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1,509 results for “Calibrations”
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the 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. We also </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 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</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 v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (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>
Calibrating phylogenies assuming bifurcation or budding alters inferred macroevolutionary dynamics in a densely sampled phylogeny of bivalve families
<p>Analyses of evolutionary dynamics can be profoundly affected by age calibrations of phylogenetic nodes under different models of lineage branching. Most time-calibrated molecular phylogenies of extant taxa assume a purely bifurcating model, where nodes are calibrated using the daughter lineage with the older first occurrence in the fossil record. Lineages can also split via budding, in which a parent lineage persists following the origin of a daughter lineage, and nodes are calibrated using the age of the lineage with the younger first occurrence. Here, we use the extensive fossil record of bivalve molluscs for a large-scale empirical test of how the choice of branching model affects macroevolutionary analyses. We time-calibrated 91% of nodes in a phylogeny of 97 extant bivalve families using 86 calibration points ranging in age from 2.59 to 485 Ma. Allowing budding-based calibrations minimizes conflict between the tree topology and timing of evolutionary events in the fossil record, reducing the summed duration of inferred "ghost lineages," from 6.76 billion yrs (Gyr; bifurcating model) to 1.00 Gyr (budding model). Adding 31 extinct paraphyletic families – many major groups contain such extinct taxa – shifts deep splits further back in time and raises ghost-lineage totals to 7.86 Gyr (bifurcating) and 1.92 Gyr (budding), but more accurately reflects the time since separation of lineages. Lineage-through-time plots from phylogenetic data scaled under a bifurcating model of evolution push more inferred bivalve diversification into the Paleozoic, conflicting with other palaeontological evidence on the magnitude of the end-Paleozoic extinction and subsequent recovery, and strongly reduce the magnitude of the Cenozoic diversification of the group. Consideration of the hypothesized branching model within a given clade is essential when node-calibrating phylogenies, and for a major clade with a robust fossil record, an evolutionary model that allows budding and does not force bifurcations is the most appropriate one, and likely common for many other clades as well.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: Calibration Data
<p>Calibration data accompanying our work, "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse.</p>
Operando electronic conductivity data - Calibration files
<p>Supplemental calibration files for the operando electronic conductivity data.</p> <p>Contains calibration files for:</p> <p>- temperature controllers (ods + json)</p> <p>- flow meters (xlsx + json)</p> <p>- gas chromatograph (json only)</p>
Spectro-photometric distances and self-calibrated abundances for Apogee DR16 RGB stars within the Milky Way disk
<p>The data file contains 48,853 RGB stars from Apogee DR16 within the Milky Way disk, for which we determine spectro-photometric parallax estimates (as described in Hogg et al. 2019, AJ, 158, 147), as well as self-calibrated stellar element abundances. The data set is described in detail and analyzed in Eilers et al. 2022 (arXiv: 2112.03295).</p>
Millstätter See seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Millstätter See, a lake in the Eastern European Alps, Austria. Together with a bathymetric dataset (10.5281/zenodo.5875923) and a core/seismic dataset from Wörthersee (10.5281/zenodo.5875576), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>28 core sections (individual short cores or sections of long cores - see <em>MillstaetterSee_core_data.xlsx</em> for information) were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000 and/or CT scanning. The generated data are provided in the folders <em>Grain Size.zip </em>and<em> CT data MI17-04.zip </em>(as .dcm files).</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
Woerthersee seismic and core data for the publication "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)"
<p>This dataset comprises the core data and the 3.5 kHz seismic data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset from Millstättersee (core and seismic data: 10.5281/zenodo.5875911; bathymetric data: 10.5281/zenodo.5875923), this is the basis for the publication Daxer et al. "High-resolution calibration of seismically-induced lacustrine deposits with historical earthquake data in the Eastern Alps (Carinthia, Austria)".</p> <p>24 short cores were analysed with a multi-sensor core logger (MSCL) and photographed with a smartcube camera image scanner and an ITRAX core scanner. The generated data are available in the folders <em>MSCL.zip</em> and <em>Photos.zip</em>. Some core sections were also analysed with a Malvern Mastersizer 3000. The generated grain-size data are provided in the folder <em>Grain Size.zip</em>.</p> <p>The seismic profiles are provided as .SGY files (<em>Seismic Pinger Data.zip</em>).</p>
New calibrated models for the TRGB luminosity and a thorough analysis of theoretical uncertainties
<p>The files contain the grid models for TRGB stars based on <strong>Saltas & Tognelli, 2022, MNRAS, in press (arXiv: 2203.02499)</strong>. See the paper for details on the physics, and the attached readme file for a description of the structure of the attached files. If you use these data in your research please cite the above article. <br> </p>
Time to Update the Split-Sample Approach in Hydrological Model Calibration v1.1
<p><strong>Time to Update the Split-Sample Approach in Hydrological Model Calibration</strong></p> <p>Hongren Shen<sup>1</sup>, Bryan A. Tolson<sup>1</sup>, Juliane Mai<sup>1</sup></p> <p><sup>1</sup>Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada</p> <p>Corresponding author: Hongren Shen (hongren.shen@uwaterloo.ca)</p> <p><strong>Abstract</strong></p> <p>Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling.</p> <p><strong>Version updates</strong></p> <p><strong>v1.1 Updated on May 19, 2022.</strong> We added hydrographs for each catchment.</p> <p><strong>There are <em>8 parts</em> of the zipped file attached in v1.1. You should download all of them and <em>unzip all those eight parts together</em>.</strong></p> <p>In this update, we added two zipped files in each gauge subfolder:</p> <p> (1) GR4J_Hydrographs.zip and</p> <p> (2) HMETS_Hydrographs.zip</p> <p>Each of the zip files contains 50 CSV files. These CSV files are named with keywords of model name, gauge ID, and the calibration sub-period (CSP) identifier.</p> <p>Each hydrograph CSV file contains four key columns:</p> <p> (1) Date time (note that the hour column is less significant since this is daily data);</p> <p> (2) Precipitation in mm that is the aggregated basin mean precipitation;</p> <p> (3) Simulated streamflow in m3/s and the column is named as "subXXX", where XXX is the ID of the catchment, specified in the CAMELS_463_gauge_info.txt file; and</p> <p> (4) Observed streamflow in m3/s and the column is named as "subXXX(observed)".</p> <p>Note that these hydrograph CSV files reported period-ending time-averaged flows. They were directly produced by the Raven hydrological modeling framework. More information about the format of the hydrograph CSV files can be redirected to the <a href="http://raven.uwaterloo.ca/">Raven webpage</a>.</p> <p><strong>v1.0 First version published on Jan 29, 2022.</strong></p> <p><strong>Data description</strong></p> <p>This data was used in the paper entitled "Time to Update the Split-Sample Approach in Hydrological Model Calibration" by Shen et al. (2022).</p> <p>Catchment, meteorological forcing and streamflow data are provided for hydrological modeling use. Specifically, the forcing and streamflow data are archived in the Raven hydrological modeling required format. The GR4J and HMETS model building results in the paper, i.e., reference KGE and KGE metrics in calibration, validation and testing periods, are provided for replication of the split-sample assessment performed in the paper.</p> <p><strong>Data content</strong></p> <p>The data folder contains a <strong>gauge info file (<em>CAMELS_463_gauge_info.txt</em>)</strong>, which<em> </em>reports basic information of each catchment, and <strong>463 subfolders</strong>, each having four files for a catchment, including:</p> <p> (1) <strong>Raven_Daymet_forcing.rvt</strong>, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (2) <strong>Raven_USGS_streamflow.rvt</strong>, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (3) <strong>GR4J_metrics.txt</strong>, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.</p> <p> (4) <strong>HMETS_metrics.txt</strong>, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods.</p> <p><strong>Data collection and processing methods</strong></p> <p> <strong>Data source</strong></p> <ul> <li> Catchment information and the Daymet meteorological forcing are retrieved from the CAMELS data set, which can be found <a href="https://ral.ucar.edu/solutions/products/camels">here</a>.</li> <li> The USGS streamflow data are collected from the U.S. Geological Survey's (USGS) National Water Information System (NWIS), which can be found <a href="https://waterdata.usgs.gov/nwis/sw">here</a>.</li> <li>The GR4J and HMETS performance metrics (i.e., reference KGE and KGE) are produced in the study by Shen et al. (2022).</li> </ul> <p><strong> Forcing data processing</strong></p> <ul> <li>A quality assessment procedure was performed. For example, daily maximum air temperature should be larger than the daily minimum air temperature; otherwise, these two values will be swapped.</li> <li>Units are converted to Raven-required ones. Precipitation: mm/day, unchanged; daily minimum/maximum air temperature: deg_C, unchanged; shortwave: W/m2 to MJ/m2/day; day length: seconds to days.</li> <li>Data for a catchment is archived in a RVT (ASCII-based) file, in which the second line specifies the start time of the forcing series, the time step (= 1 day), and the total time steps in the series (= 12784), respectively; the third and the fourth lines specify the forcing variables and their corresponding units, respectively.</li> <li>More details of Raven formatted forcing files can be found in the Raven manual (<a href="http://raven.uwaterloo.ca/">here</a>).</li> </ul> <p><strong> Streamflow data processing</strong></p> <ul> <li>Units are converted to Raven-required ones. Daily discharge originally in cfs is converted to m3/s.</li> <li>Missing data are replaced with -1.2345 as Raven requires. Those missing time steps will not be counted in performance metrics calculation.</li> <li>Streamflow series is archived in a RVT (ASCII-based) file, which is open with eight commented lines specifying relevant gauge and streamflow data information, such as gauge name, gauge ID, USGS reported catchment area, calculated catchment area (based on the catchment shapefiles in CAMELS dataset), streamflow data range, data time step, and missing data periods. The first line after the commented lines in the streamflow RVT files specifies data type (default is HYDROGRAPH), subbasin ID (i.e., SubID), and discharge unit (m3/s), respectively. And the next line specifies the start of the streamflow data, time step (=1 day), and the total time steps in the series(= 12784), respectively.</li> </ul> <p><strong>GR4J and HMETS metrics </strong></p> <p>The GR4J and HMETS metrics files consists of reference KGE and KGE in model calibration, validation, and testing periods, which are derived in the massive split-sample test experiment performed in the paper.</p> <ul> <li>Columns in these metrics files are gauge ID, calibration sub-period (CSP) identifier, KGE in calibration, validation, testing1, testing2, and testing3, respectively.</li> <li>We proposed 50 different CSPs in the experiment. "CSP_identifier" is a unique name of each CSP. e.g., CSP identifier "CSP-3A_1990" stands for the model is built in Jan 1st 1990, calibrated in the first 3-year sample (1981-1983), calibrated in the rest years during the period of 1980 to 1989. Note that 1980 is always used for spin-up.</li> <li>We defined three testing periods (independent to calibration and validation periods) for each CSP, which are the first 3 years from model build year inclusive, the first 5 years from model build year inclusive, and the full years from model build year inclusive. e.g., "testing1", "testing2", and "testing3" for CSP-3A_1990 are 1990-1992, 1990-1994, and 1990-2014, respectively.</li> <li>Reference flow is the interannual mean daily flow based on a specific period, which is derived for a one-year period and then repeated in each year in the calculation period. <ul> <li>For calibration, its reference flow is based on spin-up + calibration periods.</li> <li>For validation, its reference flow is based on spin-up + calibration periods.</li> <li>For testing, its reference flow is based on spin-up +calibration + validation periods.</li> </ul> </li> <li>Reference KGE is calculated based on the reference flow and observed streamflow in a specific calculation period (e.g., calibration).<strong> Reference KGE is computed using the KGE equation with substituting the "simulated" flow for "reference" flow in the period for calculation</strong>. Note that the reference KGEs for the three different testing periods corresponds to the same historical period, but are different, because each testing period spans in a different time period and covers different series of observed flow.</li> </ul> <p><strong>More details of the split-sample test experiment and modeling results analysis can be referred to the paper by Shen et al. (2022).</strong></p> <p><strong>Citation</strong></p> <p><strong>Journal Publication</strong></p> <p>This study:</p> <p>Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. <a href="https://doi.org/10.1029/2021WR031523">https://doi.org/10.1029/2021WR031523</a></p> <p>Original CAMELS dataset:</p> <p>A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, <a href="http://doi.org/10.5194/hess-19-209-2015">http://doi.org/10.5194/hess-19-209-2015</a></p> <p><strong>Data Publication</strong></p> <p>This study:</p> <p>H. Shen, B. A. Tolson, and J. Mai (2022). Time to Update the Split-Sample Approach in Hydrological Model Calibration. Zenodo. <a href="http://doi.org/10.5281/zenodo.5915374">http://doi.org/10.5281/zenodo.5915374</a></p> <p>Original CAMELS dataset:</p> <p>A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR.<a href="http:// https://dx.doi.org/10.5065/D6MW2F4D"> https://dx.doi.org/10.5065/D6MW2F4D</a></p>
FaIR v1.6.2 calibrated and constrained parameter set
<p>These .json files provides the 2237 ensemble members that are used to run the FaIR simple climate model in the IPCC's Sixth Assessment Report contributions to Working Group 1 and Working Group 3.</p> <p>v1.1 of this dataset includes two versions of the files:</p> <ul> <li>the original full parameter set `fair-1.6.2-wg3-params.json` as in v1.0</li> <li>reduced file size versions `fair-1.6.2-wg3-params-slim.json` and `fair-1.6.2-wg3-params-common.json` where only the parameters that vary between ensemble members are included in the former and everything else is in the latter. This is optimised for running the Working Group 3 climate assessment.</li> </ul> <p>Drawn from an initial prior ensemble of 1 million, the following constraints are placed upon the results:</p> <ul> <li>representation of observed warming from 1850-2019, within observational uncertainty</li> <li>representation of observed ocean heat content change 1971-2018, within observational uncertainty</li> <li>reproduction of near-present day atmospheric concentrations of CO2 from the carbon cycle</li> <li>airborne fraction of CO2 with a distribution similar to the assessed range of Chapter 5, IPCC Working Group 1 AR6</li> <li>equilibrium climate sensitivity with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>transient climate response with a similar distribution to Chapter 7, IPCC Working Group 1, AR6</li> <li>projected future warming that is of a similar distribution to the SSP scenarios of Chapter 4, IPCC Working Group 1, AR6, when run with prescribed concentrations</li> </ul> <p><strong>Changelog</strong></p> <ul> <li>v1.1: inclusion of lightweight parameter sets.</li> <li>v1.0: original upload full parameter sets.</li> </ul>
Zagreb Calibration Benchmark Dataset
<p>Zagreb Calibration Benchmark Dataset is multi-modal high-resolution dataset geared at evaluating calibration solutions.</p>
Dataset of PMU calibrator calibration.
<p>Calibration data for calibration of Phasor Measurement Unit calibrator. Data includes GNU Octave script to process and display results.</p> <p>The calibration method relies on the properties of a two channel digitizer with a time base common to<br>both ADCs. Because of the common time base, time errors of samples acquired by first channel are almost the same as the time errors of the samples acquired by second channel. The first channel is used to calibrate the time base and the second channel is used to sample the synchrophasor waveform.<br> </p> <p>Data are part of paper published in 25th IMEKO TC4 International Symposium, 23rd International Workshop on ADC and DAC Modelling and Testing, IMEKO TC-4 2020, Brescia, Italy / September 12-14, 2022.</p> <p>Title: Simple method for calibration of PMU calibrators<br>Authors: Martin Šíra and Stanislav Mašláň<br>Contact: Czech Metrology Institute, Okružní 31, 638 00 Brno, msira@cmi.cz<br> </p> <p>Part of project Quantum traceability for AC power standards, QuantumPower, Project Number: 19RPT01. This project (19RPT01) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.</p> <p>https://www.euramet.org/research-innovation/search-research-projects/details/project/quantum-traceability-for-ac-power-standards/?L=0&tx_eurametctcp_project%5Baction%5D=show&tx_eurametctcp_project%5Bcontroller%5D=Project&cHash=05c245c325fe8dcb6a358fe6e192e199</p>
Data for remote sensing tool calibration
<p>This dataset contains the in-situ data and the extracted pixel band information used to calibrate and develop an open-source remote sensing tool. The remote sensing tool provides near real-time water quality conditions of lakes/reservoirs in the USA. </p>
The use of GRDC gauging stations for calibrating large-scale hydrological models
<p>The Global Runoff Data Centre provides time series of observed discharges that are very valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology which, though investigated several times, has not been satisfactorily solved. Grid-based hydrological models need to fit the reported station location to the river network depending on the resolution, to compare simulated discharge with observed discharge. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the wrong basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcseconds) river network, and we provide source codes and high- and low-resolution watershed boundaries.</p> <p>Same as release on Github: https://github.com/iiasa/CWATM_grdc_calibration_stations/releases/tag/V1.0</p>
Opt2Q Calibrations -- Gelman Rubin Convergence Metrics
<p>Gelman Rubin Convergence metrics for calibrations detailed at</p> <p><a href="https://doi.org/10.5281/zenodo.4768814">https://doi.org/10.5281/zenodo.4768814</a><br> <a href="https://doi.org/10.5281/zenodo.4768842">https://doi.org/10.5281/zenodo.4768842</a><br> <a href="https://doi.org/10.5281/zenodo.4768806">https://doi.org/10.5281/zenodo.4768806</a><br> <a href="https://doi.org/10.5281/zenodo.4768370">https://doi.org/10.5281/zenodo.4768370</a></p>
Preliminary updates to the NIRCam photometric calibration
<p>Reposted from https://github.com/gbrammer/grizli/pull/107 to make a more permanent reference.</p> <p>As of late August 2022, a number of groups have found that there still seem to be large corrections to the individual NIRCam SCA photometric calibration required, even after the CRDS updates in late July. There is a series of ongoing calibration programs that will establish the absolute calibration of all of the modes at high precision. For now, however, those calibration data are incomplete.</p> <p>I have derived some corrections to the <code>jwst_0942.pmap</code> context using the following:</p> <ol> <li>The flux calibration standard <code>J1743045</code> suggests that the absolute NIRISS calibration is quite good, with just a correction of 1/1.04 required for F200W</li> <li>The flux standard also suggests that NIRCam LW broadbands are fairly well calibrated, perhaps with the exception of a 1/0.912 correction needed for F444W</li> <li>The flux standard data are currently only available for the B module in some filters. To calibrate the A module and also the additional broadband SW filters, I derived <em>relative</em> corrections using the wealth of data taken of the LMC astrometric calibration field, where certain patches of the field have been observed by more than one NIRCam SCA. The corrections are derived by measuring photometry in the NIRISS broad bands and then calculating scale factors of all of the NIRCam SW SCAs relative to these.</li> <li>Relative A/B corrections for the NIRCam LW broad-band filters are also derived from the LMC data.</li> </ol> <p>The tabulated values in <code>grizli/data/photom_correction.yml</code> are the median ratios of the derived flux densities in the indicated filters to some reference, either relative or absolute. To apply the corrections relative to exposures calibrated with the current CRDS_CONTEXT, the values should be <em>divided</em> from the SCI and ERR arrays, which is now implemented in <code>utils.drizzle_from_visit</code>.</p> <p>The LMC mosaics are available at <a href="https://s3.amazonaws.com/grizli-v2/JwstMosaics/lmc-astrometric-field/lmc.html">https://s3.amazonaws.com/grizli-v2/JwstMosaics/lmc-astrometric-field/lmc.html</a>. The full notebook used to derive the LMC corrections is at <a href="https://s3.amazonaws.com/grizli-v2/JwstMosaics/lmc-astrometric-field/astrometric-field-photometry.ipynb">https://s3.amazonaws.com/grizli-v2/JwstMosaics/lmc-astrometric-field/astrometric-field-photometry.ipynb</a>.</p> <p>This release includes a table (summary_0989.txt) of my photometric calibration determinations and comparisons of these to the CRDS references.</p> <p>An official update from the NIRCam team is now available in the JWST CRDS system in <a href="https://jwst-crds.stsci.edu/context_table/jwst_0989.pmap">jwst_0989.pmap</a>. Please cite this DOI if you use the "v4" mosaics of JWST imaging fields produced by G. Brammer.</p> <pre><code># det filt pupil mjsr_0942 gbr ila mjsr_gbr file gbr_inv mjsr_0989 file_0989 ratio_989_gbr # mjsr_0942 : PHOTMJSR from jwst_0942.pmap # gbr : multiplicative corrections from G. Brammer # ila : multiplicative corrections from I. Labbe # mjsr_gbr = gbr * mjsr_0942 # 0942 numbers from jwst_nircam_photom_0[file].fits # mjsr_0989 : PHOTMJSR from jwst_0989.pmap # 0989 numbers from jwst_nircam_photom_0[file_0989].fits # ratio_989_gbr = mjsr_0989 / mjsr_gbr NRCA1 F090W CLEAR 3.762 0.826 --- 3.108 106 1.210 2.996 116 0.964 NRCA1 F115W CLEAR 3.299 0.914 0.876 3.014 106 1.094 2.963 116 0.983 NRCA1 F150W CLEAR 2.460 0.936 0.895 2.303 106 1.068 2.251 116 0.978 NRCA1 F200W CLEAR 2.211 0.865 0.846 1.913 106 1.155 1.892 116 0.989 NRCA2 F090W CLEAR 3.872 0.833 --- 3.224 100 1.201 3.282 110 1.018 NRCA2 F115W CLEAR 3.395 0.908 0.867 3.084 100 1.101 3.101 110 1.006 NRCA2 F150W CLEAR 2.531 0.937 0.894 2.371 100 1.067 2.384 110 1.005 NRCA2 F200W CLEAR 2.275 0.889 0.857 2.023 100 1.125 2.081 110 1.029 NRCA3 F090W CLEAR 4.026 0.762 --- 3.066 102 1.313 2.954 112 0.963 NRCA3 F115W CLEAR 3.531 0.862 0.840 3.044 102 1.160 2.949 112 0.969 NRCA3 F150W CLEAR 2.632 0.878 0.852 2.310 102 1.139 2.204 112 0.954 NRCA3 F200W CLEAR 2.366 0.800 0.794 1.892 102 1.251 1.828 112 0.966 NRCA4 F090W CLEAR 3.839 0.753 --- 2.891 105 1.328 2.910 115 1.007 NRCA4 F115W CLEAR 3.366 0.813 0.784 2.738 105 1.229 2.776 115 1.014 NRCA4 F150W CLEAR 2.509 0.837 0.811 2.100 105 1.195 2.102 115 1.001 NRCA4 F200W CLEAR 2.256 0.791 0.778 1.785 105 1.264 1.805 115 1.012 NRCB1 F090W CLEAR 3.874 0.894 --- 3.463 108 1.119 3.564 118 1.029 NRCB1 F115W CLEAR 3.397 0.959 0.897 3.258 108 1.043 3.272 118 1.004 NRCB1 F150W CLEAR 2.532 0.946 0.906 2.394 108 1.058 2.412 118 1.008 NRCB1 F200W CLEAR 2.276 0.873 0.866 1.987 108 1.145 2.022 118 1.018 NRCB2 F090W CLEAR 4.009 0.842 --- 3.377 107 1.187 3.427 117 1.015 NRCB2 F115W CLEAR 3.515 0.897 0.870 3.154 107 1.114 3.128 117 0.992 NRCB2 F150W CLEAR 2.621 0.896 0.879 2.348 107 1.117 2.305 117 0.982 NRCB2 F200W CLEAR 2.356 0.829 0.847 1.953 107 1.206 1.952 117 0.999 NRCB3 F090W CLEAR 3.706 0.914 --- 3.386 103 1.095 3.425 113 1.012 NRCB3 F115W CLEAR 3.250 0.995 0.933 3.234 103 1.005 3.186 113 0.985 NRCB3 F150W CLEAR 2.423 0.986 0.936 2.388 103 1.015 2.352 113 0.985 NRCB3 F200W CLEAR 2.178 0.901 0.888 1.963 103 1.110 1.957 113 0.997 NRCB4 F090W CLEAR 3.769 0.818 --- 3.085 099 1.222 3.043 109 0.987 NRCB4 F115W CLEAR 3.305 0.831 0.811 2.746 099 1.204 2.698 109 0.983 NRCB4 F150W CLEAR 2.464 0.849 0.822 2.092 099 1.178 2.029 109 0.970 NRCB4 F200W CLEAR 2.215 0.812 0.795 1.799 099 1.231 1.771 109 0.985 NRCBLONG F277W CLEAR 0.422 1.000 --- 0.422 104 1.000 0.417 114 0.989 NRCALONG F277W CLEAR 0.433 1.107 --- 0.480 101 0.903 0.491 111 1.024 NRCBLONG F300M CLEAR 0.952 1.000 --- 0.952 104 1.000 0.949 114 0.997 NRCALONG F300M CLEAR 0.977 1.059 --- 1.035 101 0.944 1.013 111 0.979 NRCBLONG F356W CLEAR 0.372 1.000 --- 0.372 104 1.000 0.376 114 1.011 NRCALONG F356W CLEAR 0.381 1.088 --- 0.415 101 0.919 0.405 111 0.976 NRCBLONG F410M CLEAR 0.811 1.042 1.031 0.845 104 0.960 0.875 114 1.036 NRCALONG F410M CLEAR 0.832 1.031 1.031 0.858 101 0.970 0.870 111 1.015 NRCBLONG F444W CLEAR 0.335 1.096 1.031 0.367 104 0.912 0.373 114 1.015 NRCALONG F444W CLEAR 0.343 1.136 1.031 0.390 101 0.880 0.393 111 1.007 # # These are in 0989 but not in my own analysis NRCA1 F140M CLEAR 5.380 1.000 --- 5.380 106 1.000 5.075 116 0.943 NRCA1 F182M CLEAR 3.788 1.000 --- 3.788 106 1.000 3.490 116 0.921 NRCA1 F210M CLEAR 4.678 1.000 --- 4.678 106 1.000 4.404 116 0.941 NRCA1 F150W2 F162M 5.106 1.000 --- 5.106 106 1.000 4.663 116 0.913 NRCA2 F140M CLEAR 5.537 1.000 --- 5.537 100 1.000 5.355 110 0.967 NRCA2 F182M CLEAR 3.898 1.000 --- 3.898 100 1.000 3.779 110 0.970 NRCA2 F210M CLEAR 4.814 1.000 --- 4.814 100 1.000 4.841 110 1.006 NRCA2 F150W2 F162M 5.255 1.000 --- 5.255 100 1.000 5.007 110 0.953 NRCA3 F140M CLEAR 5.758 1.000 --- 5.758 102 1.000 5.011 112 0.870 NRCA3 F182M CLEAR 4.053 1.000 --- 4.053 102 1.000 3.375 112 0.833 NRCA3 F210M CLEAR 5.006 1.000 --- 5.006 102 1.000 4.235 112 0.846 NRCA3 F150W2 F162M 5.464 1.000 --- 5.464 102 1.000 4.516 112 0.827 NRCA4 F140M CLEAR 5.489 1.000 --- 5.489 105 1.000 4.714 115 0.859 NRCA4 F182M CLEAR 3.864 1.000 --- 3.864 105 1.000 3.328 115 0.861 NRCA4 F210M CLEAR 4.773 1.000 --- 4.773 105 1.000 4.223 115 0.885 NRCA4 F150W2 F162M 5.210 1.000 --- 5.210 105 1.000 4.371 115 0.839 NRCB1 F140M CLEAR 5.539 1.000 --- 5.539 108 1.000 5.441 118 0.982 NRCB1 F182M CLEAR 3.900 1.000 --- 3.900 108 1.000 3.776 118 0.968 NRCB1 F210M CLEAR 4.816 1.000 --- 4.816 108 1.000 4.760 118 0.988 NRCB1 F150W2 F162M 5.257 1.000 --- 5.257 108 1.000 5.068 118 0.964 NRCB2 F140M CLEAR 5.733 1.000 --- 5.733 107 1.000 5.196 117 0.906 NRCB2 F182M CLEAR 4.036 1.000 --- 4.036 107 1.000 3.592 117 0.890 NRCB2 F210M CLEAR 4.984 1.000 --- 4.984 107 1.000 4.517 117 0.906 NRCB2 F150W2 F162M 5.440 1.000 --- 5.440 107 1.000 4.804 117 0.883 NRCB3 F140M CLEAR 5.300 1.000 --- 5.300 103 1.000 5.324 113 1.005 NRCB3 F182M CLEAR 3.731 1.000 --- 3.731 103 1.000 3.627 113 0.972 NRCB3 F210M CLEAR 4.608 1.000 --- 4.608 103 1.000 4.562 113 0.990 NRCB3 F150W2 F162M 5.029 1.000 --- 5.029 103 1.000 4.891 113 0.973 NRCB4 F140M CLEAR 5.390 1.000 --- 5.390 099 1.000 4.557 109 0.846 NRCB4 F182M CLEAR 3.794 1.000 --- 3.794 099 1.000 3.268 109 0.861 NRCB4 F210M CLEAR 4.686 1.000 --- 4.686 099 1.000 4.137 109 0.883 NRCB4 F150W2 F162M 5.115 1.000 --- 5.115 099 1.000 4.269 109 0.835 NRCBLONG F250M CLEAR 1.632 1.000 --- 1.632 104 1.000 1.646 114 1.008 NRCALONG F250M CLEAR 1.673 1.000 --- 1.673 101 1.000 1.679 111 1.004 NRCBLONG F335M CLEAR 0.851 1.000 --- 0.851 104 1.000 0.851 114 1.000 NRCALONG F335M CLEAR 0.873 1.000 --- 0.873 101 1.000 0.989 111 1.133 NRCBLONG F360M CLEAR 0.870 1.000 --- 0.870 104 1.000 0.878 114 1.009 NRCALONG F360M CLEAR 0.892 1.000 --- 0.892 101 1.000 0.929 111 1.041 </code></pre> <p> </p> <p> </p>
Calibration Data for Choice Prediction Competition 2018 (CPC18)
<p>These are the complete raw data for the Calibration Set of a choice prediction competition for decisions under risk, under ambiguity, and from experience (titled CPC18), organized by the authors and their colleagues. The data includes 510,750 consequential choices of human decision makers choosing between two risky and/or uncertain prospects with up to 10 possible outcomes each.</p> <p>Part of these data (published herein at: https://zenodo.org/record/321652#.WZlniSgjE2z) were previously used in CPC15 (a previous choice prediction competition), and are described in the paper: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. <em>Psychological Review,</em> 124(4), 369-409.<em> </em>DOI: 10.1037/rev0000062 </p> <p>More details about the new competition (CPC18) can be found at https://cpc18.wordpress.com/</p> <p>Keywords: Choice between gambles; Decision making; Prospect theory; Decisions from experience; Description-experience gap Decisions under uncertainty</p>
Dataset for "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency"
<p>The dataset contains the data used in the article "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency".</p> <p>A low-cost monitoring system composed of 6 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115C0-003), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from November 2022 to June 2023. It contains the median of the PM2.5, relative humidity, temperature, and atmospheric pressure measurements of the low-cost sensors, after being aggregated to either one minute or one hour.</p> <p>The official measurements of the beta attenuation device are also provided.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Supplementary data accompanying Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibration' published in Geochimica et Cosmochimica Acta
<p>This data accompanies Hess et al. (2025) 'The I/Ca paleo-oxygenation proxy in planktonic foraminifera: A multispecies core-top calibrations' published in Geochimica et Cosmochimica Acta.</p> <p>Data columns and explanation:</p> <table> <tbody> <tr> <td>reference</td> <td>reference for the I/Ca and Mg/Ca data</td> </tr> <tr> <td>site</td> <td>site name</td> </tr> <tr> <td>sample_depth_cm</td> <td>sample depth (cm below sediment surface)</td> </tr> <tr> <td>basin</td> <td>ocean basin</td> </tr> <tr> <td>site_depth_km</td> <td>site water depth (km)</td> </tr> <tr> <td>species</td> <td>foraminifera species</td> </tr> <tr> <td>calcification_depth</td> <td>foraminifera calcification depth</td> </tr> <tr> <td>size_fraction</td> <td>foraminifera size fraction</td> </tr> <tr> <td>cleaning_oxidative_reductive</td> <td>cleaning applied to sample before trace element analysis (O = oxidative, O+R = oxidative and reductive)</td> </tr> <tr> <td>local_O2_variability</td> <td>indication of whether the site experiences local O2 variability, rows with "yes" are excluded from Figure 4</td> </tr> <tr> <td>MgCa</td> <td>Mg/Ca (mmol/mol)</td> </tr> <tr> <td>MgCa_corr</td> <td>Mg/Ca corrected for effect of reductive cleaning, as necessary (mmol/mol)</td> </tr> <tr> <td>T_anand</td> <td>calcification temperature calculated from Mg/Ca using Anand et al. (2003) multispecies equation</td> </tr> <tr> <td>T_Hollstein_multispec</td> <td>calcification temperature calculated from Mg/Ca using Hollstein et al. (2017) multispecies equation</td> </tr> <tr> <td>T_Hollstein_spec</td> <td>calcification temperature calculated from Mg/Ca using species-specific Hollstein et al. (2017) equations</td> </tr> <tr> <td>T_Cleroux</td> <td>calcification temperature calculated from Mg/Ca using species-specific Cléroux et al. (2008) equations</td> </tr> <tr> <td>depth_anand</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_anand</td> </tr> <tr> <td>depth_Hollstein_multispec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_multispec</td> </tr> <tr> <td>depth_Hollstein_spec</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Hollstein_spec</td> </tr> <tr> <td>depth_Cleroux</td> <td>calcification depth from water column temperature data (Moffett et al., 2020) and T_Cleroux</td> </tr> <tr> <td>ICa</td> <td>I/Ca (µmol/mol)</td> </tr> <tr> <td>ICa_corr</td> <td>I/Ca corrected for effect of reductive cleaning, as necessary (µmol/mol)</td> </tr> <tr> <td>O2av_0-500m</td> <td>average oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-500m</td> <td>minimum oxygen concentration in the top 500 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_alldepths</td> <td>minimum oxygen concentration at any depth in the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2av_0-100m</td> <td>average oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> <tr> <td>O2min_0-100m</td> <td>minimum oxygen concentration in the top 100 m of the water column at this site, calculated from Moffett et al. (2020) CTD data</td> </tr> </tbody> </table>
Fig. 12 Calibration between U in A Unitary Association-based conodont biozonation of the Smithian-Spathian boundary (Early Triassic) and associated biotic crisis from South China
Fig. 12 Calibration between U-Pb ages and fossiliferous beds, UAZs, δ13Crecord, climatic proxies and conodont diversity. A U–Pb ages (after carb Widmann et al., 2020). B Geological timescales with Early Triassic substages C ammonoid biochronozones modified from Brayard and Bucher (2008), D Conodont beds from South China (this work), E Newly established conodont UAZs from South China. See Figs. 3, 4, 5, 6, 7, 8, 9. F Evolution of the composite δ13Crecord during the Smithian and early Spathian from Qiakong, Laren, Shanggang, Lilong (see also Figs. 3, 4, 5, 6). G Temporal carb evolution of species- genera- and subfamily-diversity (after Fig. 10), H δ18Cfrom conodont apatite from Pakistan (after Goudemand et al., 2019). phos I: Palynological events (after Hermann et al. 2011)
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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OpenNeuro
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