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1,509 results for “Calibrations”
Monitoring small mammal abundance using NEON data: Are calibrated indices useful?
<p>Small mammals are important to the functioning of ecological communities with changes to their abundances used to track impacts of environmental change. While capture-recapture estimates of absolute abundance are preferred, indices of abundance continue to be used in cases of limited sampling, rare species with little data, or unmarked individuals. Improvement to indices can be achieved by calibrating them to absolute abundance but their reliability across years, sites, or species is unclear. To evaluate this, we used the US National Ecological Observatory Network (NEON) capture-recapture data for 63 small mammal species over 46 sites from 2013–2019. We generated 17,155 absolute abundance estimates using capture-recapture analyses and compared these to two standard abundance indices, and three types of calibrated indices. We found that neither raw abundance indices nor index calibrations were reliable approximations of absolute abundance, with raw indices less correlated with absolute abundance than index calibrations (raw indices overall R<sup>2</sup> < 0.5, index calibration overall R<sup>2</sup> > 0.6). Performance of indices and index calibrations varied by species, with those having higher and less variable capture probabilities performing best. We conclude that indices and index calibration methods should be used with caution with a count of individuals being the best index to use, especially if it can be calibrated with capture probability. None of the indices we tested should be used for comparing different species due to high variation in capture probabilities. Hierarchical models that allow for sharing of capture probabilities over species or plots (i.e., joint likelihood models) may offer a better solution to mitigate the cost and effort of large-scale small mammal sampling while still providing robust estimates of abundance.</p>
Data accompanying "Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat"
<p>Introduction to the dataset: </p> <p>In this manuscript, we analyse recordings of brown long-eared bats in the wild collected with a 4-microphone array, which we use to measure the distance between the bat and the microphone.<br> The distance is then used to correct the recordings for distance-dependent frequency and amplitude attenuation.<br> To ensure that our measurements and corrections are correct, we fully calibrated the microphone array and analysis set-up. <br> The calibration includes a comparison between real distances and calculated distances between a loudspeaker and the microphone array as well as comparisons between the real amplitude and the calculated amplitude of the loudspeaker.<br> To obtain the original amplitude of the calibration recording, we calibrated the loudspeaker as well</p> <p>In this folder, we provide the original bat recordings as well as the datasheet with all analysed call measurements, and the original data from the microphone calibration.</p> <p>DATA & FILE OVERVIEW</p> <p>Folder list: <br> - Bat data: folder containing the original Plecotus recordings and raw call parameters<br> - Calibration data : folder containing the array calibration raw data, as well as codes and recordings necessary to calibrate the loudspeaker. <br> </p>
Multicomponent ocean-bottom seismic data decomposition using separate calibration filters
<p>All relevant pictures in the article</p>
Trajectory Based RFI Subtraction and Calibration for Radio Interferometry
<p>Data and python notebooks for creating all the figures and tables in the paper titled "Trajectory Based RFI Subtraction and Calibration for Radio Interferometry".</p>
PASTA-ice sea ice image classification: calibration files and training data
<p>PASTA-ice is a Python-based classification algorithm for aerial sea-ice images. The data set contains calibration files for the CANON EOS-1D Mark III cameras that were implemented in helicopters and POLAR aircraft of the Alfred-Wegener-Institute. Furthermore, it contains training data of labeled sea ice surfaces observed during RV Polarstern cruise PS106 that can be used to train the implemented random forest classifier. Files with extension "sediments" were extended with exemplary data of sediment-loaden snow at the MOSAiC expedition. </p> <p>The PASTA-ice algorithm is available under: <a href="https://github.com/nielsfuchs/pasta_ice">https://github.com/nielsfuchs/pasta_ice</a></p>
AWI CANON Sea ice image albedo retrieval calibration files
<p>The dataset contains calibration data, compiled for a Python based retrieval of a broadband albedo estimate from CANON EOS-1D Mark III cameras deployed as aerial imaging system over Arctic sea ice. </p> <p>It contains information on the relative spectral response function of the camera sensor (RSR_CANON), calibration coefficients to convert spectral albedo in the three RGB color bands into broadband albedo under different sky conditions (spec2broad_params) and calibration coefficients to convert RGB intensity into radiance dependent on exposure settings (Calib_coefficients_14mm_diff_settings).</p>
Eurex-LUNa Abisko Trials Stereo Camera Calibration
<p>Bottom Stereo Camera Calibration Images for AUV Deepleng. They should be used for both determining intrinsic as well as extrinsic calibration parameters for the bottom-looking stereo camera. This is necessary for the image data published with DOI 10.5281/zenodo.7035119 .</p> <p>Contact: tom.creutz@dfki.de, bilal.wehbe@dfki.de</p> <p> </p> <p>Funded by BMWi (Kennziffer 50 NA 2002)</p>
Nanoindentation data set: Calibration and data analysis routines for nanoindentation with spherical tips
<p>These data comprise of nanoindentation experiements, which are described and analysed in the manuscript: <em>Calibration and data analysis routines for nanoidentation with spherical tips.</em></p> <p> </p>
2023/02/22 SCExAO/VAMPIRES Polarization Calibration Difference Images
<p>Polarization calibration data taken on 2023/02/22 (UT) for SCExAO/VAMPIRES. Data have been processed using the VAMPIRES Data Processing Pipeline (Lucas et al,, 2023 in prep). Included README describes experiment design and data outputs.</p>
LiDAR-camera calibration dataset
<p>This dataset provides several LiDAR-camera data sequences recorded in the rosbag2 format to test LiDAR-camera extrinsic calibration algorithms. The ros2 bag files were recorded with two LiDAR-camera configurations (LiDAR: Livox Avia / Ouster OS1-32, camera: STC-MCS500POE).</p> <p>livox.tar.gz contains five rosbags with the following topics:</p> <ul> <li>Topic: /livox/points | Type: sensor_msgs/msg/PointCloud2 |</li> <li>Topic: /livox/imu | Type: sensor_msgs/msg/Imu |</li> <li>Topic: /livox/lidar | Type: livox_interfaces/msg/CustomMsg |</li> <li>Topic: /image | Type: sensor_msgs/msg/Image |</li> <li>Topic: /camera_info | Type: sensor_msgs/msg/CameraInfo |</li> </ul> <p><br> ouster.tar.gz contains two rosbags with the following topics:</p> <ul> <li>Topic: /camera_info | Type: sensor_msgs/msg/CameraInfo |</li> <li>Topic: /image | Type: sensor_msgs/msg/Image |</li> <li>Topic: /points | Type: sensor_msgs/msg/PointCloud2 |</li> </ul> <p><br> </p>
Dataset for: Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems
<p>Dataset containing the response data for the calibration of the MB5 microbarometer (Figure 5), the WNRS (Figure 6) and the sensor (Figure 8).</p>
Ultrasound Plane Wave Raw Data 75 Angles - Breast Phantom and Calibration Phantom Dataset
<p><strong>Summary</strong></p> <p>This dataset is a collection of raw ultrasound plane wave data from a breast mimicking phantom and a calibration phantom. The breast phantom data contains samples of hyperechoic lesions, hypoechoic lesions and no lesions. Whereas the calibration phantom contains samples to experimentally validate resolution and contrast. This dataset was acquired to do experimental validation of physics based deep learning for image registration, about which a paper is a current work in progress.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>One the one hand this dataset contain 220 samples of in-vitro breast phantom ultrasound plane wave data (identified by CIRS073_RUMC). This data consists of three lesion types: hyperechoic lesions, hypoechoic lesions, and no lesions. On the other hand this dataset contains data from a calibration phantom (identified by CIRS040GSE). This data consists of: 5 samples of hypoechoic cysts, 5 samples from wire targets of 100 micrometer, 5 samples with -6dB and -3dB lesions in the field of view, and 5 samples with +3dB and +6dB in the field of view. All recordings were obtained twice: once in a low attenuating area 0.7 dB/cm/mHz, and once in a high attenuating area 0.95 dB/cm/mHz . The calibration phantom used is: Multi-Purpose Multi-Tissue Ultrasound Phantom Model 040GSE (CIRS, Norfolk, USA).</p> <p><em>Acquisition Information</em></p> <p>Data was acquired using a Verasonics Vantage T256 R256 system with an L12-5 50 mm linear array ultrasound transducer operating at a center frequency of 7.8 MHz. Plane wave data has been acquired consisting of 75 steering angles with an angle range of -16 degrees to +16 degrees. Verasonics scripts used for the acquisition are provided along with the data.</p> <p><em>Image Reconstruction</em></p> <p>The <a href="https://github.com/RALSchoop/DeepUS">GitHub repository</a> contains code to subsample any amount of angles from the 75 acquired angles and to do image reconstruction with the f-k migration algorithm on this dataset. This processing code is available in Python.</p> <p><strong>Involved Parties</strong></p> <p>This dataset was produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands in collaboration with the Medical Ultrasound Imaging Center in Radboud UMC Nijmegen, The Netherlands.</p> <p><strong>Contact Details</strong></p> <p>r [dot] schoop [at] nki [dot] nl</p>
2023/06/02 SCExAO/VAMPIRES Polarization Calibration Difference Images
<p>Polarization calibration data taken on 2023/06/02 (UT) for SCExAO/VAMPIRES. Data have been processed using the VAMPIRES Data Processing Pipeline (Lucas et al,, 2023 in prep; <a href="http://github.com/scexao-org/vampires_dpp">link</a>). Included README describes experiment design and data outputs.</p>
Minimising statistical errors in calibration of quantum-gate sets
<p>Data used to conduct the research documented in the paper https://arxiv.org/pdf/2206.03417.pdf.</p> <p>Organisation of files:</p> <ol> <li>Data used for the statistical analysis <ul> <li>data_statistical_analysis.zip</li> </ul> </li> <li>Data used for experimental considerations <ul> <li>GSC Retuning.zip</li> <li>Initial Gate Optimization: <ul> <li>Xtheta_20_B1__7__-1_Ec0_n1e+03.zip</li> <li>Ytheta_20_B1__7__-1_Ec0_n1e+03.zip</li> </ul> </li> </ul> </li> </ol>
Calibration dataset for WBAT (SN253120)
<p>Calibrations in Ny-Ålesund, Svalbard for Arrested Fish Kept Alive for Broadband Acoustic Net (AFKABAN) experiments in January 2023. The calibrations were performed with two spheres for each 120 kHz and 200 kHz.</p>
2023/09 VAMPIRES HWP Calibration Data
<p>This includes data from two different dates' HWP calibrations. The "standard" dataset includes collapsed, calibrated data and a result CSV for the standard filters (625-50, 675-50, 725-50, 750-50, 775-50, Open) and multiband mode. The "nb" dataset includes the four narrowband filters ("Halpha", "Ha-Cont", "SII", "SII-Cont"). Data were reduced using the <a href="http://github.com/scexao-org/vampires_dpp">VAMPIRES data processing pipeline</a>.</p>
Data from interlaboratory comparison of air thermometer calibrations, ATM ILC, 2019-2021
<p>The dataset contains data from an interlaboratory comparison conducted in EURAMET in 2019-2021, with the final report published in Oct 2023. The report and the ILC protocol are available in a different upload. </p> <p>This dataset consists of 3 files:</p> <ol> <li>participant information.xlsx: information on the setups used by each participant, their role in the ILC, temperatures measured at, the probes they measured on, the volume of chambers and the nature of the thermostat (bath, climate chamber or a subchamber).</li> <li>preprocessed data and drift.xlsx: preprocessed probe resistance, reference temperature, link corrected resistance, unilateral degree of equivalence, normalised error, pressure (if available), humidity (if available), pilot measurements in liquid and air for each probe, the list of points excluded from the consensus computations.</li> <li>selfheating.xlsx: probe-by-probe selfheating data from a number of participants (but not all). </li> </ol> <p> </p>
Dataset for New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids
<p>Data set for the publication named: "New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids"</p>
Dataset:A Modular Universal Divider for Calibration of UHVDC, and Composite Waves up to 1400 KV
<p>With the development of new high voltage cables and switching elements for continuously higher system voltages, especially for HVDC operation, the need for new reference measuring systems for on-site calibration to support activities in type and routine testing. Traceability in testing the fundamental voltage stress, high voltage DC or AC, is superimposed with a Switching or Lightning Impulses has up to now been lacking. This paper describes the design of a new universal RCR divider developed at RISE for a reference measurement system for traceability of ultra-high voltage (UHV) composite and combined waves.</p> <p>In composite/combined wave measurements the RCR divider is rated 500, 330, 600 and 700 kV for DC, AC<sub>rms</sub>, Switching Impulse (SI) and Lightning Impulse (LI) respectively. For UHVDC measurements the extended measurement uncertainty is better than 40 μV/V. In a composite/combined wave measurement, the extended measurement uncertainties for the divider are better than 150 and 400 μV/V for DC and AC and 0.5% for peak voltage of SI and LI. Two voltage dividers have been built for the purpose to measure both composite and combined waves. This divider is modular, where the HV arms can be stacked to 1000, 660, 1200 and 1400 kV and beyond.</p>
The data and codes corresponding to "Calibration of an SKA-Low prototype station using holographic techniques"
<p>The data and codes for our paper "Calibration of an SKA-Low prototype station using holographic techniques" being submitted to Radio Science. The data were acquired on 30-11-2022 around noon. The contents of this dataset are as follows.</p> <ol> <li>channel_cont_20221130_17259_0.hdf5 - voltage dump file with the AAVS2 antenna voltages.</li> <li>chan_204_20221130T04473900.uvfits - channelised and correlated uvfits file for the AAVS2 voltage dump. This can be imported into conventional radio astronomy packages such as miriad.</li> <li>miriad_mfcal4holo.sh - script to process the uvfits file in miriad.</li> <li>chan_204_20221130T04473900.uv_aver_phc.zip - final calibrated miriad data as a zip file.</li> <li>antenna_locations_aavs2.txt contains the AAVS2 antenna locations in metres.</li> <li>channel_4_1_1669783636.945334.dada - beamformed voltage stream from EDA2 with the beam towards the Sun.</li> <li>Holography_combined.ipynb - jupyter notebook to perform all the holographic techniques with the data given above and generate all the figures used in the paper.</li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.