </li> </ul>
Dataset for: Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts
<p>Points clouds of Fjallsjökull calving front, as derived by a Raspberry Pi and a Unoccupied Aerial Vehicle. For each sensor, eight sub-sections are analysed. Point clouds are provided in .las format. Each sub-section is generated within its own spatial reference (matching that of the other sensor to allow for comparison). </p>
Structural Health Monitoring via Thermoelastic Stress Analysis with low-cost sensors for the characterization of composite GFRP materials
Open the record for dataset details and reuse information.
Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT
<p>This zip file contains the dataset used to produce Figure 4 of "Distributed Sensing with Low-cost Mobile Sensors towards a Sustainable IoT".</p> <p>It contains two folders, the first for the stationary sensors and the second for the mobile sensors.</p>
Dataset for "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments"
<p>The dataset contains the data used in the article "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments".</p> <p>A low-cost monitoring system composed of 14 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115S0-XXX), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from October 2020 to November 2021. It contains the PM2.5, relative humidity, and temperature measurements of the low-cost monitoring system and the official measurements of the beta attenuation device.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Data Set For: Raw Data Collected From NO2, O3 And NO Air Pollution Electrochemical Low-Cost Sensors
<p>This data set contains data from two Captor nodes. These are prototypes nodes that were developed at the Universitat Politècnica de Catalunya (UPC) in order to study the effects of the sensor data gathering process on the use of low-cost sensors for air quality monitoring. Specifically, the captors nodes have tropospheric ozone, nitrogen dioxide, and nitrogen monoxide electrochemical sensors. They also have a temperature and relative humidity sensor inside the box. <br> Two Captor nodes were placed in a reference station in Barcelona (Spain) for four months (January 2021 to May 2021), at a sampling frequency of 0.5 Hz. The data set contains a "readme" file with a brief description of the five files that make up the data set.</p>
Supporting data to "Open-source, low-cost, in-situ turbidity sensor for river network monitoring"
<p>This folder contains the Supporting Dataset that is part of the Manuscript "Open-source, low-cost, in-situ turbidity sensor for river network monitoring."</p>
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Ambient air ozone concentrations using electrochemical low-cost sensors: Italy and Austria, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in Italy and Austria. Sensors are electrochemical. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network.</p>
Particulate matter observations using HM-3301 low-cost sensor
<p>The low-cost sensor was installed on September 17, 2019, and data has been taken until November 12, 2019, with some days of inactivity. During this period, the sensor has been located in both indoor and outdoor environments. In total, three different periods can be identified. The first period was from September 17 to October 14 where the sensor was installed in an indoor environment (an office at Universitat Jaume I, Castelló, Spain); the second period was from October 15 to October 30 in an outdoor environment (Vila-real, , Castelló, Spain); and finally, third period from October 31 to November 12 in an indoor environment (an office at Universitat Jaume I, Castelló, Spain). In total 3,677 observations were collected during these three periods with a rate of 15 minutes. Each observation contains a timestamp and PM1, PM2.5 and PM10 values. </p>
Input data for manuscript "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry"
<p>Input data for Fagundes et al. (2021), "An open-source low-cost sensor for SNR-based GNSS reflectometry: Design and long-term validation towards sea level altimetry", GPS Solutions (in press). <a href="https://www.researchgate.net/publication/341946011">preprint</a></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.
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.