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1,509 results for “Calibrations”
Raw data for: Frequency-comb-calibrated swept-wavelength interferometry
<p>This document includes the raw data and Matlab codes to obtain the figures included in the paper, 'Frequency-comb-calibrated swept-wavelength interferometry'.</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>
Survey response of the H2020 Water-ForCE expert meeting on In situ calibration and validation of satellite products of water quality and hydrology
<p>These slides provide an overview of the expert survey held in advance of the H2020 Water-ForCE workshop:</p> <p><strong>In situ calibration and validation of satellite products of water quality and hydrology</strong></p> <p>which was held as three virtual meeting sessions on 17, 18 and 20th May 2021:</p> <ol> <li>Data availability, Accessibility and quality gaps</li> <li>Emerging technologies to address current gaps</li> <li>Data harmonization and sharing</li> </ol> <p>Source data have been removed from the graphs included in these slides but may be requested from the authors.</p> <p>The workshop and outcomes are part of the H2020 funded project Water-ForCE: Water Scenarios For Copernicus Exploitation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004186</p>
Dataset for the publication of "WRF model parameter calibration to improve the prediction of tropicalcyclones over the Bay of Bengal using Machine Learning-basedMultiobjective Optimization"
<p>The dataset consists of the modified WRF model software, that can be extracted and used in any Linux system with preinstalled required software.</p> <p>The namelists_file.zip consists of the namelist.input files that are used for the default and calibration simulations with different driving data namely, FNL files at 1deg with two nested domains, ERA files at 1deg with two nested domains, ERA files at 0.25deg with a single domain, and the ERA files at 0.25deg with two nested domains.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Global PM2.5 Dataset: Hybrid Calibration of CAMS and MERRA-2 PM2.5 Reanalysis Products during 2017-2019
<p>By integrating two reanalysis PM<sub>2.5</sub> products (CAMSRA, MERRA-2), a <strong>global daily hybrid-calibrated PM<sub>2.5</sub> concentration dataset</strong> is generated through the proposed CM-HC scheme with ERT model. This products include <strong>1095</strong> global PM<sub>2.5</sub> GeoTIFF files, starting from Jan 01, 2017 to Dec 31, 2019 (about <strong>0.95GB</strong> memory after uncompressing this zip file).</p> <p>To prove the superiority of this product, some experiments are implemented as follows: comparing with 1) two original products; 2) results of two separate calibration schemes with ERT; 3) results of CM-HC with other three ML models (RF, GBDT, XGBoost). Above analyses include two aspects, that is, the accuracy results and mapping effects. More details can be viewed at the thesis.</p> <p>One <strong>GeoTIFF</strong> file includes one-day calibrated PM<sub>2.5</sub> data. It is worth noting that the files contain the data of ocean area, however, our thesis only shows the results of land area in order to visually display the mapping effect before and after calibrating. Also, there is no site on the sea to validate in our study.</p>
Fig. 3 in An Early Miocene Dome-Skulled Chalicothere from the ''Arikaree'' Conglomerates of Darton: Calibrating the Ages of High Plains Paleovalleys Against Rocky Mountain Tectonism
Fig. 3. Geologic crosssection from Spoon
Automated, high-throughput image calibration for parallel-laser photogrammetry
<p>This contains the data required to recreate the analyses in this paper. The code for performing the machine learning and image processing methods presented in the paper are available as supplemental files to the manuscript, and are also available at https://github.com/ejlevy/Photogrammetry_Coding_InterLaser_Distance.</p> <p>Paper abstract: <span>Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject's body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. </span></p>
X-ray diffraction data for urotropine under high pressure - part 1 - data from ID15B beamline - He pressure transmitting medium and calibration
<p>X-ray diffraction data for urotropine collected at the ID15B beamline of the European Synchrotron Radiation Facility under high pressure. Calibration data and data collected at He used as pressure transmitting medium.</p>
Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration
<p>Data and code for the manuscript <em>Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration</em> accepted for publication to<em> </em> <em>Earth and Space Science</em> in October 2021.</p> <p>The dataset contains 7 month of total losses (transmitted - received power levels) and retrieved water vapor density from a 4.87 km long full-duplex E-band commercial microwave link (CML) operating at 73.5 and 83.5 GHz in Prague, CZ. The CML was operated as a part of a mobile phone backhaul. Furthermore, observations of air temperature, and air relative humidity from sites close to the CML end nodes are provided. Finally, theoretical gaseous attenuation calculated from the air temperature and relative humidity is included as a part of the dataset.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. All time series are regular and have 5-min temporal resolution. Metadata are stored in text files.</p> <p>The code is in a form of R Markdown files and html notebooks. Results presented in the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration and in its Supporting information are fully reproducible using this dataset.</p>
Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1"
<p>The domain file and surface data file that used to run ELMv1, and processed ISIMP2a runoff data that used in <a href="https://gmd.copernicus.org/preprints/gmd-2021-401/">https://gmd.copernicus.org/preprints/gmd-2021-401/</a></p> <p>ELM_runoff_parameter_post.nc contains the ELM runoff generation relevant parameter posteriors at a global half degree spatial resolution.</p>
The global water resources and use model WaterGAP v2.2e: streamflow calibration and evaluation data basis
<p>The data collection covers the streamflow data used for calibrating and validating the global water use and availability model WaterGAP v2.2e. The collection is a result of a data selection, assessment and merge effort from three data sources (GRDC, GSIM, ADHI) for in total 1509 stations. The stations are co-registered to the DDM30 (Döll & Lehner 2002) according their best hydrological fit.</p> <p>Please see the readme.md for details. Version 1.1 contains now the shapefiles as a zip instead single files which makes downlad more convenient.</p> <p> </p>
Woerthersee sediment core data for the publication "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria"
<p>This dataset comprises sediment core data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset comprising the seismic data (10.5281/zenodo.6479186), this is the basis for the publication Daxer et al. "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria".</p> <p>The files contain the following data:</p> <ul> <li>Core images Long Cores.zip: Core images of the Wörthersee Kullenberg-type long cores acquired with an ITRAX core scanner</li> <li>Core images Short Cores.zip: Core images of the Wörthersee gravity short cores (hammer-coring or trigger cores of the Kullenberg system) acquired with an ITRAX core scanner; provided as .tif files</li> <li>CT data WOER18-L5-X-Dicom.zip: X-ray computed tomography data acquired with a Siemens SOMATOM Definition AS (voxel size 0.2 x 0.2 x 0.3 mm); provided in DICOM format</li> <li>MSCL data.zip: Data acquired with a Geotek Multi-sensor core logger (e.g. magnetic susceptibility and gamma density); provided as Excel spreadsheets</li> <li>XRF data.zip: X-ray fluorescence data acquired with a ITRAX core scanner; provided in .csv format</li> </ul>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Calibration of BAM using the SLICS simulation
<p>The calibration of the BAM kernel and bias using the SLIC simulation as a reference reaches percent accuracy in the halo power spectrum.</p>
data-informed calibration of WRF-Hydro data1
<p>This is the first part of a dataset used to produce the results in a data-informed calibration study of WRF-Hydro. </p> <p>1. It contains the simulated hourly soil moisture time series from 2016/10/1 to 2017/05/31 before and after calibration at seven in-situ stations. The 7 lines in the .txt files are for the 7 stations in the following order:</p> <p>stat1=['sld',36.461040, -121.381170]<br> stat2=['lgs', 37.261590, -122.132870]<br> stat3=['gry', 37.071800, -121.478800]<br> stat4=['lwd', 35.937080, -121.108030]<br> stat5=['blueoak',37.381667,-121.744722]<br> stat6=['hastings',36.386389,-121.550278]<br> stat7=['norris',35.534167,-121.083333]</p> <p>2. It contains the hourly simulated streamflow simulations from 2016/10/1 to 2017/05/31 before and after calibration at 9 USGS stream gages. The lines in the txt files are in the following order:</p> <p><br> gageID=["11169500","11169800","11159200","11148900","11160000","11157500","11149900","11152050","11152000"]</p> <p>3. It also contains the spatially-distributed soil parameters before and after calibration. The "smcmax", "dksat","bexp" parameters in the soil_properties.nc are for soil porosity, saturated hydraulic conductivity, and b coefficient, respectively. </p> <p> </p>
A Building Imagery Database for the Calibration of Machine Learning Algorithms
<p>An open database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This open database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade.</p> <p><strong>Full Changelog</strong>: <a href="https://github.com/vsilva028/ML/commits/v1.0.0">https://github.com/vsilva028/ML/commits/v1.0.0</a></p>
Figure-2. Calibration curve of hydroxyproline solution at 559-nm
<p>Figure-2. Calibration curve of hydroxyproline solution at 559-nm</p>
Calibrating angular momentum transport in intermediate-mass stars from gravity-mode asteroseismology
<p>Best-matching MESA stellar evolution models and GYRE stellar pulsation models of the seven stars presented in Mombarg (2023).</p> <p>'MESA_inlist_template_for_GYRE' is the inlist for MESA to compute the models used for the asteroseismic modelling, 'GYRE_inlist_template.in' is the inlist for GYRE to compute the eigenmodes. </p> <p>'MESA_inlist_template_AM_transport' is the inlist for MESA to compute rotating models with angular momentum transport that were used to predict the near-core and surface rotation frequencies.</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>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.