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125 results for “open models”
OpenFOAM cases of the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers"
<p>This dataset contains the<em> underling data</em> for the paper "Development and validation of an open-source CFD model for the efficiency assessment of data centers”, submitted for the consideration and open review in Open Research Europe (ORE).</p> <p><strong>Validation1.tar.xz:</strong> OpenFOAM files and scripts for the simulation of flow and thermal structures in an enclosed environment (Wang and Chen, 2009).</p> <p><em>Wang, Miao; Chen, Qingyan (2009). Assessment of Various Turbulence Models for Transitional Flows in an Enclosed Environment (RP-1271). HVAC&R Research, 15(6), 1099–1119. doi:10.1080/10789669.2009.10390881</em></p> <p><strong>Validation2-kOmegaSSTModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using k-omega SST turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation2-RNGkEpsilonModel.tar.xz:</strong> OpenFOAM files and scripts for the simulation of forced convection in a room (Zhang et al. 2007) using RNG k-epsilon turbulence model. </p> <p><em>Zhao Zhang, Wei Zhang, Zhiqiang John Zhai & Qingyan Yan Chen (2007) Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature, HVAC&R Research, 13:6, 871-886, DOI: 10.1080/10789669.2007.10391460</em></p> <p><strong>Validation3.tar.xz:</strong> OpenFOAM files and scripts for the simulation of strong natural convection in a model fire room (Murakami et al. 1995).</p> <p><em>Murakami, S., S. Kato, and R. Yoshie. 1995. Measurement of turbulence statistics in a model fire room by LDV. ASHRAE Transactions 101(2):287–301.</em></p> <p><strong>Validation4.tar.xz:</strong> OpenFOAM files and scripts for the simulation of thermal distribution in an open-aisle data center (Abdelmaksoud et al. 2013).</p> <p><em>W.A. Abdelmaksoud, T.Q. Dang, H. Ezzat Khalifa, R.R. Schmidt Improved computational fluid dynamics model for open-aisle air-cooled data center simulations J. Electron. Packag., 135 (2013), pp. 030901-30913</em></p> <p><strong>Results_Validation1.tar.xz:</strong> Simulation results of the Validation case 1.</p> <p><strong>Results_Validation2.tar.xz:</strong> Simulation results of the Validation case 2.</p> <p><strong>Results_Validation3.tar.xz:</strong> Simulation results of the Validation case 3.</p> <p><strong>Results_Validation4.tar.xz:</strong> Simulation results of the Validation case 4.</p> <p><strong>layout.csv:</strong> Input file for the Validation case 4.</p>
AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE
<p>This repository contains all geometrical data and metadata belonging to the paper AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE by the MAGIC Amsterdam research consortium. The following contents are uploaded:</p><p><strong>shapeVectors_original.csv</strong> | shape vectors of the original data<br><strong>shapeVectors_rescaled.csv</strong> | shape vectors of the rescaled data<br>678 x 62589 matrices where the rows are samples and the columns are shape vectors. The shape vectors are formatted<i> [x1, x2, x3, ..., y1, y2, y3, ..., z1, z2, z3, ...].</i></p><p><strong>PCA_coeff_original.csv</strong> | principal component coefficients of the original data<br><strong>PCA_coeff_rescaled.csv</strong> | principal component coefficients of the rescaled data<br>62589 x 677 matrices where each row of these matrices is a variable (x-, y-, or z-coordinate of a vertex) and each column is a principal component.</p><p><strong>PCA_score_original.csv</strong> | principal component scores of the original data<br><strong>PCA_score_rescaled.csv</strong> | principal component scores of the rescaled data<br>678 x 677 matrices where rows correspond to samples and columns correspond to principal components.</p><p><strong>PCA_latent_original.csv</strong> | principal component variances of the original data<br><strong>PCA_latent_rescaled.csv</strong> | principal component variances of the rescaled data<br>677 x 1 vectors where each element is an eigenvalue of a principal component.</p><p><strong>PCA_mu_original.csv</strong> | mean of the original data<br><strong>PCA_mu_rescaled.csv</strong> | mean of the rescaled data<br>1 x 62589 vectors that represent the average shape vector. All (centered) data can be reconstructed as follows: <i>shapeVectors = PCA_score * PCA_coeff' + PCA_mu.</i></p><p><strong>PCA_standardDeviations_original.csv</strong> | standard deviations of each sample for each principal component of the original data.<br><strong>PCA_standardDeviations_rescaled.csv</strong> | standard deviations of each sample for each principal component of the rescaled data.<br>677 x 678 matrices where the rows are principal components and the columns are samples. The standard deviations were calculated as follows: <i>PCA_standardDeviations = PCA_score' ./ sqrt(PCA_latent).</i></p><p><strong>metadata.csv</strong> | This matrix contains the age in years (first column) and biological sex (second column, 1 = male and 2 = female) for all samples (rows).</p><p><strong>connectivityList.csv</strong> | This matrix defines the mesh of the 3D model of the mandible. The vector in each row represents which vertices define a triangle. Indexing starts at 0, so for use in e.g. Matlab, add 1 to all elements.</p>
Open Soil Spectral Library (training data and calibration models)
<p><strong>Open Soil Spectral Library</strong> contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set:</p> <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> <p>Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe.</p> <p>To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <strong><a href="https://github.com/traversc/qs">QS data format</a></strong> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a></p> <p>Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a></p> <p><a href="https://soilspectroscopy.org/"><strong>Soil Spectroscopy for the Global Good</strong></a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>).</p> <p>Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>. </p> <p>For more advanced uses of the soil spectral libraries <strong>we advise to contact the original data producers</strong> especially to get help with using, extending and improving the original SSL data.</p>
Towards an open-source landscape for 3D CSEM modelling
<p>Accompanying data to journal article</p> <blockquote> <p>Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>.</p> </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> <p>The Marlim R3D model can be found at:</p> <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>
Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the entire ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a> since git is not suited for handling large changing files. Instead, we provide separate data bundles and cutouts to be downloaded and extracted, as noted in the documentation.</p> <p>The provided <strong>cutouts </strong>are merged spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V003">CMSAF SARAH-3</a> solar surface radiation dataset for the years 1996, 2010, 2012, 2013, 2019, 2020 and 2023. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p>Solar irradiation data is taken from SARAH-3 while all other weather data is from ERA5.</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-3</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Drücke, Jaqueline; Trentmann, Jörg; Schröder, Marc; Selbach, Nathalie; Hollmann, Rainer (2023): Surface Radiation Data Set - Heliosat (SARAH) - Edition 3, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V003, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003" target="_blank" rel="noopener">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003</a>.</li> <li><strong>Terms of Use:</strong> All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.</li> </ul>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City
<p>SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City Authors</p> <ul> <li>Therrien, J-D<sup>1</sup></li> <li>Maere, T.<sup>1</sup></li> <li>Sanchez-Quete, F.<sup>2</sup></li> <li>Tsitouras, A.<sup>2</sup></li> <li>Goitom, E.<sup>3</sup></li> <li>Cloutier, F.<sup>4</sup></li> <li>Dufour, D.<sup>4</sup></li> <li>Proulx, F. <sup>4</sup></li> <li>Nicolaï, N.<sup>1</sup></li> <li>Philippe, R.<sup>1</sup></li> <li>Tohidi, M.<sup>1</sup></li> <li>Dorner, S.<sup>3</sup></li> <li>Frigon, D.<sup>2</sup></li> <li>Vanrolleghem, P.A.<sup>1</sup></li> </ul> <p>Affiliations</p> <ul> <li><sup>1</sup> model<em>EAU</em>, Département de génie civil et de génie des eaux, Université Laval</li> <li><sup>2</sup> Microbial Community Engineering Lab (MiCEL), Department of Civil Engineering, McGill University</li> <li><sup>3</sup> Polytechnique Montréal</li> <li><sup>4</sup> Ville de Québec</li> </ul> <p>General Remarks</p> <p>Wastewater-based surveillance of SARS-CoV-2 virus can detect between 1 and 30 infected individuals per 100,000 (including asymptomatic ones) by analyzing the population's sewage. As such, this method is very attractive since it costs only a fraction of clinical testing (as low as 1%). Human faeces may contain the virus a few days before a person becomes ill. Thus, this approach allows for detection of outbreaks 2-7 days before the increase in reported cases stemming from clinical screening tests (Bibby et al., 2021). Wastewater-based surveillance complements clinical testing by geolocating outbreaks, which may help targeting intensive screening programs. Moreover, it provides a quick indication of whether new public health measures (e.g., masks, social distancing, confinement, and curfew) are effective.</p> <p>Sampling</p> <p>The reported dataset contains open data collected in the province of Québec as part of the SARS-CoV-2 wastewater-based surveillance program <a href="https://www.centreau.ulaval.ca/en/covid/">CentrEau</a>-COVID. Four of the largest cities in the province (Montréal, Laval, Québec City, and Trois-Rivières), as well as the municipalities of four rural regions (Mauricie, Centre-du-Québec, Bas-St-Laurent, and Gaspésie) participated in the program. The entire dataset includes 31 sampling sites covering approximately half the population of the province of Québec (population size of 8.5 million). The timeframe covered by the dataset varies for each site. The earliest surveillance program was launched in March 2020, others followed soon after. Samples were collected using various methods, such as 24h composite samples, grab samples, and passive sampling using variations on the Moore swab method (Schang et al., 2020)</p> <p>Analysis</p> <p>Prior to the analysis of the samples for SARS-CoV-2, physiochemical parameters such as total suspended solids (TSS), turbidity, conductivity, ammonium concentration, and pH were measured. The samples were subsequently concentred by filtration using a MEC filter (0.45 um), followed by total RNA extraction using the Qiagen AllPrep PowerViral DNA/RNA Kit (Qiagen, USA) with some modifications (beta-mercaptoethanol concentration raised to 10% and lysis performed at 55 °C for 30 minutes) (Ahmed et al., 2020). SARS-CoV-2 viral RNA was detected by a one-step RT-qPCR. To assess the RNA recovery rate of the procedure, samples were spiked before extraction with a known concentration of Bovine Respiratory Syncytial Virus (BRSV) using the Zoetis INFORCE 3 vaccine (Zoetis, USA). In addition to SARS-CoV-2, samples were assessed for Pepper Mild Mottle Virus (PMMoV), the daily load of which is hypothesized to represent the fecal load contributions to the samples at a given site and time. PCR conditions and primer used to collect viral data are described in the files <code>primers.md</code> and <code>PCR conditions.md</code>.</p> <p>Compilation</p> <p>The measurements on wastewater samples carried out by the participating laboratories of this study are found in the <code>WWMeasure</code> table. The values provided by municipalities come from laboratories accredited by the Centre d'expertise en analyse environnementale du Québec (CEAEQ), in compliance with the latter's quality assurance protocols. The COVID-19-related public health data found in the <code>CPHD</code> table were collected from the Institut National de Santé Publique du Québec (INSPQ)'s public reports. Wastewater data taken in-situ at the sampling sites (e.g., the flow at pumping stations or water resource recovery facilities (WRRFs)) are found in the <code>SiteMeasure</code> table and were taken by the institutions responsible for managing the sites. All of the data, stemming from multiple sources, were combined into the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM)</a> standard format using the <a href="https://github.com/modelEAU/ODM-Import">ODM-Import python package</a> (see also Structure).</p> <p>Validation</p> <p>Wastewater and sample data were manually assessed for quality by our research collaborators. Data points for which the quality appeared to be uncertain were tagged with the value <code>True</code> in the <code>qualityFlag</code> column. Conversely, data deemed of good quality have a quality flag of <code>False</code>. Data that were not checked have a quality flag of <code>NA</code>. Textual comments describing the issues with the data points in more detail are also included in the dataset using the <code>notes</code> column of the relevant tables. Note that data validation was carried out by the data custodians responsible for each city in the dataset according to available resources. As the project continues and data validation is undertaken on more sections of the dataset, data may be re-analyzed, flagged, or commented as needed. Revisions to the dataset will be reported to the best of our ability.</p> <p>Structure</p> <p>The data contained in this dataset has been structured according to the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM) for Wastewater-Based Surveillance</a>. This model provides a standardized dictionary to collect and share data and metadata stemming from wastewater-based surveillance programs. By convention, it splits all data into 10+ thematic tables with each record representing a unique measurement, i.e., long format. For convenience, the <code>wide</code> folder presents the data found in all the other tables in a wide format, i.e., multiple measurements are aligned by <code>timestamp</code>, with each column representing a different parameter.</p> <p>Acknowledgements</p> <p>The authors would like to acknowledge that this dataset was collected thanks to the financial support of the Fonds de Recherche du Québec, the Molson Foundation, the Trottier Family Foundation, CentrEau and NSERC. The authors would also like to acknowledge the efforts of Douglas Manuel (Ottawa Hospital) and Howard Swerdfeger (Public Health Agency of Canada) for their original idea for the Open Data Model and continued development.</p> <p>References</p> <ol> <li> <p>Ahmed, W., Bertsch, P.M., Bivins, A., Bibby, K., Farkas, K., Gathercole, A., Haramoto, E., Gyawali, P., Korajkic, A., McMinn, B.R., Mueller, J.F., Simpson, S.L., Smith, W.J.M., Symonds, E.M., Thomas, K. v., Verhagen, R., Kitajima, M., 2020. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Science of the Total Environment 739. <a href="https://doi.org/10.1016/j.scitotenv.2020.139960">https://doi.org/10.1016/j.scitotenv.2020.139960</a></p> </li> <li> <p>Bibby, K., Bivins, A., Wu, Z., North, D., 2021. Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Research 202, 117438. <a href="https://doi.org/10.1016/j.watres.2021.117438">https://doi.org/10.1016/j.watres.2021.117438</a></p> </li> <li> <p>Schang, C., Crosbie, N., Nolan, M., Poon, R., Wang, M., Jex, A., Scales, P., Schmidt, J., Thorley, B.R., Henry, R., Kolotelo, P., Langeveld, J., Schilperoort, R., Shi, B., Einsiedel, S., Thomas, M., Black, J., Wilson, S., McCarthy, D.T., 2020. Passive sampling of viruses for wastewater-based epidemiology: a case-study of SARS-CoV-2 [WWW Document]. URL <a href="https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true">https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true</a> (accessed 1.18.21).</p> </li> </ol>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Dataset: An Open-hardware Platform for MPSoC Thermal Modeling
<p>This repository contains the experimental data for the paper</p> <p>"An Open-hardware Platform for MPSoC Thermal Modeling"</p> <p>published at 2019 samos conference</p> <p>http://samos-conference.com</p> <p>The data is released under a CC-BY Creative Commons license.<br> If you use this dataset, cite the following paper:<br> Federico Terraneo, Alberto Leva, William Fornaciari, "An Open-hardware Platform for MPSoC Thermal Modeling", 2019 IEEE International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS)</p>
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
Data from the OPERAS business models survey on open access books
<p>OPERAS (the European Research Infrastructure for the development of open scholarly communication in the social sciences and humanities) has conducted a survey of publishing organisations throughout Europe to identify and better understand existing and potential business models to support the Open Access publication of research monographs. The results of the survey are used to inform the formulation of recommendations about how to create a sustainable open access book publishing ecosystem within Europe.</p> <p>The survey was designed to serve two core aims: <br> 1. To further, better or improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs;<br> 2. To identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in SSH. </p> <p>The survey was open between 16 February and 14 April 2021.</p> <p>The results are presented in two versions of the white paper of the Open Access Business Models Special Interest Group: Stone, Graham, Błaszczyńska, Marta, Lebon, Chloé, Morka, Agata, Mosterd, Tom, Mounier, Pierre, Proudman, Vanessa, Speicher, Lara, & Melinščak Zlodi, Iva. (2021). Collaborative models for OA book publishers (1.0). Zenodo. https://doi.org/10.5281/zenodo.5494731 and the second version to be published in Spring 2023.</p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
Results of the expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps
<p>This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.</p> <p>Note: check the most recent version in the sidebar</p> <table> <tbody> <tr> <td>Current version</td> <td>v.0.2</td> </tr> <tr> <td>Date</td> <td>2024/01/10</td> </tr> <tr> <td>Respondants</td> <td>30</td> </tr> </tbody> </table> <p><strong>Available files:</strong></p> <table> <tbody> <tr> <td>File</td> <td>Type</td> <td>Description</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_public.csv">responses_v01_public.csv</a></td> <td>CSV table</td> <td>Survey raw results (anonymous)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_stats.csv">responses_v01_stats.csv</a></td> <td>CSV table</td> <td>Questions statistics</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_mean_sd.jpg">responses_v01_mean_sd.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (mean and standard deviation)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_bands.jpg">responses_v01_bands.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (uncertainty bands)</td> </tr> </tbody> </table> <p>The column descriptions in the statistical table are as follows:</p> <p>Prefixes:</p> <ul> <li>HABITAT: habitat suitability score</li> <li>WEIGHT: Threat weight</li> <li>MAX_DIST: Maximum distance of negative influence (impact)</li> </ul> <p>Suffixes:</p> <ul> <li>mean: Average</li> <li>std: Standard deviation</li> <li>min: Minimum value</li> <li>p05: 5th percentile</li> <li>p25: 25th percentile</li> <li>p50: 50th percentile (median)</li> <li>p75: 75th percentile</li> <li>p95: 95th percentile</li> <li>max: Maximum value</li> </ul> <p>These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.</p>
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
The Utopian Model: How The Neuro Has Become An Open Science Institution
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to Dylan Roskams-Edris from Open Science Alliance Officer at Tanenbaum Open Science Institute and The Neuro in Canada. We discussed how The Neuro made itself into the worlds first open neuroscience institution, the challenges and opportunities of embracing Open Science at an institutional level, how Open Science itself needs to be more open, and the potential for scientists working in such a system.</p> <p><strong>Episode Links: </strong></p> <p> <a href="https://www.linkedin.com/in/dylan-roskams-edris-26690598/?originalSubdomain=ca">Dylan Roskams-Edris</a></p> <p><a href="https://twitter.com/dylanwre?lang=en">Twitter</a></p> <ul> <li><a href="https://t.co/N3BFnpiVgv?amp=1">The Neuro</a> <ul> <li><a href="https://twitter.com/TheNeuro_MNI">Twitter</a></li> </ul> </li> </ul>
Techno-economic dataset for open modelling of decarbonization pathways in The Philippines
<p><span>This</span><span> file contains the data and data sources updated for the paper :</span></p> <p> </p> <p><span>'</span><span>The Philippines’ Energy Transition: Assessing Emerging Technology Options using OSeMOSYS (Open Source Energy Modelling System)'</span></p> <p><span>All other data in the model used for the paper is from the Philippines Starter Data kit (Allington, 2021). Renewable Energy Costs are updated from (Alexander, 2023).</span></p> <p> </p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.