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1,772 results for “sensors”
SINS database - Node 11 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
Estimations of Sensor Misorientation for Broadband Seismic Stations in and around Africa
<p>To ensure the accuracy of future rotation-based seismological studies using data recorded by broadband seismic stations in Africa and environs, we investigate the sensor orientation of 1075 stations belonging to 41 seismic networks deployed in and around the African continent in the past three decades. We applied three independent waveform-based orientation estimation methods that involve the measurement of P-wave particle motion based on the principal component analysis, minimizing the P-wave energy on the transverse component of motion, and measuring intermediate-period Rayleigh-wave arrival angles from teleseismic earthquakes. This dataset is the compilation of the entire result of this study.</p>
Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (<a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><br> In particular, the dataset contains: </p> <ol> <li>Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)</li> <li>Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)</li> </ol> <p><strong>Hardware:</strong><br> --------------</p> <ol> <li>Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB</li> <li>Carbon monoxide sensor: Alphasense CO-B4</li> <li>GPS receiver: u-blox EVK-6p</li> </ol> <p><strong>Data files format: </strong><br> -------------------------<br> co_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)</li> </ol> <p>o3_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] </li> <li>Temperature [in °C]</li> <li>Relative Humidity [in %]</li> </ol> <p><strong>Data quality:</strong><br> ------------------<br> The data has NOT been post-processed!<br> In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.<br> Reference data can be obtained from <a href="http://www.ostluft.ch">www.ostluft.ch</a>, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.</p> <p><strong>Plot Coverage Map (MATLAB):</strong><br> --------------------------------------------<br> The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). </p> <p><strong>References:</strong><br> -----------------<br> The dataset (and related aspects) has partly been used and is described in more detail in the following publications:</p> <ol> <li>Balz Maag et al. <strong>SCAN: Multi-Hop Calibration for Mobile Sensor Arrays</strong>. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.</li> <li>Olga Saukh et al. <strong>Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks</strong>. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensor Nodes on Public Transport Networks</strong>. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.</li> <li>Olga Saukh et al. <strong>On Rendezvous in Mobile Sensing Networks</strong>. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.</li> <li>Jason Jingshi Li et al. <strong>Sensing the Air we Breathe – The OpenSense Zurich Dataset</strong>. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensors with Checkpointing Constraints</strong>. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.</li> <li>David Hasenfratz et al. <strong>On-the-fly Calibration of Low-Cost Gas Sensors</strong>. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. </li> </ol> <p><br> For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
COMPOSITION Fill Level Sensors Datasets
<p>The Smart Waste Management scenario is one of the main use cases of the COMPOSITION EU Project. In this scenario a real-time monitoring framework has set up for the monitoring of bins' fill level in pilot partners' sites. The deployed datasets contains measurement coming from the deployed bins fill level sensors. These datasets are public available after partners, KLEEMANN Hellas SA and ELDIA SA consent to share their data for a period from 1 to 2 months.</p>
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
Sensor data set radial forging at AFRC testbed v2
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 81 parts were forged over one day of operation. A machine failure occurred during the forging of part number 70, and this part was re-run once the malfunction had been fixed. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. The CMM records 18 dimensional measurements.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in 81 csv files in the folder “Scope Traces”, named “Scope0001.csv” to “Scope0081.csv”. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file “ForgedPartDataStructureSummaryv3.xlsx” <strong>(NOTE: Some columns do not have sensor descriptions as this information is not available).</strong></li> <li>The CMM data is provided in the file “CMMData.xlsx”.</li> </ul> <p><strong>Further Information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:</p> <p><a href="https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0">https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0</a></p> <p>These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.</p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology
<p>This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern.</p> <p>The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.</p>
DiY Sensor Dataset for Air Pollution Monitoring
<p>The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.</p>
Real-time monitoring of a 3D blood-brain barrier model maturation and integrity with a sensorized microfluidic device
<p><span>A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood-brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional <em>in vivo</em> models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. <em>In vitro</em> models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequency-dependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics <em>in vivo</em> shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine.</span></p>
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Loon Stratospheric Sensor Data
<p>This data set contains flight data from Loon, a Google project and later an Alphabet subsidiary that provided wireless internet access to rural areas using high-altitude balloons. The data set includes GPS and sensor data from all 2,131 flights across the project's entire nine-and-a-half year history, from the first sounding balloon experiment in August, 2011, through the last balloon landing in May, 2021. In total the data set comprises over 218 flight-years of data (over 127 million telemetry points), the vast majority from altitudes between 50 and 110 hPa (approximately 15.5 to 21 km above sea level).</p> <p>Please see README.md within the dataset for more details.</p>
Atmosphéries and the poetics of the in situ: the role and impact of sensors in data-to-sound transposition installations - Supplementary audio material
<p>This audio file wishes to give an insight into NXI Gestatio Design Lab's <em>Atmosphéries</em> research program, and to accompany the publication "Atmosphéries and the poetics of the in situ: the role and impact of sensors in data-to-sound transposition installations". The file consists in a 7-minutes recording of the <em>Meridian Probe</em>, the last instrument designed within the <em>Atmosphéries</em> program, which allows to generate sound from on-site atmospheric data. The recorded extract then acts as a sonic representation of the atmospheric conditions found at the place of exhibition, at the time of recording - in this case, gardens of the Bussy-Rabutin's Castle (France), respectively July 31st, 2021, 17h17. For further information about the <em>Meridian Probe</em>'s design and functioning, we invite you to read the aforementioned publication.</p>
Digitized Raw Output Signals of a Low-cost Aerosol PM Sensor Sharp GP2Y
<p>The data contains the digitized traces of low-cost PM sensor Sharp GP2Y1010AU0F pulse outputs during calibration and environmental measurements. The work was done within the Aeromet EMPIR project 19ENV08.</p> <p>Further details are given in the article:</p> <p>Bučar, K.; Malet, J.; Stabile, L.; Pražnikar, J.; Seeger, S.; Žitnik, M. Statistics of a Sharp GP2Y Low-Cost Aerosol PM Sensor Output Signals. Sensors 2020, 20, 6707. <a href="https://doi.org/10.3390/s20236707">https://doi.org/10.3390/s20236707</a></p> <p>Also see the included README.pdf file.</p> <p> </p>
At-sensor-radiance data from aerial imaging for Lake Mulargia (Sardinia, Italy) (2020/09/24)
<p>This dataset contains the radiance data collected from Hyspex images of Lake Mulargia (Sardinia, Italy) for the VNIR bands. The acquisition was done by CGR Spa (Italy).</p>
Series AC Arc Fault Detection Method Based on High-Frequency Coupling Sensor and Convolution Neural Network
<p>The data provided can be used for the development of methods for the detection of arcing faults in a domestic low-voltage electrical networks (230V - 50 Hz). The data files are current and voltage signatures experimentally measured.</p> <p>Test for to produce an arcing fault : Open contact electrodes and Carbonized path wires</p> <p>The ReadMe file describes :</p> <p>- the test set up and the the procedure followed to make the measurements</p> <p>- the list of household appliances and their main characteristics.</p> <p>- the name of the data files</p> <p>- the type of arcing faults</p>
Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)
<p><strong>General information on the data set</strong></p> <p>The dataset was generated with two different measurement systems at the ZeMA testbed for electromechanical cylinders.</p> <p> </p> <p><strong>All relevant information can be found within the hdf5 file.</strong></p> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in MATLAB:</strong></p> <pre><code># available structures inside file dataset = 'axis11_2kHz_ZeMA_PTB_SI.h5'; h5disp(dataset) % general attributes about file attr = h5info(dataset).Attributes; project = jsondecode(attr(1,1).Value) person = jsondecode(attr(2,1).Value) publication = jsondecode(attr(3,1).Value) experiment = jsondecode(attr(4,1).Value)</code></pre> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in Python:</strong></p> <pre><code>import h5py import json # open file h5file = h5py.File("axis11_2kHz_ZeMA_PTB_SI.h5", "r") # general attributes about file for key in h5file.attrs: print(key) val = json.loads(h5file.attrs[key]) for subkey, subval in val.items(): print(" ", subkey, " : ", subval) # available structures inside file h5file.visit(print) # proper exit h5file.close()</code></pre> <p> </p> <p><strong>Metadata output of the hdf5 file:</strong></p> <ul> <li><strong>For the dataset:</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/' Attributes: 'Project': '{ "fullTitle":"Metrology for the Factory of the Future", "acronym":"Met4FoF", "websiteLink":"www.met4fof.eu", "fundingSource":"European Commission (EC)", "fundingAdministrator":"EURAMET", "funding programme":"EMPIR", "fundingNumber":"17IND12", "acknowledgementText":"This work has received funding within the project 17IND12 Met4FoF from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. The authors want to thank Clifford Brown, Daniel Hutzschenreuter, Holger Israel, Giacomo Lanza, Bj\u00f6rn Ludwig, and Julia Neumann fromPhysikalisch-Technische Bundesanstalt (PTB) for their helpful suggestions and support." }' 'Person': '{ "dc:author":[ "Tanja Dorst", "Maximilian Gruber", "Anupam Prasad Vedurmudi" ], "e-mail":[ "t.dorst@zema.de", "maximilian.gruber@ptb.de", "anupam.vedurmudi@ptb.de" ], "affiliation":[ "ZeMA gGmbH", "Physikalisch-Technische Bundesanstalt", "Physikalisch-Technische Bundesanstalt" ] }' 'Publication': '{ "dc:identifier":"10.5281/zenodo.5185953", "dc:license":"Creative Commons Attribution 4.0 International (CC-BY-4.0)", "dc:title":"Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)", "dc:description":"The data set was generated with two different measurement systems at the ZeMA testbed. The ZeMA DAQ unit consists of 11 sensors and the SmartUp-Unit has 13 differentsignals. A typical working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke of the electromechanical cylinder. The data set does not consist of the entire working cycles. Only one second of the return stroke of every 100rd working cycle is included. The dataset consists of 4776 cycles. One row represents one second of the return stroke of one working cycle.", "dc:subject":[ "dynamic measurement", "measurement uncertainty", "sensor network", "digital sensors", "MEMS", "machine learning", "European Union (EU)", "Horizon 2020", "EMPIR" ], "dc:SizeOrDuration":"24 sensors, 4776 cycles and 2000 datapoints each", "dc:type":"Dataset", "dc:issued":"2021-09-10", "dc:bibliographicCitation":"T. Dorst, M. Gruber and A. P. Vedurmudi : Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit), Zenodo [data set], https://doi.org/10.5281/zenodo.5185953, 2021." }' 'Experiment': '{ "date":"2021-03-29/2021-04-15", "DUT":"Festo ESBF cylinder", "identifier":"axis11", "label":"Electromechanical cylinder no. 11" }'</code></pre> <p> </p> </li> <li><strong>Example for one sensor (BMA 280, acceleration) of the PTB SmartUp Unit (SUU) and one sensor of ZeMA DAQ (pressure):</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/PTB_SUU' Group '/PTB_SUU/BMA_280' Group '/PTB_SUU/BMA_280/Acceleration' Attributes: 'qudt:hasQuantityKind': '[ "qudt:Acceleration", "qudt:Acceleration", "qudt:Acceleration" ]' 'misc': '{ "interpolation_scheme":"cubic" }' 'si:unit': '"\\metre\\second\\tothe{-2}"' 'sosa:madeBySensor': '"BMA 280"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration uncertainty", "Y acceleration uncertainty", "Z acceleration uncertainty" ]' Dataset 'qudt:value' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration", "Y acceleration", "Z acceleration" ]' Group '/ZeMA_DAQ' Group '/ZeMA_DAQ/Pressure' Attributes: 'qudt:hasQuantityKind': '"qudt:Pressure"' 'sosa:madeBySensor': '"Festo VPPM"' 'si:unit': '"\\pascal"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure uncertainty"' Dataset 'qudt:value' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure"' 'misc': '{ "raw_data":false, "comment":"Converted from ADC values based on appropriate conversion." }'</code></pre> </li> </ul>
Satellite Sensor Relative Spectral Response data
<p>Satellite Spectral Response functions for a number of imaging sensors. Currently supports:</p> <ul> <li>Himawari-8 AHI</li> <li>Himawari-9 AHI</li> <li>GOES-16 ABI</li> <li>GOES-17 ABI</li> <li>GOES-18 ABI</li> <li>GOES-19 ABI</li> <li>NOAA AVHRR/1, AVHRR/2, AVHRR/3 (NOAA-6 errors in ch1 & 2 fixed)</li> <li>Metop AVHRR/3</li> <li>TIROS-N AVHRR/1</li> <li>Envisat AATSR</li> <li>Sentinel-3A, B, C & D SLSTR</li> <li>Sentinel-3A&B OLCI - mean rsr</li> <li>Meteosat SEVIRI</li> <li>Terra/Aqua MODIS</li> <li>Suomi-NPP VIIRS</li> <li>JPSS-1 (NOAA-20) VIIRS</li> <li>JPSS-2 (NOAA-21) VIIRS</li> <li>Sentinel-2A MSI</li> <li>Sentinel-2B MSI</li> <li>Landsat-8 OLI & TIRS</li> <li>Landsat-9 OLI & TIRS</li> <li>HY-1C COCTS</li> <li>Metop-SG-A1 MetImage - NB! simulated data - not measured!</li> <li>Sentinel-3B OLCI - mean rsr</li> <li>FY-3D MERSI-2</li> <li>FY-3F MERSI-3</li> <li>FY-4A AGRI</li> <li>FY-4B AGRI</li> <li>FY-3B VIRR</li> <li>FY-3C VIRR</li> <li>GEO-KOMPSAT-2A AMI</li> <li>Meteosat-12 FCI (version from EUMETSAT, April 2022)</li> <li>MTG-I1 FCI (same as above)</li> <li>Electro-L N2 MSU-GS</li> <li>Arctica-M 1 MSU-GS/A</li> <li>DSCOVR EPIC</li> <li>Sentinel-C MSI</li> </ul> <p> </p> <p>Changelog for the Relative Spectral Response data<br>=================================================</p> <p>Version v1.5.0 (Tue Sep 30 09:53:59 PM CEST 2025)</p> <p>-------------------------------------------</p> <p>* Added SLSTR RSRs for Sentinel-3C and -D. Updated with the latest versions from ESA for Sentinel-3A and -B.</p> <p> </p> <p>Version v1.4.1 (Tue Oct 29 03:45:40 PM CET 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Landsat-8 and -9 OLI & TIRS. Original RSR as provided by NASA. Previously we had only Landsat-8 OLI</p> <p> </p> <p>Version v1.4.0 (Tue Sep 24 03:35:15 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Sentinel-2C MSI and updated the MSI RSR for Sentinel 2A & B</p> <p> </p> <p>Version v1.3.2 (Mon Jul 15 12:32:12 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Corrected the file name for the RSRs of Mersi-3 onboard FY-3F</p> <p> </p> <p>Version v1.3.1 (Mon Jul 15 11:12:10 AM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-3 sensor onboard FY-3F</p> <p> </p> <p>Version v1.3.0 (Fri May 3 04:11:43 PM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-1 sensor onboard FY-3A/B/C<br> * Added SRFs for the Mersi-RM sensor onboard FY-3G<br> * Added SRFs for the GHI (Geostationary High-speed Imager) sensor onboard FY-4B<br> * Added SRFs for GOCI-II (Geostationary Ocean Color Imager: Follow-on) sensor onboard GK-2B (GEO-KOMPSAT-2B)</p> <p> </p> <p>Version v1.2.4 (Sat Oct 21 12:25:00 PM CEST 2023)</p> <p>-------------------------------------------</p> <p>* Normalized RSR responses for the EPIC sensor. Now values are scaled to be</p> <p> between 0 and 1.</p> <p> </p> <p>Version v1.2.3 (Fri Oct 20 01:58:29 2023)</p> <p>-------------------------------------------</p> <p>* Added RSR file for EPIC on DSCOVR (responses not normalized)</p> <p> </p> <p>Version v1.2.2 (Tue Nov 15 14:44:03 2022)<br>-------------------------------------------</p> <p> * Added RSR file for AGRI onboard FY-4B<br> * Corrected RSR file for AGRI onboard FY-3B<br> Values were between 0 and 100, now scaled to between 0 and 1</p> <p> </p> <p>Version v1.2.1 (Mon Oct 24 16:26:14 2022)<br>-------------------------------------------</p> <p> * Changed name of the FY-3D MERSI-2 RSR file, removing the hyphen:<br> New name = rsr_mersi2_FY-3D.h5<br> </p> <p>Version v1.2.0 (Tue Sep 27 21:08:13 2022)<br>-------------------------------------------</p> <p> * Added VIIRS RSR for JPSS-2/NOAA-21:<br> Based on the J2_VIIRS_RSR_DAWG_At-Launch_Public_Release_V2_Jul2019.zip<br> The data read are the Detector wise files under J2_VIIRS_Detector_RSR_V2:</p> <p> J2_VIIRS_RSR_DNBLGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_I1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I4_Detector_V2F.txt<br> J2_VIIRS_RSR_I5_Detector_V2F.txt<br> J2_VIIRS_RSR_M10_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M11_Detector_V2F.txt<br> J2_VIIRS_RSR_M12_Detector_V2F.txt<br> J2_VIIRS_RSR_M13_Detector_V2F.txt<br> J2_VIIRS_RSR_M14_Detector_V2F.txt<br> J2_VIIRS_RSR_M15_Detector_V2F.txt<br> J2_VIIRS_RSR_M16A_Detector_V2F.txt<br> J2_VIIRS_RSR_M1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M4_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M5_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M6_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M7_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M8_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M9_Detector_WVCOR_Fused_V2F.txt</p> <p> The two files here were not considered:<br> J2_VIIRS_RSR_DNBMGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_M16B_Detector_V2F.txt</p> <p> * Correted errors concerning NOAA-6 channel 1 and 2. These proved to be wrong<br> (a factor of 10 scale error), as the files they were based on were<br> wrong. For AVHRR-1 we have been using the ascii files from NOAA STAR<br> (https://www.star.nesdis.noaa.gov/smcd/spb/fwu/homepage/AVHRR/spec_resp_func/index.html). Example<br> of the start of the file for NOAA-6 channel-1:</p> <p> %> cat NOAA_6_A103C001.txt<br> Wavelegth (nm) Normalized RSF<br> 5600.000000 0.071000<br> 5700.000000 0.449000<br> 5800.000000 0.739000<br> 5900.000000 0.813000<br> 6000.000000 0.806000<br> 6200.000000 0.919000<br> 6400.000000 1.000000<br> ...</p> <p> Here we have instead used the xls files on which the NOAA-STAR files are based: AVHRR1_SRF_only.xls</p> <p> For TIROS-N there seem to be a typo in the wavelengths array for channel-1<br> on TIROS-N: A 640 nm should most likely have been 840 nm. This has been<br> corrected in the hdf5 file.<br> </p>
Cow drinking monitoring tests with ultrasonic sensor setup Aug 12th and Sep 20th, 2022
<p>We tested a device that could potentially be developed for use in large scale drinking monitoring of individual animals. Our setup consisted of two plastic cylindrical containers having the inner diameter of 275 millimeters. On top of the other cylinder were placed two ultrasonic sensors that measured the distance to the water level in the container. The containers were connected with a hose (inner diameter 25 millimeters) so that the water level on both containers was always the same. Faucets shown on the image were completely open during the experiments that took place on August 12th and September 20th, 2022. Cows were able to freely access and drink from the second container which had the height of 100 centimeters in the first experiment and reduced height of 80 centimeters in the second experiment. </p> <p>Two ultrasonic sensors were used and they were JSN-SR04T-2.0 ja Maxbotix MB7389. Sensors were connected to circuit board V2 ESP8266 Development Board (CH341) from which measurements were collected to a PC programmatically at about 0.3 second intervals. Both sensors had the resolution of 1 millimeter which corresponds to 0.6 liters in the used containers.</p> <p>Image of the setup and its installation during the experiment are shown in the photographs. There was a small leakage in the container as is seen in the data (slow and constant decrease of water level). In the first experiment, the leakage was compensated by continuous flow of water after 17:30. On other times the containers were filled manually.</p> <p>Videos are taken with no particular plan but, instead, when something interesting was seen. Timestamp in the filename refers to the starting time of the video in format YYYYMMDD_HHMMSSsss.</p> <p>All times refer to Finnish time UTC+3.</p> <p>Data and videas are shared with the following license: Creative Commons ByAttribution (CC-BY).</p>
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.