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3,479 results for “Italie”
National Checklists 2017: Italy 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 Italy collected using effechecka and geonames polygons
National Checklists 2019: Italy 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 Italy collected using effechecka and geonames polygons
Partial bibliography on J.E.Eielson and R. Deustua in Italy.
<p>Partial bibliography about J.E.Eielson and R. Deustua in Italy. LATILMA studied the relationship between Italy and Peru and this led to a focus on the work of some poets belonging to the “Generation of 50” who lived in Italy. These include J.E.Eielson and R.Deustua, about whom I have written two papers that are currently being evaluated.</p> <p>This bibliography includes information about the corpus, secondary bibliography but also theoretical and methodological references cited in the aforementioned articles, not exclusively related to the corpus.</p>
Northern Italy gap-filled MODIS Land Surface Temperature 1km daily
<p>Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.</p> <p>The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.</p> <ul> <li>Spatial resolution: 1 km</li> <li>Temporal resolution: 1 day</li> <li>Temporal extent: 2014-2018</li> <li>Units: Celsius</li> <li>Aggregation method: average</li> <li>Format: stored as a <a href="https://grass.osgeo.org/">GRASS GIS</a> 8+ project</li> <li>Software used: GRASS GIS 8.4.0</li> </ul> <p>Reference:<br><br>Metz, M.; Andreo, V.; Neteler, M. <em>A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data</em>. Remote Sens. 2017, 9, 1333. <a href="https://doi.org/10.3390/rs9121333">https://doi.org/10.3390/rs9121333</a></p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod09a1v006/">https://lpdaac.usgs.gov/products/mod09a1v006/</a></p> <p>Data provided by:</p> <p>mundialis GmbH & Co. KG<br>Koelnstrasse 99<br>53111 Bonn, Germany<br><a href="https://www.mundialis.de">https://www.mundialis.de</a></p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Surface water loss hotspots and areas of human pressure in Italy
<p>In Italy, surface water bodies are the main source of water withdrawals. However, growing human pressures are significantly changing surface water availability, gradually reducing its extent.</p> <p>We analyze the influence of human activities on surface water losses occurred in Italy between 1984 and 2021. To do so, we identify three areas of human pressure, i.e., regions of human activities that heavily rely on the use of surface water:</p> <ol> <li>Irrigated area (IRR);</li> <li>Built-up area (BUP), indicating areas of human settlements (urban and industrial areas);</li> <li>Anthropogenic area (ANT), indicating areas of either irrigation practices or human settlements.</li> </ol> <p>Here, we provide the datasets describing the spatial distribution of surface water loss (SWL), irrigated areas, built-up areas, and anthropogenic areas, and the land cover classification for 2021 across Italy (LC). Such datasets have been derived from remotely-sensed products. In particular, the location of SWL is determined using the Transitions layer of the Global Surface Water dataset (Pekel et al., 2016), whereas the maps of irrigated and built-up areas are obtained from the Corine Land Cover (CLC) 2018 dataset (EEA, 2018). Finally, the land cover map is extracted from the ESA WorldCover map (version 2) for the year 2021 (Zanaga et al., 2022).</p> <p>In the map of SWL, irrigated areas, built-up areas, and anthropogenic areas the value 1 indicates the presence of SWL or irrigated area or built-up area or anthropogenic area, respectively. The 2021 land cover map follows the classification system of the ESA WorldCover map (11 classes).</p> <p>References:</p> <p><em>Pekel, JF.; Cottam, A.; Gorelick, N.; Belward, A.S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422.</em></p> <p><em>European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA).</em></p> <p><em>Zanaga, D.; Van De Kerchove, R.; Daems, D.; De Keersmaecker, W.; Brockmann, C.; Kirches, G.; Wevers, J.; Cartus, O.; Santoro, M.; Fritz, S.; Lesiv, M.; Herold, M.; Tsendbazar, N.E.; Xu, P.; Ramoino, F.; Arino, O. ESA WorldCover 10 m 2021 v200, 2022.</em></p>
Improvement of regulations interpretation and formalisation for information need definition - Municipality of Ascoli Piceno, Italy
<p>CHEK Digital Building Permit Maturity Model (CDBPMM) as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit'.</p> <p>(CHEK) https://chekdbp.eu </p> <p>It is described in the CHEK project deliverable D2.1.</p> <p>This project has received funding from the European Union's Horizon Europe program under Grant Agreement No.101058559.</p> <p>The aim of CHEK is to remove barriers preventing municipalities from adopting digital building permit processes by developing, connecting, and aligning scalable solutions in the regulatory and policy context, in open standards and interoperability (geospatial and BIM), in closing knowledge gaps through education, in renewing municipal processes, and in deploying technology. </p>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
SERENA EJP Soil: Green House Gas Regulation Application Emilia-Romagna, Italy (Summer)
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
SERENA EJP Soil - Map of Soil Sealing of Italy
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
Trentino region (Italy) - NEVERMORE Climate Dataset
<p>The dataset consist of the historical and climate projection (CMIP6) for gridded atmospheric variables and the climate hazards/extreme events alongside the return values (likelihood) of hazards/extreme events. The dataset was developed during NEVERMORE project as part of WP3 from CMCC and NCSRD.</p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy (dataset)
<p>Dataset inherent to the following article:</p> <p>Medici, M., Canavari, C., Castellini, A., 2021. <em>Exploring the economic, social, and environmental dimensions of community-supported agriculture in Italy</em>, Journal of Cleaner Production, 316, 128233, DOI: <a href="https://doi.org/10.1016/j.jclepro.2021.128233">10.1016/j.jclepro.2021.128233</a></p>
Risk-perception, attitudes and behavioural intentions to spend on experiences in the post-Corona crisis: data from Italy, Denmark, China and Japan
<p>A cross-sectional survey conducted in Japan (n=1,111), Denmark (n=1,028), China (n=1,019) and Italy (n=1,014) during 10-24th of July 2020.</p> <p>Data format: sav (SPSS) and csv.</p>
UAV-based data for Lake Mulargia (Sardinia, Italy) (2020/09/23)
<p>This dataset contains MicaSense-derived data of Lake Mulargia (Sardinia, Italy) for the 23 September 2020. The acquisition was done by CGR Spa (Italy). Available products are: True-color image (RGB), at-sensor-radiance (TOA), and Bottom-of-atmosphere reflectance data.</p>
DESIS-derived water quality parameters for Lake Mulargia (Sardinia, Italy) (2020/08/17)
<p>This dataset contains DESIS-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 17 August 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). DESIS data courtesy of the German Aerospace Center (DLR, 2020).</p>
PRISMA-derived water quality parameters for Lake Mulargia (Sardinia, Italy) (2020/07/08)
<p>This dataset contains PRISMA-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 8 July 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). PRISMA data courtesy of the Italian Space Agency (ASI, 2020).</p>
Direct sun retrievals of nitrogen dioxide (NO2) total columns from Brewer #067, Rome, Italy (reprocessed with algorithm BNALG2)
<p>Cloud-screened and quality-filtered direct sun retrievals of nitrogen dioxide (NO2) vertical column densities (VCDs) derived from MkIV Brewer #067 measurements in Rome (wavelengths 425.02, 431.40, 437.35, 442.83, 448.08, and 453.20 nm) and processed using the Brewer Nitrogen Dioxide Algoritm BNALG2. Calibration is carried out with Bootstrap Estimation techniques. The values represent averages of 5 samples.</p> <p>In the latest version, days with obviously erroneous data (NO2 VCD > 99.9% percentile) have been removed.</p> <p>A detailed description of the method has been accepted as a research article by the ESSD journal (H. Diémoz et al., Advanced NO2 retrieval technique for the Brewer spectrophotometer applied to the 20-year record in Rome, Italy, Earth Syst. Sci. Data, 2021).</p>
SPHERA High Resolution Reanalysis over Italy - Hourly accumulated total precipitation
<p><strong>Please refer to the latest-released version (v2) of this dataset</strong></p> <p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at a hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly accumulated total precipitation for the period 1995-2020.</p> <p><strong>Update 09/10/2025</strong>: tpH Dataset Version 2 Released:</p> <p>A new version of the dataset (v2) has been published, incorporating the following improvements and corrections:</p> <ul> <li>Precipitation data have been cleaned to remove duplicated fields that were inadvertently included in the initial release. Additionally, the data have been decumulated to represent hourly precipitation values. In the original version, precipitation was reported as accumulations increasing over the day from 00 UTC to 23 UTC.This format has now been replaced by actual hourly precipitation totals, offering a representation that is more relevant and useful for most applications.</li> <li>Grid inconsistencies present in some GRIB messages have been resolved to ensure structural uniformity across the dataset.</li> <li>Data have been rescued for some of the data holes. In the cases when only 1 hour was missing from the original extraction, the field has been produced by averaging the two fields associated with the previous and next hours to ensure the most continuous data series as possible. Particularly this is the case for the following grib messages: <ul> <li>23 UTC of 31 December 1995</li> <li>23 UTC of 31 December 1998</li> <li>23 UTC of 19 February 2020</li> <li>23 UTC of 13-17-22-30 July 2020</li> <li>23 UTC of 4-14 August 2020</li> </ul> </li> </ul> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and the hourly surface air temperature at 2-meter height:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Italy, climate data analyst based on era5 land data
<p>These plots illustrate the results of a climatic analysis conducted in Italy using the ERA5 Land (Copernicus Climate Service), since 1950.</p>
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OpenNeuro
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