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47,155 results for “evaluations”
For our world without sound. The opportunistic debitage in the Italian context: a methodological evaluation of the lithic assemblages of Pirro Nord, Cà Belvedere di Montepoggiolo, Ciota Ciara cave and Riparo Tagliente.
<p>Raw data concerning the technological analysis of both the experimentation and archaeological collections.</p>
Dataset for the fine-tuning of parameters and hyper parameters, and the evaluation of the Scorca agent
<p># Scorca Data<br>This repository contains data collected during multiple tests of the Scorca agent.</p> <p>This data was used to generate the various plots seen in the ICAART paper titled ***Knowledge Modelling, Strategy Designing, and Agent Engineering for Reconnaissance Blind Chess***, and to forge decisions for the agent, i.e. with which strategy to go and which hyperparameters to use. </p> <p>It also contains multiple scripts used to generate the plots in the paper.</p> <p>## Project overview</p> <p>The project is organized into several key directories, each containing specific components of the Scorca agent's testing and data analysis:</p> <p>- /experiments: Contains various experimental data and scripts.<br> - /entropy_comp: Data and scripts related to entropy computation experiments.<br> - /piece_states_removal: Information on experiments involving the removal of piece states.<br> - /sense_comp: Contains sub-directories for adapted entropy, likely senses, and opponent move weight analysis.<br>- /misc_src: Miscellaneous source files and scripts.<br> - /efficiency: Scripts and data related to the efficiency analysis of the agent.<br> - /enemy_bot_movement: Data on enemy bot movement patterns and strategies.<br> - /history: Historical data and analysis scripts.<br> - /naive_entropy: Scripts for naive entropy calculations.<br>- /sense_comp: Sense computation related files.<br> - /all_possible_states: Tracking and analysis of all possible board states.<br> - /likely_senses: Data on the most likely senses used in various game scenarios.<br> - /opp_move_weight: Analysis of opponent move weight in different contexts.</p> <p>For the agent source code, please refer to the Scorca GitHub repository: https://github.com/Robinbux/Scorca.</p> <p>- Contact Robin in case of any inquiry (rb.stoehr@gmail.com)<br>- The licence is CC-BY 4.0.</p>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Evaluation datasets and results for the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays"
<p>Event-logs and Business Process Simulation Models used in the experimentation of the paper "Enhancing Business Process Simulation Models with Extraneous Activity Delays", where the '<em>inputs</em>' folder contains all the files used as input, and the '<em>output</em>' folder the results of the evaluation.</p> <p> </p> <p><em><strong>Inputs</strong></em>: event-logs, BPS models, and simulation parameters used as input in the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: real-life event logs, corresponding to two disjoint subsets of traces from an Academic Credentials' process, and the BPIC 2012 and BPIC 2017 event logs (filtered as explained in the paper), and the BPS model (plus simulation parameters) used as input for each dataset in the presented approach.</li> <li><em><strong>Synthetic</strong></em>: simulated event-logs and corresponding BPS models (plus simulation parameters) for four different processes with 0, 1, 3 and 5 timer events.</li> </ul> <p><em><strong>Outputs</strong></em>: results of the experimentation.</p> <ul> <li><em><strong>Real-life</strong></em>: results corresponding to the evaluation with real-life event logs. Each of the folders is composed by the original and the enhanced BPS models, 10 event logs simulated with each of them, two folders with the best iteration of the two hyperparameter optimization processes, and the values for the injected timers in each case. In addition, a CSV file with the EMD metrics (cycle time and absolute hour event distribution) for each dataset is provided.</li> <li><em><strong>Synthetic</strong></em>: results corresponding to the simulated event-logs. <ul> <li>Before-After: BPS models and discovered timer events for the four synthetic processes, with five timers placed before and after different activity instances.</li> <li>Complete: BPS models and quality measures (precision, recall, and SMAPE of the discovered timers) for the four synthetic processes with zero, one, three, and five timer events.</li> <li>Individual: event logs enhanced with the discovered extraneous delay for each activity instance, for the four synthetic processes with zero, one, three, and five timer events; and SMAPE of the estimations.</li> </ul> </li> </ul>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC
<p>This data set is used for the training, validation and evaluation of retrievals of temperature and specific humidity profiles, as well as integrated water vapour from simlulated or measured microwave brightness temperatures (TBs), which are described in <strong>[1]</strong>.</p> <p>The data set consists of yearly files (2001-2018, 6-hourly resolution) that include data from the European Centre for Medium-Range Weather Forecasts's ERA5 reanalysis <strong>[2]</strong> and simulated TBs in the microwave spectrum. TB simulations were performed with PAMTRA <strong>[3,4]</strong> on the native ERA5 model level resolution at frequencies of a low frequency Humidity and Temperature Profiler (HATPRO, 22-58 GHz) and of a Low Humidity Profiler (LHUMPRO-243-340, aka MiRAC-P, 175-340 GHz). Afterwards, the ERA5 model level data has been interpolated to a new height grid (dimension 'z'), of which the lowest 43 indices equal the height grid of the retrieval that is developed with this data set. The upper 11 indices are included for additional TB simulations needed for the information content estimation performed and are not used for the retrievals to avoid the tropopause.</p> <p>The trained retrieval is applied to observations from the HATPRO and MiRAC-P that were installed onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.</p> <p><strong>[3]:</strong> Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geoscientific Model Development, 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020.</p> <p><strong>[4]:</strong> Mech, M., Maahn, M., Ori, D., Kneifel, S., and Orlandi, E.: PAMTRA Package – Passive and Active Microwave TRANsfer, available at: https://github.com/igmk/pamtra (last access: 6 September 2020), 2019c.</p>
Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"
<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> <p>See http://arxiv.org/abs/2411.04543.</p> <p>This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>: </p> <p>In order to use these data files, </p> <p>please follow: (github readme)</p> <h2>Data from <em>Planck</em> release</h2> <h3><em>Planck</em> Release 1, 2013</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2013</strong></p> <p>HFI_CompMap_ThermalDustModel_2048_R1.20.fits</p> <p><a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light</strong></p> <p><strong>Relation to this work: used to filter out regions with strong zodiacal emission</strong></p> <p>HFI_SkyMap_217_2048_R1.10_nominal.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a></p> <p>HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 2, 2015</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of synchrotron emission</strong></p> <p><strong>Relation to this work: used to remove synchrotron emission from full-sky maps</strong></p> <p>COM_CompMap_Synchrotron-commander_0256_R2.00.fits<br><a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of free-free emission</strong></p> <p><strong>Relation to this work: used to remove free-free emission from full-sky maps</strong></p> <p>COM_CompMap_freefree-commander_0256_R2.00.fits<br><a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of carbon monoxide</strong></p> <p><strong>Relation to this work: used to remove carbon monoxide emission from full-sky maps</strong></p> <p>COM_CompMap_CO21-commander_2048_R2.00.fits<br><a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of 94/100 GHz molecular emission lines</strong></p> <p><strong>Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps</strong></p> <p>COM_CompMap_xline-commander_0256_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: Galactic plane masks with no apodization</strong></p> <p><strong>Relation to this work: used to mask Galactic plane</strong></p> <p>HFI_Mask_GalPlane-apo0_2048_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: point source masks</strong></p> <p><strong>Relation to this work: used to mask point sources in full-sky maps and inpaint them </strong></p> <p>HFI_Mask_PointSrc_2048_R2.00.fits<br><a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination)</strong></p> <p>COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a></p> <p><strong>Format: .FITS file (table)</strong></p> <p><strong>Type: <em>Planck</em> catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: to mask compact sources</strong></p> <p>COM_PCCS_030_R2.04.fits<br><a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a></p> <p>COM_PCCS_044_R2.04.fits<br><a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a></p> <p>COM_PCCS_070_R2.04.fits<br><a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a></p> <p>COM_PCCS_100-excluded_R2.01.fits<br><a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a></p> <p>COM_PCCS_100_R2.01.fits<br><a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a></p> <p>COM_PCCS_143-excluded_R2.01.fits<br><a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a></p> <p>COM_PCCS_143_R2.01.fits<br><a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a></p> <p>COM_PCCS_217-excluded_R2.01.fits<br><a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a></p> <p>COM_PCCS_217_R2.01.fits<br><a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a></p> <p>COM_PCCS_353-excluded_R2.01.fits<br><a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a></p> <p>COM_PCCS_353_R2.01.fits<br><a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a></p> <p>COM_PCCS_545-excluded_R2.01.fits<br><a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a></p> <p>COM_PCCS_545_R2.01.fits<br><a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a></p> <p>COM_PCCS_857-excluded_R2.01.fits<br><a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a></p> <p>COM_PCCS_857_R2.01.fits<br><a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 3, 2018</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of CMB anisotropies (SMICA from <em>Planck</em> 2018)</strong></p> <p><strong>Relation to this work: used to remove CMB anisotropies from full-sky maps</strong></p> <p>COM_CMB_IQU-smica_2048_R3.00_full.fits<br><a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: <em>Planck</em> 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: used to obtain dust data maps at these bands</strong></p> <p>HFI_SkyMap_100_2048_R3.01_full.fits<br><a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_143_2048_R3.01_full.fits<br><a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_217_2048_R3.01_full.fits<br><a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_353_2048_R3.01_full.fits<br><a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_545_2048_R3.01_full.fits<br><a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_857_2048_R3.01_full.fits<br><a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a></p> <p>HFI_RIMO_R3.00.fits<br><a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a></p> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Melis O. Irfan et al. 2019</strong></p> <p>beta.fits<br><a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a></p> <p>tau.fits<br><a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a></p> <p>temp.fits<br><a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a></p> <p> </p>
Inter-Chemical Correlation results for the study: HHEARx2017-1598 (Evaluation of Environmental Exposures in TEDDY)
Title: Evaluation of Environmental Exposures in TEDDY <br>Species: Homo sapiens <br>Number of samples: 1025 <br>Number of named analytes: 45 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=37 <br>
Bridge2AI Grand Challenge AI-Readiness Evaluation Data Year 2 of 4
<p>This excel workbook and set of radar plots contains current and projected AI-readiness evaluation datasets of four NIH Bridge2AI Program Grand Challenges in Functional Genomics, Clinical Care Informatics, Precision Public Health, and Return to Health (Salutogenesis). These evaluations were collected in late 2024, at the conclusion of Year 2 of the 4-year Bridge2AI program, by Grand Challenge (GC) representatives on the Bridge2AI Standards Working Group, in consultation with their GC leadership team, and will be updated in subsequent years and the program progresses. They assess biomedical AI readiness of existing collected data only. </p>
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Expert judgements for evaluating deduplication of OpenAIRE Research Graph
<p>Expert judgments used to evaluate the deduplication algorithm used for constructing the OpenAIRE Research Graph.</p> <p>Each expert assigned each group with one of the following predetermined classes (that also indicate whether contained entities are equivalent or not):</p> <ul> <li>AMBIGUOUS: At least one DOIs is invalid (no metadata are available) (N/A)</li> <li>DELETED-DUPLICATES: DOIs once pointing to the same research object, currently deleted. (TRUE)</li> <li>MULTI-PUBLISHED: Article published in more than one locations (full or abstract) (TRUE)</li> <li>VERSIONS: Multiple versions of the same research object (e.g. pre-prints, post-prints etc). (TRUE)</li> <li>ERRONEOUS: Unrelated set of objects. (FALSE)</li> <li>PAPER-EXTENSIONS: Extended version of a conference paper in a journal. (FALSE)</li> <li>PART-OF-A-GROUP: Multiple parts of the same research object (e.g. multi-part publication, photos of the same collection etc). (FALSE)</li> <li>SUPPLEMENTARY: A publication and its supplementary material (including errata). (FALSE)</li> </ul> <p>The following are provided:</p> <ul> <li>OpenAIRE identifier</li> <li>Judgement</li> <li>Count of numbers in group</li> <li>DOIs in group</li> </ul>
Dataset to manuscript: Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems
<p>Raw data to the manuscript entitled "Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems" by Severin-Luca Bellè, Jean Riotte, Norman Backhaus, Muddu Sekhar, Pascal Jouquet and Samuel Abiven. </p> <p>Data files include all raw data of farmer interviews (20211209_Biochar_India_rawdata_Bellè_Abiven_farmer_interviews) and all raw data from the soil incubation study (20211209_Biochar_India_rawdata_Bellè_Abiven_soil_incubation). </p> <p>File ending with var_names is the README file. </p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
GERONTE H2020 project - GERDAT005 - Intrinsic capacity evaluation and intervention protocol
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a protocol for the evaluation of intrinsic capacity and frailty, with subsequent interventions aimed at optimizing health status and support for older patients with multimorbidity and cancer.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of Geronte is to take account of intrinsic capacity. Most older patients who are diagnosed with cancer also suffer from other illnesses and impairments that could affect their prognosis, priorities and ability to tolerate and benefit from treatment. For tailored oncologic decision making, it is essential to obtain a complete overview of the patient’s health status. This dataset contains the protocol for evaluation intrinsic capacity and potential interventions for impairments or vulnerabilities that were identified in this evaluation. It is going to be used in the assessment and management of older patients with multimorbidity and cancer, within the GERONTE care pathway.</p>
Evaluation of PD-L1 expression in vulvar cancer
<p><strong>background & Aims: </strong>Vulvar cancer accounts for about 4% of all female genital malignancies. Squamous cell carcinoma is the most common histological type.PD-L1 is a membranous protein of nucleated cells. By binding to its PD-1 receptor, PD-L1 inactivates T lymphocytes. It plays a key role in the treatment of some cancers. Our aims were to study the expression profile of PD-L1 in vulvar cancer by immunohistochemistry and to correlate its expression with survival rates. <strong>Methods:</strong> A retrospective study was conducted in the Pathology Department of Saleh Azaiez Institute, involving 55 patients followed for vulvar cancer over a period of 13 years from January 2008 to December 2021. Clinicopathologic data were collected from medical records and pathology reports. Immunohistochemical analysis was performed using an automaton (Leica Biosystems™). <strong>Results:</strong> PD-L1 expression in vulvar squamous cell carcinoma was observed in 44% of cases. This expression was noted in 11% of cases at the level of lymphocytes whose dissection was negative in all cases with a maximum tumor size of 40 mm. PDL1 was expressed in tumor cells in 33% of cases where there was a positive inguinal dissection in 22% of cases and a maximum tumor size of 90 mm. <strong>Conclusions:</strong> The immune checkpoint inhibitor PD-1/PD-L1 seems to have an important prognostic effect on the development of some cancers (breast cancer, lymphoma). Indeed, the expression of PD-L1 in tumors is often considered a factor of poor prognosis due to its immunosuppressive activity in tumor tissues. Many studies have attempted to determine the prognostic factors of vulvar cancer in order to optimize therapeutic management. PD-L1 expression in vulvar squamous cell carcinoma has not been studied before, hence the interest of our study which suggested, as in the literature, a prognostic role for this protein.</p>
Programming Problems Submitted for Evaluation of LLMs GPT-3.5 and Gemini Pro 1.0
<p>Problems extracted from platforms LeetCode and BeeCrowd for evaluation of LLMs GPT3.5 and Gemini Pro 1.0.</p> <p>The data from the plataforms has the following columns and values:</p> <p> </p> <table> <tbody> <tr> <td><strong>LeetCode Data</strong></td> <td> </td> <td><strong>BeeCrowd Data</strong></td> <td> </td> </tr> <tr> <td><strong>Column</strong></td> <td><strong>Doc</strong></td> <td><strong>Column</strong></td> <td><strong>Doc</strong></td> </tr> <tr> <td>problem_level</td> <td>easy | medium | hard</td> <td>problem_level</td> <td><1...10></td> </tr> <tr> <td>problem_link</td> <td><LeetCode link for the problem></td> <td>problem_link</td> <td><BeeCrowd link for the problem></td> </tr> <tr> <td>prompt</td> <td><text submitted to LLM></td> <td>prompt</td> <td><text submitted to LLM></td> </tr> <tr> <td>response_code</td> <td><code provided by the LLM></td> <td>response_code</td> <td><code provided by the LLM></td> </tr> <tr> <td>response_evaluation</td> <td>True | False</td> <td>response_evaluation</td> <td>True | False</td> </tr> <tr> <td>execution_time_ms</td> <td><time></td> <td>execution_time_ms</td> <td><time></td> </tr> <tr> <td>memory_usage_mb</td> <td><memory></td> <td>error_generated</td> <td>Wrong Answer | Time Limit Exceeded | Memory Limit Exceeded....</td> </tr> <tr> <td>error_generated</td> <td>Wrong Answer | Time Limit Exceeded | Memory Limit Exceeded....</td> <td>attempts_number</td> <td>1 | 2 | 3</td> </tr> <tr> <td>attempts_number</td> <td>1 | 2 | 3</td> <td>author</td> <td><author's name></td> </tr> <tr> <td>contains_image</td> <td>True | False</td> <td>source</td> <td><origin institution></td> </tr> <tr> <td>related_topic_1</td> <td><topic></td> <td>origin_country</td> <td><origin country></td> </tr> <tr> <td>related_topic_2</td> <td><topic></td> <td>contains_image</td> <td>True | False</td> </tr> <tr> <td>related_topic_3</td> <td><topic></td> <td>related_topic_1</td> <td><topic></td> </tr> <tr> <td>related_topic_4</td> <td><topic></td> <td> </td> <td> </td> </tr> <tr> <td>related_topic_5</td> <td><topic></td> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p>
Sample data for evaluating Scholix relationship SubTypes for linked data publications
<p>Scholix links provide a standardized framework for establishing connections between research publications and their associated datasets or related data publications, thereby fostering improved discoverability, reusability, and reproducibility of research data.<br><br>This dataset aims to facilitate the evaluation of the degree of relatedness between literature publications and their associated linked data publications. It comprises 3,600 tuples, each representing a pair of a literature publication (A) and a linked data publication (B) connected through Scholix links.</p> <p><strong>Dataset Contents</strong></p> <p>1. <em>Scholix Links</em>: The dataset includes 450 Scholix links for each of the eight most frequently observed relationship types between literature and linked data publications, as expressed in the "RelationshipType - SubType" field of Scholix metadata:</p> <ul> <li>IsSupplementedBy</li> <li>IsReferencedBy</li> <li>IsRelatedTo</li> <li>References</li> <li>Documents</li> <li>Cites</li> <li>IsSupplementTo</li> <li>IsCitedBy</li> </ul> <p>2. <em>Publication Metadata</em>: In addition to the Scholix links, the dataset is augmented with metadata for each publication, including titles and author names. This metadata was harvested from the Crossref and DataCite APIs.</p> <p>3. <em>Relatedness Measures</em>: To estimate the degree of relatedness between literature and linked data publications, the dataset includes numeric measures for the similarity of authors' lists and publication titles for each tuple.</p> <p><strong>Data Sources</strong></p> <ul> <li>Scholix links were harvested from the Scholexplorer API.</li> <li>Publication metadata (titles and author names) were obtained from the Crossref and DataCite APIs.</li> </ul> <p>This dataset can be valuable for researchers and practitioners working on linked literature and data publications, evaluating the quality of existing links, or developing algorithms to identify related publications across different domains.</p>
Dataset for: Evaluating phylogenetic methods for quantifying risks and opportunities presented by forks in open source software (master dissertation).
<p>This is the data for my master dissertation [1]. If you wish to get a copy, download it from Zenodo and open docs/master.pdf.</p> <p>Data acquisition and encoding techniques are described in paragraph 3.1.1 (table 3.1).</p> <p>The data is described in more detail in paragraph 4.1 (table 4.2).</p> <p>* fork1_all.csv: MySQL server / MariaDB server<br> * fork2_all.csv: Linux kernel / Android kernel<br> * fork3_all.csv: Apache OpenOffice / LibreOffice</p> <p>==Cite==<br> [1] A. Ortiz-Troncoso. Evaluating phylogenetic methods for quantifying risks and opportunities presented<br> by forks in open source software (master dissertation). Zenodo, 2018. doi: http://doi.org/10.5281/zenodo.1158292</p>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
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