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412 results for “sensor data”
Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic (Data)
<p>Origin projects and figures used for the article "Enhancement of real-time resonance tracking in electro-thermally actuated cantilever sensor with optimized phase characteristic", published in the proceedings of the 29th Micromechanics and Microsystems Europe Workshop; 26.08.2018 to 29.08.2018; Smolenice Castle, Slovakia.</p>
Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors (Data)
<p>Origin projects and figures used for the article "Enhancement of unsteady frequency responses of electro-thermal resonance MEMS cantilever sensors", published in the proceedings of the 30th Micromechanics and Microsystems Europe Workshop; 18.08.2019 to 20.08.2019; Wolfson College, Oxford, United Kingdom.</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
Data for A Locally Activatable Sensor for Robust Quantification of Organellar Glutathione
<p>Supporting data to paper A Locally Activatable Sensor for Robust Quantification of Organellar Glutathione,</p> <p>including NMR, MS, microscopy, etc</p>
GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)
<p>Video recording of the presentation for the publication N. Souli et al., "GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion," 2020 22nd International Conference on Transparent Optical Networks (ICTON), Bari, Italy, 2020, pp. 1-4, doi: 10.1109/ICTON51198.2020.9203087.</p>
Impacts of pressure, temperature, and rel. humidity on NDIR CO2 sensors (DATA)
<p>These files support the experiments demonstrated in the peer-reviewed article "Low-complexity methods to mitigate the impact of environmental variables on low-cost UAS-based atmospheric carbon dioxide measurements", available via open access at the European Geophysical Union's Atmospheric Measurements Techniques journal.</p>
Code and data for: Decoupling channel count from field-of-view and spatial resolution in single-sensor imaging systems for fluorescence image-guided surgery
<p><em>Significance</em></p> <p>Near-infrared fluorescence image-guided surgery is often thought of as a spectral imaging problem where the channel count is the critical parameter, but it should also be thought of as a multiscale imaging problem where the field-of-view and spatial resolution are similarly important.</p> <p><em>Aim</em></p> <p>Conventional imaging systems based on division-of-focal-plane architectures suffer from a strict relationship between the channel count on one hand and the field-of-view and spatial resolution on the other, but bioinspired imaging systems that combine stacked photodiode image sensors and long-pass/short-pass filter arrays offer a weaker tradeoff.</p> <p><em>Approach</em></p> <p>In this paper, we explore how the relevant changes to the image sensor and associated image processing routines affect image fidelity during image-guided surgeries for tumor removal in an animal model of breast cancer and nodal mapping in women with breast cancer.</p> <p><em>Results</em></p> <p>We demonstrate that a transition from a conventional imaging system to a bioinspired one, along with optimization of the image processing routines, yields improvements in multiple measures of spectral and textural rendition relevant to surgical decision-making.</p> <p><em>Conclusions</em></p> <p>These results call for a critical examination of the devices and algorithms that underpin image-guided surgery to ensure that surgeons receive high-quality guidance and patients receive high-quality outcomes as these technologies enter clinical practice.</p>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: VelocityProfilies_2018_Musumarra
<p>The experimental campaign was devoted to study the velocity profile inside the small scale flume for several bottom configurations. In particular, the following configurations were considered: thin sand (D<sub>50</sub>=0.25 mm); coarse sand (D<sub>50</sub>=0.56 mm); mixed sand: 10% coarse sand and 90% thin sand; mixed sand: 20% coarse sand and 80% thin sand; mixed sand: 30% coarse sand and 70% thin sand; mixed sand: 40% coarse sand and 60% thin sand; small gravel (diameter between 3 and 5 mm); gravel (diameter between 9 and 14 mm); small gravel and thin sand; gravel and thin sand.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Hydralab+ HSVA-01 TA Kvaerner sensor_data
<p>sensor_data as described in the data storage report</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>
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>
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>
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>
Data from laboratory granular-flow experiments with acoustic sensors
<p>Experimental data of dynamic pressures generated by dry granular flows moving down and impacting on a plate embedded in an inclined chute facility. The data consists of basal impact pressures measured with a pressure sensor for variable slope angle ranging from 30° to 38° with an initial mass of 100 kg.</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.