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zenodo44/100

Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"

<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes:&nbsp;ALPHA-3<br> Unit:&nbsp;Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro&nbsp;(aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND&nbsp;(Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND&nbsp;(Open-Cycle&nbsp;Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind&nbsp;(aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit&nbsp;(offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV&nbsp;(Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump&nbsp;(aggregated for different combinations of building, e.g. residential and non-residential,&nbsp;and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump&nbsp;(aggregated for different combinations of building, e.g. residential and non-residential,&nbsp;and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro&nbsp;(aggregated for different equivalent hydropower systems)</li> </ul>

opencc-by-4.0Nov 2020View details →
zenodo44/100

Planetesimal gradual accretion and thermal dynamo results

<p>Numerical modelling code&nbsp;used to produce data&nbsp;and&nbsp;figures in the publication Dodds et al., 2020 &#39;The thermal evolution of planetesimals during accretion and differentiation: consequences for dynamo generation by thermally-driven convection.&#39;&nbsp;</p> <p>Data used to produced figures in above publication also included here.</p> <p>Please contact Kathryn Dodds with any questions.</p>

opencc-by-4.0Sep 2020View details →
zenodo44/100

Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study

<p>Collection of data and scripts related to the paper:</p> <p>Jonas&nbsp;Gossen et al. &quot;A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics&quot;&nbsp;</p> <p>https://www.biorxiv.org/content/10.1101/2020.12.14.422634v2&nbsp; &nbsp;doi:&nbsp;https://doi.org/10.1101/2020.12.14.422634</p> <p>ACS Pharmacology and Translational Science&nbsp; 2021 DOI:&nbsp;10.1021/acsptsci.0c00215</p> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong></p> <p>include simulation of the binding pocket druggability, physical-chemical properties, &nbsp;and the binding site composition</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Protease_clean.ipynb">Protease_clean.ipynb</a>&nbsp; - Jupyter Notebook containing&nbsp; analysis of the generated data</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/allTables.zip">allTables.zip</a>&nbsp; - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Every10-ligand_6LU7_R3.5.zip">Every10-ligand_6LU7_R3.5.zip</a>&nbsp;-&nbsp;results of TRAPP pocket analysis on the MD frames</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/PDB-Giulia.zip">PDB-Giulia.zip</a>&nbsp;- TRAPP pocket analysis of 40 PDB complexes of main protease</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/TRAPP_properties_PDB.xlsx">TRAPP_properties_PDB.xlsx</a>&nbsp;- binding pocket properties for&nbsp;40 PDB complexes of main protease summarized in a table</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/DrugPDB_3structures.xlsx">DrugPDB_3structures.xlsx</a>&nbsp;-&nbsp;binding pocket properties for 3 PDB structures&nbsp;</p> <p><strong>2. Docking &amp; Screening Results</strong></p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/TRAPP_secondSelection_VS.csv">TRAPP_secondSelection_VS.csv</a>&nbsp;- docking/screening of selected structures from TRAPP analysis</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Fred_VS.csv">Fred_VS.csv</a>&nbsp;- docking of PDB structures using Fred</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Glide_VS.csv">Glide_VS.csv</a>&nbsp;- docking of PDB structures using Glide</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS1.xlsx">TableS1.xlsx</a> -&nbsp;&nbsp;Available structures of SARS-CoV-2 Mpro selected for binding site analyses.&nbsp;</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2A.xlsx">TableS2A.xlsx</a>&nbsp;-&nbsp;SiteScore&nbsp;analysis of all the deposited X-ray crystal structures for the Mpro.</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2B.xlsx">TableS2B.xlsx</a>&nbsp;-&nbsp;&nbsp;SiteScore&nbsp;analysis of the MSM ensemble (4-macrostates).</p>

opencc-by-4.0Nov 2020View details →
zenodo44/100

UniDAM results with Gaia eDR3 parallaxes

<p>Results of UniDAM run with Gaia eDR3 parallaxes included.</p> <p>+------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | &nbsp; &nbsp; &nbsp; &nbsp; Survey &nbsp; &nbsp; &nbsp; &nbsp; | Input catalogue size | Stars with estimates &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Reference &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp;done using &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp;Gaia eDR3 parallaxes | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | APOGEE (DR16) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 473307 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;326884 | Ahumada et al. (2020) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Bensby &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;714 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 547 | Bensby et al. (2014) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Gaia-ESO (DR3) &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;25533 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 20127 | Gilmore et al. (2012) G.Gilmore&amp; S.Randich (2016) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | GALAH (DR3) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 564620 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;505403 | Buder et al. (2020) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | GCS &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;13565 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;7633 | Casagrande et al. (2011) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;|<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | LAMOST (DR6) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;5581266 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 4377103 | Luo et al. (2015) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | LAMOST MRS (DR6) &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 328187 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;223407 | Luo et al. (2015) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | RAVE (DR6) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 491349 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;347211 | Steinmetz et al. (2020) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | SEGUE &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 235595 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;180012 | Yanny et al. (2009) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Total (unique sources) | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;5856273 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 4616931 | &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+</p>

opencc-by-4.0Dec 2020View details →
zenodo44/100

Mobility analytic results

<p>The dataset contains information about the trips made by the participants with the MyCorridor app within the context of the second iteration phase in the MyCorridor project. The collected information is related to certain trip characteristics as trip length, trip distance, number of transfers and distribution of service clusters. Moreover, the data shows the number of users and trips.</p>

opencc-by-4.0Jan 2021View details →
zenodo44/100

HGSVC2 full eQTL results

<p>Full summary statistics of the QTL mappings performed on the&nbsp;GEUVADIS and deep 1000GP RNA-sequencing samples presented in the: &quot;Structural variation characterization from the de novo assembly of 64 haplotype-resolved human genomes of diverse ancestry&quot; paper.<br> <br> **Updated release now including sQTL and updated eQTL results</p>

opencc-by-4.0Nov 2020View details →
zenodo44/100

Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects

<p>This dataset and its associated report contain the results of an online survey on using a&nbsp;model-based requirements engineering approach in three European projects.&nbsp;</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

DMS multiphase chemistry mechanism and model results

<p><strong>Open Access DMS multiphase chemistry mechanism</strong></p> <p>This repository contains the complete DMS multiphase chemistry mechanism developed and applied in Wollesen de Jonge et al. (2021). The DMS multiphase mechanism is located in the folder named <strong>DMS_chemistry</strong>. The executable mechanism consist of a number of Fortran f90 files, which were generated with the kinetic pre-processor (KPP) (Damian et al., 2002) using the KPP input file <em>DMSchem.def</em> and <em>DMSchem.kpp</em> file. Both the executable Fortran code and the KPP input files are stored in the subfolder <strong>DMS_multiphase_chem</strong>. The <em>DMSchem.def</em> file list all reactions and reaction rates in the DMS multiphase chemistry mechanism similar to the supplementary Tables S1 in Wollesen de Jonge et al. (2021). &nbsp;The executable DMS multiphase chemistry mechanism consist of the following Fortran f90 files:</p> <p><em>DMSchem_Main.f90</em></p> <p><em>DMSchem_Function.f90</em></p> <p><em>DMSchem_Initialize.f90</em></p> <p><em>DMSchem_Integrator.f90</em></p> <p><em>DMSchem_Jacobian.f90</em></p> <p><em>DMSchem_JacobianSP.f90</em></p> <p><em>DMSchem_LinearAlgebra.f90</em></p> <p><em>DMSchem_mex_Fun.f90</em></p> <p><em>DMSchem_mex_Jac_SP.f90</em></p> <p><em>DMSchem_Model.f90</em></p> <p><em>DMSchem_Monitor.f90</em></p> <p><em>DMSchem_Parameters.f90</em></p> <p><em>DMSchem_Precision.f90</em></p> <p><em>DMSchem_Rates.f90</em></p> <p><em>DMSchem_Util.f90</em></p> <p><em>DMSchem_Global.f90</em></p> <p>These f90-files can be linked and compiled with gfortran using the provided <em>Makefile</em>. &nbsp;</p> <p>The subfolder <strong>photolysis</strong> contain vectors with absorption cross sections (cs), quantum yields (qy) and the spectral actinic flux of the UV-lamps in the AURA chamber.</p> <p>A simplified model setup is provided to illustrate how the DMS-multiphase chemistry routines can be run. The DMS multiphase chemistry mechanism is called and run from a program named <em>main.f90</em>.</p> <p>In the <em>main.f90</em> program the temperature, humidity, pressure and initial concentrations of all gas and aqueous phase species in the DMS multiphase chemistry are declared.</p> <p>After this the main program call the subroutines <em>getKVALUES</em> and <em>getJVALUES</em> from the Fortran module <em>reaction_rates.f90</em>.</p> <ul> <li><em>getKVALUES</em> calculates a number of complex reaction rates (mainly pressure dependent three-body reactions).</li> <li><em>getJVALUES</em> calculates all gas phase photolysis rates in the DMS multiphase chemistry mechanism using the absorption cross sections, quantum yields and the spectral actinic flux files stored in the <strong>photolysis</strong> subfolder</li> </ul> <p>The <em>main.f90</em> program saves the concentration of all species in a file called <em>conc.dat</em>, the time step vector (<em>time.dat</em>) and all species names in <em>SPC_NAMES.dat.</em></p> <p>A short Matlab script called <em>plot_concentrations.m</em> is provided to illustrate how the concentrations of all species listed in <em>SPC_NAMES.dat</em> can be plotted along the saved time vector.</p> <p>The simplified model (only used for demonstration purpose) can be compiled and executed with GFortran using the provided <em>Makefile</em> by typing the following commands in the command line (terminal):</p> <p>make</p> <p>./main.exe</p> <p>&nbsp;</p> <p><strong>Stored model results presented in Wollesen de Jonge et al. (2021)</strong></p> <p>We have saved all model data from each simulated smog chamber experiment and atmospheric relevant base case and sensitivity run presented in Wollesen de Jonge et al. (2021) in the form of &#39;OutputTable&#39; files.</p> <p>The columns in the tables related to the DMS smog chamber experiments are classified as follows:</p> <ul> <li><em>1 time</em> [h] (simulation time starting from -1 h hour and ending at 15 h, time = 0 h is defined as the time when the UV-lights were turned on in the AURA smog chamber).</li> <li><em>2 PN_1.7nm</em> [#/cm^3] (Total particle number concentration of particles 1.7 nm in diameter).</li> <li><em>3 PN_2.5nm</em> [#/cm^3] (Total particle number concentration of particles 2.5 nm in diameter)</li> <li><em>4 PN_10nm</em> [#/cm^3] (Total particle number concentration of particles 10 nm in diameter)</li> <li><em>5 PM_SO4</em> [&mu;g/m^3] (Sulfate particle mass)</li> <li><em>6 PM_CH3SO3</em> [&mu;g/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li><em>7 PM_NH4</em> [&mu;g/m^3] (Ammonium particle mass)</li> <li><em>8 DMS</em> [ppb<sub>v</sub>] (Dimethyl sulfide gas phase concentration)</li> <li><em>9 O3</em> [ppb<sub>v</sub>] (Ozone gas phase concentration)</li> <li><em>10 PV</em> [&mu;m^3/cm^3] (Total particle volume concentration)</li> <li><em>11 PM</em> [&mu;g/m^3] (Total particle mass concentration)</li> <li><em>12 NH3</em> [ppb<sub>v</sub>] (Ammonia gas phase concentration)</li> <li><em>13 MSIA</em> [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li><em>14 SO2</em> [ppb<sub>v</sub>] (Sulfur dioxide gas phase concentration)</li> <li><em>15 DMSO</em> [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li><em>16 HPMTF</em> [#/cm^3] (Hydroperoxymethyl thioformate&nbsp; gas phase concentration)</li> <li><em>17 H2O2</em> [ppb<sub>v</sub>] (Hydrogen peroxide gas phase concentration)</li> <li><em>18 HO2</em> [#/cm^3] (Hydroperoxyl radical gas phase concentration)</li> <li><em>19 OH</em> [#/cm^3] (Hydroxyl radical gas phase concentration)</li> <li>20-219<em> dN/dlogDp</em> [#/m^3] (Particle number size distributions)</li> </ul> <p>&nbsp;</p> <p>The header line, rows 20-219, give the corresponding aerosol particle geometric mean diameters (<em>Dp</em>) in unit m, which were used to represent the modelled particle number size distributions (<em>dN/dlogDp</em>). The gas-phase concentrations given in unit ppb are given at the standard temperature and pressure of 273.15 K and 1E5 Pa.</p> <p>&nbsp;</p> <p>The columns in the tables related to the atmospheric relevant runs are classified as follows:</p> <ul> <li><em>1 time</em> [h] ] (simulation time).</li> <li><em>2 DMS</em> [#/cm^3]&nbsp; (Dimethyl sulfide gas phase concentration)</li> <li><em>3 H2SO4</em> [#/cm^3] (Sulfuric acid gas phase concentration)</li> <li><em>4 MSA</em> [#/cm^3] (Methane sulfonic acid gas phase concentration)</li> <li><em>5 HPMTF</em> [#/cm^3] (Hydroperoxymethyl thioformate&nbsp; gas phase concentration)</li> <li><em>6 MSIA</em> [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li><em>7 DMSO</em> [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li><em>8 UVflux</em> [] (Relative UV light intensity, i.e. <em>UVflux </em>= 1 maximum sunlight, <em>UVflux </em>= 0 no sunlight)</li> <li><em>9 sinkCL</em> [#/cm^3/s] (DMS loss rate by reactions with Cl radicals)</li> <li><em>10 sinkOHabs</em> [#/cm^3/s] (DMS loss rate by reactions with OH via the abstraction pathway)</li> <li><em>11 sinkBrO</em> [#/cm^3/s] (DMS loss rate by reactions with BrO radicals)</li> <li><em>12 sinkOHadd</em> [#/cm^3/s] (DMS loss rate by reactions with OH via the addition pathway)</li> <li><em>13 sinkNO3</em> [#/cm^3/s] (DMS loss rate by reactions with NO<sub>3</sub> radicals)</li> <li><em>14 sinkO3aq</em> [#/cm^3/s] (DMS loss rate by reactions with O<sub>3</sub> in the aqueous phase)</li> <li><em>15 PM_CH3SO3</em> [&mu;g/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li><em>16 PM_SO4</em> [&mu;g/m^3] (Sulfate particle mass)</li> <li><em>17 PM_NH4</em> [&mu;g/m^3] (Ammonium particle mass)</li> <li><em>18 PM_NO3</em> [&mu;g/m^3] (Nitrate particle mass)</li> <li>19-218 <em>dN/dlogDp</em> [#/m^3]. (Particle number size distributions)</li> </ul> <p>&nbsp;</p> <p>In this case, the header line, rows 19-218, give the corresponding geometric mean diameters in unit m.</p> <p>The modelled smog chamber experiments result files were named according to the date when the DMS experiments were performed:</p> <p>Exp. DMS1 - 20180405</p> <p>Exp. DMS2 - 20180519</p> <p>Exp. DMS3 - 20180521</p> <p>Exp. DMS4 - 20180523</p> <p>Exp. DMS5 - 20180526</p> <p>Exp. DMS6 - 20190226</p> <p>Exp. DMS7 - 20190301</p> <p>&nbsp;</p> <p><strong>Details for each output table are given here: </strong></p> <p>OutputTable_AtmMain: Base case atmospheric model run</p> <p>OutputTable_lowWindAtm: Atmospheric model run with 2 m/s wind speed</p> <p>OutputTable_PolAtm: Atmospheric model run with higher O<sub>3</sub> and NO<sub>x</sub> concentrations</p> <p>OutputTable_woAqAtm: Atmospheric model simulation without aqueous phase chemistry reactions</p> <p>OutputTable_woCloudAtm: Atmospheric model simulation without clouds</p> <p>&nbsp;</p> <p>OutputTable20180405_1: Base case smog chamber simulation experiment DMS1</p> <p>OutputTable20180519_1: Base case smog chamber simulation experiment DMS2</p> <p>OutputTable20180521_1: Base case smog chamber simulation experiment DMS3</p> <p>OutputTable20180523_1: Base case smog chamber simulation experiment DMS4</p> <p>OutputTable20180526_1: Base case smog chamber simulation experiment DMS5</p> <p>OutputTable20190226_1: Base case smog chamber simulation experiment DMS6</p> <p>OutputTable20190301_1: Base case smog chamber simulation experiment DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180519_2: MSIA+OH rate analogues to Yin et al, exp. DMS2</p> <p>&nbsp;OutputTable20180519_3: MSIA+OH rate analogues to Lucas &amp; Prinn et al. , exp. DMS2</p> <p>&nbsp;OutputTable20180519_4: MSIA+OH rate set to 0, exp. DMS2</p> <p>&nbsp;OutputTable20180519_5: No gas partitioning to the liquid water film on the chamber walls, exp. DMS2</p> <p>&nbsp;OutputTable20180519_6: MCM gas-phase chem. setup, exp. DMS2</p> <p>&nbsp;OutputTable20180519_9: CH3SOO isomerization set to 0, exp. DMS2</p> <p>&nbsp;OutputTable20180519_10: HPMTF pathway set to 0, exp. DMS2</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20190226_2: HPMTF pathway analogues to Veres et al. , exp. DMS6</p> <p>&nbsp;OutputTable20190226_3: HPMTF pathway analogues to Yin et al. , exp. DMS6</p> <p>&nbsp;OutputTable20190226_4: HPMTF patway set to 0 , exp. DMS6</p> <p>&nbsp;OutputTable20190226_5: No gas partitioning to the liquid water film on the chamber walls , exp. DMS6</p> <p>&nbsp;OutputTable20190226_6: CH3SOO isomerization set to 0 , exp. DMS6</p> <p>&nbsp;OutputTable20190226_7: MSIA+OH rate set to 0 , exp. DMS6</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_12: O3 wall accommodation coefficient = 1E-8, exp. DMS1</p> <p>&nbsp;OutputTable20180519_12: O3 wall accommodation coefficient = 1E-8, exp. DMS2</p> <p>&nbsp;OutputTable20180521_12: O3 wall accommodation coefficient = 1E-8, exp. DMS3</p> <p>&nbsp;OutputTable20180523_12: O3 wall accommodation coefficient = 1E-8, exp. DMS4</p> <p>&nbsp;OutputTable20180526_12: O3 wall accommodation coefficient = 1E-8, exp. DMS5</p> <p>&nbsp;OutputTable20190226_12: O3 wall accommodation coefficient = 1E-8, exp. DMS6</p> <p>&nbsp;OutputTable20190301_12: O3 wall accommodation coefficient = 1E-8, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_13: O3 wall accommodation coefficient = 1E-6, exp. DMS1</p> <p>&nbsp;OutputTable20180519_13: O3 wall accommodation coefficient = 1E-6, exp. DMS2</p> <p>&nbsp;OutputTable20180521_13: O3 wall accommodation coefficient = 1E-6, exp. DMS3</p> <p>&nbsp;OutputTable20180523_13: O3 wall accommodation coefficient = 1E-6, exp. DMS4</p> <p>&nbsp;OutputTable20180526_13: O3 wall accommodation coefficient = 1E-6, exp. DMS5</p> <p>&nbsp;OutputTable20190226_13: O3 wall accommodation coefficient = 1E-6, exp. DMS6</p> <p>&nbsp;OutputTable20190301_13: O3 wall accommodation coefficient = 1E-6, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS1</p> <p>&nbsp;OutputTable20180519_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS2</p> <p>&nbsp;OutputTable20180521_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS3</p> <p>&nbsp;OutputTable20180523_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS4</p> <p>&nbsp;OutputTable20180526_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS5</p> <p>&nbsp;OutputTable20190226_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS6</p> <p>&nbsp;OutputTable20190301_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS1</p> <p>&nbsp;OutputTable20180519_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS2</p> <p>&nbsp;OutputTable20180521_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS3</p> <p>&nbsp;OutputTable20180523_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS4</p> <p>&nbsp;OutputTable20180526_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS5</p> <p>&nbsp;OutputTable20190226_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS6</p> <p>&nbsp;OutputTable20190301_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_16: DMS wall accommodation coefficient = 1E-8, exp. DMS1</p> <p>&nbsp;OutputTable20180519_16: DMS wall accommodation coefficient = 1E-8, exp. DMS2</p> <p>&nbsp;OutputTable20180521_16: DMS wall accommodation coefficient = 1E-8, exp. DMS3</p> <p>&nbsp;OutputTable20180523_16: DMS wall accommodation coefficient = 1E-8, exp. DMS4</p> <p>&nbsp;OutputTable20180526_16: DMS wall accommodation coefficient = 1E-8, exp. DMS5</p> <p>&nbsp;OutputTable20190226_16: DMS wall accommodation coefficient = 1E-8, exp. DMS6</p> <p>&nbsp;OutputTable20190301_16: DMS wall accommodation coefficient = 1E-8, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_17: DMS wall accommodation coefficient = 1E-6, exp. DMS1</p> <p>&nbsp;OutputTable20180519_17: DMS wall accommodation coefficient = 1E-6, exp. DMS2</p> <p>&nbsp;OutputTable20180521_17: DMS wall accommodation coefficient = 1E-6, exp. DMS3</p> <p>&nbsp;OutputTable20180523_17: DMS wall accommodation coefficient = 1E-6, exp. DMS4</p> <p>&nbsp;OutputTable20180526_17: DMS wall accommodation coefficient = 1E-6, exp. DMS5</p> <p>&nbsp;OutputTable20190226_17: DMS wall accommodation coefficient = 1E-6, exp. DMS6</p> <p>&nbsp;OutputTable20190301_17: DMS wall accommodation coefficient = 1E-6, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS1</p> <p>&nbsp;OutputTable20180519_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS2</p> <p>&nbsp;OutputTable20180521_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS3</p> <p>&nbsp;OutputTable20180523_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS4</p> <p>&nbsp;OutputTable20180526_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS5</p> <p>&nbsp;OutputTable20190226_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS6</p> <p>&nbsp;OutputTable20190301_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180405_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS1</p> <p>&nbsp;OutputTable20180519_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS2</p> <p>&nbsp;OutputTable20180521_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS3</p> <p>&nbsp;OutputTable20180523_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS4</p> <p>&nbsp;OutputTable20180526_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS5</p> <p>&nbsp;OutputTable20190226_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS6</p> <p>&nbsp;OutputTable20190301_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180519_20: Liquid water content on walls (LWC wall) = 0.3 mg/m^3, exp. DMS2</p> <p>&nbsp;OutputTable20190226_20: Liquid water content on walls (LWC wall) = 1.5 g/m^3, exp. DMS6</p> <p>&nbsp;OutputTable20190301_20: Liquid water content on walls (LWC wall) = 15 g/m^3, exp. DMS7</p> <p>&nbsp;</p> <p>&nbsp;OutputTable20180519_21: Liquid water content on walls (LWC wall) = 30 mg/m^3, exp. DMS2</p> <p>&nbsp;OutputTable20190226_21: Liquid water content on walls (LWC wall) = 150 g/m^3, exp. DMS6</p> <p>&nbsp;OutputTable20190301_21: Liquid water content on walls (LWC wall) = 1000 g/m^3, exp. DMS7</p> <p>&nbsp;</p> <p><strong>References</strong></p> <p>Wollesen de Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., L&uuml;demann, D., Bilde, M., and Roldin, P.: Secondary aerosol formation from dimethyl sulfide &ndash; improved mechanistic understanding based on smog chamber experiments and modelling, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-2020-1324">https://doi.org/10.5194/acp-2020-1324</a> (2021) &nbsp;&nbsp;</p> <p>Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567&ndash;1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Archaeobotanical results from Ballynacarriga 3, Cork, Ireland

<p>Dataset that resulted from the archaeobotanical analysis of samples from an archaeological excavation of a multi-period site (primarily Late Neolithic) at Ballynacarriga 3, Co. Cork, Ireland. Saved as .csv file.</p>

opencc-by-4.0Mar 2014View details →
zenodo44/100

Archaeobotanical results from Mitchelstown 1, Cork, Ireland

<p>.csv file with results of archaeobotanical analysis from Mitchelstown 1, County Cork, Ireland. This dataset has been subject to minor modifications after peer review. This file supersedes the version published at&nbsp;DOI:10.5281/zenodo.7702 (https://zenodo.org/record/7702#.UuUbs9JFDwc).</p>

opencc-by-4.0Jan 2014View details →
zenodo44/100

Dataset with the results of the e-infrastructures Austria National Survey about Research Data

<p>This is the dataset accompanying the report with the results of our national survey regarding the management of research data</p>

opencc-by-4.0May 2016View details →
zenodo44/100

Listening test results for sound field synthesis localization experiment -- head movement data

<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>

opencc-by-4.0Nov 2016View details →
zenodo44/100

Results from the RDM Survey - LEARN project (December 2016)

<p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the first one published at http://doi.org/10.5281/zenodo.61903. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>

opencc-by-4.0Feb 2017View details →
zenodo44/100

ReliSA/dataset_optimal-set-ilp-2015-07: Published results

<p>The dataset as used for the results reported in the paper &quot;<a href="https://doi.org/10.1007/978-3-662-49192-8_37">Jakub Danek, Premek Brada: Finding Optimal Compatible Set of Software Components Using Integer Linear Programming</a>. SOFSEM 2016: 457-468&quot; (https://link.springer.com/chapter/10.1007%2F978-3-662-49192-8_37).</p>

opencc-by-nc-4.0Apr 2017View details →
zenodo44/100

OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results

<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers.  The survey was coducted between 20 January and 23 February 2017.  It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms.  The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>

opencc-by-4.0Apr 2017View details →
zenodo44/100

Model Results HJ Andrews WS1

Results for Hj Andrews Watershed 1 for four different hydrological models (FLEX, HYMOD, TUW and HYPE). It contains the random parameterizations for the models and accompanying objective function values.

opencc-by-4.0May 2017View details →
zenodo44/100

Final Results from the RDM Survey - LEARN project (June 2017)

<p> </p> <p>Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the previous ones published at http://doi.org/10.5281/zenodo.61903 and http://doi.org/10.5281/zenodo.290635. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>

opencc-by-4.0Jun 2017View details →
zenodo44/100

Montenegro results from the monitoring of pesticide residues in food

<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called &ldquo;maximum residue levels&rdquo; or briefly &ldquo;MRLs&rdquo;, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.&nbsp;The chemical monitoring data collected and published by EFSA include the analytical results provided by&nbsp;EU Member States, Iceland,&nbsp;Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia.&nbsp;</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample &nbsp;with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p>&nbsp;</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2022 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2021 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2020&nbsp;- Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2019 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2018 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>MOPER_2017 - Center for Eco-Toxicological Research - Administration for Food safety, Veterinary and Phytosanitary Affairs,</p> <p>&nbsp;</p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>

opencc-by-4.0Jan 2021View details →
zenodo44/100

Template for HFLAV results in Zenodo

<h2>HFLAV results for Unitarity Triangle March 2024</h2> Cite the results presented as<br> S. Banerjee et al., <i>Averages of b-hadron, c-hadron, and tau-lepton properties as of 2023</i>, <a href="https://arxiv.org/abs/2411.18639">arXiv:2411.18639</a>, with specific result from <a href="https://doi.org/10.5072/zenodo.16917540">doi:10.5072/zenodo.16917540</a>.<br> Alternatively use the bibtex record<br> <code> @article{HeavyFlavorAveragingGroupHFLAV:2024ctg,<br> author = "Banerjee, Swagato and others",<br> collaboration = "Heavy Flavor Averaging Group (HFLAV)",<br> title = "{Averages of $b$-hadron, $c$-hadron, and $\tau$-lepton properties as of 2023}",<br> eprint = "2411.18639",<br> archivePrefix = "arXiv",<br> primaryClass = "hep-ex",<br> month = "11",<br> year = "2024"<br> note = "{with specific result from \href{https://doi.org/10.5072/zenodo.16917540}{{\texttt{doi:10.5072/zenodo.16917540}}}}"<br> }<br> </code>

opencc-by-4.0May 2024View details →
zenodo44/100

Suplementary data, results and scripts: "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer"

<p>This repository contains supplementary data, models and scripts associated with "Reconstruction of Cell-specific Models Capturing the Influence of Metabolism on DNA methylation in Cancer".</p><p>Folders content:</p><p>'data_results_matlabscripts': data, result files and scripts (original python scripts and adapted MATLAB scripts)</p><p>'supplementary_figures': supplementary figures</p><p>'supplementary_tables': supplementary tables</p>

opencc-by-4.0Oct 2023View details →

ScienceDex guides

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

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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