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zenodo32/100

Validation data Set: Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry

Validation dataset for paper published in the Journal of Chromatography A.   Clément Douillet, Mary Moloney, Melissa Di Rocco, Christopher Elliott, Martin Danaher, Development and validation of a quantitative method for 15 antiviral drugs in poultry muscle using liquid chromatography coupled to tandem mass spectrometry, Journal of Chromatography A, Volume 1665, 2022, 462793, ISSN 0021-9673, Abstract: The objective of this work was to develop a quantitative multi-residue method for analysing antiviral drug residues and their metabolites in poultry meat samples. Antiviral drugs are not licensed for the treatment of influenza in food producing animals. However, there have been some reports indicating their illegal use in poultry. In this study, a method was developed for the analysis of 15 antiviral drug residues in poultry muscle (chicken, duck, quail and turkey) using liquid chromatography coupled to tandem mass spectrometry. This included 13 drugs against influenza and associated metabolites, but also two drugs employed for the treatment of herpes (acyclovir and ganciclovir). The method required the development of a novel chromatographic separation using a hydrophilic interaction chromatographic (HILIC) BEH amide column, which was necessary to retain the highly polar compounds. The analytes were detected using a triple quadrupole mass spectrometer operating in positive electrospray ionization mode. A range of different sample preparation protocols suitable for polar compounds were evaluated. The most effective procedure was based on a simple acetonitrile-based protein precipitation step followed by a further dilution in a methanol/water solution. The confirmatory method was validated according to the EU 2021/808 guidelines on different species including chicken, duck, turkey and quail. The validation was performed using various calibration curves ranging from 0.1 µg kg−1to 200 µg kg−1, according to the analyte. Depending on the analyte sensitivity, decision limits achieved ranged from 0.12 µg kg−1 for arbidol to 34.7 µg kg−1 for ribavirin. Overall, the reproducibility precision values ranged from 2.8% to 22.7% and the recoveries from 84% to 127%. The method was applied to 120 commercial poultry samples from the Irish market, which were all found to be residue-free. Keywords: Antiviral drug residues; Influenza; HILIC; LC-MS/MS; Poultry muscle

opencc-by-4.0Feb 2022View details →

zenodo32/100

Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"

Files description Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth. File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx Files have been organized as follows: Lipidomics <blockquote> 1) Control samples: lip_controls.rar 2) Watering low As exposure: lip_water_1.rar 3) Watering high As exposure: lip_water_1000.rar 4) Soil low As exposure: lip_soil_5.rar 5) Soil high As exposure: lip_soil_50.rar 6) QC samples: lip_qcs.rar </blockquote> Metabolomics (positive ionization mode) <blockquote> 1) Control samples: met_pos_controls.rar 2) Watering low As exposure: met_pos_water_1.rar 3) Watering high As exposure: met_pos_water_1000.rar 4) Soil low As exposure: met_pos_soil_5.rar 5) Soil high As exposure: met_pos_soil_50.rar 6) QC samples: met_pos_qcs.rar </blockquote> Metabolomics (negative ionization mode) <blockquote> 1) Control samples: met_neg_controls.rar 2) Watering low As exposure: met_neg_water_1.rar 3) Watering high As exposure: met_neg_water_1000.rar 4) Soil low As exposure: met_neg_soil_5.rar 5) Soil high As exposure: met_neg_soil_50.rar 6) QC samples: met_neg_qcs.rar   </blockquote>   Experimental details <blockquote> Arsenic Exposure Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure. For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies. For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L-1). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L-1 as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L-1, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels. Lipidomic Analysis The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da. The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min-1. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H2O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20. Metabolomic analysis The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from m/z 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV. The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min-1, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B. </blockquote>   Funding: This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP).

opencc-by-4.0Dec 2021View details →

zenodo32/100

Coupled lithospheric deformation in the Qinling Orogen, central China: Insights from seismic reflection and surface-wave tomography

Data of geochronology of intrusive plutons and selected zircon Hf values shown in Figure S1 and the references cited

opencc-by-4.0Mar 2022View details →

zenodo32/100

Data related to "Bilayer WSe2 as a natural platform for interlayer exciton condensates in the strong coupling limit"

Data related to "Bilayer WSe2 as a natural platform for interlayer exciton condensates in the strong coupling limit"

opencc-by-4.0Jan 2022View details →

zenodo32/100

Data Bundle for PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System

While small data files used in PyPSA-Eur-Sec are included directly in the git repository, larger ones are collected in this data bundle. The data bundle’s size is around 680 MB. Licenses Different licenses apply to the various components of this data bundle (mostly attribution). For details see <a href="https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements">https://pypsa-eur-sec.readthedocs.io/en/latest/installation.html#data-requirements</a> Changelog 0.3.1 <ul> <li>Fix IRENASTAT encoding</li> </ul> Changelog 0.3.0 <ul> <li>Add <a href="https://pxweb.irena.org/pxweb/en/IRENASTAT">IRENASTAT</a> country-level power generation capacities.</li> </ul> Changelog 0.2.0 <ul> <li>add hydrogen salt cavern storage potential (h2_salt_caverns_GWh_per_sqkm.geojson)</li> </ul>

openother-atApr 2022View details →

zenodo32/100

Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"

those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU).

opencc-by-4.0May 2022View details →

zenodo32/100

Dataset of "A hierarchy of global ocean models coupled to CESM1"

Data associated with the following publication: Hsu, T. Y., Primeau, F. W., & Magnusdottir, G. (2022). A Hierarchy of Global Ocean Models Coupled to CESM1. Paper Abstract: We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.   Methods Description of methods used for generation of data:  The data is generated with EMOM, a hierarchy of ocean models that are applied in CESM1. The detailed description of the model is in the paper the dataset is presented in (i.e. A Hierarchy of Global Ocean Models Coupled to CESM1). Methods for processing the data: This dataset consists of a set of atmospheric and oceanic fields produced by the NCAR CESM1 climate model. The data is in NETCDF format and has been post-processed and formatted using the NCO command language (see http://nco.sourceforge.net/ for more details). Software-specific information needed to interpret the data: The data is in NetCDF format. Usage Notes This README file was generated on 20200416 by Tien-Yiao Hsu Dataset of the paper A Hierarchy of Global Ocean Models Coupled to CESM1                      Email: tienyiah@uci.edu       OrcID: 0000-0002-8121-1525     Associate Contact Information       Name: Francois Primeau       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: fprimeau@uci.edu     Associate Contact Information       Name: Gudrun Magnusdottir       Institution: University of California, Irvine       Institutions ROR: [UCI = https://ror.org/04gyf1771]       Address:                   Department of Earth System Science         Croul Hall         Irvine, CA 92697-3100         Email: gudrun@uci.edu   3. Date of data organized : 20220201   4. Information about funding sources that supported the collection of the data:     Funder name: Department of Energy     Funder uri: https://www.energy.gov/   5. Contextual description of the data:          The data used to produce the figures in the paper.   -------------------------- SHARING/ACCESS INFORMATION --------------------------      Licenses/restrictions placed on the data:        CREATIVE COMMONS ATTRIBUTION 4.0 INTERNATIONAL CC-BY   --------------------- DATA & FILE OVERVIEW ---------------------   We separate sets of data in terms of folders.    1. AMOC      This directory contains the AMOC streamfunction output from     simulations OGCM_CTL and OGCM_EXP.   2. hierarchy_statistics          This directory contains the statistics (mean, variability, ...)    and diagnosed quantities (ex: EOF, heat transport) of the hierarchy    output.      The output of CTL run of year 21 to 120 is in CTL_21-120.    The output of EXP run of year 81 to 180 is in EXP_81-180.   3. hierarchy_average      This directory is similar to is similar to hierarchy_statistics,     containing CTL and EXP. The difference is that it is the raw, unprocessed    mean data that contains the complete output variables.     -------------------------- METHODOLOGICAL INFORMATION --------------------------     1. Description of methods used for generation of data:       The data is generated with EMOM, a hierarchy of ocean models that is    applied in CESM1. The detail description of the model is in the paper    the dataset is preseted in (i.e. A Hierarchy of Global Ocean Models     Coupled to CESM1).   2. Methods for processing the data:      The output data is mostly the mean and variance of the climate variables.   3. Software-specific information needed to interpret the data:      The data is in NetCDF format.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: AMOC ---------------------------------------------   # Filename: MOC_[CTL|EXP].nc   Variable list:     1. MOC        Unit: Sv        The monthly mean value of streamfunction of the meridional overturning      circulation in ocean basins.     # Filename MOC_[CTL|EXP]_timeseries.nc   Variable list:     1. AMOC_max       Unit: Sv       The annual maximum value of the Atlantic Meridional Overturning Circulation.     2. AMOC_max_lat       Unit: degree north       The latitude of the location where AMOC_max occurs.     3. AMOC_max_z       Unit: m       The depth of the location where AMOC_max occurs.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_average ---------------------------------------------   In this directory, each sub-directory is of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. A sub-directory has three files: atm.nc, ocn.nc and ocn_regrid.nc.    atm.nc is the averaged data of atmosphere model output of year 21-121 of each model run on f09 grid. ocn.nc is the averaged data of ocean model output of year 21-121 of each model run on g16 grid. ocn_regrid.nc is the regrided version ocn.nc from grid g16 onto f09.   Details of the atm.nc variables can be found in CAM4 documentation https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html   Details of the ocn.nc variables can be found in POP2 documentation https://ncar.github.io/POP/doc/build/html/users_guide/model-diagnostics-and-output.html   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: hierarchy_statistics ---------------------------------------------   This directory conatins CTL and EXP runs folder where the statistics time is 21-121 for CTL and 81-180 for EXP.   Each experiment folder contains sub-directories of the form [MODEL_NAME]_[CTL|EXP] where MODEL_NAME can be SOM, MLM, EMOM or POP2. Each of these directories has the same analysis listed below.   # Filename: atm_analysis_[AAO|AO|ENSO|NAO|PDO].nc     Description: This file contains the derived climate variability patterns (i.e.                 AAO, AO, ENSO, NAO, PDO).      Variable list:         1. PCAs(modes, Ny, Nx)            Unit: None            The normalized PCAs. Different modes of the PCAs are separated according          to the first dimension.         2. PCAs_ts(time, modes)            Unit: None            The timeseries of projected PCAs onto the anomalies (i.e. the inner product of PCAs and anomalous fields).   # Filename: atm_analysis_mean_anomaly_[VARNAME].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [ICEFRAC|TAUX|TAUY|SST]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The mean values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.                    2. [VARNAME]_[TIMESCALE]A            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The anomalous values of each grid point. TIMESCALE = [M|S|A] where M stands for monthly,          S for seaonal (MAM, JJA, SON, and DJF), A for annnual.            3. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = N / m^2                TAUY    = N / m^2                SST     = K            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.          4. [VARNAME]_[TIMESCALE]ASTD            Unit: ICEFRAC = None                TAUX    = (N / m^2)^2                TAUY    = (N / m^2)^2                SST     = K^2            The variance of the anomalous values of each grid point. TIMESCALE = [M|S|A]           where M stands for monthly, S for seaonal (MAM, JJA, SON, and DJF), A for annnual.    # Filename: atm_analysis_mean_var_[T|U].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [T|U]     Variable list:         1. [VARNAME]_[TIMESCALE]M            Unit: T       = K                U       = m / s            The mean values of each grid point. TIMESCALE = [M|A] where M stands for monthly, A for annnual.          2. [VARNAME]_[TIMESCALE]ASTD            Unit: T       = K                U       = m / s            The standard deviation of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.          3. [VARNAME]_[TIMESCALE]AVAR            Unit: T       = K                U       = m / s            The variance of the anomalous values of each grid point. TIMESCALE = [M|A]           where M stands for monthly, A for annnual.    # Filename: atm_analysis_SST_CORR.nc     Description: This file contains the year-to-year correlation of monthly anomalous SST.     Variable list:         1. CORR(months, Ny, Nx)            Unit: None            The year-to-year correlation of monthly anomalous SST.   # Filename: ice_analysis_mean_anomaly_[aice|vice].nc     Description: This file contains the mean, standard deviation of the denoted field.                VARNAME = [aice|vice].                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for aice = None                The unit for vice = m   # Filename: ocn_analysis_mean_anomaly_STRAT.nc     Description: This file contains the mean, standard deviation of the denoted field.                STRAT is the difference of mean ocean temperatures T_top - T_bot.                T_top is the mean temperature of the top 50m of the ocean where as                T_bot is the mean temperature of the ocean between depth 50m to 503.7m.                  The variables are exactly of the same structure as described in                 atm_analysis_mean_anomaly_[VARNAME].nc                  The unit for STRAT = K   # Filename: atm_analysis_AHT_OHT.nc     Description: This file contains the indirectly derived atmosphere heat transport.      Variable list:         1. AHT(time, lat_bnd)            Unit: W            The monthly atmospheric heat transport.         2. AHT_AM(year, lat_bnd)                     Unit: W                    The annual atmospheric heat transport.         3. AHT_MEAN(lat_bnd)                     Unit: W                    The time-averaged atmospheric heat transport.         4. AHT_TFLX_CONV(time, lat)                     Unit: W / m                    The monthly-zonally-averaged atmospheric heat convergence.         5. AHT_TFLX_CONV_MEAN(lat)                               Unit: W                    The time-zonally-averaged atmospheric heat convergence.   # Filename: ocn_analysis_OHT.nc     Description: This file contains the derived ocean heat transport.      Variable list:         1. ADVT(time, lat)            Unit: K / s / m^3            The monthly vertically-integrated temperature tendency due to advection and horizontal diffusion.         2. ADVT_MEAN(lat)            Unit: K / s / m^3            The time-averaged ADVT.         3. OHT(time, lat_bnd)            Unit: W            The total monthly ocean heat transport.         4. OHT_MEAN(lat_bnd)            Unit: W            The time-average of OHT.         5. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         6. OHT_ADVT_MEAN(time, lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.         7. OHT_ADVT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to advection and horizontal diffusion.         8. OHT_ADVT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_ADVT.           9. OHT_WKRSTT(time, lat_bnd)            Unit: W            The monthly ocean heat transport due to weak-restoring.         10. OHT_WKRSTT_MEAN(lat_bnd)            Unit: W            The time-averaged of OHT_WKRSTT.         11. SHF(time, lat)            Unit: W            The monthly surface heat flux.         12. SHF_MEAN(lat)            Unit: W            The time-average of SHF.         13. WKRSTT(time, lat)            Unit: K / s / m^2            The vertically integrated monthly weak-restoring.         14. WKRSTT_MEAN(lat)            Unit: W            The time-average of WKRSTT.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/importance_of_KH ---------------------------------------------   This directory conatins the average of CAM4 and EMOM output of field during year 21-30.   The meaning of the variable can be found in official website https://www.cesm.ucar.edu/models/cesm1.0/cam/docs/ug5_1/hist_flds_fv_cam4_trop_bam.html     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_mean_temp ---------------------------------------------   This directory conatins the annual average of ocean mean temperature of the top 33 layers (507.33m) in the EXP run (sea-ice loss run)   # Filename: paper2021_[MODEL_NAME]_EXP.ocn_mean_T.nc     Description: This file contains the annual average of ocean mean temperature of the top 33 layers (507.33m).        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.           2. SALT(time, Nz)            Unit: kg / m^3 (PSU)                    Ocean salinity.     --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/ocean_heat_content_trend ---------------------------------------------   This directory conatins the difference of the ocean state between the year 181 and year 81 of the EXP run.   # Filename: OHC_diff_[MODEL_NAME].nc     Description: The difference of the ocean state between the year 181 and year 81 of the EXP run.        Variable list:         1. TEMP(time, Nz)            Unit: degC                    Ocean temperature.   --------------------------------------------- DATA-SPECIFIC INFORMATION FOR DIRECTORY: supp/vice_target_file ---------------------------------------------   This directory conatins the sea-ice forcing used to derive Q-flux (CTL) and the forcing applied in EXP run.    # Filename: forcing.vice.[GRID].paper2021_[RUN]_POP2.nc     Description: This the sea-ice forcing used in the [RUN] in the grid of [GRID].                GRID = [f09|gx1v6]                [RUN] = [CTL|EXP]        Variable list:         1. vice_target(time, nlat, nlon)            Unit: m^3 / m^2 (volume density)                    Total ice volume.

opencc-by-4.0Jun 2022View details →

zenodo32/100

Effects of convective mergers on the evolution of microphysical and electrical activity in a severe squall line simulated by WRF coupled with explicit electrification scheme

These are important data supporting the conclusion of the paper are available in the main text.The STORM973 dataset including BLNET data and radar data are provided here.Some of the E-WRF outputs are also provided here. The corresponding scripts are based on NCL (version 6.6.2.).

opencc-by-4.0Dec 2021View details →

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Improved boundary conditions for coupled geospace models: an application in modeling spacecraft surface charging environment

These are simulation results from running the RAM-SCB model coupled with the BATS-R-US model via the Space Weather Modeling Framework (SWMF). Two simulations are conducted. One named as "Simulation I" contains simulation outputs from using the traditional outer boundary conditions for the RAM-SCB (i.e., assuming a Kappa distribution with MHD parameters obtained from the BATS-R-US model). The other one named as "Simulation II" contains simulation output from using the new outer boundary conditions for the RAM-SCB (i.e., combining the Denton's empirical electron flux distribution (E<40 keV) with the MHD-parameterized Kappa distribution).  In "simulation_data.zip", three types of simulation results are included:  <ul> <li>"BC_simulation_?.zip": the outer boundary conditions of the electron flux at 6.5 Re at five different times (hour=6, 7, 10, 14, 19)</li> <li>"ram_e_d20130317_flux_simulation_?.zip": the differential electron flux in the equatorial plane within 6.5 Re during the March 17, 2013 event</li> <li>"RBSPB20130317.nc": the differential electron flux, and other parameters (ion flux, magnetic fields) along the RBSP-B trajectory.</li> </ul> In "simulation20180410_alongRBSPa.zip", model results during the storm of 2018/04/10 are saved, including the differential electron flux and other parameters (ion flux, magnetic fields) along the RBSP-A trajectory. Both types of simulations are conducted. In "simulation20180826_alongRBSPa.zip", model results during the storm of 2018/08/25-26 are saved, including the differential electron flux, and other parameters (ion flux, magnetic fields) along the RBSP-A trajectory. Both type of simulations are conducted.

opencc-by-4.0Oct 2021View details →

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High cooperativity coupling to nuclear spins on a circuit QED architecture - Open Access Data SAet

Open data set supporting figures of the related publication.

opencc-by-4.0Dec 2021View details →

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Wave-current Coupling Effects on the Variation Modes of Pore Pressure Response in a Sandy Seabed: Physical Modeling and Explicit Approximations

This is experimental data of combined wave-current induced pore pressure. The corresponding test condition is given in the title of each excel. The channels 4, 2, 1 represent the wave height data measured by the WHGs just above PPTs, in the upstream, and in the downstream, respectively. The channels 6, 8, 3, 7 are pore pressure data monitored by PPTs installed at 0, 6, 9, and 15 cm below the seabed surface, respectively.

opencc-by-4.0Aug 2022View details →

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Closed-loop optimization of general conditions for heteroaryl Suzuki coupling

No description provided.

openother-openSep 2022View details →

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Dataset for "Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model" by Kim et al., 2022 in Geophysical Research Letters

Kiel Climate Model pre-industrial simulation data used in the Geophysical Research Letters publication titled “Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model” by Kim et al., 2022

opencc-by-4.0Sep 2022View details →

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Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)

Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)

opencc-by-4.0Oct 2022View details →

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Second Harmonic Generation from Grating-Coupled Hybrid Plasmon-Phonon Polaritons - Experimental and Simulation Data

Experimental and simulation data for "Second Harmonic Generation from Grating-Coupled Hybrid Plasmon-Phonon Polaritons" by Marcel Kohlmann et al., recently (10/22) accepted for publication in Applied Physics Letters. A preprint is available on the <a href="https://arxiv.org/abs/2209.00375">arXiv</a>.  Content: We provide matlab code to generate the figures from the experimental and simulated data. fig1.m: generates fig1c,d of the paper fig2_4.m generates fig 2 and 4 fig3.m generates fig3 experimental_data.zip contains all experimental data, please unzip before running the scripts comsol_data.zip contains all Comsol simulation output data. Please unzip before running the scripts. The remaining matlab scripts are needed in the process. "passler_epsTarray_generator.m" is also part of the transfer matrix implementation available on <a href="https://doi.org/10.5281/zenodo.7034720">Zenodo</a>. Please contact <a href="mailto:alexander.paarmann@fhi-berlin.mpg.de">Alex Paarmann</a> for any questions.

opencc-by-4.0Dec 2021View details →

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Coupling Silicon Lithography with Metal Casting-Data Set

Dataset corresponding to the article Coupling Silicon Lithography with Metal Casting <a href="https://doi.org/10.1016/j.apmt.2022.101647">https://doi.org/10.1016/j.apmt.2022.101647</a>

opencc-by-4.0Jun 2022View details →

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Golgi cell gap junction coupling in cerebellum cortex model WT and KO conditions

Golgi cell gap junction coupling in cerebellum cortex model WT and KO conditions

opencc-by-4.0Oct 2022View details →

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Gap junction coupling of Golgi cells in cerebellar cortex model

Gap junction coupling of Golgi cells in cerebellar cortex model

opencc-by-4.0Oct 2022View details →

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Copper Hose Coupling EOA:2022.133

Copper hose coupling from the wreck of the Earl Of Abergavenny. ID: EOA:2022.133 Collection: Earl of Abergavenny Classification: Trade/Crew Measurements: Length 82mm Diametre 48mm Date made: as yet unknown Display: not on display Manufacturer/Creator: as yet unknown Credit: Portland Museum Trust This is one of several artefacts that may suggest there was a hand pump onboard the Abergavenny. The biggest pump would undoutedly have been for the bilge, and would have been instrumental in trying to save the ship from foundering, however this may have been for a smaller hand pump. Does anybody have any more information? Could anyone help with a positive identification? For more information about Portland Museum's Diving into the Digital Archives of the Earl of Abergavenny project click [here](https://portlandmuseum.co.uk/earl-of-abergavenny/) Source: Objaverse 1.0 / Sketchfab

opencc-by-nc-sa-2.0Aug 2022View details →

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NMR and HRMS data - Intermolecular Pummerer Coupling with Carbon Nucleophiles in Non-Electrophilic Media

HRMS and NMR characterisation data for all the new compounds reported in the paper Intermolecular Pummerer Coupling with Carbon Nucleophiles in Non-Electrophilic Media Angew. Chem. Int. Ed. 2017, DOI: 10.1002/anie.201709715

opencc-by-4.0Oct 2017View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record