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134 results for “SIM”
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Sensitivity enhancement using chemically reactive gas cluster ion beams in secondary ion mass spectrometry (SIMS)
<p>We report for the first time on significant molecular secondary ion yield increases by modifying the chemistry of a water cluster primary ion beam. This was demonstrated using 70 keV ion beams of 0.15 eV/amu. For the neutral drug Bezafibrate, secondary ion yield enhancements ×5-10 were observed when replacing the Ar carrier gas in a water gas cluster ion beam (GCIB) source with a mixture containing 12% CO2 and 2% O2 in Ar. For the cationic drug Ranitidine the ion yield enhancements using the CO2-containing carrier gas were up to ×20-50 in positive mode and ×2-4 in negative mode. The extent of molecular fragmentation was very similar from both cluster beams. We conclude that additional chemically reactive species are present in the impact zone using the (H2O/CO2)n projectile which promote the formation of secondary ions of both polarity through projectile impact-induced chemical reactions. This methodology can be applied to further extend the capabilities of high-resolution 3-dimensional mass spectral imaging using reactive GCIB-SIMS.</p>
MeV TOF SIMS determination of deposition order between optically distinguishable and indistinguishable inks
<p>In the forensic investigation of questioned documents, it is often very important to know the deposition order of ink traces from two different writing tools at their intersection on a paper. In the present work, intersections of inks from several writing tools were studied using optical techniques that are standardly applied for questioned documents examination in a forensic laboratory, and an accelerator-based Ion Beam Analysis (IBA) technique called Secondary Ion Mass Spectrometry using MeV ions (MeV SIMS) that is applied in an accelerator facility. MeV SIMS provides molecular information about the studied inks from writing tools, which is an added value and can be also applied for the determination of deposition order but was so far relatively rarely used in forensic studies. Aim of this paper is to compare performance of optical techniques and MeV SIMS for several combinations of intersecting lines. Cases were divided into those in which optical techniques can distinguish used inks and those which are optically completely indistinguishable. In the latter cases, we show that although mass spectra of used inks (from blue ballpoint pens) had extremely small differences, these in combination with advanced and most importantly objective multivariate algorithms could be very beneficial in resolving the deposition order at the intersection of optically indistinguishable inks. In general, MeV SIMS proved to be more efficient for oil-based inks while difficulties were encountered with water-based ones, similar to optical methods.</p>
WP2_SIM-TST_DSB-SSBchirpPerformance_V1
<p>WP2_SIM_DSB-SSBperformance_V1.xslx</p> <p>WP2_TST_VCSEL20GHz-S-parametersBTB_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters10km_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters24km_V1.txt</p> <p>WP2_TST_VCSEL20GHz-S-parameters34km_V1.txt</p> <p>The results of the simulations performed to assess the performance of a directly-modulated source in different chirp conditions are reported in the Excel file. A standard dual sideband system and a single sideband condition are considered.</p> <p>The S-parameters of a short-cavity high-bandwidth VCSEL measured at various lengths (BTB, 10 km, 24 km, 34 km) are reported in the .txt files. The measured S-parameters are useful for calculating the transfer function of the SM fiber due to the interplay between the source chirp and the fiber chromatic dispersion, needed to obtain the chirp parameters <em>alfa</em> and <em>k</em> of the source.</p>
Transkribus - Handwritten Text Recognition for Premodern Documents (SIMS 2020 Lightning Talk)
<p>Transkribus is a platform for text recognition and can be used via the Transkribus Expert Software (available after registration: transkribus.eu). Through Transkribus different tools for document analysis and text recognition can be directly applied. The intro demonstrates very briefly how Transkribus can help with regards to premodern documents especially since a variety of pre-trained models are already available: for Latin (prints and handwriting), for early modern vernaculars in French, Dutch, English, and German. For more information go to transkribus.eu.</p> <p>Presented as a Schoenberg Symposium 2020 Lightning Talk</p>
RAPID input and output files corresponding to "RAPID Applied to the SIM-France Model"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, Cédric H., Florence Habets, David R. Maidment and Zong-Liang Yang (2011), RAPID applied to the SIM-France model, Hydrological Processes, 25(22), 3412-3425. DOI: 10.1002/hyp.8070. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The hydrographic network of SIM-France, as published in Habets, F., A. Boone, J. L. Champeaux, P. Etchevers, L. Franchistéguy, E. Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P. Quintana Seguí, F. Rousset-Regimbeau, and P. Viennot (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research: Atmospheres, 113(D6), DOI: 10.1029/2007JD008548.</li> <li>The observed flows are from Banque HYDRO, Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations. Available at http://www.hydro.eaufrance.fr/index.php.</li> <li>Outputs from a simulation using SIM-France (Habets et al. 2008). The simulation was run by Florence Habets, and produced 3-hourly time steps from 1995-08-01T00:00+02:00 to 2005-07-31T21:02+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.1.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to one study domain:</p> <ul> <li>The river network of SIM-France is made of 24264 river reaches. The temporal range corresponding to this domain is from 1995-08-01T00:00+02:00 to 2005-07-31 T21:00+02:00.</li> </ul> <p> </p> <p><strong>Description of files </strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_France.csv.</em> This CSV file contains the river network connectivity information and is based on the unique IDs of the SIM-France river reaches (the IDs). For each river reach, this file specifies: the ID of the reach, the ID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the IDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of ID. The values were computed based on the SIM-France FICVID file. This file was prepared using a Fortran program.</li> <li><em>m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005/07/31T21:00+02:00. The values were computed using the outputs of SIM-France. This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_1km_hour.csv.</em> This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The values were computed based on the following information: ID, size of the side of the grid cell, Equation (5) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_ttra_length.csv. </em>This CSV file contains a second guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The values were computed based on the following information: ID, size of the side of the grid cell, travel time, and Equation (9) in David et al. (2011).</li> </ul> <ul> <li><em>k_modcou_0.csv.</em> This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> </ul> <ul> <li><em>k_modcou_1.csv.</em> This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_2.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_3.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_4.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_a.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_b.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_c.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_0.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_1.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_2.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_3.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_4.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_a.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_b.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_c.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>rivsurf_France.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the SIM-France domain. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_adour.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Adour River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_allier.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Allier River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_ardeche.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Ardeche River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_dordogne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Dordogne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonne_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonneariege.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne and Ariege River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_herault.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Herault River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loir.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loir River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire_amont_nevers.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_lot.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Lot River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_meuse.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Meuse River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_oise.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Oise River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone_suisse.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_saone.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Saone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine_amont.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_tarn.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Tarn River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_vienne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Vienne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p4_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pa_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pb_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pc_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_p0_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s_pougny.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_1995_1996_full.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_1996_full_nash.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record and for which RAPID simulations led to a positive efficiency value. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_2005_70.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with 70% daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>Qobs_1995_1996_full.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash_93.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the daily values is from 1995-11-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_2005_70.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_2005_70.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobsbarrec_1995_1996_full_nash.csv. </em>This CSV file contains the reciprocal of the averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the computation of the average is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>forcingtot_id_1995_1996_full.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the SIM-France domain. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_garonne_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Garonne River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_loire_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Loire River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_pougny.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream of Lake Geneva. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_seine_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Seine River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>Qfor_1995_1996_full.csv. </em>This CSV file is identical to <em>Qobs_1995_1996_full.csv.</em></li> <li><em>Qfor_1995_1996_full_93.csv. </em>This CSV file is identical to <em>Qobs_1995_1996_full_nash_93.csv.</em></li> <li><em>Qinit_93.csv. </em>This CSV file contains the final state of RAPID after a simulation ending on 1995-11-31T00:00+02:00</li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>A small bug in RAPID v1.1.0 was discovered and fixed on 2011-07-16 that had an impact on the optimization of parameters when using forcing data to replace upstream simulations. This bug led to erroneous results for only two of the basins where upstream forcing was used: Garonne River Basin, downstream; and Rhone River Basin, downstream. The bug had no influence on: Loire River Basin, downstream, and Seine River Basin, downstream; or on any of the other simulations. This should not affect the conclusions of David et al. (2011) since only a few locations were impacted. </p> <p> </p> <p><strong>Funding</strong></p> <p>This work was partially supported by the French Mines Paristech, by the French Agence Nationale de la Recherche under the Vulnérabilité de la nappe du Rhin (VulNaR) project, by the French Programme Interdisciplinaire de Recherche sur l’Environnement de la Seine (PIREN-Seine) project, by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G, by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems, and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - Prod6
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) is the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations enable the optimization of the instrument configuration and the estimation of observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the CTAO Instrument Response Functions based on the Prod6 observatory model (to be published).</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (Prod6 includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php" target="_blank" rel="noopener">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/" target="_blank" rel="noopener">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/" target="_blank" rel="noopener">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run `./build_all prod6 qgs2` (see `–help` for more options). Note that CORSIKA 7.7550 requires the corsika-77550.patch file available in bernlohr-1.68.tar.gz. Runing without it would lead to run-time errors. Placing the patch file in the top level of the directory structure is enough for the `build_all` script to pick it up.</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTAO Simulation Telescope Models provided by the CTAO Central Organisation and Consortium (version prod6 v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: https://zenodo.org/records/14198379/export/bibtex</p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253" target="_blank" rel="noopener">arXiv:0808.2253</a></p>
ASVspoof2019LA-Sim: Augmented Dataset for An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
<p>This is the dataset we augmented to study the channel effects for anti-spoofing. For more details, please refer to our Interspeech 2021 paper: "An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems".</p> <p>Proceeding: <a href="https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html">https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html</a></p> <p>Arxiv: <a href="https://arxiv.org/pdf/2104.01320.pdf">https://arxiv.org/pdf/2104.01320.pdf</a></p> <p>Code: <a href="https://github.com/yzyouzhang/Empirical-Channel-CM">https://github.com/yzyouzhang/Empirical-Channel-CM</a></p> <p>Contact: you.zhang@rochester.edu</p> <p><strong>Version 1.0</strong> contains the <strong>training</strong> and the <strong>development</strong> set. We have added the <strong>evaluation</strong> set in <strong>version 1.1 </strong>but deleted the training set due to the size limitation, but you can still access the training set in version 1.0.</p> <p>Please check it out.</p> <p>To extract the files, please use the following commands:</p> <pre><code class="language-bash">cat eval.tar.gz-part* > eval.tar.gz tar -xvzf *.tar.gz</code></pre> <p>After concatenation, to make sure the download is complete, you can check with the following:</p> <pre><code>md5sum *.tar.gz 15dea7d28b126994bb6b159778f706af dev.tar.gz 0615052b34ca6c7f58505eaa8647844f eval.tar.gz 3058dd9d407f3c9ae697acca8c34a6c3 train.tar.gz</code></pre> <p>Thanks.</p>
Low energy MeV SIMS yield measurements of various inorganic samples
<p>The low energy range (a few 100 keV to a few MeV) primary ion mode in MeV Secondary Ion Mass Spectrometry (MeV SIMS) and its potential in exploiting the capabilities of conventional (keV) SIMS and MeV SIMS simultaneously was investigated. The aim is to see if in this energy range both types of materials, inorganic and organic, can be simultaneously analyzed. A feasibility study was conducted, first by analyzing the dependence of secondary ion yields in Indium Tin Oxide (ITO – In2O5Sn) and leucine (C6H13NO2) on various primary ion energies and charge states of Cu beam, within the scope of equal influence of electronic and nuclear stopping. Expected behavior was observed for both targets (mainly nuclear sputtering for ITO and electronic sputtering for leucine). MeV SIMS images of samples containing separate regions of Cr and leucine were obtained using both keV and MeV primary ions. Based on the image contrast and measured data, the benefit of a low energy beam is demonstrated by Cr+ intensity leveling with leucine [M+H]+ intensity, as opposed to a significant contrast at higher energy. It is estimated that by lowering the energy, leucine [M+H]+ yield efficiency lowers roughly 20 times as a price for gaining about 10 times larger efficiency of Cr+ yield, while leucine [M+H]+ yield still remains sufficiently pronounced.</p>
MeV SIMS analysis of irradiation effects on molecular signatures
<p>Characterizing the effect of MeV ion beam irradiation on biological tissues is important for proton beam therapy, which is routinely used as a form of cancer treatment. It is also important for optimizing protocols for multimodal elemental and molecular imaging. Elemental mapping of trace elements in tissues has been carried out for a long time using nuclear microprobe analysis. However, the effect of MeV ion beams on biological samples is largely unexplored. These effects have been explored in Surrey using two mass spectrometry imaging (MSI) techniques – matrix-assisted laser desorption electrospray (MALDI) and desorption electrospray ionization (DESI). The combination of these techniques with ion beam analysis (IBA) presents a few challenges, namely substrate compatibility and de-localization of elemental markers during measurements. As such, MeV-secondary ion mass spectrometry (SIMS) is being explored as an alternative technique for molecular imaging of biological tissues. MeV SIMS, unlike conventional keV SIMS, allows the detection of intact molecules, making it a prime candidate for the molecular analysis of biological samples. This presents an opportunity to benchmark the capabilities of MeV SIMS against established and widely used techniques such as DESI and MALDI. Experiments carried out at Surrey (reported at the ICNMTA 2020) observed that proton beam-induced damage could be mitigated through the application of a MALDI matrix (employed in MALDI as an ionization aid and sample protection). Thus, the role of this matrix is explored in MeV SIMS experiments.</p>
Development of MeV TOF-SIMS capillary microprobe at the Ruđer Bošković Institute in Zagreb
<p>New Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) setup using MeV heavy ions for the excitation is developed at the Ruđer Bošković Institute accelerator facility. To focus heavy MeV ions to micron dimensions, conical glass capillary is used instead of quadrupole magnetic lenses. The setup uses a continuous primary beam where START signal for TOF is obtained from the PIN diode placed behind the thin transmission sample. Results showing measured energy spectra for several primary heavy ions are presented and compared with theoretical simulations. . The first mass spectra obtained with the new setup using reflectron-type TOF analyzer are given together with the mass and spatial resolution values of the new setup. </p>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - prod3b
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) will be the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations allow us to optimise the instrument configuration and to estimate the observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the <a href="https://doi.org/10.5281/zenodo.5163272">CTAO Instrument Response Functions - prod3b</a>.</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (prod3b includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run ‘./build_all prod3-la-palma qgs2' (see ‘--help’ for more options).</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTA Simulation Telescope Models provided by the CTA Observatory and Consortium (version prod3b v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: <a href="https://zenodo.org/record/6219128/export/hx">https://zenodo.org/record/6219128/export/hx</a></p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253">arXiv:0808.2253</a></p> <p>[2] A. Acharyya, for the CTA Consortium (2019), Monte Carlo studies for the optimisation of the Cherenkov Telescope Array layout, <a href="https://arxiv.org/abs/1904.01426">arXiv:1904.01426</a></p>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - prod5
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) will be the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations allow us to optimise the instrument configuration and to estimate the observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the <a href="https://doi.org/10.5281/zenodo.5499839">CTAO Instrument Response Functions - prod5</a>.</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (prod5 includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run ‘./build_all prod5 qgs2’ (see ‘--help’ for more options).</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTA Simulation Telescope Models provided by the CTA Observatory and Consortium (version prod5 v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: <a href="https://zenodo.org/record/6218687/export/hx">https://zenodo.org/record/6218687/export/hx</a></p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253">arXiv:0808.2253</a></p> <p>[2] O.Gueta for the CTA Consortium and the CTA Observatory, The Cherenkov Telescope Array: layout, design and performance, Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), Berlin, Germany; <a href="https://arxiv.org/abs/2108.04512">arXiv:2108.04512</a></p>
CLDF dataset derived from Sims' "Diachrony of Tone in Proto-Rma" from 2020
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sims, Nathanial A. (2020): Reconsidering the diachrony of tone in Rma. Journal of the Southeast Asian Linguistics Society 13.1. 53-85.</p> </blockquote>
Herbarium specimen image of Passiflora edulis Sims, part of the collection of Royal Botanic Gardens, Kew
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.<br>- Two PNG files containing segmented image overlays of the scanned herbarium sheet. The _all extension indicates that all labels, color charts and pieces of text have received a different color against a black background color. The _sel extension indicates that these elements are white if they're barcode labels, yellow if they're color charts and red if they're anything else.
WP4_SIM_linewidth&bandwidth_V1
<p>WP4_SIM_LO-linewidth&frequencyDeviation_V1.docx</p> <p>WP4_SIM_photodiodeBandwidth_V1.xlsx</p> <p>Simulations results evaluating the performance of a coherent detector as a function of local oscillator (LO) frequency drifts and non-negligible LO linewidths, with different PD electrical bandwidths.</p> <p>The received signal is obtained by direct modulation with DMT of a SC-long wavelength VCSEL.</p>
Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0
<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0. </p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>
Dual color DMD-SIM by temperature-controlled laser wavelength matching [raw datasets]
<p>Raw data set accompanying the publication "Dual color DMD-SIM by temperature-controlled laser wavelength matching".</p>
SatQKD_sim_data
<p>Raw output data from simulations for practical performance limits in SatQKD. The source code used to generate datasets is archived at https://github.com/cnqo-qcomms/SatQuMA/. Dataset categorised by data for each figure, as they appear in <em>Finite key performance of satellite quantum key distribution under practical constraints</em>, Jasminder S. Sidhu, Thomas Brougham, Duncan McArthur, Roberto G. Pousa, Daniel K. L. Oi (2023):</p> <p>arXiv version: <a href="https://arxiv.org/abs/2301.13209">https://arxiv.org/abs/2301.13209</a></p> <p>Accepted in Nature Communications Physics (Jun 2023).</p>
SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark
<p>In this repository, we present the PacBio and ONT simulated datasets used for benchmarking transcriptome reconstruction tools, as evaluated in the manuscript titled "<i>SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark</i>". The dataset includes simulated long reads, short reads, CAGE peaks, and a reduced reference annotation. Additionally, we have included reconstructed transcriptomes from each method, along with SQANTI3 output files. The SQANTI-SIM software can be accessed on GitHub at the following URL: <a href="https://github.com/ConesaLab/SQANTI-SIM">https://github.com/ConesaLab/SQANTI-SIM</a>.</p>
ScienceDex guides
Understand access before you commit
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.