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1,509 results for “Calibrations”
Sex differences in fearful personality traits are explained by facultative calibration to physical strength
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Software and Calibration for Photothermal Threshold Quantum Yield
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Seasonally optimized calibrations improve low-cost sensor performance: Long-term field evaluation of PurpleAir sensors in urban and rural India
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A comparison of the temporal and spatial properties of trans-saccadic perceptual re-calibration and saccadic adaptation
<p>This Dataset was used in the paper entitled "A comparison of the temporal and spatial properties of trans-saccadic perceptual re-calibration and saccadic adaptation", published in the Journal of Vision.</p> <p>Inside the folder are three subfolders, one for Experiment 1, one for the Adjustment condition of Experiment 2 and one for the Saccade Adaptation condition of Experiment 2.</p> <p>Each subfolder contains a DATA FORMAT DESCRIPTION.txt file that details the meaning of the single files.</p>
AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2009-2015) by calibrating GPM IMERG at daily scale using APHRODITE
<p>The datasets provide a new AIMERG precipitation data (0.1°/ half-hourly, 2009-2015, Asia) with better quality, calibrated by GPM IMERG (0.1°/half-hourly) and APHRODITE (0.25°/Daily) at daily scale for the Asian applications.</p> <p><strong>How to cite: Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., and Hong, Y.: AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating GPM IMERG at a daily scale using APHRODITE, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-250, 2020.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E-150°E, 15°S-55°N, land.</p> <p>AIMERG (0.1°/ half-hourly, 2000-2008, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/10.5281/zenodo.3609352</a></p>
AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000-2008) by calibrating GPM IMERG at daily scale using APHRODITE
<p>The datasets provide a new AIMERG precipitation data (0.1°/ half-hourly, 2000-2008, Asia) with better quality, calibrated by GPM IMERG (0.1°/half-hourly) and APHRODITE (0.25°/Daily) at daily scale for the Asian applications.</p> <p><strong>How to cite: Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., and Hong, Y.: AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating GPM IMERG at a daily scale using APHRODITE, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-250, 2020.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E-150°E, 15°S-55°N, land.</p> <p>AIMERG (0.1°/ half-hourly, 2009-2015, Asia) is available at <a href="http://10.5281/zenodo.3609507">http://10.5281/zenodo.3609507</a></p>
Neutron powder diffraction from NIST 660c LaB6 standard for calibration of Wombat high-intensity neutron powder diffractometer
<p>Calibration data for the Wombat high intensity powder diffractometer, ANSTO, collected at a nominal wavelength of 1.64A from NIST 660c LaB<sub>6</sub> standard using the [224] reflection from a single crystal Ge monochromator, [335] cut. Note that B in this standard is enriched to 99% with isotope <sup>11</sup>B. The sample was loaded in a 6mm diameter vanadium cylindrical sample can.</p>
Dataset from UNIBO for the deliverables 7.1 and 7.2, and SydILUC model setup, calibration and validation
<p>STAR-ProBio_ILUCLiteratureReview_v1.0 _Spreadsheet: Inventory of existing key drivers and parameters for ILUC quantification and future strategies to reduce ILUC risks, and collection of standardisation work related to the sustainability of biofuels and biomaterials.</p> <p>STAR-ProBio_ModelParameters_v13.0 _Spreadsheet: Inventory of parameters and relationships used in the SydILUC model, with related metadata, i.e. source (literature), units, suggested update period, etc... </p> <p>STAR-ProBio_CalibrationGlobalFAOSTAT_v3.0_Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model</p> <p>STAR-ProBio_MaizeNationalFAOSTAT_v1.0 _Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model – for the regional version of the model</p> <p>STAR-ProBio_BioplasticProductionSydILUC_v1.0 _Spreadsheet: Collection of all the yields for different bioplastics from different sources (sugar, starch, oil) – from the documents by “Biopolymers facts and statistics” by IfBB</p>
HORUS pressure dependent calibrations using the APACHE calibrator
<p>Dataset for the pressure dependent calibrations of HORUS using the newly developed All Pressure Altitude Calibrator for HOx Experimentation (APACHE) chamber.</p>
Supplementary material 7 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S5. Time-calibrated tree of European butterflies Section IV: Nymphalidae Part II: Subfamilies Libytheinae, Danainae & Satyrinae
Supplementary material 4 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S2. Time-calibrated tree of European butterflies Section I: Papilionidae, Hesperiidae & Pieridae
Supplementary material 6 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Figure S4. Time-calibrated tree of European butterflies Section III: Nymphalidae Part I: Subfamilies Limenitidinae, Heliconiinae, Apaturinae & Nymphalinae
Supplementary material 1 from: Wiemers M, Chazot N, Wheat CW, Schweiger O, Wahlberg N (2020) A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938: 97-124. https://doi.org/10.3897/zookeys.938.50878
Table listing of European butterfly species with higher taxonomy, voucher codes and accession numbers for the sequences used to build the phylogeny.
A calibrated groundwater model (Modflow-NWT) data repository in the Koga Irrigation Project area, Ethiopia
<p>The repository includes the research data pertaining to the Modflow-NWT based groundwater model developed for the Koga irrigation project area, Ethiopia. The database constitutes three archived data folders namely, 1. MainData (mostly excel files which include model forcings, data used in model calibration, citizen science data, etc.), 2. GIS (mostly geospatial files to assist readers with the spatial locations of the irrigation project structures, as well as the important data and administrative locations), 3. ModelFiles (mostly text files which include model inputs and outputs).</p> <p>The data has been used in preparation of the manuscript titled, "A numerical framework to advance agricultural water management under hydrological stress conditions in a data scarce environment", published in the Agricultural Water Management journal (<a href="http://dx.doi.org/10.1016/j.agwat.2021.106947">10.1016/j.agwat.2021.106947</a>). Readers are requested to go through this article to find more details on the data. The model simulations ranged from 1st January 2008 to 15th August 2019.</p> <p> </p>
ATTA-Ar calibration data for GRL submission
<p>ATTA-Ar calibration data for GRL submission</p>
AFM calibration - AFMBioMed Summer school 2020
<p>A 10 min video tutorial to perform proper AFM calibration.</p> <p>The video presents:</p> <ul> <li>Calibration for force measurements</li> <li>Explain both contact and non-contact calibration</li> <li>Provide a spring constant calibration process (Sader method)</li> <li>InvOLS calibration in liquid</li> </ul>
Data/code for Interpretable, calibrated neural networks for analysis and understanding of neutron spectra
<p>Simulated neutron spectra from inelastic neutron scattering on the double perovskite PCSMO. The dataset is related to the publication <em>Interpretable, calibrated neural networks for analysis and understanding of neutron spectra</em>. The paper is yet to be submitted, but the references will be added in due course.</p> <p># Pre-trained model weights</p> <p>`model-weights.tgz`</p> <p>This file contains the weights for the models reported in the paper which can be downloaded and used to re-produce the results.</p> <p>Please download the `model-weights.tgz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p># Data Generation</p> <p>`data_generation.tar.gz`</p> <p> Large data files for github repository associated with the publication _Interpretable, calibrated neural networks for analysis and understanding of neutron spectra_ These files relate to generation of the data in the paper.</p> <p>Please download the `data_generation.tar.gz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github.</p> <p>#Training data</p> <p>`<a href="https://zenodo.org/api/files/47a40b89-ae6f-4655-aa4b-2d8fc11ec597/pcsmo-simulated-spectra.tgz?versionId=1ff107a9-85b9-4b46-9c77-84319ca9d59a">pcsmo-simulated-spectra.tgz</a>`</p> <p>Contains the data used to train the networks. Extract this file to a data directory and then point the `train.py` files for the various models to look for this `<datadir>` in the specified location in those files.</p>
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 of probability predictions from machine-learning and statistical models
<p>This data set describes the occurrence (yes/no) of a bird, the Southern Whiteface (<i>Aphelocephala leucopsis)</i> in Australia. A suite of environmental variables is provided, which are used in the paper to illustrate a statistical problem. The data are meant to allow reproduction of the analysis in this paper. They are not intended for actual ecological analysis. The data come as .Rdata-file, i.e. as an R-dataset (described technically here: https://www.loc.gov/preservation/digital/formats/fdd/fdd000470.shtml).</p> <p>Here is the paper's abstract:</p> <p><span>Aim: Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine-learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say, 0.7 should indicate that out of 100 cases with these environmental conditions, and hence the same predicted probability, the species should be present in 70 and absent in 30.</span><br> <span>Innovation: A simple calibration plot shows that this is not necessarily the case, particularly not for over-fitted models or algorithms that use non-likelihood target functions. As a consequence, "raw" predictions from such model could easily be off by 0.2, are unsuitable for averaging across model types, and resulting maps hence be substantially distorted. The solution, a flexible calibration regression, is simple and can be applied whenever deviations are observed.</span><br> <span>Conclusion: "Raw", uncalibrated probability predictions should be calibrated before interpreting or averaging them in a probabilistic way.</span></p>
Data from: Calibrating divergence times on species trees versus gene trees: implications for speciation history of Aphelocoma jays
Estimates of the timing of divergence are central to testing the underlying causes of speciation. Relaxed molecular clocks and fossil calibration have improved these estimates; however, these advances are implemented in the context of gene trees, which can overestimate divergence times. Here we couple recent innovations for dating speciation events with the analytical power of species trees, where multilocus data are considered in a coalescent context. Divergence times are estimated in the bird genus Aphelocoma to test whether speciation coincided with mountain uplift or glacial cycles. Gene trees and species trees show general agreement that diversification began in the Miocene amid mountain uplift. However, dates from the multilocus species tree are more recent, occurring predominately in the Pleistocene, consistent with theory that divergence times can be significantly overestimated with gene-tree based approaches that do not correct for genetic divergence that predates speciation. In addition to coalescent stochasticity, Haldane's Rule could account for differences in timing estimates between mitochondrial and nuclear genes. By incorporating a fossil calibration applied to the species tree, in addition to the process of gene lineage coalescence, the present approach provides a more biologically realistic framework for dating speciation events, and hence for testing the links between diversification and specific biogeographic and geologic events.
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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.