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1,509 results for “Calibrations”
Datasets used for the spatial mode-based calibration (SMoC) paper
<p>Datasets for a journal paper "Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models".</p> <p>In the SMoC paper, the performance of SMoC is evaluated by applying it to forecasts of substantive precipitation events over the Brisbane Drainage Basin in eastern Australia. This repository provides datasets of precipitation forecasts (3 years) and observations (33 years) at a grid spacing of 0.05° x 0.05° and on a daily basis for this case study: (1) Forecasts: "3-year forecast data.nc"; (2) Corresponding observations: "3-year observation data.nc"; (3) Long-term observations: "30-year observation data.nc". These three files are compressed into "Forecast and observation data.zip".</p> <p>Author name: Pengcheng Zhao. Affiliation: Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne. Email: pengcheng@student.unimelb.edu.au.</p>
A low-cost wireless bite force measurement device - Calibration data
<p>Raw data for a low-cost wireless bite force measurement device calibration. The folder contains the data recorded during 3 different sessions for both the 1D load cell "1D-TAS606" sensor and two accurate 6 axis force transducer "3D-FT Sensors"</p>
Dataset for publication: "Design and Verification of a Calculable Composite Voltage Calibrator"
<p>Dataset for publication: "Design and Verification of a Calculable Composite Voltage Calibrator". Excel file contains the data for Figures 5, 8, and 9.</p>
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification - Features
<p>A release of the sources employed in the FRMIL work - "Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification", presented at MICCAI 2022.</p> <p>We release the Camelyon16 Whole Slide Image (WSI) extracted patch-level features for reproducibility.</p>
Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis - Single molecule tracking - raw data - calibration images
<p>This dataset contains the images used for channel calibration for the single molecule data that was analyzed in the manuscript "Organizing Structural Principles of the Interleukin-17 Ligand-Receptor Axis"</p>
Dataset and data processing tools of EPL 139 (2022) 55002 — Harmonic calibration of quadrature phase interferometry
<p>Dataset for <em>EPL</em> <strong>139</strong> (2022) 55002 — Harmonic calibration of quadrature phase interferometry</p> <p><strong>Scripts: </strong>the main Matlab script to analyse all data is <strong>analyseall.m</strong>, and uses all other scripts and functions (<strong>*.m</strong>). See comments in this main script for details. The script <strong>DefineDatasets.m,</strong> and its output <strong>Dataset.mat</strong>, were run before to define the main parameters of the 20 datasets, and define the frequency range where to estimate the background noise to be subtracted from the spectra of the driven cantilever when computing the total harmonic distortion.</p> <p><strong>Data:</strong> the 100 data files (.mat) have the following name scheme: [driving-frequency]-[Amplitude]-(low frequency external phase)-001 to 005.mat. For example 1kHz-10nm-w1Hz150nm-001.mat correspond to a driving of the cantilever at 1kHz, with an oscillation amplitude close to 10nm, with a low frequency driving of the external optical phase at 1Hz and equivalent 150nm amplitude. 001 stand for the first of the five files corresponding to this measurement. Note that the low frequency driving is optional. The main driving frequency is either directly given in kHz, or corresponds to one of the 2 first resonance modes of the cantilever (mode1 at 13.4kHz, and mode2approx at 84.6 kHz). Each of these data files contains 5 variables: Cx and Cy are the 2 outputs of the quadrature phase interferometer (1s of acquisition, 2e6 samples), fs is the sampling frequency (2 MHz), Ix and Iy are the mean total intensities on the photodiodes corresponding to Cx and Cy (units: A, this information can be useful to estimate the expected shot noise floor on the measurement).</p>
Calibration data for IMACLIM-R electricity nexus - alpha version
<p>First release of the calibration data for the electricity nexus of IMACLIM-R (<a href="https://www.iamconsortium.org/resources/model-resources/imaclim-r/">https://www.iamconsortium.org/resources/model-resources/imaclim-r/</a>).</p> <p>Ongoing/missing:</p> <ul> <li>operation and maintenance costs</li> <li>installed capacity</li> </ul>
Drop characteristics from the UNIGE calibration device for non-catching precipitation instruments
<p>These data are from the paper "Calibration uncertainty of non-catching precipitation gauges", by Baire, Q. et al., accepted for publication on the journal Sensors. A rain drop generator, developed at the University of Genova, is presented in that paper using two high-precision syringe pumps, with a capacity of 20 and 1 ml (20 and 4.5 mm piston diameter, respectively), to produce water drops of the required volume. An electric field, generated by a high voltage trigger, allows releasing each single drop on demand. Each drop is generated at the tip of a suitable nozzle by dispensing the necessary volume to achieve the desired drop size and then detached by exploiting a 5 kV potential difference, where the water is negatively charged and attracted by a metal ring (positively charged), positioned just below the tip of the nozzle. By using different nozzles/needles and the proper syringe pump, drops of various size are produced.</p> <p>Validation of drop size measurements was obtained by weighing the total volume of samples of about 20 to 45 drops with a precision balance having a resolution of 0.001 g. Drops were released at 1.20 m above the center of the measurement plane of the camera.</p> <p>Results are summarized in Table 2 in the paper in terms of the average drop diameter obtained from the software and the balance, and their difference. Drop size characteristics as obtained from the photogrammetric system are included in the database for the generated sets of drops. A second set of tests was conducted using the photogrammetric system alone, without weighing the overall water volume. Statistics of the detected drop diameter and fall velocity are listed in Tables 3 and 4 in the paper, together with the number of released drops during each test, while the single drop size and fall velocity characteristics are included in the dataset.</p>
Calibrated Western USA Noah-MP Parameter Set Forced by ERA5-WRF
<p>These three files include the default (original) Noah-MP parameter set for the Western USA, and parameter sets based on calibration to daily streamflow data and monthly streamflow data. </p>
South Pacific precipitation dataset: PACRAIN, TRMM, ERA5 and calibrated precipitation series.
<p>This dataset contains a set of stations located in the South Pacific region, obtained from the PACRAIN (Pacific Rainfall Database). Similarly, the TRMM (Tropical Rainfall Measuring Mission) satellite precipitation series and the ERA5 reanalysis precipitation series have been extracted for the aforementioned stations. For the stations corresponding to IDs NZ75400, NZ82400, NZ84317, NZ99701, SP00646, US14000 and US14690, the following <strong>daily</strong> variables are included:</p> <p> </p> <ul> <li>TRMM raw</li> <li>ERA5 raw</li> <li>PACRAIN </li> <li>Weather type associated</li> <li>TRMM calibrated (scaling) </li> <li>TRMM calibrated (empirical quantile mapping) </li> <li>TRMM calibrated (scaling conditioned) </li> <li>TRMM calibrated (empirical quantile mapping conditioned) </li> </ul> <p>where the last four are a consequence of the application of 4 different calibration methods. A summary of the stations is shown in the following table:</p> <table> <caption> </caption> <tbody> <tr> <td>Station Id</td> <td>Station Name</td> <td>Longitude</td> <td>Latitude</td> <td>Start</td> <td>End</td> <td>% Missing Data</td> <td>Altitude</td> </tr> <tr> <td>NZ75400</td> <td>Kolopelu (Wallis and Futuna)</td> <td>-178.12</td> <td>-14.32</td> <td>1998-01-01</td> <td>2012-01-01</td> <td>9.74</td> <td>36</td> </tr> <tr> <td>NZ82400</td> <td>Alofi (Niue)</td> <td>-163.93</td> <td>-19.07</td> <td>1998-01-01*</td> <td>2010-09-02</td> <td>2.68</td> <td>59</td> </tr> <tr> <td>NZ84317</td> <td>Rarotonga (Cook Islands)</td> <td>-159.8</td> <td>-21.2</td> <td>1999-09-28</td> <td>2012-01-12</td> <td>11.36</td> <td>4</td> </tr> <tr> <td>NZ99701</td> <td>Raoul Island (New Zealand)</td> <td>-177.93</td> <td>-29.23</td> <td>1998-01-01*</td> <td>2012-01-01</td> <td>0.72</td> <td>49</td> </tr> <tr> <td>SP00646</td> <td>Port Vila (Vanuatu)</td> <td>168.3</td> <td>-17.72</td> <td>2000-01-26</td> <td>2013-06-01</td> <td>18.13</td> <td>24</td> </tr> <tr> <td>US14000</td> <td>Aoloau (American Samoa)</td> <td>-170.77</td> <td>-14.3</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>21.72</td> <td>408</td> </tr> <tr> <td>US14690</td> <td>Nu'uuli (American Samoa)</td> <td>-170.70</td> <td>-14.32</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>0.037</td> <td>3</td> </tr> </tbody> </table> <p> </p>
Data and code: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize
<p>Data and code supporting the research article: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize - M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T.K.D. Weber. This includes R code for the implementation of the SPASS phenology maize model; Jags in R to implement the Bayesian multi-level models .</p>
Opt2Q Example cell_viability_calibration_results
<p>A model of apoptosis (aEARM) was calibrated to published cell viability data using PyDREAM. The posterior sample for the model parameters and log-posterior traces are included as .npy files. The .txt files show the Gelman Rubin metric for each of the model parameters. The data was generated using https://github.com/LoLab-VU/aEARM_cell_viability_calibration</p>
Nonlinear self calibrated spectrometer with single GeSe-InSe heterojunction device
<p>Computational spectrometry is an emerging field that employs photodetection in conjunction with numerical algorithms for spectroscopic measurements. Compact single photodetectors made from layered materials are particularly attractive since they eliminate the need for bulky mechanical and optical components used in traditional spectrometers and can easily be engineered as heterostructures to optimize device performance. However, such photodetectors are typically nonlinear devices, which adds complexity to extracting optical spectra from their response. Here, we train an artificial neural network (ANN) to recover the full nonlinear spectral photoresponse of a single GeSe-InSe p-n heterojunction device. The device has a spectral range of 400-1100 nm, a small footprint of ~25×25 〖μm〗^2, and a mean reconstruction error of 〖2×10〗^(-4) for the power spectrum at 0.35 nm. Using our device, we demonstrate a solution to metamerism, an apparent matching of colors with different power spectral distributions, which is a fundamental problem in optical imaging.</p>
Pylake example: Active calibration bead-bead coupling data
<p>A dataset acquired on a LUMICKS C-Trap used to demonstrate coupling between beads in active calibration.</p> <p>The dataset contains dual trap data with 2.1 micron beads captured in each trap.</p> <p>Files starting with bead1, bead2, bead3 are different bead pairs, while bead4, bead4b and bead4c contain technical replicates for a fourth bead.</p> <p> </p>
Data for: Domestic cat accelerometer data calibrated with behaviours
<p>Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies, however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (<em>Felis catus</em>) as a model organism for terrestrial mammalian behaviours.</p> <p>Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with; (i) additional descriptive variables; (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 second); and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models which were validated against calibrated cat behaviours before identifying behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours.</p> <p>RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardized durations of behaviours, and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast paced behaviours (e.g. locomotion), while lower frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours.</p> <p>While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare, and management of wild animals.</p>
Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes
<p><strong>Supplemental files contain all necessary datasets to reproduce the analysis and figures in "Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes" Code and additional files can be found at https://github.com/FowlerLab/VEP-calibrations</strong></p>
Boron isotope pH calibration of a larger benthic nummulitid foraminifer using LA-MC-ICPMS
<p>This repository is associated with the Submission of the manuscript entitled: "Boron isotope pH calibration of a larger benthic nummulitid foraminifer using LA-MC-ICPMS" submitted to the Geochimica et Cosmochimica-Acta journal.</p> <p>This repository contains the supplementary materials and the data for all figures associated with the manuscripts.</p>
Fossil-calibrated inference of divergence times among the Volvocine algae enables reconstruction of the steps that led to differentiated multicellularity
<p>Throughout its nearly four-billion-year history, life has undergone evolutionary transitions in which simpler subunits have become integrated to form a more complex whole. Many of these transitions opened the door to innovations that resulted in increased biodiversity and/or organismal efficiency. The evolution of multicellularity from unicellular forms represents one such transition, one that paved the way for cellular differentiation, including differentiation of male and female gametes. A useful model for studying the evolution of multicellularity and cellular differentiation is the volvocine algae, a clade of freshwater green algae whose members range from unicellular to colonial, from undifferentiated to completely differentiated, and whose gamete types can be isogamous, anisogamous, or oogamous. To better understand how multicellularity, differentiation, and gametes evolved in this group, we used comparative genomics and fossil data to establish a geologically calibrated roadmap of when these innovations occurred. Our results, presented as ancestral-state reconstructions, show that multicellularity arose independently twice in this clade. Our chronograms indicate multicellularity evolved during the Carboniferous-Triassic periods in Goniaceae + Volvocaceae, and possibly as early as the Cretaceous in Tetrabaenaceae. Using divergence time estimates we inferred when, and in what order, specific developmental changes occurred that led to differentiated multicellularity and oogamy. We find that in the volvocine algae the temporal sequence of developmental changes leading to differentiated multicellularity is much as proposed by David Kirk, and that multicellularity is correlated with the acquisition of anisogamy and oogamy. Lastly, morphological, molecular, and divergence time data suggest the possibility of cryptic species in Tetrabaenaceae.</p>
Ionospheric Connection Explorer (ICON) EUV Lunar Calibration Data
<p>This dataset provides the processed lunar calibraton data for the Ionospheric Connection Explorer Extreme Ultraviolet Spectrometer (ICON EUV) that were used in the study by Sirk et al. (2024, submitted).</p>
Dataset of paper "Real Driving Emissions—Event Detection for Efficient Emission Calibration"
<p>This dataset includes signal traces and resulting data used in the publication "Real Driving Emissions—Event Detection for Efficient Emission Calibration", MDPI Gases, 2024 (doi: 10.3390/gases4030010).</p> <p>The time trace data is available in a frequency of 1 Hz.</p> <p>The general vehicle specifications are listed below. For further information please refer to the paper.</p> <table> <tbody> <tr> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Criteria</strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p>Vehicle mass</p> </td> <td> <p>> 2000 kg</p> </td> <td> <p>Drivetrain</p> </td> <td> <p>All-wheel drive (AWD)</p> </td> </tr> <tr> <td> <p>Engine</p> </td> <td> <p>Inline 4 cylinder</p> </td> <td> <p>Gearbox</p> </td> <td> <p>Automatic Transmission (AT)</p> </td> </tr> <tr> <td> <p>Max. power</p> </td> <td> <p>> 180 kW</p> </td> <td> <p>EATS</p> </td> <td> <p>Three-way catalytic converter (TWC) and Gasoline particulate filter (GPF)</p> </td> </tr> <tr> <td> <p>Max. torque</p> </td> <td> <p>> 300 Nm</p> </td> <td> <p>Fuel</p> </td> <td> <p>Gasoline</p> </td> </tr> </tbody> </table> <p> </p> <p>Description of files:</p> <ul> <li><strong>Data_Measurement_NOx_Events_HistoryInfluence</strong>: Profile of examplary cycle in 1 Hz with time [s], speed [km/h], NOx mass flow [g/s], downstream lambda sensor voltage (U_HEGO) [V], flag fuel cut-off (b_fco) [-], distance specific NOx intensity for window layout (20 %, 50 % and 80 % History) [g/km], flag for critical window intensity (b_SE for 20 %, 50 % and 80 % history) [-]. (Refers to figure 9 and 10)</li> <li><strong>Data_NOx_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of NOx intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 8)</li> <li><strong>Data_PN_RelativePositionOfEmissionIntensityPeak</strong>: Map of relative position of PN intensity peak within the detected event based on the window layout. The position is displayed over the Window Duration [s] in rows and the History Share [%] in columns. (Refers to figure 7)</li> <li><strong>Data_NOx_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for NOx emissions. (Refers to figure 6)</li> <li><strong>Data_PN_WindowDuration_NumberEvents_AverageDuration_20percHistory</strong>: Overview of statistics for number of detected events in an examplary cycle and average duration of these events for different window durations and a window layout that considers 20 % history for PN emissions. (Refers to figure 5)</li> <li><strong>Data_Threshold_Statistics_NOx</strong>: Data for statistical evaluation of the threshold impact for event detection for the NOx emissions. The data are binned at a step size of 0.0001 g/km from 0 to 0.06 g/km (Threshold). For these bins, the detectable share of samples (relative) is listed for only urban samples, rural samples, motorway samples and total number of samples. (Refers to figure 4)</li> <li><strong>Data_Threshold_Window_Speed_Distance_Intensity_NOx</strong>: Data for threshold analysis. Contains the information of the analysis windows for each measurement sample that show a distance of > 0.002 km. For each window, the average window speed [km/h], the travelled window distance [km] the distance specific NOx emissions [g/km] and the integrated NOx emissions within this window [g] are displayed. (Refers to figure 3)</li> <li><strong>Overview_FinalEvent_Duration</strong>: Contains a list with the number of events and the duration of the events for the final event detection results with the named threshold values</li> <li><strong>Overview_FinalEvent_Profiles</strong>: Contains a list with the explicit data of the final event set. The time based data are displayed in a 1 Hz frequency over rows. The first column names the ID of the event, then the time in the cycle [s], the vehicle speed [km/h], the flag fuel cut-off (b_fco) [-], the downstream lambda sensor voltage (U_HEGO) [V], the Lambda value [-] and the NOx mass flow [g/s] are displayed.</li> </ul>
ScienceDex guides
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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.