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36 results for “Sensor Calibration”
Continuous stream CO2 and temperature data and sensor calibration grab samples from five NEON sites (CARI, COMO, KING, MART, WALK), August 2021-April 2024.
This package contains: 1) sensor-based measurements of dissolved CO2 concentration and temperature, and 2) dissolved CO2 concentration from grab samples that were used to calibrate the sensor data, collected at five stream sites in the NEON network (CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN) between August 2021 - April 2024. The grab sample dataset contains a combination of samples collected by NEON (DP1.20097.001) and additional samples collected by project personnel. All samples were collected using the headspace equilibration method, and dissolved CO2 concentrations were calculated using the 'neonDissGas' R package (https://github.com/NEONScience/NEON-dissolved-gas). The sensor dataset contains CO2 concentrations measured with an eosGP CO2 gas probe, averaged to 15-minute intervals and corrected to align with grab sample concentrations using a site-specific grab versus sensor regression. Due to inaccuracies in the eosGP temperature data, we instead include the temperature data from NEON that was used to convert CO2 between units of ppmv and umol/L (DP1.20053.001 for CARI, KING, MART, and WALK, and data from the multiparameter sonde for COMO). All NEON data used in this data package references the RELEASE-2025 version of each data product (downloaded February 2025).
Evaluation and Calibration of a Low-cost Particle Sensor in Ambient Conditions Using Machine Learning Methods
<p>Particle sensing technology has shown great potential for monitoring particulate matter (PM) with very few temporal and spatial restrictions because of its low-cost, compact size, and easy operation. However, the performance of low-cost sensors for PM monitoring in ambient conditions has not been thoroughly evaluated. Monitoring results by low-cost sensors are often questionable. In this study, a low-cost fine particle monitor (Plantower PMS 5003) was co-located with a reference instrument, named Synchronized Hybrid Ambient Real-time Particulate (SHARP) monitor, in Calgary Varsity air monitoring station from December 2018 to April 2019. The study evaluated the performance of this low-cost PM sensor in ambient conditions and calibrated its readings using simple linear regression (SLR), multiple linear regression (MLR), and two more powerful machine learning algorithms using random search techniques for the best model architectures. The two machine learning algorithms are XGBoost and feedforward neural network (NN). Field evaluation showed that the Pearson r between the low-cost sensor and the SHARP instrument was 0.78. Fligner and Killeen (F-K) test indicated a statistically significant difference between the variances of the PM<sub>2.5 </sub>values by the low-cost sensor and by the SHARP instrument. Large overestimations by the low-cost sensor before calibration were observed in the field and were believed to be caused by the variation of ambient relative humidity. The root mean square error (RMSE) was 9.93 when comparing the low-cost sensor with the SHARP instrument. The calibration by the feedforward NN had the smallest RMSE of 3.91 in the test dataset, compared to the calibrations by SLR (4.91), MLR (4.65), and XGBoost (4.19). After calibrations, the F-K test using the test dataset showed that the variances of the PM<sub>2.5</sub> values by the NN and the XGBoost and by the reference method were not statistically significantly different. From this study, we conclude that feedforward NN is a promising method to address the poor performance of the low-cost sensors for PM<sub>2.5</sub> monitoring. In addition, the random search method for hyperparameters was demonstrated to be an efficient approach for selecting the best model structure.</p>
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</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>
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
Dataset for "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency"
<p>The dataset contains the data used in the article "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency".</p> <p>A low-cost monitoring system composed of 6 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 HPMA115C0-003), 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 November 2022 to June 2023. It contains the median of the PM2.5, relative humidity, temperature, and atmospheric pressure measurements of the low-cost sensors, after being aggregated to either one minute or one hour.</p> <p>The official measurements of the beta attenuation device are also provided.</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>
Rawdata for: Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors, Karbach et al., 2024
<p>Rawdata for publication: Karbach et al.: Preparation of low-concentration H2 test gas mixtures in ambient<br>air for calibration of H2 sensors, AMT, 2024</p> <p>DOI: 10.5194/amt-17-4081-2024</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>
Dataset for the "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration"
<p>This dataset supports findings of the study presented in paper "Comparison of fiber interferometric sensor with a commercial interferometer for a Kibble balance velocity calibration".</p>
Datasets for article "Robot Self-Calibration Using Actuated 3D Sensors"
<p>Real and sythetic datasets used in artilcle "Robot Self-Calibration Using Actuated 3D Sensors". For each recodring of a calibration scene is there is a ROS bag file holding a single message of type vision_3d_msgs/Actuated3dRecording.</p>
NDVI datasets related with the article "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance"
<p>AHVRR GIMMS and MODIS NDVI input datasets and main results on seasonal and long-term NDVI dynamics related with the manuscript "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance" recently published in Remote Sensing 8(2), 159, doi: 10.3390/rs8020159.</p>
MINTS Light Sensor Calibration Dataset
<p>MINTS Light Sensor Calibration Dataset, </p> <p>Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA</p>
Small and big pot calibration for 5TE and 10HS sensors.
<p>This is a dataset of data used to calibrate 10HS and 5TE sensors for soil moisture. It contains information on repetitions measured, transforming data and sample size estimation.</p>
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.
Random gas mixtures for efficient gas sensor calibration: Dataset
<p>The dataset was created at the Lab for Measurement Technology (Saarland University). It consists the raw signals (proportional to the logarithmic conductance) of two temperature-modulated semiconductor metaloxide gas sensors (ScioSense AS-MLV and AS-MLV-P2, former AppliedSensor, ams).</p> <p>An exact description of the measurement setup and its results can be found in the open access article:</p> <p>Baur, T., Bastuck, M., Schultealbert, C., Sauerwald, T., and Schütze, A.: Random gas mixtures for efficient gas sensor calibration, J. Sens. Sens. Syst., 9, 411–424, 2020, https://doi.org/10.5194/jsss-9-411-2020</p> <p><strong>Dataset:</strong><br> The mat-file comprises diffrent datasets:</p> <ul> <li><strong>as_mlv: </strong>raw signal (~ logarithmic conductance) of the AS-MLV containing 12202 sensor cycles with 12000 data points (@ 100 Hz)</li> <li><strong>as_mlv_p2: </strong>raw signal (~ logarithmic conductance) of the AS-MLV-P2 containing 12202 sensor cycles with 12000 data points (@ 100 Hz)</li> <li><strong>as_mlv_targets and as_mlv_p2_targets:</strong> <ul> <li><strong>acetone: </strong>acetone concentration in ppb</li> <li><strong>benzene</strong>: benzene concentration in ppb</li> <li><strong>formaldehyde</strong>: formaldehyde concentration in ppb, NaN values are undefined formaldehyde concentrations</li> <li><strong>toluene</strong>: toluene concentration in ppb</li> <li><strong>carbon_monoxide</strong>: carbon monoxide concentration in ppb</li> <li><strong>hydrogen</strong>: hydrogen concentration in ppb</li> <li><strong>humidity</strong>: relative humidity in %RH</li> <li><strong>voc_sum_ppb: </strong>sum of all VOC concentations (acetone, toluene, formaldehyde, benzene) in ppb </li> <li><strong>voc_sum_ugm3: </strong>sum of all VOC concentations (acetone, toluene, formaldehyde, benzene) in µg/m<sup>3</sup></li> <li><strong>count</strong>: number of the gas mixture exposure, each gas mixture exposure contains approx.10 sensor cycles</li> <li><strong>measurement</strong>: number of the measurement, each measurement contains approx.100 gas exposures</li> <li><strong>ranges</strong>: marked cycles for the data evaluation with group number</li> </ul> </li> </ul> <p> </p>
Calibration data for Climate Change Tower temperature sensors - may 2017
<p>Calibration data for the temperature sensors hosted at Climate Change Tower in Ny-Ålesund, Svalbard, during May 2017. Each namefile contains the temperature point (e.g. m25 is -25 °C, while p5 is +5 °C) and the sensor type ("ref" for reference, "DUT" for Device under test).</p> <p>Reference files contain data for the 2 reference sensors employed, while DUT files contain data for the 4 CCT sensors.</p>
Characterisation and calibration of PM sensors at high temporal resolution
<p>This repository contains the data used for the Chapter 7 of my PhD thesis:</p> <blockquote> <p>Bulot, Florentin (2022) Systematic studies of commodity particulate matter air pollution sensors. <em>University of Southampton, Doctoral Thesis</em>, 272pp. http://eprints.soton.ac.uk/id/eprint/458166</p> </blockquote> <p>It contains three files:</p> <ul> <li>fidas_10s.csv - contains the PM concentration data from the Fidas 200S</li> <li>rh_temperature.csv - contains the relative humidity and temperature data recorded by each of the SHT35 sensors in each air quality monitor</li> <li>sensors_10s_avg.zip - a zip file containing sensors_10s_avg.csv which contains the PM concentration data measured by the commodity PM sensors from Plantower PMS5003 and Sensirion SPS30 models</li> </ul>
ECOSTRESS At-sensor Calibrated Radiance Daily L1B Global 70m V001
The ECO1BRAD Version 1 data product was decommissioned on May 21, 2025. Users are encouraged to use the [ECO_L1CT_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CT_RAD.002) Version 2 and [ECO_L1CG_RAD](https://doi.org/10.5067/ECOSTRESS/ECO_L1CG_RAD.002) Version 2 data products.The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. .The ECO1BRAD Version 1 data product provides at-sensor calibrated radiance values retrieved for five thermal infrared (TIR) bands operating between 8 and 12.5 microns. Additionally, the digital numbers (DN) for the shortwave infrared (SWIR) band are provided. The TIR bands are spatially co-registered to produce a variable spatial resolution between 70 meters (m) to 90 m at the edge of the swath. The ECO1BRAD data product is provided as swath data and does not contain geolocation information. The corresponding [ECO1BGEO](https://doi.org/10.5067/ECOSTRESS/ECO1BGEO.001) data product is required to georeference the ECO1BRAD data product. The geographic coverage of acquisitions for the ECO1BRAD Version 1 data product extends to areas outside of those indicated on the coverage map. However, corresponding higher-level products over these areas are not available at this time.The ECO1BRAD Version 1 data product contains variables of radiance values for the five TIR bands, DN values for the SWIR band, associated data quality indicators, and ancillary data.Known Issues* Cannot perform spatial query on ECO1BRAD in NASA Earthdata Search: ECO1BRAD does not contain spatial attributes, so granules cannot be searched by geographic location. Users should search for ECO1BRAD data products by orbit number instead.* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Missing scan data/striping features: During testing, an instrument artifact was encountered in ECOSTRESS bands 1 and 5, resulting in missing values. A machine learning algorithm has been applied to interpolate missing values. For more information on the missing scan filling techniques and outcomes, see Section 3.3.2 of the User Guide.* Scan overlap: An overlap between ECOSTRESS scans results in a clear line overlap and repeating data. Additional information is available in Section 3.2 of the User Guide.* Scan flipping: Improvements to the visualization of the data to compensate for instrument orientation are discussed in Section 3.4 of the User Guide.* Cold bias: ECOSTRESS Level-1 Radiance data shows high correlation with in-situ ground measurements (R2 = 0.99 in all bands). Currently, ECOSTRESS has a cold bias of approximately 0.7 Kelvin (K), which will be corrected through calibration in future data releases.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.
EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001
The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/).The EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation (EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. Each EMITL1BRAD granule consists of two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Radiance (EMIT_L1B_RAD) and Observation (EMIT_L1B_OBS). The Radiance file contains the at-sensor radiance measurements of 285 bands with a spectral range of 381-2493 nanometers (nm) and with a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Observation file contains viewing and solar geometries, timing, topographic, and other information related to the observation. Each NetCDF4 file holds a location group containing geometric lookup tables (GLT), which are orthorectified images that provide relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length.Known Issues* Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.
Pressure Correction in the Calibration of Heat Dissipation Sensors
<p>Heat dissipation sensors (HDSs) infer soil matric potential based on the measured rate of temperature rise from a heat source. The process for calibrating these sensors may use a pressure plate apparatus (PPA) to remove water to a certain calibration point that is controlled by pressure. Conventionally, the pressure within the PPA is released before the temperature change calibration points are measured (D<i>T</i><sub>0</sub>). This conventional HDS calibration approach can take several weeks. We propose that the equilibrated temperature change at pressure (D<i>T</i><sub>p</sub>) can be corrected empirically to the value at zero pressure. Based on measurements of 14 HDSs between 0.02 and 0.1 MPa, D<i>T</i><sub>p</sub> was on average 6.2% larger than D<i>T</i><sub>0</sub>. After pressure correction, using an empirical equation, the difference between the corrected D<i>T</i><sub>p</sub> and D<i>T</i><sub>0</sub> was only 0.28%. These results demonstrate that this empirical approach provides sufficient accuracy to achieve HDS calibration without performing a PPA pressure release.</p>
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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.