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1,509 results for “Calibrations”
Data for: A novel and enhanced calibration of the tilting weir as a flow measurement structure
<p>To achieve reliable and equitable water distribution it is necessary to accurately estimate the volumetric discharge of water flow through rivers and canals. One such method of accomplishing this is by using hydraulic structures, such as the tilting weir. This structure not only can be used to measure the discharge of water on a continuous basis, but also allows for regulating the water level upstream of the structure. Presently, tilting weirs are often used to control water levels, but the literature on their utilization as flow measurement structures has remained sparse. This study presents a thorough analysis of laboratory experiments on scaled models of tilting weirs, where the flow depth upstream of the structure (i.e., head) and the inclination angle are used to calibrate an equation to estimate the discharge to a high degree of accuracy. Operational guidelines to ensure minimum uncertainty in measurement are also given.</p> <p>The data published here include head and discharge measurements for all experiments conducted, including information on the experimental location, date, and model dimensions. Publication of this data is intended to allow other researchers to verify the calibration procedure laid out in the primary article, and as a comparative dataset for future calibrations.</p>
Calibres découpés
Cette série de calibre à été découpée dans le carton à l'échelle 1:1. Elle représente toute les pièces du "puzzle" que constitue le vitrail de la tête dite "Christ de Wissembourg" chaqure calibre va permettre de servir de guide pour la découpe des pièces en verre. Source: Objaverse 1.0 / Sketchfab
Carton vitrail pour calibre
Ce carton comporte le dessin du chemin de plomb et l'emplacement des pièces en verre pour la fabrication d'un vitrail. Source: Objaverse 1.0 / Sketchfab
Fossil calibrated molecular phylogenies of Southern cave weta
<p>Aim: The biota of continents and islands are commonly considered to have a source-sink relationship, but small islands can harbour distinctive taxa. The distribution of four monotypic genera within the southern subfamily Macropathinae on young oceanic islands indicates a role for long-distance dispersal and extinction. We used molecular dating to estimate the timing of the southern radiation and infer potential processes involved.</p> <p>Location: Subantarctic islands, South Africa, South America, Australia, Aotearoa/New Zealand.</p> <p>Taxon: Southern hemisphere camel crickets subfamily Macropathinae within the Orthopteran family Rhaphidophoridae (Cave crickets/camel crickets/cave weta/tokoriro).</p> <p>Methods: Phylogenetic relationships were inferred from whole mtDNA genomes and nuclear sequences (45S cassette; four histones). We used a fossil and one palaeogeographic event to calibrate a molecular clock analysis.</p> <p>Results: We confirm that neither the Australian nor Aotearoa/New Zealand Rhaphidophoridae fauna are monophyletic. The Macropathinae radiation may have begun in the late Jurassic but trans-oceanic dispersal is required to explain the distribution of some lineages within the subfamily Macropathinae. Dating the most recent common ancestor of seven island endemic species with their nearest mainland relative suggests that each existed long before land surface on their island home was available.</p> <p>Main conclusions: If our molecular clock analysis is a good time estimate, then our data from the island endemic species suggest a failure to sample mainland species (New Zealand, Australia, or elsewhere) due to either extinction or lack of investment into taxonomy and species discovery.</p>
Low resolution local L-sigma calibration dataset
<p>Measurements of the H-alpha luminosity and intrinsic velocity dispersion of 347 H II regions across 9 spiral galaxies. These properties were measured from integral field unit (IFU) spectroscopy taken by the Mulit-Object Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT). The L-sigma relation calibrated from these data is valid for low spectral (R ~ 3000) and spatial (~500 pc) resolution observations of giant H II regions. </p>
Calibration and testing of HyperProbe1 and HyperProbe1.1
<p>The datasets presents the preliminary testing and technical characterisation of the laboratory versions of the HyperProbe system, named <strong>HyperProbe1 </strong>and <strong>HyperProbe1.1</strong>. These procedures include characterisation of (1) its spectral power emission, (2) spectral resolution, (3) image quality parameters such as spatial resolution, field of view (FOV) , contrast, signal-to-noise ratio (SNR), as well as (4) its temporal resolution.</p>
Stellar chromospheric activity database of solar-like stars based on the Ca II H and K lines of LAMOST Low-Resolution Spectroscopic Survey: the bolometric and photospheric calibration
<p>This database contains the stellar chromospheric activity parameters of solar-like stars based on the Ca II H and K lines from the spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). We extract 1,122,495 LRS spectra of solar-like stars from the LAMOST Data Release 8 v2.0 and provide the chromospheric activity parameters $S_{\rm tri}$, $S_{\rm MWO}$, $R_{\rm HK}$ and $R'_{\rm HK}$, as well as their uncertainties in the database. $R_{\rm HK}$ and $R'_{\rm HK}$ are derived from the method in the classic literature (denoted with classic) and the method based on the PHOENIX model (denoted with PHOENIX). These stellar chromospheric activity indexes and their uncertainties, along with the relevant spectroscopic parameters from the LAMOST catalogs, are saved in a CSV-format file (name: CaIIHK_Activity_Indexes_LAMOST_DR8_LRS.csv).</p> <p> </p> <p>Columns in the catalog of the database:</p> <p>obsid - Unique observation identifier of LAMOST LRS spectrum</p> <p>obsdate-Spectral observation date</p> <p>fitsname - FITS file name of LAMOST LRS spectrum</p> <p>snrg - Signal-to-noise ratio in the $g$ band ($\mathrm{S/N}_g$) of LAMOST LRS spectrum</p> <p>snrr - Signal-to-noise ratio in the $r$ band ($\mathrm{S/N}_r$) of LAMOST LRS spectrum</p> <p>teff - Effective temperature ($T_\mathrm{eff}$) derived from the LASP (unit: K)</p> <p>teff_err - Uncertainty of $T_\mathrm{eff}$ derived from the LASP (unit: K)</p> <p>logg - Surface gravity ($\log\,g$) derived from the LASP (unit: dex)</p> <p>logg_err - Uncertainty of $\log\,g$ derived from the LASP (unit: dex)</p> <p>feh - Metallicity ([Fe/H]) derived from the LASP (unit: dex)</p> <p>feh_err - Uncertainty of [Fe/H] derived from the LASP (unit: dex)</p> <p>rv - Radial velocity ($V_r$) derived from the LASP (unit: km/s)</p> <p>rv_err - Uncertainty of $V_r$ derived from the LASP (unit: km/s)</p> <p>ra_obs - Right ascension (RA) of fiber pointing (unit: degree)</p> <p>dec_obs - Declination (DEC) of fiber pointing (unit: degree) </p> <p>gaia_source_id - Source identifier in Gaia DR3 catalog</p> <p>gaia_g_mean_mag - $G$ magnitude in Gaia DR3 catalog</p> <p>S_tri* - $S_{\rm tri}$ index of LAMOST LRS </p> <p>S_tri_err* - Uncertainty of $S_{\rm tri}$ </p> <p>S_MWO* - $S_{\rm MWO}$ </p> <p>S_MWO_err* - Uncertainty of $S_{\rm MWO}$ </p> <p>log_R_HK_classic* - $\log\,R_{\rm HK,classic}$ </p> <p>log_R_HK_classic_err* - Uncertainty of $\log\,R_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic* - $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_p_HK_classic_err* - Uncertainty of $\log\,R'_{\rm HK,classic}$ </p> <p>log_R_HK_PHOENIX* - $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_HK_PHOENIX_err* - Uncertainty of $\log\,R_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX* - $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>log_R_p_HK_PHOENIX_err* - Uncertainty of $\log\,R'_{\rm HK,PHOENIX}$ </p> <p>(Note: The columns denoted by an asterisk represent the parameters provided by this database, and the remaining columns are collected from the data release of LAMOST DR8 V2.0. If the values are not available, they will be filled with '-9999' in this database.)</p>
Data and Code from: Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
<p>This record contains the data used in the paper, "Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO", and a link to the corresponding code for calibration of camera networks. The full method and a description of the dataset is given in the companion paper, currently on arXiv: https://arxiv.org/abs/1903.06811 .</p><p>And the code is on Github: https://github.com/amy-tabb/calico</p><p> </p>
Deep Madagascar Basin (DMB) Experiment CTD data for Deep SOLO Salinity Calibration
<p>This dataset contains CTD (Conductivity-Temperature-Depth) measurements taken during the Deep Madagascar Basin (DMB) Experiment from May 2 to 7, 2023, at the exit of three fracture zones of the Southwest Indian Ridge (SWIR): Atlantis II, Novara, and Melville. These measurements were used to compute the optimized CPcor coefficients for three Deep SOLO floats deployed in the same area during the DMB Experiment. </p> <p>The vertical profiles of temperature (C) and salinity (PSS-78) are available in a Matlab file, one for each of the three fracture zones. The file contains three structure arrays: ctd_atlantis, ctd_novara, and ctd_melville. Variables at each array: lon (longitude), lat (latitude), time, press (pressure in dbar), temp (in-situ temperature in degrees C), and sal (salinity in PSS-78).</p> <p> </p> <p> </p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</p>
Considering Time and Feature Entropy in Calibrated Recommendations
<p>Dataset and results from the paper "Considering Time and Feature Entropy in Calibrated Recommendations" authored by Diego Corrêa da Silva, Dietmar Jannach and Frederico Araújo Durão.</p>
Data for publication: Developing self-calibrating system for fiber Bragg grating based guided wave sensing under changing temperature conditions
<p>Data for section 3 and section 4 Figure 7- Figure 12 i n. txt format for easy viewing without need for special licenses</p> <p>The work related to the automated system was supported by the Project ‘Guided waves based reference-free SHM using fiber Bragg grating sensors (REFFREE)’<br>(2020/39/D/ST8/00188) Granted by National Science Center, Poland.</p>
Simulation for the calibration of radiation housekeeping monitor onboard BepiColombo/MMO and application to the inner heliosphere exploration
<p>I appreciate your interest in this paper.</p> <p>These archived data are used for Kinoshita et al., (2024).</p> <p>If you have further questions about these data, please feel free to contact me.</p> <p>E-mail: g-kinoshita20686@g.ecc.u-tokyo.ac.jp</p> <p> </p> <p>Sincerely,</p> <p>Gaku Kinoshita</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Pylake tutorial dataset: Force calibration
<p>A dataset acquired on a LUMICKS C-Trap used to demonstrate passive and active force calibration of an optical trap.</p> <p>The dataset contains:</p> <p>- A passive force calibration deep in the flowcell.</p> <p>- An active calibration near the surface of the flowcell.</p> <p>- A calibration on a fast detector.</p> <p>- A calibration spectrum with a noise peak.</p> <p>- A calibration with a noise floor.</p> <p>- A calibration item with the data inside the calibration marker.</p>
Advances in traceable calibration of cylinder pressure transducers
<p>"Pressure signals generated with VTT MIKES primary standard.xlsx". Dynamic pressure signals of VTT MIKES primary standard. </p> <p>"Calibration results of dynamic pressure sensors.xlsx". Calibration results for a commercial sensor and VTT cylinder pressure sensor (CPS) at different temperatures.</p>
Dataset for quantifying avian inertial properties using calibrated computed tomography
<p>Estimating centre of mass and mass moments of inertia is an important aspect of many studies in biomechanics. Characterising these parameters accurately in three dimensions is challenging with traditional methods requiring dissection or suspension of cadavers. Here, we present a method to quantify the three-dimensional centre of mass and inertia tensor of birds of prey using calibrated computed-tomography (CT) scans. The technique was validated using several independent methods, providing body segment mass estimates within approximately 1% of physical dissection measurements and moment of inertia measurements with a 0.993 R<sup>2</sup> correlation with conventional trifilar pendulum measurements. Calibrated CT offers a relatively straightforward, non-destructive approach that yields highly detailed mass distribution data that can be used for three-dimensional dynamics modelling in biomechanics. Although demonstrated here with birds, this approach should work equally well with any animal or appendage capable of being CT scanned.</p>
Dataset for XBeach model calibration and simulation of sample storms in an intermediate-to-dissipative, microtidal coast (Sabaudia, Italy)
<p>The archive contains XBeach input data and datasets used for the calibration of the hydrodynamic model XBeach and subsequent simulation of six sample storms at different tidal levels at the intermediate-to-dissipative, microtidal coast of Sabaudia (Tyrrhenian Sea, Italy).</p> <p>VERSION v2 (January 24, 2022): uploaded a new version of the dataset to support the revised version of the manuscript.</p>
Application of an improved calibration flight scheme in aeromagnetic interference compensation
<p><span>In aeromagnetic measurement, accurate compensation for the interference magnetic field generated by the aircraft platform due to the aircraft maneuvering in the Earth's magnetic field is an important prerequisite for the accurate indentification of the magnetic anomaly signal of the detection target. The flaws of the traditional calibration flight scheme in aeromagnetic interference compensation are firstly analyzed, and then an improved scheme is proposed, featuring smaller calibration flight areas, more simplified maneuvers, shorter flight route and less influence from the magnetometer's direction. The improved scheme can enhance the robustness of the aeromagnetic interference compensation matrix, and improve the compensation efficiency, and thus both the solved magnetic compensation coefficients and compensation effects are significantly improved. The experimental results of aeromagnetic interference compensation in an area of Inner Mongolia of China show that the average accuracy of the repeated survey lines, which are compensated by the improved scheme, can be increased by 5 nT and 3 nT, respectively, compared with the uncompensated results and the results compensated by the traditional scheme. Therefore, the effectiveness and superiority of the improved calibration flight scheme is fully proved.</span></p>
Example data for "Calibration of X-ray computed tomography for surface topography measurement using metrological characteristics"
<p>Example raw data used to create figures during the conference paper "<a href="https://www.euspen.eu/knowledge-base/ICE21165.pdf"><em>Calibration of X-ray computed tomography for surface topography measurement using metrological characteristics</em></a>", presented at euspen’s 21st International Conference & Exhibition 2020</p> <p>Data in the ".datx" format are acquired using a ZYGO NexView NX2 coherence scanning interferometer.</p> <p>Data in the ".stl" format are acquired using a Nikon MCT 225 X-ray computed tomography instrument.</p>
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