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1,048 results for “Performance evaluation”
Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets
<p>Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong></p> <p><strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019).</p> <p><strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript.</p> <p><strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github</p> <p>The scripts for the dataset generation, processing and visualisation of results are available at <a href="https://github.com/Ebony-Watson/scProximitE">our github for the scProcimitE package</a>, and documentation is available <a href="https://ebony-watson.github.io/scProximitE/">here</a>.</p>
Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance
<p>This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes:<br> - A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model).<br> - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation"
<p>Data for a publication "Amino-Modified ZIF-8 for Enhanced CO2 Capture: Synthesis, Characterization and Performance Evaluation".</p> <p><strong>Versions of dataset:</strong></p> <p><strong><span>V1: </span></strong><span>First dataset regarding the data used in the article.</span></p> <p><strong><span>V2:</span></strong><span> The dataset </span><span>was newly reorganized</span><span>, containing the </span><span>data,</span><span> that </span><span>were used</span><span> for the published article. </span><span>More information can be found</span><span> in the README file.</span></p> <p><strong>Article abstract</strong></p> <p>The urgent need for sustainable and innovative approaches to mitigate the increasing levels of atmospheric CO<sub>2</sub> necessitates the development of efficient methods for its removal. In this study, we focus on the new, innovative approach for synthesis and functionalization of metal-organic framework (MOF) ZIF-8 in one step at room temperature to enhance its capacity for CO<sub>2</sub> capture. Specifically, we investigated the impact of four amino-compounds, namely tetraethylenepentamine (TEPA), hexadecylamine (HDA), <a title="Learn more about ethanolamine from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemical-engineering/ethanolamine">ethanolamine</a> (ELA), and cyclopropylamine (CPA), on the <a title="Learn more about chemical structure from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/structure-composition">chemical structure</a>, size, surface area and porosity, and CO<sub>2</sub> capturing of ZIF-8 powder. By varying concentrations of the amino-compounds, we examined their influence on the ZIF-8 properties. Our findings demonstrate that each amino-compound and its respective concentration exhibit distinct effects on the characteristics of ZIF-8. Notably, the ZIF-8 sample functionalized with the highest presented concentration of TEPA exhibited significant improvement in CO<sub>2</sub> trapping efficiency, with a 33.3% enhancement. Moreover, least concentrated samples with added HDA or CPA demonstrated notable improvements with enhancements of 46.6% and 18.6%, respectively. These results highlight the potential of simple synthesis and functionalization techniques for MOFs in enhancing their CO<sub>2</sub> capture capabilities. The findings from this study offer new opportunities for the development of strategies to mitigate CO<sub>2</sub> emissions using MOFs.</p>
Evaluating Multi-Tenant Live Migrations Effects on Performance
<p>Results for Evaluating Multi-Tenant Live Migrations Effects on Performance article (Coopis 2018)</p> <p>Framework used to generate this data available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms</a></p> <p>Code for data interpretation available on <a href="https://github.com/guillaumerosinosky/migration_bpms">https://github.com/guillaumerosinosky/migration_bpms/coopis2018/xp_paper.ipynb</a></p> <p>Files description :</p> <ul> <li>png images : BPM process schemas used for the experimentations (AdditionalApproval, HumanTask and M3Process)</li> <li>xp1.csv : data for the <em>Migration duration </em>experiment</li> <li>xp3.csv : data for the <em>Migration effects on migrated tenant</em> and <em>Migration effects on co-located tenants</em> experiments</li> </ul>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Laboratory Tests
<p>A study was conducted to evaluate the repeatability of the Flexibility Index of asphalt mixtures obtained according to AASHTO TP-124-16. Three asphalt concrete samples were mixed and compacted using the Superpave Gyratory Compactor in one laboratory. The samples were then cut to specific dimensions for semi-circular bend testing based on the AASHTO Specifications at a single laboratory using a dedicated cutting equipment. The samples were randomized and distributed equally among three different testing labs.</p> <p>The process was repeated three times and in some instances the rate of loading was varied.</p> <p>This experiment allowed to study the repeatability of the the Flexibility Index</p>
Evaluation of Materials for Asphalt Mixture Performance, Semi-Circular Bend Field Material
<p>The data contained herein is part of a study conducted with support from the Utah Department of Transportation. In the study, seven asphalt mixtures from across the state of Utah were collected at the plant (prior to delivery) and at laydown (prior to compaction). The mixtures were sealed in metal containers and brought to three different laboratories where the asphalt mixtures were compacted using a Superpave gyratory compactor into cylinders. Each cylinder was cut using a masonry saw to create semi-circular samples with a notch in the middle based on the specification from AASHTO T124-16. The samples were tested following the procedures outlined in the specification with some exceptions where the loading rate was changed. The results were used to developed specification limits.</p>
Mowgli: DBMS Performance & Scalability Evaluation Data Sets
<p>These data sets contain the performance and scalability evaluation data created by the <a href="https://omi-gitlab.e-technik.uni-ulm.de/mowgli/getting-started">Mowgli</a> framework for Apache Cassandra and Couchbase, operated on a private Openstack and the Amazon EC2 cloud.</p>
Data used in the manuscript - A Hierarchical Approach for Evaluating Athlete Performance with an Application in Elite Basketball
<p>The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data. </p> <p>The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.</p> <p>Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.</p> <p>However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset. </p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Experiment Results: Evaluation of different parallelisation strategies with regard to the performance of parallel program execution
<p><br> Modern processors achieve an increase in performance by adding multiple cores. This means that during software development, care must be taken to parallelise the program sequences. To make predictions about the performance of a software design, there is Palladio. This is very accurate for single core processors.<br> In this bachelor thesis the influence of the chosen parallelization strategy on the performance of software is examined. Different hardware requirements are used for this purpose. They are used to generate individual work packages. These are executed by different parallelization strategies. The used parallelization strategies are: Java Threads, Java ParallelStreams, OpenMp and Akka Actor. Runtime and cache behavior are measured during each execution. In addition, the experiments are performed on different servers. The evaluation is done using acceleration curves and the Cache Miss Rate. The results show that the parallelization strategies differ only slightly in the work packages used.</p>
Scalability, dynamicity and performance evaluation results of Mantus framework
<p>Datasets used for experimental results (Figure 5): (a) Compositional weaver efficiency; (b) incremental weaving efficiency; (c) relative overhead of weaving in workflow; (d) weaver efficiency vs. aspect complexity.</p> <p>Type of data: raw and processed</p> <p>Hardware/software used: Intel Xeon E5-2650 Haswell at 2.60GHz with 64 GB of RAM; Testing input for all Mantus benchmarks: OpenStack-based ORBITS template described in paper, composed of a controller node and of 3 different group instances of compute nodes (Xen, KVM, LXC), with two virtual networks and relative network resources.</p> <p>Data format: CSV</p> <p>Source: Experiments</p> <p> </p>
Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
<p>Dataset for article entitled "<strong>Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"</strong></p>
Data for Tekran Model 3425 performance evaluation report for elemental mercury
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the Tekran Model 3425 is published in this repository. The files of the following experiments can be found here:</p><ul><li>m1<ul><li>Calibration Tekran mercury gas generator m1 20230612</li><li>Calibration_Tekran_m1</li></ul></li><li>m2<ul><li>Calibration Tekran mercury gas generator m2 20230619</li><li>Calibration_Tekran_m2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator m3 20230626</li><li>Calibration_Tekran_m3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator m4 20230629</li><li>Calibration_Tekran_m4</li></ul></li><li>short-term drift<ul><li>m2<ul><li>Calibration Tekran mercury gas generator short term drift m2</li><li>Tekran_Short_Term_M2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator short term drift m3</li><li>Tekran_Short_Term_M3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator short term drift m4</li><li>Tekran_Short_Term_M4</li></ul></li><li>m5<ul><li>Calibration Tekran mercury gas generator short term drift m5</li><li>Tekran_Short_Term_M5</li></ul></li></ul></li><li>stability<ul><li>Calibration Tekran mercury gas generator 20230609 stability</li></ul></li></ul>
Data for PSA 10.536 Elemental Hg generator performance evaluation report
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the PSA 10.536 elemental Hg generator is published in this repository. The files of the following experiments can be found here:</p><ul><li>range1 m1<ul><li>Calibration_PSA_range1_m1_20221130</li><li>multi_point_calibration_PSA_range1_m1</li></ul></li><li>range1 m2<ul><li>Calibration_PSA_range1_m2_20221209</li><li>multi_point_calibration_PSA_range1_m2</li></ul></li><li>range1 m3<ul><li>Calibration_PSA_range1_m3_20221214</li><li>multi_point_calibration_PSA_range1_m3</li></ul></li><li>range1 m4<ul><li>Calibration_PSA_range1_m4_20230915</li><li>multi_point_calibration_PSA_range1_m4</li></ul></li><li>range1 m5<ul><li>Calibration_PSA_range1_m5_20230919</li><li>multi_point_calibration_PSA_range1_m5</li></ul></li><li>range2 m1<ul><li>Calibration_PSA_range2 m1 20221006</li><li>multi_point_calibration_PSA_range2_m1</li></ul></li><li>range2 m2<ul><li>Calibration_PSA_range2 m2 20221011</li><li>multi_point_calibration_PSA_range2_m2</li></ul></li><li>range2 m3<ul><li>Calibration_PSA_range2 m3 20221012</li><li>multi_point_calibration_PSA_range2_m3</li></ul></li><li>short-term drift<ul><li>m1<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 1</li><li>PSA_short_term_drift_M1</li></ul></li><li>m2<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 2</li><li>PSA_short_term_drift_M2</li></ul></li><li>m3<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 3</li><li>PSA_short_term_drift_M3</li></ul></li><li>m4<ul><li>Calibration mercury gas generator range1 short term drift 20221012 deel 4</li><li>PSA_short_term_drift_M4</li></ul></li></ul></li><li>stability<ul><li>Calibration mercury gas generator range2 stability 20220811</li><li>Calibration mercury gas generator stability 20221018</li></ul></li></ul>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Papers and KPIs for the Evaluation of Renewable Energy Communities' Performance
<p>This database contains the methodology used to explore and identify the papers that containes KPIs related to the evaluation of RECs performance. This methodology is divided into three phases: </p> <p>1.1) <em>Papers Exploration – </em>Comprehensive search of papers in the field of RECs using Scopus and Web Of Science databases;</p> <p>1.2) <em>Papers Screening</em> – Initial screening of collected literature based on research domain and accessibility;</p> <p>1.3) <em>Papers Eligibility</em> – Further filtering papers by extracting those that explicitly define KPIs through mathematical formulations in the context of the RECs.</p> <p> In the <em>Papers Exploration</em> step, the authors conducted a systematic review of the state-of-the-art of literature on performance metrics in the context of the renewable energy community. The search was conducted in March 2024 using the search engines Scopus and Web Of Science (the used queries are detailed explain in thte database). The output of this phase is a large database of the most recent and relevant studies, cataloged by the following information: authors, article title, abstract, author keywords, index keywords, and year of publication. At this stage, only journal articles and research works published after 2010 were considered. In the <em>Papers Screening</em> phase, the articles are further filtered by the authors screening manually all papers based on keywords, titles, and abstracts, removing articles not relevant to the context of the RECs. In addition, articles for which it was not possible to access the full text are excluded. In the <em>Papers Eligibility</em> phase, the articles are entirely read to identify those articles that directly address the use of performance metrics. The eligibility criterion used by the reviewers’ team refers to the explicit definition of KPIs through mathematical formulas combined with their direct usage to evaluate RECs’ performances. The main objective of this phase is therefore to identify those articles that explicitly define and use KPIs, so that they can later be collected and labeled, based on their definition and usage.<br><br></p> <p>In additions, KPIs are extracted from the papers deemed elegible generating Tables A1, A2, A3 and A4. In these tables, similar KPIs are aggregated together in one single mathematical definition based on the methodology described in <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4991932">Key Performance Indicators for Renewable Energy Communities: A Comprehensive Review by Lorenzo Giannuzzo, Minuto Francesco Demetrio, Daniele Salvatore Schiera, Samuele Branchetti, Carlo Petrovich, Angelo Frascella, Nicola Gessa, Andrea Lanzini :: SSRN</a></p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
Supplementary Information for "Performance evaluation of adaptive introgression classification methods"
<p>Supplementary information : supplementary figures and tables from "<em>Performance evaluation of adaptive introgression classification methods</em>", Romieu <em>et al., </em>2024 manuscript. ROC values, curves and score value by non-AI windows type for various demographic scenarios.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.