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200 results for “Euros”
Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel, Agile and Astra.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1968 cc 55 Kw Euro 6 diesel engine: update 2
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1968 cc 55 kW Euro 6 diesel engine that has been applied in the Volkswagen Caddy.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 5a diesel engine: update 2
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 5a diesel engine that has been applied in the Volkswagen Polo, Seat Ibiza, Skoda Fabia and Skoda Roomster.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
1 2 1 in Euro-Mediterranean fauna of Campodeinae (Campodeidae, Diplura)
1 2 1
2 Euro
This model has been made and UV unwrapped **on Zbrush.** Textures were made **on Substance Painter** 2048x2048jpeg. The compotition **on Maya** File: -Fbx ; -Obj; -Stl; -Mtl; -Bmp; If you want, follow me on my instagram page: ' https://instagram.com/kekkart_?utm_medium=copy_link '. Page andaccount Facebook: ' https://www.facebook.com/francesca.cel.5 '. Source: Objaverse 1.0 / Sketchfab
2 Euro Germany
This coin model was scanned by the Beta version of the D3D-s scanner. Note how accurately even the smallest details are captured. Look at the precision of the edges. Find more information about the scanner at www.d3d-s.com Source: Objaverse 1.0 / Sketchfab
1 Euro (Dutch)
Photogrammetry exercise. Setup: OpenScanner (www.openscan.eu), light tent, 5 softboxes, Sony Alpha 77 II, SAL100M28, 3 chunks with 238 RAW images total, Adobe Lightroom, Agisoft Metashape Standard, Adobe Photoshop, Blender. Source: Objaverse 1.0 / Sketchfab
Lietuva 10 Euro Cent
This coin model was scanned by the Beta version of the D3D-s scanner. Note how accurately even the smallest details are captured. Look at the precision of the edges. Find more information about the scanner at www.d3d-s.com Size 20 mm Source: Objaverse 1.0 / Sketchfab
1 Euro (German)
Another photogrammetry test. Photos taken with newly built coin holders. Setup: OpenScanner (www.openscan.eu) in a light tent with 3 soft boxes, Sony Alpha 77 II, SAL100M28 (100mm / f2.8 Macro), 3 chunks with 539 RAW images total, processed in lightroom, Agisoft Metashape, minor texture editing in Photoshop. I finally solved the problem with hard chunk borders, therefore now missing some detail und depth in the model. Source: Objaverse 1.0 / Sketchfab
Bias-corrected EURO-CORDEX daily temperature and precipitation dataset for Hungary
<p>These datasets contain bias-corrected regional climate model outputs for daily precipitation, near surface mean-, minimum- and maximum temperature for the historical period 1993-2005 on a regular 0.11°x0.11° lon/lat grid (between latitudes 45.6825°N and 48.6525°N, and longitudes 15.9075°E and 22.9475°E).</p> <p> </p> <p>The datasets contain daily outputs of the following high-resolution (0.11°) regional climate models from the framework of EURO-CORDEX (Jacob et al., 2014):</p> <p>-CCLM</p> <p>-HIRHAM</p> <p>-RACMO</p> <p>-RCA</p> <p>-REMO</p> <p> </p> <p>The reference dataset is HUCLIM, which covers Hungary for the period 1971-2022 (downloaded in 2023) produced by the HungaroMet Hungarian Meteorological Service.</p> <p>File format: NetCDF</p> <p>All bias-corrected data produced by the use of HUCLIM have been created following the work of Mezghani et al. (2017).</p> <p> </p> <p>References:</p> <p>Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevel, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B. and Yiou, P. (2014) EURO-CORDEX New high resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578. <a href="https://doi.org/10.1007/s10113-013-0499-2" target="_blank" rel="noopener">https://doi.org/10.1007/s10113-013-0499-2</a></p> <p>Mezghani, A., Dobler, A., Haugen, J.E., Benestad, R.E., Parding, K.M., Piniewski, M., Kardel, I. and Kundzewicz, Z.W. (2017) CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations. Earth Syst. Sci. Data, 9, 905–925. <a href="https://doi.org/10.5194/essd-9-905-2017" target="_blank" rel="noopener">https://doi.org/10.5194/essd-9-905-2017</a></p> <p> </p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Input datasets for Euro-Calliope
<p><a href="https://euro-calliope.readthedocs.io/">Euro-Calliope</a> is a set of models of the European energy sytem and an automatic workflow to generate them. Euro-Calliope is based on a variety of input datasets each of which describes a certain aspect of the energy system, like generation potentials, historical generation, and energy demand. The workflow building all models does not contain data but instead automatically derives input data from their source where possible. For some input datasets this is not possible and this folder includes these datasets.</p>
Euro-Calliope: Pre-built models
<p>Ready to use models of the European electricity system built for use in <em>Calliope</em>. Models are available on three different spatial resolutions: continental, national, and regional.</p> <p>In addition, Euro-Calliope models can be built manually which adds more configuration options. To learn how to build Euro-Calliope manually, head over to <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>At a glance</strong></p> <p>Euro-Calliope models the European electricity system with each location representing an administrative unit. It is built on three spatial resolutions: on the continental level as a single location, on the national level with 34 locations, and on the regional level with 497 locations. At each location, renewable generation capacities (wind, solar, bioenergy) and balancing capacities (battery, hydrogen) can be built. In addition, hydro electricity and pumped hydro storage capacities can be built up to the extent to which they exist today. All capacities are used to satisfy electricity demand on all locations where demand is based on historic data. Locations are connected through transmission lines of either unrestricted capacity or projections. Using <a href="https://www.callio.pe">Calliope</a>, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. Due to the flexibility of Calliope and the availability of the routines building the model all components can be adapted to the modeller’s needs.</p> <p><strong>Prepare</strong></p> <ol> <li> <p>Install a Gurobi license on your computer (<a href="https://www.gurobi.com/downloads/end-user-license-agreement-academic/">academic license</a> comes at no cost), or <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/#manual-changes">choose a different solver</a>.</p> </li> <li> <p>Install Calliope and all required dependencies. The easiest way to do so is using <a href="https://conda.io/">conda</a> or <a href="https://mamba.readthedocs.io/">mamba</a>. Using conda, you can install Calliope:</p> </li> </ol> <pre><code>cd pre-built-euro-calliope-v1.1.0 conda env create -f environment.yaml conda activate euro-calliope</code></pre> <p><strong>Run</strong></p> <p>There are three models in the directory of the pre-builts – one for each of the three spatial resolutions continental, national, and regional. You can run all three models out-of-the-box, but you may want to modify the model. By default, the model runs for the first day of January only. To run the example model on the continental resolution type:</p> <pre><code>calliope run ./continental/example-model.yaml</code></pre> <p><strong>Customise</strong></p> <p>The pre-built models are examples and very likely require customisation to fit your purpose. Once you’ve managed to run them, it’s a good point in time to learn about <a href="https://euro-calliope.readthedocs.io/en/latest/model/customisation/">model customisation options in Euro-Calliope</a>.</p> <p><strong>More information</strong></p> <p>For more information on Euro-Calliope and how to use and modify the models, see <a href="https://euro-calliope.readthedocs.io">Euro-Calliope’s documentation</a>.</p> <p><strong>License and attribution</strong></p> <p>Euro-Calliope is developed and maintained within the <a href="https://www.callio.pe">Calliope project</a>.<br> <br> This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>Contains modified Copernicus Atmosphere Monitoring Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>Contains modified data from <a href="https://www.renewables.ninja/">Renewables.ninja</a>.</p> <p>Contains modified data from <a href="https://open-power-system-data.org">Open Power System Data</a>.</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
Euro PM2021: Use of research software
<p>For the presentation „<a href="https://zenodo.org/deposit/7127468">Wissenschaftliche Forschungssoftware nachhaltig entwickeln und nutzen</a>“ I examined the extent to which the used software is mentioned in the <a href="https://europm2021.com/">Euro PM2021</a> conference proceedings.</p> <p>The three sheets of the „Software at Euro PM2021.xlsx“ file contain the following information:</p> <ul> <li>„Euro PM2021 Suchbegriffe“: the search strings used for finding software. Searching was done with <a href="https://github.com/phiresky/ripgrep-all">ripgrep-all</a> v0.9.6 on the pdf files from the official <a href="https://www.epma.com/publications/euro-pm-proceedings/category/euro-pm2021-congress-proceedings">Euro PM2021 Congress Proceedings</a> available from EPMA.</li> <li>„Euro PM2021 Paper mit Software“: all the papers that yielded some results.</li> <li>„Abouaf-Implementierung“: This has nothing to do with Euro PM2021, but is another part of the mentioned presentation. This is a (albeit most likely not complete) list of scientific literature that (re-)implement the Abouaf densification model for Hot Isostating Pressing.</li> </ul>
Finnish coins before Euro
These coins were used in Finland between 1992 - 2001. Photos taken with D5300 + 40mm Micro Nikkor. Models created with Agisoft + Blender. Source: Objaverse 1.0 / Sketchfab
1 Euro (Italian)
Another photogrammetry test to prepare for a historical coin photoscan, this time testing new coin holders to avoid adhesion paste. Setup: OpenScanner (www.openscan.eu) in a light tent with no artificial lightsource, Sony Alpha 77 II, SAL100M28 (100mm / f2.8 Macro) 2 chunks with 206 RAW images each, processed in lightroom, Agisoft Metashape, minor texture editing in Photoshop. I now figured out how to avoid the ugly edges at the chunk borders. Perhaps re-uploading this in the future. Source: Objaverse 1.0 / Sketchfab
Pre-built Sector-coupled Euro-Calliope Model
<p><strong>Sector-coupled Euro-Calliope subnational-scale pre-built models</strong></p> <p>Built using <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a> commit hash: 6fd0bf3dce2a0799ac9821b50e9b1513fa783018</p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2010 - 2018 input data. To run the model you will need to do the following:</p> <p>a. Install a specific conda environment to be working with the correct version of Calliope (<code>conda env create -f requirements.yml</code>)</p> <p>b. Include specific scenarios to pick up the relevant sectors. For the study accompanying this release, The following scenarios were included <code>"industry_fuel_shared,transport,heat,config_overrides,res_2h,gas_storage,link_cap_dynamic,freeze-hydro-capacities,add-biofuel"</code>, where:</p> <ul> <li> <p><code>industry_fuel_shared</code>: Includes all non-electrical industry demands and the necessary technologies to generate those fuels synthetically. This includes e.g. annual methanol requirements for the chemical industry. <code>shared</code> refers to the fact that all regions' annual demand is pooled and can be met across all regions. The other option is to set this to <code>industry_fuel_isolated</code>, where a region must meet its own annual demand by generation of fuel within the region.</p> </li> <li> <p><code>transport</code>: This ensures ICE and EV light and heavy vehicle technologies and annual demands are in the model. It also includes reference to constraints required to make smart-charging of EVs work (e.g. weekly demand requirements).</p> </li> <li> <p><code>heat</code>: This ensures that all heat provision technologies and hourly demands are in the model. Carriers added are <code>heat</code> (space heating and hot water) and <code>cooking</code>. Technologies added can be found in <code>heat-techs.yaml</code>.</p> </li> <li> <p><code>config_overrides</code>: This includes high-level simplifications, such as removal of technologies that are considered redundant (e.g. less interesting combined heat and power technologies).</p> </li> <li> <p><code>res_2h</code>: Sets the model with a 2h resolution. Can be omitted or can be one of <code>res_2h</code>, <code>res_3h</code>, <code>res_6h</code>, <code>res_12h</code>. The full hourly resolution model takes ~2 days to complete.</p> </li> <li> <p><code>gas_storage</code>: Includes underground methane storage facilities, based on latest data on a national level. Can be omitted to remove the option of this technology.</p> </li> <li> <p><code>link_cap_dynamic</code>: Sets a limit on transmission line capacities. The limits are chosen subjectively based on current capacity, such that lines with smaller current capacities can proportionally increase much more (e.g. 100x) than larger lines (e.g. 2x). See <code>national/links.yaml</code> for other override options to apply here.</p> </li> <li> <p><code>freeze-hydro-capacities</code>: Sets hydro capacities to equal "today's" capacities. This seems more reasonable than setting current capacities as upper limits, as this causes the model to install no hydro.</p> </li> <li> <p><code>add-biofuel</code>: Enables a biofuel supply stream with a distinct <code>biofuel</code> carrier, with annual limits on biofuel that can be provided (based on JRC residuals). This differs from Euro-Calliope v1.0 which is a black box technology converting biofuel to electricity directly.</p> </li> </ul> <p>c. decide on a SPORES run to undertake, e.g. the scenario <code>spores_supply</code> will run SPORES for primary energy supply technologies. SPORES scenarios can be found in the file <code>spores.yaml</code>.</p> <p>d. pick your run year, by pointing to the relevant model config file (e.g. <code>model-2018.yaml</code> for the 2018 weather year).</p> <p>e. run the model via the dedicated scripts found in this directory. These scripts include the addition of custom constraints and have been copied directly from the workflow, where they would normally be initiated as part of the internal process. However, you can load and run them in an interactive session / with your own python script to call them:</p> <p><code class="language-python">[1] import create_input </code></p> <p><code class="language-python">[2] create_input.build_model(path_to_model_yaml, scenarios_string, path_to_netcdf_of_model_inputs) </code></p> <p><code class="language-python">[3] import run </code></p> <p><code class="language-python">[4] run.run_model(path_to_netcdf_of_model_inputs, path_to_netcdf_of_results)</code></p> <p> </p> <p><code class="language-python">Note: The only difference between this version and v0.1 is that spurious hidden files specific to MacOS have been removed from the dataset.</code></p>
Isoprene concentrations data used in the paper "Assessment of isoprene and near surface ozone sensitivities to water stress over the Euro-Mediterranean region"
<p>Isoprene concentrations collected by E. Bourtsoukidis and J. Williams during a field-campaign that took place in Cyprus (site field: Ineia; Latitude: 34.96° N, Longitude: 32.39° E) during the summer 2014 (from July 7 to August 3; data collected every 45 minutes) using the technique of gas chromatography - mass spectrometry (GC-MS) (Derstroff et al., 2017). These data have been used to validate isoprene concentrations simulated by the regional climate model RegCM applied in the study "<em>Assessment of isoprene and near surface ozone sensitivities to water stress over the Euro-Mediterranean region</em>" (https://doi.org/10.5194/egusphere-2022-1522).</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.