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1,509 results for “Calibrations”
Profile Data for Stylus Device Calibration using Ball Standard
<p>X and Z profile data for the simulation of Stylus device calibration using Ball standard</p> <p>M1000: Number of profile points -20 means: 20 points at the left of the peak</p>
Calibrating a high-speed contact-resonance profilometer (Data)
<p>Raw data, scripts and figures used for the article "Calibrating a high-speed contact-resonance profilometer", published in <em>Journal of Sensors and Sensor Systems</em> on 07 Jul 2020.</p> <p>The data/scripts can be opened/executed by the software "Matlab"</p>
Traces used for calibration of NPB LU with SMPI / SimGrid
<p>These traces were used to calibrate the NAS NPB Benchmark LU with SMPI.</p> <p>The archives contain the traces for 12 cores, all run on 1 single node (that contained 12 cores).</p> <p>The extracted size should be around 7 GB for the MPI one and 600 MB for SMPI.</p> <p> </p> <p>The .org-file contains the analysis used to obtain the required input files for SimGrid. You can load them in org-mode and then execute via "C-c C-v b" the whole buffer. Make sure to extract the archives in /tmp/ or change the paths accordingly.</p>
Text-fig. 3. Progonomys morganae molars from the upper part of the Nagri Formation. a) YGSP 33180, holotype, from Y 450 and b) 54091 from Y 450; both left M1 with buccal side upward. c) 34159 from Y259; and d) 36168 from Y 311, left and right m1, respectively, with lingual side upward. Scale for all is 1 mm. in Early Late Miocene Murine Rodents From The Upper Part Of The Nagri Formation, Siwalik Group, Pakistan, With A New Fossil Calibration Point For The Tribe Apodemurini (Apodemus/Tokudaia)
Text-fig. 3. Progonomys morganae molars from the upper part of the Nagri Formation. a) YGSP 33180, holotype, from Y 450 and b) 54091 from Y 450; both left M1 with buccal side upward. c) 34159 from Y259; and d) 36168 from Y 311, left and right m1, respectively, with lingual side upward. Scale for all is 1 mm.
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
Matlab code to calibrate a structured-PDE model to data from in vitro experiments
<p>Matlab code for the calibration of a PDE model of evolutionary dynamics of a well-mixed population of aggressive breast cancer cells from in vitro data on MCF7-sh-WISP2 cell line and bootstrapping for uncertainty quantification. For more details, see the associated publication: "Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally-informed mathematical modelling", by L. Almeida, J. Denis, N. Ferrand, T. Lorenzi, A. Prunet, M. Sabbah, C. Villa (corresponding author, author of code), 2023. In press in the journal of the Royal Society Interface.</p>
Supplementary Data for: A time-calibrated 'Tree of Life' of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world
<p>This compendium of files includes the dated phylogenetic tree in Newick format (<strong>Data S1</strong>), the list of statistical routines used for the three empirical case studies (<strong>Data S2</strong>), and the high-resolution version of the figures in the supplementary materials and main text (<strong>Data S3</strong>) for the <em>Earth-Science Reviews</em> paper "A time-calibrated ‘Tree of Life’ of aquatic insects for knitting historical patterns of evolution and measuring extant phylogenetic biodiversity across the world", which is under consideration. The best-scoring molecular tree (<strong>Data S1</strong>) can be opened using freely available programs like R (R Development Core Team, 2021), Dendroscope (Huson and Scornavacca, 2012), and FigTree (Rambaut, 2018).</p> <p>Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es OR Jorge.Garcia-Giron@oulu.fi) if you face any trouble downloading, opening, or using these files.</p> <ul> <li>Huson, D. H., & Scornavacca, C. (2012). Dendroscope 3: An interactive tool for rooted phylogenetic trees and networks. <em>Systematic Biology</em>, <em>61(6)</em>, 1061–1067.</li> <li>R Development Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/</li> <li>Rambaut, A. (2018). FigTree. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. http://tree.bio.ed.ac.uk/software/figtree/</li> </ul>
Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model
<p>=========================================================================<br>Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model<br>=========================================================================</p><p>-----------------------<br>INTRODUCTION<br>-----------------------</p><p>This dataset contains the data and scripts required to reproduce the figures and tables presented in the study:<br>"Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model" in <i>The Cryosphere</i>.</p><p>We perform an ensemble of simulations of the Antarctic ice sheet between 1950 and 3014, forced by a panel of CMIP6 climate models, starting from present-day geometry with the Kori-ULB ice-sheet model v0.9. We calibrate our ensemble in a Bayesian framework to produce observationally-calibrated Antarctic projections used to investigate the future trajectory of the Antarctic ice sheet related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the surface mass balance on the other. All simulations are performed at a spatial resolution of 16 km.</p><p>Hindcasts of the behaviour of the AIS over the period 1950-2014 CE are reproduced using changes in oceanic and atmospheric boundary conditions derived from the CMIP5 climate model NorESM1-M. As of the year 2015 CE, climate projections derived from a subset of CMIP6 climate models (MRI-ESM2-0, IPSL-CM6A-LR, CESM2-WACCM and UKESM1-0-LL) are used as forcing until the year 2300 CE. Afterwards, no climate trend is applied. The forcing applied is derived from both the Shared Socioeconomic Pathways (SSP) 5-8.5 and 1-2.6 scenarios. </p><p>------------------------------<br>PROVIDED SCRIPTS: <br>------------------------------</p><p> - 'KoriModelAll.m' and 'KoriInputParams.m': Kori-ULB ice flow model (more info at https://github.com/FrankPat/Kori-ULB)<br> - 'Compute_Bayesian_Weight.m': calculation of the ensemble likelihood weights used in the Bayesian calibration.<br> - 'Plot_parameter_space_distributions.m': calculation and plots of prior and posterior parameter probability distributions.<br> - 'Plot_sea_level_distributions.m': calculation and plots of prior and posterior sea-level distributions.<br> - 'Plot_mass_balance_components_distributions.m': calculation and plots of mass balance components distributions.<br> - 'Plot_mean_thickness_change.m': calculation and plots of calibrated mean thickness change.<br> - 'Plot_ungrounded_probability.m': calculation and plots of the marginal probability of being ungrounded.<br> - 'Plot_SMB_sensitivity.m': Calculation and plots of surface mass balance sensitivity.<br> - 'run_MISMIPplus.m' and 'MISMIPplus.m': run and compare MISMIP+ experiment</p><p>-------------------------<br>PROVIDED DATA: <br>-------------------------</p><ul><li>'LHSensemble.mat': 100x9 matrices containing the values of the 100-member ensemble sampled (using maximin Latin Hypercube) within the parameter space in Table 1.<ul><li>1rst column ((:,1)) contains values of atmospheric present-day climatology (CLIMatm): MARv3.11 (1) - RACMOv2.3p2 (2)</li><li>2nd column ((:,2)) contains values of oceanic present-day climatology (CLIMocn): Jourdain2020 (1) - Schmidtko2014 (2)</li><li>3rd column ((:,3)) contains values of the atmospheric lapse rate (°C/km)</li><li>4th column ((:,4)) contains values of the thickness of the thermally-active layer influencing surface refreezing (m)</li><li>5th column ((:,5)) contains values of the contains values of the Degree day factor for the melting of ice (mm/PDD)</li><li>6th column ((:,6)) contains values of the contains values of the Degree day factor for the melting of snow (mm/PDD)</li><li>7th column ((:,7)) contains values of the applied Sub-shelf melt parameterisation: Quadratic-local Antarctic slope parameterisation (1) - PICO model (2) - Plume model (3) - ISMIP6 Nonlocal quadratic parameterisation (4) - ISMIP6 Nonlocal quadratic parameterisation including dependency on local slope (5)</li><li>8th column ((:,8)) contains values of the effective ice-ocean heat flux: [0.1 x 10^-5 - 10 x 10^-5] m/s for gammaT* in PICO - [1 x 10^-4 - 10 x 10^-4] for Cd^1/2Gamma_TS in Plume - [1 x 10^-4 - 10 x 10^-4] for K in Quadratic-local Antarctic slope parameterisation - [1 x 10^4 - 4 x 10^4] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation - [1 x 10^6 - 4 x 10^6] m/yr for gamma0 in ISMIP6 Nonlocal quadratic parameterisation with slope dependency</li><li>9th column ((:,9)) contains values of the CMIP6 climate model applied for climate forcing: MRI-ESM2-0 (1) - UKESM1-0-LL (2) - CESM2-WACCM (3) - IPSL-CM6A-LR (4)<br><br>'LHval' and 'LHS' contain the absolute values and the values of the parameters scaled linearly between 0 and 1 (0: minimum value, 1:maximum value) of the nine parameters, respectively.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014).<ul><li>H_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with ice thickness field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid.</li><li>MASK_ensemble: 4D matrix of dimension [X, Y, snap_time, ensemble member] with grounded mask field (in meters) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid. <br>It distinguishes grounded ice (1: grounded) from ocean or floating ice (0: ocean/floating).</li><li>mbcomp_ensemble: 3D matrix of dimension [time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr). <br>The components mbcomp include the following ice-sheet aggregated and grounded ice sheet components:<br> (1) Ice-sheet aggregated surface mass balance<br> (2) Ice-sheet aggregated accumulation<br> (3) Ice-sheet aggregated surface melt<br> (4) Ice-sheet aggregated runoff<br> (5) Ice-sheet aggregated rain<br> (6) sub-shelf melt<br> (7) dynamic ice loss (calving)<br> (8) surface mass balance over the grounded ice sheet<br> (9) accumulation over the grounded ice sheet<br> (10) surface melt over the grounded ice sheet<br> (11) runoff over the grounded ice sheet<br> (12) rain over the grounded ice sheet <br> (13) Net mass balance (rate of HAF change)</li><li>SLC_ensemble: 2D matrix of dimension [ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) <br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'CONTROL_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 when considering constant present-day conditions as of the year 2015.<br> </li><li>'SSP126_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA.mat': contains the variables H_ensemble, MASK_ensemble, mbcomp_ensemble and SLC_ensemble (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario. It also contains the variable Runoff_ensemble, a 4D matrix of dimension [X, Y, snap_time, ensemble member] with surface runoff field (in m/yr i.e.) for the 100 ensemble members at different years (snap_time). X and Y represent spatial coordinates on a grid, as used in Fig. 7.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN.mat' contains the following variables describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) integrated over 27 drainage basins (http://imbie.org/imbie-2016/drainage-basins/).<ul><li>SLC_ensemble_basin: 3D matrix of dimension [basin, ensemble member, time] with timeseries (yearly values at years time) of the ice-sheet sea-level contribution (in m) by basin</li><li>mbcomp_ensemble_basin: 4D matrix of dimension [basin, time, mbcomp, ensemble member] with timeseries (yearly values at years time) of various mass balance components for the 100 ensemble members (in gigatons per year, Gt/yr) by basin. The components mbcomp include the same ice-sheet aggregated and grounded ice-sheet components as in 'HIST_ENSEMBLE_DATA.mat'.<br> </li></ul></li><li>'HIST_ENSEMBLE_DATA_BASIN_NO_ELEVATION_DATA.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when neglecting the melt-elevation feedback.<br> </li><li>'HIST_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'HIST_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the historical period (1950-2014) when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP126_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA°BASIN') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP1-2.6 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN.mat': contains the variables SLC_ensemble_basin and mbcomp_ensemble_basin (as in 'HIST_ENSEMBLE_DATA') describing the evolution of the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under a SSP5-8.5 scenario when neglecting the melt-elevation feedback.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_HYDROFRAC.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when including surface melt-driven hydrofracturing of the ice shelves (estimated following Pollard et al., 2015).<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_NO_ELEVATION_FEEDBACK_ATM_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario when neglecting the melt-elevation feedback and considering constant oceanic present-day conditions as of the year 2015.<br> </li><li>'SSP585_ENSEMBLE_DATA_BASIN_OCEAN_ONLY.mat': same as 'SSP585_ENSEMBLE_DATA_BASIN.mat' for the 100-member ensemble of simulations of the Antarctic ice sheet over the period 2015-3014 under an SSP5-8.5 scenario considering constant atmospheric present-day conditions as of the year 2015.<br> </li><li>'GCM_SSPXXX_mean_aTs.mat': Timeseries of the regionally-averaged (between 90–60°S) annual near-surface (2-m) air temperature anomaly (°C) projected by the climate model 'GCM' from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) between 2015 and 2300 under the SSPXXX emission scenario, compared to the 1995-2014 reference period. SSPXXX may be 'SSP126' and 'SSP585' and GCM may be 'MRI-ESM2-0', 'CESM2-WACCM', 'IPSL-CM6A-LR', or 'UKESM1-0-LL'.<br> </li><li>'CALIBRATION DATA.mat': values ('val'), uncertainty ('sigma'), beginning ('year1') and end ('year2') of the average time period of the 12 regionally and temporally aggregated IMBIE data used in the Bayesian calibration (Table 2 in this study, coming from Table 2 from Otosaka et al., 2023)<br> </li><li>'INIT_MAR_aNorESM1-M_1950.mat' and 'INIT_RACMO_aNorESM1-M_1950.mat': Ice-sheet initial states at year 1950 obtained with the 1995-2014 atmospheric climatology from MARv3.11(Kittel eta l.,2021) or RACMOv2.3p2 (van Wessem et al., 2018), respectively, adjusted with a 1945-1955 anomaly from NorESM1-M. H is the ice thickness (in meters), B is the bedrock topography (in meters), and u is the surface velocity (in m/yr). These files were provided as input files to Kori-ULB to produce the projections. More info on the input files and their variables can be found here: https://github.com/FrankPat/Kori-ULB.</li></ul><p>----------------------------------------------------------<br>MATLAB FUNCTIONS USED IN SCRIPTS: <br>----------------------------------------------------------</p><p>- imagescn: imagesc with transparent NaNs, by Chad Greene (2023), downloaded from MATLAB Central File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/61293-imagescn), <br>- brewermap: provides all ColorBrewer colorschemes for MATLAB, by Stephen23. Downloaded from https://github.com/DrosteEffect/BrewerMap.<br>- crameri: returns perceptually-uniform scientific colormaps created by Fabio Crameri (requires CrameriColourMaps8.0.mat)</p><p>----------------------------------------------------------------------------------<br>EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:<br>----------------------------------------------------------------------------------</p><p>- BedMachine data used for the present-day grounding lines in Figures 2 and 7: It is BedMachine v2 (Morlighem et al., 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2.<br>- The delineation of the 27 Zwally Basins used to identify and separate the West and East Antarctic ice sheets and the Antarctic Peninsula can be found at http://imbie.org/imbie-2016/drainage-basins/<br>- Outputs from MAR(CNRM-CM6-1) and MAR(CESM2) used in Figures 7 and S10. The data can be downloaded at 10.5281/zenodo.4529004 and 10.5281/zenodo.4529002, respectively. It was then interpolated to the 16-km grid used by Kori-ULB.<br>- CESM2-WACCM outputs used in Figure 7 were downloaded from the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/) and interpolated to the 16-km grid used by Kori-ULB.<br>- The CMIP6 forcing data used in this study (and plotted in Figures S6 and S7) are accessible through the CMIP6 search interface (https://esgf-node.llnl.gov/search/cmip6/). They have been interpolated to the interpolated to the 16-km grid used by Kori-ULB.</p><p>---------------------<br>REFERENCES: <br>---------------------</p><p>Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021.</p><p>Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8</p><p>Otosaka, I. N., Shepherd, A., Ivins, E. R., Schlegel, N.-J., Amory, C., van den Broeke, M. R., Horwath, M., Joughin, I., King, M. D., Krinner, G., Nowicki, S., Payne, A. J., Rignot, E., Scambos, T., Simon, K. M., Smith, B. E., Sørensen, L. S., Velicogna, I., Whitehouse, P. L., A, G., Agosta, C., Ahlstrøm, A. P., Blazquez, A., Colgan, W., Engdahl, M. E., Fettweis, X., Forsberg, R., Gallée, H., Gardner, A., Gilbert, L., Gourmelen, N., Groh, A., Gunter, B. C., Harig, C., Helm, V., Khan, S. A., Kittel, C., Konrad, H., Langen, P. L., Lecavalier, B. S., Liang, C.-C., Loomis, B. D., McMillan, M., Melini, D., Mernild, S. H., Mottram, R., Mouginot, J., Nilsson, J., Noël, B., Pattle, M. E., Peltier, W. R., Pie, N., Roca, M., Sasgen, I., Save, H. V., Seo, K.-W., Scheuchl, B., Schrama, E. J. O., Schröder, L., Simonsen, S. B., Slater, T., Spada, G., Sutterley, T. C., Vishwakarma, B. D., van Wessem, J. M., Wiese, D., van der Wal, W., and Wouters, B.: Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020, Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, 2023.</p><p>Pollard, D., DeConto, R. M., and Alley, R. B.: Potential Antarctic Ice Sheet retreat driven by hydrofracturing and ice cliff failure, Earth and Planetary Science Letters, 412, 112–121, https://doi.org/10.1016/j.epsl.2014.12.035, 2015.<br> <br>van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018.</p>
Supplementary material for: Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.
<h1>Calibrating coordinate system alignment in a scanning transmission electron microscope using a digital twin.</h1> <h2>Supplementary material</h2> <p>This deposition contains supplementary material for a paper on coordinate system calibration in 4D STEM. A preprint of the paper is available at <a href="https://arxiv.org/abs/2403.08538">https://arxiv.org/abs/2403.08538</a>.</p> <h2>Contents</h2> <div> <div><code>20221025_154811.zip</code>: Overfocused 4D STEM test dataset</div> <div> </div> <div><code>overfocus.sif</code>: Apptainer image with complete software stack. <code>apptainer run --writable overfocus.sif</code> to execute. It starts a Jupyterlab instance with two notebooks, one to genreate test data and the other to perform the interactive adjustment. This documents the software version that was used for the figures in the paper.</div> <div> </div> <div><code>requirements.txt</code>: Python package versions of dependencies in <code>overfocus.sif</code>. </div> <div> </div> <div><code>COM - Jupyter Notebook - Google Chrome 2023-01-25 12-40-07_processed.mp4</code>: Screen capture video with explanation of the first live calibration with an early prototype.</div> <div> </div> <div><code>video description.docx</code>: Explanation of the plots and adjustment process in the screen capture video.</div> <div> </div> <div><code>Microscope-Calibration.tar.gz</code>: Repository archive of the software and examples for calibration in the version used in the paper.</div> <div> </div> <div><code>TemGym.tar.gz</code>: Repository archive of TemGym Basic in the version used in the paper.</div> </div>
On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications: calibration dataset
<p>Studying the geometric distribution of the light field in terms of absolute radiometry required expensive and complex instruments. New types of compact 360-degree cameras have recently appeared on the consumer technology market. Some of these allow users to access raw imagery, offering sensor-level data that can be directly exploited for absolute light quantification. This paves the way for easy-to-use, inexpensive and accessible radiance cameras that can be operated in a wide range of natural environments. </p> <p>This dataset presents raw format images captured with the camera Insta360 ONE for its calibration and characterization. These experiments include geometric calibration, relative illumination evaluation, spectral response determination, absolute spectral radiance calibration, as well as linearity and dark frame analysis. In addition, we are providing data from a calibration validation experiment based on co-located measurements of the sky's downward radiance using a 360-degree calibrated camera and a scientific radiometer: the Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.).</p> <p>This repository contains raw files as taken by the camera's imaging sensor. In most cases, no data processing has been carried out. The entire data set is contained in a .zip file which includes the following sub-folders (in alphabetical order):</p> <ul> <li><strong>absolute-radiance</strong>: data (.dng, .tsv , .hdf5) acquired for <em>absolute spectral radiance</em> calibration</li> <li><strong>darkframe: </strong>DNG raw images taken for <em>dark frame</em> analysis</li> <li><strong>geometric: </strong>DNG images used for the <em>geometric calibration</em></li> <li><strong>immersion-factor: </strong>DNG images for the calculation of the immersion factor</li> <li><strong>linearity:</strong> DNG images for <em>linearity</em> assessment (gain and exposure time)</li> <li><strong>relative-illumination: </strong>DNG images for <em>roll-off (relative illumination) </em>characterization</li> <li><strong>relative-spectral-response: </strong>data (.asc, .dng) taken for relative spectral response characterization</li> <li><strong>verification&validation: </strong>time series data (.tsv, .dng) of the calibration validation experiment </li> </ul> <p>Each folder contains a <strong>README </strong>file explaining the additional subfolders and their files. As you may notice, some of the subfolders are named "lensclose" or "lensfar". They refers to the data acquired with the fish-eye optic assembly that is closer or farther from the top of the 360-degree camera respectively. Some calibrations were performed both in water and in air (geometric calibration, relative-illumination). In that regard, the images were placed in folders refering to "air" and "water". The routines (coded in python) for the data processing can be found in the following <a href="https://github.com/RaphaelLarouche/radiance_camera_insta360/tree/master_v01">Github repository</a> (master_v01) or the <a href="../records/4660994">Zenodo stored version</a>. The useful scripts are located in the <em>calibration</em> directory, and the<strong> README</strong> for each folder points to the revelant code for analysis of the files they contain. For additional information, all the methodologies are described in the <a href="https://arxiv.org/abs/2305.07103">arXiv preprint</a>. </p>
Fig. 4 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 4 - Paleogeographic distribution of the conodont Chiosella timorensis around the Olenekian-Anisian/Early-Middle Triassic boundary (based on Map 48 from Scotese, 2014): Numbers of figured occurrences are those from Fig. 3. A – Tethys occurrences; B – Panthalassa occurrences; C – Arctic occurrences, uncertain; D and E – primary location of allochthonous occurrences in Japanese Islands and Far East Russia, and their tectonic transport pathways. 1 - Chios, Greece; 2 - Perşani Mountains; 3 - Capelluzzo, Southern Apennines; 4 - Sosio Valley, Sicilia; 5 - Kçira, Albania; 6 - Deşli Caira, Romania; 7 - Gebze, Turkey; 8 - Wadi Alwa, Oman; 9 - Salt Range, Pakistan; 10 - Dolpo, Nepal; 11- Spiti, India; 12 - Kashmir, India; 13 - Southeastern Pamirs, Tajikistan; 14 – Tulong and Dibucuo, Tibet; 15 - South China, Guandao, Ganheqiao, Qingyan; 16 -South China, Wantou and Youping; 17 - Kodiang, Malaysia; 18 - Western Thailand; 19 - Kamura and Taho-attol carbonates; 20 - Honshu Island-pelagic chert; 21 - Koryak Upland; 22 - Zyryanka, Kolyma river; 23 - Dalnegorsk, Sikhote-Alin; 24 - Chernaya River, South Primorye; 25 - Mount Lilu, Timor-Leste; 26 - Nifukoko, West Timor; 27 - Western Australia, Carnarvon, Perth & Canning basins; 28 - Northwestern Nevada; 29 - Great Valley, California; 30 - Sheep Creek, Idaho; 31 - Ursula Creek and Subsurface British Columbia; 32 - Quesnellia; 33 - Stikinia; 34 - Brooks Range, Alaska; 35 - Svalbard.
Fig. 3 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 3 - Present-day geographic distribution of the conodont Chiosella timorensis occurrences and basic references. A – Tethys-originating occurrences (1-18, 25-27); B – Panthalassa-originating occurrences (19-24, 28-33; C – Arctic occurrences, uncertain (34-35). Europe: 1 - Chios, Greece (Nicora, 1977; Assereto et al., 1980); 2 - Perşani Mountains, Romania (Mirăuţă & Gheorghian, 1978); 3 - Capelluzzo, Southern Apennines, Italy (Mietto et al., 1991); 4 - Sosio Valley, Sicilia, Italy (Kozur et al.,1995); 5 - Kçira, Albania (Muttoni et al., 1996, 2019); 6 - Deşli Caira, Romania (Grădinaru et al., 2007; Orchard et al., 2007a); Southwest Asia: 7 - Gebze, Turkey (Kiliç, 2021); 8 - Wadi Alwa, Oman (Orchard, 1994a); Himalayas: 9 - Salt Range, Pakistan (Sweet 1970a, 1973); 10 - Dolpo, west Nepal (Kovács & Kozur, 1980); 11 - Spiti, India (Krystyn et al., 2007; Sue et al., 2021); Garzanti et al., 1995); 12 - Kashmir, India (Chhabra, 1981; Matsuda, 1983); Central Asia: 13 - Southeastern Pamirs, Tajikistan (Bragin et al., 2016); Eastern Asia: 14 – Tulong and Dibucuo, Tibet (Tian 1982; Chen A-F et al., 2021; Wu G C. et al., 2007); 15 - South China, Guandao (Orchard et al., 2007b), Ganheqiao and Qingyan (Yao et al., 2011); 16 - South China, Wantou and Youping (Chen Y et al., 2020); Southeast Asia: 17 - Kodiang, Malaysia (Koike, 1973, 1982); 18 - Western Thailand (Kemper et al., 1976); Japanese Islands: 19 - Kamura and Taho attol carbonates, Kyushu Island and Shikoku Island (Hirsch & Ishida, 2002; Zhang L et al., 2019a; Ha et al., 2021); 20 - Honshu Island-pelagic chert (Muto et al., 2018; Muto, 2021); Far East Russia: 21 - Koryak Upland (Bragin, 1991); 22 - Zyryanka, Kolyma river; Klets (1998); 23 – Dalnegorsk, Sikhote-Alin (Buryi, 1989, 1997; Klets, 1995); 24 - Chernaya River, South Primorye (Buryi, 1979); Timor-Leste: 25 - Mount Lilu (Nogami, 1968); West Timor: 26 - Nifukoko (Orchard, 1994a); Western Australia: 27 - Carnarvon, Perth & Canning basins (McTavish, 1973; Nicoll et al., 2007; Gorter et al., 2019); Western United States: 28 - Northwestern Nevada (Collinson & Hasenmueller, 1978; Orchard, 1994a; Paull & Paull, 1998; Goudemand et al., 2012); 29 - Great Valley, California (Wardlaw & Jones, 1980); 30 - Sheep Creek, Idaho (Paull, 1988); Western Canada: 31 - Ursula Creek, British Columbia (Orchard & Tozer, 1997a); Subsurface British Columbia (Golding, 2014, 2021b); 32 - Quesnellia (Orchard & Tozer, 1997a); 33 - Stikinia (Orchard & Tozer, 1997a); Arctic North America: 34 - Brooks Range, Alaska (Wardlaw & Jones, 1980). Arctic Europe: 35 - Svalbard (Nakrem et al., 2008).
Fig. 2 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 2 - Suggested Olenekian-Anisian/Early-Middle Triassic chronostratigraphy and the presumed OAB, marked in blue, are added to the Chinese Wantou section (Chen Y et al., 2020, fig. 3), based on the re-interpretation of conodont events.
Fig. 5 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 5 - Chronostratigraphic calibration and the Spathian-Aegean/Olenekian-Anisian/Early-Middle Triassic boundary in the Deşli Caira section, Romania. Conodont biochronology - (A) after Orchard et al. (2007a), and (B) after Golding (2021). The boundary is constrained by the ammonoid biochronology – (C) after Grădinaru, in Grădinaru & Gaetani (2019). Legend: 1 - thick-bedded limestone; 2 - ammonoid occurrence.
Fig. 4 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 4 - Paleogeographic distribution of the conodont Chiosella timorensis around the Olenekian-Anisian/ Numbers of figured occurrences are those from Fig. 3. A – Tethys occurrences; B – Panthalassa occurrences; C – Arctic occurrences, uncertain; D and E – primary East Russia, and their tectonic transport pathways. 1 - Chios, Greece; 2 - Perşani Mountains; 3 - Capelluzzo, Southern Apennines; 4 - Sosio Valley, Sicilia; 5 - Alwa, Oman; 9 - Salt Range, Pakistan; 10 - Dolpo, Nepal; 11- Spiti, India; 12 - Kashmir, India; 13 - Southeastern China, Guandao, Ganheqiao, Qingyan; 16 -South China, Wantou and Youping; 17 - Kodiang, Malaysia; 18 shu Island-pelagic chert; 21 - Koryak Upland; 22 - Zyryanka, Kolyma river; 23 - Dalnegorsk, Sikhote-Alin 26 - Nifukoko, West Timor; 27 - Western Australia, Carnarvon, Perth & Canning basins; 28 - Northwestern Ursula Creek and Subsurface British Columbia; 32 - Quesnellia; 33 - Stikinia; 34 - Brooks Range, Alaska;
Fig. 1 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale
Fig. 1 - Revised Lower-Middle Triassic chronostratigraphy in the Albanian Kçira-A section, marked ord. 1 – ammonoid record in fig. 5 of Muttoni et al. (2019); 2 – conodont record in fig. 4 of Muttoni et fig. 8 of Muttoni et al. (2019). Line A - The Olenekian-Anisian/Early-Middle Triassic boundary in Muttoni et al. (2019), based on proxy for the nominated boundary; Line B - the herein assumed base of the Aegean Substage (AEG), Procarnites kokeni (Arthaber, 1908), with the FO of Ch. timorensis positioned well below the Line B; indicated by Germani et al. (1997); Line D - Bithynian-Pelsonian boundary, as indicated by Germani
Rapport du calcul des dates avec ChronoModel et test du modèle de calibration directionnelle pour les fours de tuiliers du site archéologique de Barbery (Calvados)
<p>Ce rapport a été rédigé les 20 et 22 juin 2022. Il présente les résultats des calculs des dates de la dernière cuisson des différents fours de tuiliers du site de Barbery (Calvados) obtenues après un traitement avec ChronoModel, en utilisant un modèle de calibration directionnelle.</p> <p>Ces nouveaux intervalles de datation sont ceux présentés dans l’article qui paraitra en 2024 dans la revue Archéologie médiévale : <span>A. Bocquet-Liénard, A. Dubois, M. Leclerc, P. Rohée, L. Tickner et J.-B. Vincent</span> « Les fours de tuiliers médiévaux et modernes de Barbery (Calvados) ».</p> <p>Les données archéomagnétiques des cinq fours (A3, A4, A5, A6 et A10) ont été récoltées au cours des trois campagnes de prélèvements menées au cours des opérations de fouilles. Ces campagnes ont été engagées dans le cadre du projet pluridisciplinaire mis en place depuis 2016 pour l’étude des tuileries médiévales et modernes de Barbery, qui ont produit les matériaux destinés à la couverture d'un grand nombre d'édifices de la plaine de Caen et du pays de Falaise. Ce projet vise à étudier la totalité de la chaîne opératoire depuis l’extraction de la matière première jusqu’à la diffusion des produits finis , ainsi que les structures de cuisson et leur chronologie.</p> <p> </p> <p>Le détail des mesures de déclinaison et d’inclinaison est présenté dans les trois rapports suivants :</p> <p>Fours A3 et A10 : <span>A. Bocquet-Liénard, A. Dubois, J.-B. Vincent, P. Dufresne, G. Hulin, </span><span><em>et al.</em></span><span>. </span><span><em>Les tuileries médiévales et modernes de Barbery (Calvados). Opération 2019</em></span><span>. [Rapport de recherche] Université de Caen Normandie, SRA Normandie. 2020, 230 p.</span><span><span> </span></span></p> <p>Fours A4 et A5 : <span>A. Bocquet-Liénard, A. Dubois, J.-B. Vincent</span><span><em>. Les tuileries médiévales et modernes de Barbery (Calvados). Opération 2017</em></span><span>. [Rapport de recherche] Université de Caen Normandie, SRA Normandie. 2018, 334 p.</span></p> <p>Four A3 et A6 : <span>A. Bocquet-Liénard, A. Dubois, V. Le Quellec, J.-B. Vincent, J. Lautridou, </span><span><em>et al..</em></span><span> </span><span><em>Artisanat et industrie du feu à Barbery, bas-fourneaux de l’âge du Fer et tuileries médiévales et modernes. Opération 2021.</em></span><span> [Rapport de recherche], Université de Caen Normandie, SRA Normandie. 2022, 257 p.</span></p>
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
<p>This repository contains all the data and code associated with the manuscript "Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients"</p> <p>Mass spec and absorption chromatogram data are in reaction_set_1.zip, reaction_set_2.zip, and simulated_reaction_set.zip. The chemprop model trained on the Deep4Chem dataset is in Deep4Chem_chemprop.zip.</p>
The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
<p>GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view. Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.</p>
Calibration curves for Folin-Ciocalteu assay for reliable quantification of total polyphenols.
<p><span>Five different protocols (P1-P5) for determining total polyphenol content in samples were investigated and compared. Detailed information on the protocols are provided in: <br></span></p> <p><span>P1 </span><span>Singleton, V.L.; Rossi, J.A. Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents. <em>Am J Enol Vitic.</em> <strong>1965</strong>, <em>16</em>, 144–158, doi:10.5344/ajev.1965.16.3.144.</span></p> <p><span>P2<span> </span>Singleton, V.L.; Orthofer, R.; Lamuela-Raventós, R.M. [14] Analysis of Total Phenols and Other Oxidation Substrates and Antioxidants by Means of Folin-Ciocalteu Reagent. In <em>Methods in Enzymology</em>; Oxidants and Antioxidants Part A; Academic Press, 1999; Vol. 299, pp. 152–178.</span></p> <p><span>P3<span> </span>Cicco, N.; Lanorte, M.T.; Paraggio, M.; Viggiano, M.; Lattanzio, V. A Reproducible, Rapid and Inexpensive Folin–Ciocalteu Micro-Method in Determining Phenolics of Plant Methanol Extracts. <em>Microchemical Journal</em> <strong>2009</strong>, <em>91</em>, 107–110, doi:10.1016/j.microc.2008.08.011.</span></p> <p><span>P4. Sotenko, M.; Coles, S.; Barker, G.; Song, L.; Jiang, Y.; Longhurst, P.; Romanova, T.; Shuvaeva, O.; Kirwan, K. Phytoremediation-Biorefinery Tandem for Effective Clean-up of Metal Contaminated Soil and Biomass Valorisation. <em>International Journal of Phytoremediation</em> <strong>2017</strong>, <em>19</em>, 965–975, doi:10.1080/15226514.2016.1267705.</span></p> <p><span>P5<span> </span>Tsibranska, I.; Tylkowski, B.; Kochanov, R.; Alipieva, K. Extraction of Biologically Active Compounds from <em>Sideritis</em> Ssp. L. <em>Food and Bioproducts Processing</em> <strong>2011</strong>, <em>89</em>, 273–280, doi:10.1016/j.fbp.2010.10.004.</span></p> <p> </p> <p><span><span>Total polyphenol content was determined spectrophotometrically using different variation of Folin–Ciocalteu’s method by means of UV-VIS UV-1800 Shimadzu spectrophotometer (Tokio, Japan). </span><span>The Folin-Ciocalteu reagent, a</span><span> mixture of phosphomolybdic and phosphotungstic acids reacts with phenolic compounds and is reduced, forming a blue chromophore detectable spectrophotometrically. Seven concentration levels of gallic acid standard were used to construct the calibration curves: L1 - 0.6 g/L, L2 - 0.4 g/L, L3 - 0.2 g/L, L4 - 0.1 g/L, L5 - 0.05 g/L, L6 - 0.025 g/L and L7 - 0.0125 g/L. A 1 g/L gallic acid stock solution was prepared in Milli-Q water. Each concentration level was measured in triplicate using all the investigated polyphenol determination methods. Once the calibration curves for each method were prepared, </span><span>two independent standard solutions were used for methods validation. The first one was gallic acid at a concentration of 0.2 g/L prepared in Milli-Q water. The second standard was a commercial polyphenol extract Vitaflavan at a concentration of 0.2 g/L which contains 80% of total polyphenols. Each standard solution was measured in triplicate using all the methods investigated. </span></span></p>
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.