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1,696 results for “Greece”
Atmospheric Halocarbon Observations at Finokalia, Crete, Greece
<p>Atmospheric halocarbon (HFC, HCFC) observations (mole fractions) from the site Finokalia (FKL, 35.34 °N, 25.67 °E, 250 m a.s.l.) on the island of Crete, Greece, covering the period December 2012 to August 2013). The measurements were conducted using a gas chromatograph<br> (Agilent 6890) and:mass spectrometer (Agilent 5973) (GC-MS), coupled to an adsorption desorption system (ADS) for preconcentration of samples from the air (Simmonds et al., 1995).</p> <p>The measurements are described in detail in: Schoenenberger, F., S. Henne, M. Hill, M. K. Vollmer, G. Kouvarakis, N. Mihalopoulos, S. O'Doherty, M. Maione, L. Emmenegger, T. Peter, and S. Reimann (2017), Abundance and Sources of Atmospheric Halocarbons in the Eastern Mediterranean, Atmos. Chem. Phys. Discuss., 2017, 1-46, doi: 10.5194/acp-2017-451.</p> <p>The data format is plain text character-separated and follows that used in the AGAGE community. Further details are given at the AGAGE data archive: http://agage.eas.gatech.edu/data_archive/agage/</p>
FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece
<p><strong>dataset_greece.nc</strong></p> <p><strong>This dataset is meant to be used to develop models for next-day fire hazard forecasting in Greece. It contains the following variables for the years 2009 to 2021 at a daily 1km x 1km grid.</strong><br> </p> <table> <thead> <tr> <th scope="col"><strong>Variable</strong></th> <th scope="col"><strong>Units</strong></th> <th scope="col"><strong>Long Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td>avg_d2m</td> <td>K</td> <td>Avg 2 metre dewpoint temperature</td> <td>Daily Average 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>avg_rh</td> <td>%</td> <td>Avg Relative humidity</td> <td>Daily Average Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>avg_sp</td> <td>Pa</td> <td>Avg Surface pressure</td> <td>Daily Average Surface pressure ERA5-Land</td> </tr> <tr> <td>avg_t2m</td> <td>K</td> <td>Avg 2 metre temperature</td> <td>Daily Average 2 metre temperature ERA5-Land</td> </tr> <tr> <td>avg_tp</td> <td>m</td> <td>Avg total precipitation</td> <td>Daily Average Total precipitation ERA5-Land</td> </tr> <tr> <td>avg_u10</td> <td>m/s</td> <td>Avg 10 metre U wind component</td> <td>Daily Average 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>avg_v10</td> <td>m/s</td> <td>Avg 10 metre V wind component</td> <td>Daily Average 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>burned_areas</td> <td>unitless</td> <td>Rasterized burned polygons</td> <td>EFFIS (https://effis.jrc.ec.europa.eu/) burned areas burned as raster (value 1). Starting date retrieved with intersection with MODIS active fires</td> </tr> <tr> <td>et</td> <td>kg/m^2/8day</td> <td>8-day Evapotranspiration</td> <td>Total Evapotranspiration - MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid (MOD16A2)</td> </tr> <tr> <td>evi</td> <td>unitless</td> <td>16-day EVI</td> <td>Enhanced vegetation index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2)</td> </tr> <tr> <td>fapar</td> <td>%</td> <td>Fraction of Absorbed Photosynthetically Active Radiation</td> <td>FPAR - MOD15A2H MODIS/Terra Gridded 500M (8-day composite)</td> </tr> <tr> <td>fwi</td> <td>unitless</td> <td>Fire Weather Index</td> <td>Fire Weather Index 0.25 deg - https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview</td> </tr> <tr> <td>ignition_points</td> <td>unitless</td> <td>Rasterized ignition points</td> <td>Ignition points burned as raster (value 1) on the map calculated from intersection of MODIS active fires and EFFIS (https://effis.jrc.ec.europa.eu/) burned areas</td> </tr> <tr> <td>lai</td> <td>unitless</td> <td>Leaf Area Index</td> <td>Leaf Area Index (LAI) - MOD15A2H MODIS/Terra Gridded 500M Leaf Area Index LAI (8-day composite)</td> </tr> <tr> <td>lst_day</td> <td>K</td> <td>Day Land Surface Temperature</td> <td>Day Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>lst_night</td> <td>K</td> <td>Night Land Surface Temperature</td> <td>Night Land Surface Temperature (LST) - MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid (MOD11A1)</td> </tr> <tr> <td>max_d2m</td> <td>K</td> <td>Max 2 metre dewpoint temperature</td> <td>Daily Maximum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>max_rh</td> <td>%</td> <td>Max Relative humidity</td> <td>Daily Maximum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>max_sp</td> <td>Pa</td> <td>Max Surface pressure</td> <td>Daily Maximum Surface pressure ERA5-Land</td> </tr> <tr> <td>max_t2m</td> <td>K</td> <td>Max 2 metre temperature</td> <td>Daily Maximum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>max_tp</td> <td>m</td> <td>Max Total precipitation</td> <td>Daily Maximum Total precipitation ERA5-Land</td> </tr> <tr> <td>max_u10</td> <td>m/s</td> <td>Max 10 metre U wind component</td> <td>Daily Maximum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>max_v10</td> <td>m/s</td> <td>Max 10 metre V wind component</td> <td>Daily Maximum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>max_wind_direction</td> <td>degrees</td> <td>Wind direction of Max Wind</td> <td>Daily Maximum wind speed direction calculated from the U, V components</td> </tr> <tr> <td>max_wind_speed</td> <td>m/s</td> <td>Max wind speed norm</td> <td>Daily Maximum wind speed calculated from the U, V components</td> </tr> <tr> <td>max_wind_u10</td> <td>m/s</td> <td>10 metre U wind of Max Wind</td> <td>Daily 10 metre U wind component of Maximum Wind</td> </tr> <tr> <td>max_wind_v10</td> <td>m/s</td> <td>10 metre V wind of Max Wind</td> <td>Daily 10 metre V wind component of Maximum Wind</td> </tr> <tr> <td>min_d2m</td> <td>K</td> <td>Min 2 metre dewpoint temperature</td> <td>Daily Minimum 2 metre dewpoint temperature ERA5-Land</td> </tr> <tr> <td>min_rh</td> <td>%</td> <td>Min Relative humidity</td> <td>Daily Minimum Relative humidity calculated from t2m and d2m</td> </tr> <tr> <td>min_sp</td> <td>Pa</td> <td>Min Surface Pressure</td> <td>Daily Minimum Surface pressure ERA5-Land</td> </tr> <tr> <td>min_t2m</td> <td>K</td> <td>Min 2 metre temperature</td> <td>Daily Minimum 2 metre temperature ERA5-Land</td> </tr> <tr> <td>min_tp</td> <td>m</td> <td>Min Total precipitation</td> <td>Daily Minimum Total precipitation ERA5-Land</td> </tr> <tr> <td>min_u10</td> <td>m/s</td> <td>Min 10 metre U wind component</td> <td>Daily Minimum 10 metre U wind component ERA5-Land</td> </tr> <tr> <td>min_v10</td> <td>m/s</td> <td>Min 10 metre V wind component</td> <td>Daily Minimum 10 metre V wind component ERA5-Land</td> </tr> <tr> <td>ndvi</td> <td>unitless</td> <td>16-day NDVI</td> <td>Normalized Difference Vegetation Index - MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid</td> </tr> <tr> <td>number_of_fires</td> <td>unitless</td> <td>Daily number of fires</td> <td>Daily number of fires</td> </tr> <tr> <td>smian</td> <td>unitless</td> <td>Soil moisture index anomaly</td> <td>Soil Moisture Index Anomaly 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>sminx</td> <td>unitless</td> <td>Soil moisture index</td> <td>Soil Moisture Index 10day, 5km Europe - EDO https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112</td> </tr> <tr> <td>ASPECT</td> <td>degrees</td> <td>Aspect</td> <td>Aspect calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>CLC_2006</td> <td>unitless</td> <td>Mode of Corine Land Cover 2006</td> <td>Mode of Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2006_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2006</td> </tr> <tr> <td>CLC_2012</td> <td>unitless</td> <td>Mode of Corine Land Cover 2012</td> <td>Mode of Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2012_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2012</td> </tr> <tr> <td>CLC_2018</td> <td>unitless</td> <td>Mode of Corine Land Cover 2018</td> <td>Mode of Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_0</td> <td>/100 %</td> <td>Fraction of lc class 0 (artificial_surfaces)</td> <td>Fraction of class 0 (artificial_surfaces), Corine Class Codes [1, 3, 4, 5, 6, 7, 8, 9 , 10, 11], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_1</td> <td>/100 %</td> <td>Fraction of lc class 1 (discontinuous_urban)</td> <td>Fraction of class 1 (discontinuous_urban), Corine Class Codes [2], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_2</td> <td>/100 %</td> <td>Fraction of lc class 2 (arable_land)</td> <td>Fraction of class 2 (arable_land), Corine Class Codes [12, 13, 14], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_3</td> <td>/100 %</td> <td>Fraction of lc class 3 (permanent_crops)</td> <td>Fraction of class 3 (permanent_crops), Corine Class Codes [15, 16, 17], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_4</td> <td>/100 %</td> <td>Fraction of lc class 4 (pastures)</td> <td>Fraction of class 4 (pastures), Corine Class Codes [18], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_5</td> <td>/100 %</td> <td>Fraction of lc class 5 (general_agricultural)</td> <td>Fraction of class 5 (general_agricultural), Corine Class Codes [19, 20, 21, 22], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_6</td> <td>/100 %</td> <td>Fraction of lc class 6 (forest)</td> <td>Fraction of class 6 (forest), Corine Class Codes [23, 24, 25], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_7</td> <td>/100 %</td> <td>Fraction of lc class 7 (misc_vegetation)</td> <td>Fraction of class 7 (misc_vegetation), Corine Class Codes [26, 27, 28, 29], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_8</td> <td>/100 %</td> <td>Fraction of lc class 8 (misc_no_vegetation)</td> <td>Fraction of class 8 (misc_no_vegetation), Corine Class Codes [30, 31, 32, 33, 34], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>CLC_2018_9</td> <td>/100 %</td> <td>Fraction of lc class 9 (water)</td> <td>Fraction of class 9 (water), Corine Class Codes [>=35], for every grid cell, based on Corine Land Cover 2018</td> </tr> <tr> <td>DEM</td> <td>m</td> <td>Elevation</td> <td>Averaged Digital Elevation Model EU-DEM</td> </tr> <tr> <td>POP_DENS_2009</td> <td>humans/km^2</td> <td>Population density (2009)</td> <td>Population density for year 2009 from Worldpop</td> </tr> <tr> <td>POP_DENS_2010</td> <td>humans/km^2</td> <td>Population density (2010)</td> <td>Population density for year 2010 from Worldpop</td> </tr> <tr> <td>POP_DENS_2011</td> <td>humans/km^2</td> <td>Population density (2011)</td> <td>Population density for year 2011 from Worldpop</td> </tr> <tr> <td>POP_DENS_2012</td> <td>humans/km^2</td> <td>Population density (2012)</td> <td>Population density for year 2012 from Worldpop</td> </tr> <tr> <td>POP_DENS_2013</td> <td>humans/km^2</td> <td>Population density (2013)</td> <td>Population density for year 2013 from Worldpop</td> </tr> <tr> <td>POP_DENS_2014</td> <td>humans/km^2</td> <td>Population density (2014)</td> <td>Population density for year 2014 from Worldpop</td> </tr> <tr> <td>POP_DENS_2015</td> <td>humans/km^2</td> <td>Population density (2015)</td> <td>Population density for year 2015 from Worldpop</td> </tr> <tr> <td>POP_DENS_2016</td> <td>humans/km^2</td> <td>Population density (2016)</td> <td>Population density for year 2016 from Worldpop</td> </tr> <tr> <td>POP_DENS_2017</td> <td>humans/km^2</td> <td>Population density (2017)</td> <td>Population density for year 2017 from Worldpop</td> </tr> <tr> <td>POP_DENS_2018</td> <td>humans/km^2</td> <td>Population density (2018)</td> <td>Population density for year 2018 from Worldpop</td> </tr> <tr> <td>POP_DENS_2019</td> <td>humans/km^2</td> <td>Population density (2019)</td> <td>Population density for year 2019 from Worldpop</td> </tr> <tr> <td>POP_DENS_2020</td> <td>humans/km^2</td> <td>Population density (2020)</td> <td>Population density for year 2020 from Worldpop</td> </tr> <tr> <td>POP_DENS_2021</td> <td>humans/km^2</td> <td>Population density (2021)</td> <td>Population density for year 2021 from Worldpop</td> </tr> <tr> <td>ROAD_DISTANCE</td> <td>meters</td> <td>Distance from roads</td> <td>Distance from major roads from Worldpop</td> </tr> <tr> <td>ROUGHNESS</td> <td>unitless</td> <td>Roughness</td> <td>Roughness calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>SLOPE</td> <td>degrees</td> <td>Slope</td> <td>Slope calculated from Digital Elevation Model EU-DEM</td> </tr> <tr> <td>WATERWAY_DISTANCE</td> <td>meters</td> <td>Distance from waterways</td> <td>Distance from major waterways from Worldpop</td> </tr> </tbody> </table> <p>temporal_extent : (2009-03-06, 2021-08-29)</p> <p>spatial_extent : (18.7, 34.3, 28.9, 42.3)</p> <p>crs : EPSG:4326</p> <p>license : Creative Commons Attribution v4</p> <p>creators : Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis</p> <p>contact_person : Ioannis Prapas <iprapas (at) noa.gr></p> <p>citation : Ioannis Prapas, Spyros Kondylatos, & Ioannis Papoutsis. (2021). A Datacube for the analysis of wildfires in Greece (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4943354</p> <p>acknowledgements : This work has received funding from the European Union’s Horizon 2020 research and innovation project DeepCube, under grant agreement number 101004188.</p> <p>title : FireCube: A Daily Datacube for the Modeling and Analysis of Wildfires in Greece</p>
S57 | GREEKPHARMA | Suspect Pharmaceuticals from the National Organization of Medicine, Greece
<p>This is the dataset associated with list S57 GREEKPHARMA on the NORMAN Suspect List Exchange:</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p>
Holly Church of Saint Spyridon. Saint Spyridon the Miracle-worker and Keeper of Corfu City. Old Town of Corfu. Greece. Bell tower at night (en). Ιερός Ναός Αγίου Σπυρίδωνα. Άγιος Σπυρίδων ο Θαυματουργός και Πολιούχος της Πόλης της Κέρκυρας. Παλαιά Πόλη της Κέρκυρας. Ελλάδα. Κωδονοστάσιο τη νύχτα (ελ). Ierós Naós Agíou Spyrídōna. Ágios Spyrídōn o Thaumatourgós kai Polioúchos tēs Pólēs tēs Kérkyras. Palaiá Pólē tēs Kérkyras. Elláda. Kōdonostásio tē nýchta (el).
<p>Holly Church of Saint Spyridon.</p> <p>Saint Spyridon the Miracle-worker and Keeper of Corfu City.</p> <p>Old Town of Corfu.</p> <p>Greece.</p> <p>Bell tower at night </p>
Organisation for Economic Co-operation and Development (OECD) data for Antalya (Turkey), Antwerp (Belgium), Cork (Ireland), Thessaloniki (Greece) (source: OECD)
<p>The data have been collected via the official OECD Application Programming Interface (API)<strong> </strong>and<strong> </strong>includes the following indicators:</p> <ul> <li>EmpPlaRes - Employment at place of residence</li> <li>LfPartRa - Labour Force and Participation rate</li> <li>UnemReg - Unemployment in regions </li> <li>RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2)</li> <li>GDPLT3 - Gross Domestic Product (Small regions TL3)</li> <li>RegEmIndu - Regional Employment by industry (ISIC rev 4)</li> <li>RegGVAWorker - Regional GVA per worker</li> <li>RegIncPC - Regional income per capita</li> </ul> <p>Source: https://data.oecd.org/api/</p>
Philorhizus margii median lobe of aedeagus Figurre 2 Wrase DW, Assmann T (2008) A new species of Philorhizus Hope, 1838 from Greece (Coleoptera, Carabidae, Lebiini). ZooKeys 3: 1-10. doi: 10.3897/zookeys.3.19
<p>Wrase DW, Assmann T (2008) A new species of <em>Philorhizus </em>Hope, 1838 from Greece (Coleoptera, Carabidae, Lebiini). ZooKeys 3: 1-10. doi: 10.3897/zookeys.3.19</p>
Fig. 3 Habitat of Philorhizus marggii Wrase DW, Assmann T (2008) A new species of Philorhizus Hope, 1838 from Greece (Coleoptera, Carabidae, Lebiini). ZooKeys 3: 1-10. doi: 10.3897/zookeys.3.19
<p>Habitat of Philorhizus marggii<br> Greece: Peloponnese/Taygetos: Profitis Illias, subalpine altitude, 16.V.2007, 2000-2400m a.s.l.</p> <p> </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Greece
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2022_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2021_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2020_EL: Ministry of Rural Development and Food (MINAGRIC)</li> <li>TSE_2019_EL: Ministry of Rural Development and Food (MINAGRIC)</li> </ul>
CROSSBOW HLU2-UC4-TC6 Day ahead energy Price for the demonstration period in Greece
<p>For the evaluation of the curtailment distribution algorithm, an experiment was made with the forecast generation or RES assets in the area of Crete, Greece and the simulation of 30 limitations applied on random days. This dataset contains the DA energy prices in Greece at the time the demonstration was held</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
CoMix social contact data (Greece)
<p>CoMix social contact data for Greece.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Elpida Pavi at the University of West Attica (UniWA).</p>
773782 COASTAL EU Project: stream data from SW Messinia, Greece
<p>In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements.</p> <p>Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Greece
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Greece
<p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>contains the Greek questionnaire data set for Wave 1 of Work Pachage 7 of Catch-EyoU project (Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions; funded by the European Commission under the Horizon 2020 Programme; GA number: 649538; 2015-2018; http://www.catcheyou.eu/). This dataset contains the data underlying the publications presented in the References section (see below). </p> <p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>corresponds to the responses of participants to the questionnaire study for the purposes of Catch-EyoU project, WP7, Wave 1, collected in December 2016. These were entered and coded as either numeric or alphanumeric variables. Detailed information on variable names and labels is included in the portable file itself and it is also provided in the accompanying README file.</p>
SSR genotypes of oak parents and progeny in Pentalofos Forest Greece
<p>Two files (datasets) with multi-locus genotypes at 17 nuclear microsatellites; one containing the genotypes of all 59 adult trees within a mixed oak forest stand in the Pentalofod Forest in Evros County in Greece and the other the genotypes of 110 juvenile trees in the same stand. A comparison can be performed for parentage assignement and studies of hybridization. Oak species: <em>Quercus frainetto, Q. petraea, Q pubescens.</em></p>
Ground Vertical and east Velocities for the Western Gulf of Corinth, Greece, combining InSAR and GPS
<p><strong>Data group</strong> :<br> Ground Vertical and East Velocities for the Western Gulf of Corinth combining InSAR and GPS, for the period 2002-2010</p> <p><strong>Data identifiers</strong> : </p> <ol> <li>Best constrained 951 vertical and east PS-SBAS velocities for pixels of 200m</li> <li>Best constrained 4391 vertical and east PS-SBAS velocities for pixels of 200m</li> </ol> <p><strong>Version</strong> : 1.0</p> <p><strong>Coordinate Reference System</strong>: Geographic WGS84, EPSG:4326</p> <p><strong>Citation</strong>: Elias & Briole, 2018, Ground deformations in the Corinth rift, Greece, investigated through the means of SAR multi-temporal interferometry<br> </p>
Data package for modeling the journey of Colonel William Leake in the southern Mani Peninsula, Greece, using least-cost analysis
<p>Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, <em>Travels in the Morea: Volume I </em>(Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, 'Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,' is published in <em>Journal of Archaeological Science: Reports</em> and can be accessed here: <a href="http://doi.org/10.1016/j.jasrep.2019.01.014">https://doi.org/10.1016/j.jasrep.2019.01.014</a>. The article pre-print can be accessed here: <a href="https://works.bepress.com/rebecca-seifried/11/">https://works.bepress.com/rebecca-seifried/11/</a>.</p> <p>Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled 'Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)' (<a href="http://hdl.handle.net/10027/21274">https://hdl.handle.net/10027/21274</a>). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the 'CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,' whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula.</p> <p>This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.</p>
(PRE) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria
<p>The dataset has been created from obtaining answers from 303 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Questionnaire.pdf</strong>). It is composed by several factors which are explained in different TXT files. All these factors are contained in a "<strong>all_code_final_zenodo.xlsx</strong>" along with their answers by participants. In the following a short descrition of each TXT which explian the dataset is provided.:</p> <p> * <strong>socio-economic_description_not_dependent_of_work</strong><br> - Contains all the information from participants which is irrespective of their current workplace. This file contains typical socio-demographic and cultural attributes from respondents.</p> <p> * <strong>socio-economic_description_dependent_of_work</strong><br> - Contains socio-economic and cultural information from participants which is relevant to the workplace in relation to energy efficient practices in such environment.</p> <p> * <strong>actions-at-work</strong><br> - Are a set of attributes which describe certain practices of employees in relation to energy efficiency.</p> <p> * <strong>persuasive_strategies</strong><br> - Explain the ratings from 1 to 5 that participants attributed to a set of persuasive strategies. These strategies are framed within Phychological Persuasive principles which are also explained in the file.</p> <p> * <strong>all_attributes_together</strong><br> - All the variables together without distinction of the environment where they are applicable.</p> <p>Finally, plots from every construct or attribute are provided in a zip file (<strong>plots_descriptive_analysis_per_city.zip</strong>) which contains the plots uploaded in "PNG" extension</p>
National Checklists 2017: Greece Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Greece collected using effechecka and geonames polygons
National Checklists 2019: Greece Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Greece collected using effechecka and geonames polygons
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.