Find research datasets worth reusing

Augmented emission maps: 1560 cc 73 kW Euro 6b diesel engine

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. This specific data set refers to the 1560 cc 73 kW Euro 6b diesel engine that has been applied in the - Peugeot Partner, 208, and 2008, Citroen C4 Cactus, Berlingo, C3, and DS3. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 1968 cc 100 kW Euro 5b diesel engine

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. This specific data set refers to the 1968 cc 100 kW Euro 5b diesel engine that has been applied in the Volkswagen Crafter, Passat, Sharan, and Tiguan, Audi A3, Q3, A4, A5, and A6, Seat Alhambra. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 2993 cc 190 kW Euro 6b diesel engine

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. This specific data set refers to the 2993 cc 190 kW Euro 56b diesel engine that has been applied in the BMW. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 1499 cc 96 kW Euro 6dT diesel engine

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. This specific data set refers to the 1499 cc 96 kW Euro 6dT diesel engine that has been applied in the Peugeot 308, 3008, 508, 5008, and Partner, Opel Grandland X and Combo, Citroen Berlingo, C4 Spacetourer, and C5 Aircross, DS 7 Crossback. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

opencc-by-4.0Feb 2020View details →

Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine

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. This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel Corsa, Agila, and Astra. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 1587 cc 80 kW Euro 3 petrol engine

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. This specific data set refers to the 1587 cc 80 kW Euro 3 petrol engine that has been applied in the Citroen Berlingo, C2, C3, C3 Pluriel, C4, and Xsara. Peugeot 206, 307, and Partner. NB: this is only the PSA cars. The Volvos have alliance code VOLV and have a different engine. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 1124 cc 44 kW Euro 3 petrol engine

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. This specific data set refers to the 11124 cc 44 kW Euro 3 petrol engine that has been applied in the Peugeot 106, 206, and Partner. Citroen Saxo, C2, C3, and Berlingo. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

Augmented emission maps: 1997 cc 100 kW Euro 3 petrol engine

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. This specific data set refers to the 1997 cc 100 kW Euro 3 petrol engine that has been applied in the Citroen C4, C5, C8, Xsara, and Xsara Picasso. Peugeot 206, 307, 406, 407, 607, 806, 807, and Expert. The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about: <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> 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.

opencc-by-4.0Feb 2022View details →

Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies

Bias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for: -period 1981-2099/2100, -6 RCM, -3 scenarios (RCPs 2.6, 4.5 and 8.5), -7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and -18 domains and 23 locations within these domains. Bias correction and further downscaling to 0.1° was done using <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging. Organization of the data The name of the files are domain-type.zip, where type is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: experiment/modelNumber/variable.nc for gridded and experiment/modelNumber/variable-pilotFieldNumber.txt for point data, where experiment is rcp26, rcp45 or rcp85. domain and pilotFieldNumber <table> <tbody> <tr> <td> domain </td> <td> domain location (min and max. Longitude, min and max latitude) </td> <td> pilotFieldNumber </td> <td> pilot field location (longitude, latitude) </td> <td> case study number </td> <td> country </td> <td> Name (OPTAIN case study) </td> </tr> <tr> <td> 01 </td> <td> 50.95 51.45 14.55 15.05 </td> <td> </td> <td>   </td> <td> 1 </td> <td> DEU </td> <td> Schoeps </td> </tr> <tr> <td> 02 </td> <td> 46.35 47.05 6.55 7.15 </td> <td> 2 </td> <td> 46.816667 6.95 </td> <td> 2 </td> <td> CHE </td> <td> Petite Glane </td> </tr> <tr> <td> 02_1 </td> <td> 46.75 47.25 7.25 7.75 </td> <td> 1 </td> <td> 46.983333 7.466667   </td> </tr> <tr> <td> 02_34 </td> <td> 47.35 47.85 8.35 </td> <td> 3 4 </td> <td> 47.433333 8.516667 47.683333 8.616667 </td> </tr> <tr> <td> 02_5 </td> <td> 46.15 46.65 5.95 6.45 </td> <td> 5 </td> <td> 46.4 6.233333 </td> </tr> <tr> <td> 03a </td> <td> 46.65 47.15 17.45 17.95 </td> <td> 1 2 3 4 </td> <td> 46.92649 17.68246 46.9166 17.68976 46.91283 17.69754 46.91283 17.69723 </td> <td> 3a </td> <td> HUN </td> <td> Csorsza </td> </tr> <tr> <td> 03b </td> <td> 46.45 46.95 16.65 17.15 </td> <td> </td> <td>   </td> <td> 3b </td> <td> HUN </td> <td> Felso Valicka </td> </tr> <tr> <td> 04 </td> <td> 52.35 52.85 18.45 18.95 </td> <td> 1 </td> <td> 52.597469 18.728617 </td> <td> 4 </td> <td> POL </td> <td> Upper Zglowiaczka </td> </tr> <tr> <td> 05 </td> <td> 46.35 46.85 15.35 15.85 </td> <td> </td> <td>   </td> <td> 5 </td> <td> SVN </td> <td> Pesnica </td> </tr> <tr> <td> 06 </td> <td> 46.45 46.95 16.15 16.65 </td> <td> </td> <td>   </td> <td> 6 </td> <td> HUN/SVN </td> <td> Kebele/Kobiljski </td> </tr> <tr> <td> 07 </td> <td> 49.85 50.35 4.75 5.25 </td> <td> </td> <td>   </td> <td> 7 </td> <td> BEL </td> <td> La Wimbe </td> </tr> <tr> <td> 08 </td> <td> 55.15 55.75 23.55 24.05 </td> <td> 1 2 </td> <td> 55.522057 23.799235 55.42233194 23.82580339 </td> <td> 8 </td> <td> LTU </td> <td> Dotnuvele </td> </tr> <tr> <td> 09 </td> <td> 45.45 45.95 9.65 10.15 </td> <td>   </td> <td>   </td> <td> 9 </td> <td> ITA </td> <td> Cherio </td> </tr> <tr> <td> 10 </td> <td> 59.45 59.95 10.75 11.25 </td> <td> 1 2 3 4 5 6 7 8 </td> <td> 59.71949 10.83576 59.6833306 10.8833298 59.6833306 10.8833298 59.665 10.9475 59.665 10.9475 59.841012 10.903597 59.757631 11.072031 59.539623 10.856447 </td> <td> 10 </td> <td> NOR </td> <td> Krogstad </td> </tr> <tr> <td> 11 </td> <td> 46.45 46.95 17.55 18.05 </td> <td> 1 2 </td> <td> 46.658333 17.75583 46.656944 17.75833 </td> <td> 11 </td> <td> HUN </td> <td> Tetves </td> </tr> <tr> <td> 12 </td> <td> 49.35 49.85 14.75 15.25 </td> <td> 1 </td> <td> 49.616837 15.078266 </td> <td> 12 </td> <td> CZE </td> <td> Cechticky </td> </tr> <tr> <td> 13 </td> <td> 55.85 56.35 25.85 26.45 </td> <td>   </td> <td>   </td> <td> 13 </td> <td> LVA </td> <td> Dviete </td> </tr> <tr> <td> 14 </td> <td> 59.75 60.25 17.55 18.05 </td> <td>   </td> <td>   </td> <td> 14 </td> <td> SWE </td> <td> Ingvastaan Lehstaan </td> </tr> </tbody> </table>   modelNumber <table> <tbody> <tr> <td> modelNumber </td> <td> Driving Model (GCM) </td> <td> Ensemble </td> <td> RCM </td> <td> End date </td> </tr> <tr> <td> 1 </td> <td> EC-EARTH </td> <td> r12i1p1 </td> <td> CCLM4-8-17 </td> <td> 31.12.2100 </td> </tr> <tr> <td> 2 </td> <td> EC-EARTH </td> <td> r3i1p1 </td> <td> HIRHAM5 </td> <td> 31.12.2100 </td> </tr> <tr> <td> 3 </td> <td> HadGEM2-ES </td> <td> r1i1p1 </td> <td> HIRHAM5 </td> <td> 30.12.2099 </td> </tr> <tr> <td> 4 </td> <td> HadGEM2-ES </td> <td> r1i1p1 </td> <td> RACMO22E </td> <td> 30.12.2099 </td> </tr> <tr> <td> 5 </td> <td> HadGEM2-ES </td> <td> r1i1p1 </td> <td> RCA4 </td> <td> 30.12.2099 </td> </tr> <tr> <td> 6 </td> <td> MPI-ESM-LR </td> <td> r2i1p1 </td> <td> REMO2009 </td> <td> 31.12.2100 </td> </tr> </tbody> </table>   variable <table> <tbody> <tr> <td> variable </td> <td> description </td> <td> Unit </td> </tr> <tr> <td> Tmean </td> <td> Mean temperature </td> <td> °C </td> </tr> <tr> <td> Tmin </td> <td> Min temperature </td> <td> °C </td> </tr> <tr> <td> Tmax </td> <td> Max temperature </td> <td> °C </td> </tr> <tr> <td> prec </td> <td> Precipitation </td> <td> mm </td> </tr> <tr> <td> solarRad </td> <td> Solar radiation </td> <td> MJ/m2 </td> </tr> <tr> <td> windSpeed </td> <td> Wind speed at 2m </td> <td> m/s </td> </tr> <tr> <td> relHum </td> <td> Relative humidity </td> <td> % </td> </tr> </tbody> </table>   Methodolody Bias correction was done using non-parametric empirical quantile mapping with modified method from R package <a href="https://cran.r-project.org/web/packages/qmap/index.html">qmap</a>. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010. The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging  with R packages <a href="https://cran.r-project.org/web/packages/automap/index.html">automap</a> and <a href="https://cran.r-project.org/web/packages/gstat/index.html">gstat</a> with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model <a href="https://webmap.ornl.gov/wcsdown/dataset.jsp?dg_id=10008_1">Shuttle Radar Topography Mission</a> was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z.   Funding This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862756.

opencc-by-4.0May 2022View details →

Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions

Custom 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. This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios. These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):   <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre>   This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>. 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>.

opencc-by-nc-4.0Jan 2024View details →

EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research

EA-MD-QD is a collection of large monthly and quarterly EA and EA member countries datasets for macroeconomic analysis. The EA member countries covered are: AT, BE, DE, EL, ES, FR, IE, IT, NL, PT. The formal reference to this dataset is:  Barigozzi, M. and Lissona, C. (2024) "EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research". Zenodo. Please refer to it when using the data. Each zip file contains: - Excel files for the EA and the countries covered, each containing an unbalanced panel of raw de-seasonalized data. - A Matlab code taking as input the raw data and allowing to perform various operations such as: choose the frequency, fill-in missing values, transform data to stationarity, and control for covid outliers. - A pdf file with all informations about the series names, sources, and transformation codes. This version (10.2025): Updated data as of 31-October-2025.

Figs 202–204. 202 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 202–204. 202. Plusiocampa (Stygiocampa) christiani Condé & Bareth, 1996, from Lazareva Cave, Serbia (photo: Dragan Antić). 203. Plusiocampa (Plusiocampa) lagari Sendra & Condé, 1987, from Chorros Cave, Albacete, Spain (photo: Toni Pérez). 204. Plusiocampa (Plusiocampa) hoffmanni Sendra & Paragamian sp. nov., from Splialio Sfentone Trypa Cave, Crete (photo: Kaloust Paragamian).

Figs 193–195 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 193–195. Plusiocampa (Venetocampa) ferrani Sendra & Delić sp. nov., holotype, ♂, from Ferranova buža, Vrhnika, Slovenia (MZB 2019-1031). 193. Pronotum, mesonotum and metanotum, left side. 194. Metathoracic leg. 195. Urotergites VII–VIII and abdominal segment IX, right side. Scale bar: 0.2 mm.

Figs 131–134 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 131–134. Plusiocampa (Plusiocampa) schweitzeri Condé, 1947, from Govještica, Banja stijena, Rogatica, Bosnia and Herzegovina (Coll. AS). 131. Medial antennomere, latero-distal view. 132. Medial portion of a gouge sensillum, detail. 133. Surface of metanotum, detail. 134. Distal portion of tarsus and pretarsus, lateral view.

Figs 113–118 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 113–118. Plusiocampa (Plusiocampa) lagari Sendra & Condé, 1987, adult ♂♂, from Farallón Cave, Riopar, Albacete, Spain (Coll. AS). 113. Urosternite I of a large adult ♂. 114. Urosternite I. 115. Distal portion of appendage of a urosternite. 116. Detail of posterior portion of urosternite I with glandular g1 setae. 117. Urosternite VIII. 118. Lateral part of urosternite VII, with stylus.

Figs 196–201 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 196–201. Paratachycampa hispanica Bareth & Condé, 1981, from Cova dels Encenalls, Sant Mateu, Spain (Coll. AS). 196. Lateral distal view of a medial antennomere with gouge sensilla. 197. Basal view of a gouge sensillum. 198. Pretarsus of a metathoracic leg. 199. Lateral view of pretarsus, metathoracic leg. 200. Distal part of lateral process of pretarsus, metathoracic leg. 201. Apical part of lateral process of pretarsus, metathoracic leg.

Figs 59–64 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 59–64. Plusiocampa (Plusiocampa) hoffmanni Sendra & Paragamian sp. nov., from Varathro Stou Bokou ton Poro, Krousonas, Crete (59, 62–64) and Varathro Mythia Kabathoura, Rethymno, Crete (60–61) (Coll. AS). 59. Cupuliform organ. 60. Olfactory chemoreceptors. 61. Medial antennomere. 62. Latero-distal view of medial antennomere with gouge sensilla. 63. Gouge sensilla, detail. 64. Gouge sensilla of medial antennomere.

Figs 42–43 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 42–43. Plusiocampa (Plusiocampa) bulgarica Silvestri, 1931, from Sbrikovata Cave, Pamporovo, Bulgaria (Coll. AS). 42. Pretarsus, dorsal view. 43. Pretarsus, latero-posterior view.

Figs 51–52 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 51–52. Plusiocampa (Plusiocampa) fagei Condé, 1955, from Cova Sa Gleda, Manacor, Mallorca Island (Coll. AS). 51. Lateral distal side of a medial antennomere. 52. Detail of a gouge sensillum.

Figs 30–35 in Flourishing in subterranean ecosystems: Euro-Mediterranean Plusiocampinae and tachycampoids (Diplura, Campodeidae)

Figs 30–35. Plusiocampa (Plusiocampa) bonneti condei Sendra & Escolà, 2004, from Cova del Toll, Moià, Barcelona, Spain (Coll. AS). 30. Cupuliform organ. 31. Medial antennomere. 32. Frontal process. 33. Lateral distal view of medial antennomere, with gouge and coniform sensilla 34. Protruding frontal process. 35. Surface of metanotum.