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3,479 results for “Italie”
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Italy
<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_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2022_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2021_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2020_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> <li>TSE_2019_IT: Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle d'Aosta (IZSTO)</li> </ul>
Emilia-Romagna coastal area NBS (OAL ITALY) for storm surge mitigation
<p>Within the framework of the OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM) project, the seagrass NBS is presented within a simulation design methodology consisting of the comparison between validated wave numerical simulations for the present/ future climate, and modified wave simulations with marine seagrass. Ten years of WWIII simulations have been executed to generate the wave climatology, particularly over the Emilia-Romagna coastal strip for the present (2010-19) and future climate (2040-49) using MedCordex winds (based on RCP8.5). The WWIII model was modified to include a modified bottom dissipation stress due to submerged vegetation, thereby incorporating the NBS4 as a potential mechanism for wave amplitude reduction. The seagrass species <em>‘Zostera marina’</em> was chosen in this study and an along-shore seagrass belt was first inserted in WWIII and sensitivity experiments were carried out to assess the effects of different types of seagrass landscape designs in the Bellocchio beach. Simulation experiments with and without seagrass (NBS4) were carried out for the present and future climates. Based on the present and future climate simulations, it is noted that the seagrass landscaping is an important aspect in the numerical modelling of vegetation. A combination of broken vegetation stripes and clusters were seen to be effective in reduction of wave energy at the coast in comparison to other landscape designs. The wave height comparisons in the Bellocchio beach, with and without vegetation showed a considerable reduction in wave heights specifically in the higher ranges for both the present and future climates. There exists a strong seasonality in the attenuation rates along the coastal belt with higher attenuations during winter and comparatively lower in summer. In comparison to the present climate, a slightly increased rate of mean attenuation is expected in the future scenarios. Overall, the Zostera Marina seagrass applied for the Emilia-Romagna coastal belt was found to be efficient in reduction of wave energy (> 50%). The limitation being that the experiments were done with rigid seagrass and in the future, we look for advanced parameterization using flexible seagrass.</p> <p>This dataset contains wave model outputs for the OAL-ITALY, mainly:</p> <ul> <li>Bathymetry of the model domain, Spatial maps of mean significant wave height (Hs in m) for present (2010-19) and future climate (2040-49), Seagrass belt position in the Bellocchio beach, Time-series comparison of Hs, with & without vegetation, and Wave attenuation maps.</li> </ul> <ul> <li>Selected locations (station map) for the time series in the Emilia-Romagna coastal belt during the period 2010-19, and 2040-49 (8 stations), Selected locations (station map) in the Emilia-Romagna coastal belt for the time series comparison (with and without vegetation) during the period 2010-19, and 2040-49 (5 stations).</li> </ul> <ul> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2010-19, present climate).</li> <li>WW3 time series of wave parameters (wave height, peak period, & direction) for 8 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> <li>WW3 time series of significant wave height (Hs in metres) with and without vegetation for 5 stations in the Emilia-Romagna coastal belt (2040-49, future climate).</li> </ul>
Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)
<p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li><strong>Nurseries: </strong>A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li><strong>Nurseries: </strong>ornamental plants, trees, flowers.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2019) <em>Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. </em><strong>Sustainability </strong>2019, <em>11</em>(7), 1846</p> <p>https://www.mdpi.com/2071-1050/11/7/1846</p>
Database of geo-hydrological hazards in Apulia (Italy)
<p>Geospatial database containing data on geo-hydrological processes (Landslides, Floods, Sinkholes) and/or related damage, occurred between 2008 and 2019 in the Apulia Region (Italy).</p> <p>We provide a GPKG file containing multiple layers for the different types of geometries (point, line, polygon).<br> Data are extracted from a complex relational database structure described originally here https://doi.org/10.1016/j.jenvman.2017.11.022 but recently updated and improved.<br> For the different damage and phenomena we provide information about type, data and time of occurrence, temporal and spatial accuracy, main predisposing factor, etc..<br> For floods we provides codes and information compliant with the EC Flood Directive.</p> <p>The different phenomena and damages are grouped based on the meteorological event responsible for their occurrence.</p>
CoMix social contact data (Italy)
<p>CoMix social contact data for Italy.</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 Daniela Paolotti at the ISI Foundation.</p>
Web requests analysis of Italy websites which use Google Analytics
<p>List of 504,038 domains of Italy found to contain Google Analytics.</p> <p>The front page for Italy-related domain names has been accessed through HTTPS or HTTP and analysed with webbkoll and jq to gather data about third-party requests, cookies and other privacy-invasive features. Together with the actual URL visited, the user/property ID is provided for 495,663 domains (extracted either from the cookies deposited or the URL of requests to Google Analytics). MX and TXT records for the domains are also provided.</p> <p>The most common ID found was 23LNSPS7Q6, with over 35k domains calling it (seemingly associated with italiaonline.it). The most common responding IP addresses were 3 AWS IPv4 addresses (over 40k domains) and 2 CloudFlare IPv6 addresses (over 12k domains).</p>
The soil province feature of Italy at the 1:1,000,000 scale
<p>Updated version of the feature Soil province of Italy at the 1:1,000,000 scale. The original geodatabase (version 1.0 - https://zenodo.org/record/7072306) is not replaced because collecting other features debribed by references. This version partially becomes observations raised by regional officiers of Regione Emilia Romagna and Regione Veneto: few map units have been splitted in order to better conform to regional features. This new map reports 51 map units.</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 - Italy
<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>
Human resources for research and innovation in Italy
<p>Human resources play a crucial role in enabling research and innovation. Key players include university students, PhD and master graduates, researchers holding a European grant supporting excellent researchers in carrying out ground-breaking, high-risk, high-gain, frontier research projects, entrepreneurs engaged in spin-offs, startups or innovation project supported by Horizon 2020 <em>SME instrument</em> grants.</p> <p>Data is generally available, but often it is not easy to use due to different formats and vocabularies and the variety of geographical references (city names, province, region or zip codes).</p> <p>This file collection is part of ongoing research work carried out by the sustainability unit at Area Science Park. Data is collected from a variety of open sources, curated and prepared for further analysis. The focus is on Italy; geographical references use EUROSTAT NUTS-2 and NUTS-3 taxonomy.</p> <p>Data available: </p> <ul> <li>Maps of NUTS2 and NUTS2 regions in Italy</li> <li>NUTS2 and NUTS3 names in Italian</li> <li>Universities </li> <li>Phd and Masters graduates since 2010</li> <li>University spin-offs </li> <li>Innovative Startups</li> <li>Horizon 2020 grants for researchers: "<em>Marie Skłodowska Curie</em>" and "<em>European Research Council</em>"</li> <li>Horizon 2020 grants "<em>SME instrument</em>"</li> </ul> <p>Python scripts for data preparation are available in script.zip; development version is available on <a href="https://gitlab.com/area-science-park-sustainability/it_regional_innovation">this GitLab repository</a><br> Some examples of visual representation of the data are available in .pdf format and as <a href="https://app.powerbi.com/view?r=eyJrIjoiMWMyMjA1OWQtMzJmNi00NWJmLTk1OTctMzczZWUxYjYzYzFmIiwidCI6ImQ0YWFmY2E2LWJmMzUtNDUxNS1iMDZhLTQ5NzNjZGZiYmVkMyIsImMiOjh9&pageName=ReportSection3090d63ae7727ef701e8">online interactive visualization report.</a></p>
Atomic clock dataset for 'Coherent Optical-Fiber Link Across Italy and France'
<p>Dataset of the comparison of the atomic clocks at LNE-SYRTE and INRIM via optical fibre link between October 2021 and February 2022. Results discussed in Clivati et al., Coherent Optical-Fiber Link Across Italy and France, <em>Phys. Rev. Applied, American Physical Society, </em><em> 18</em>, 054009, <strong>202<em>2</em></strong>.</p> <p>The involved atomic clocks are the Cs fountains SYRTE-F02Cs, IT-CsF2, the Rb fountain SYRTE-F02Rb and the Yb optical lattice clock IT-Yb1.</p> <p>Data is organized in folders, one for each comparison. In the folders data is separated is one file per day. Data is reported as fractional frequency ratios in bins of 864 s. Timetags are reported in modified Julian date (MJD). A validity flag is given where 0 = invalid, valid otherwise. Each folder includes a yaml file with metadata required for generalized data processing as in [Lodewyck et al., 2020]. The Python package used for data processing can be found on <a href="https://github.com/INRIM/tintervals">github.</a></p> <p> </p>
Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy'
<h2><strong>Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy' </strong></h2> <p><strong>Compendium DOI: </strong></p> <p><a href="https://doi.org/10.5281/zenodo.10639553">https://doi.org/</a><a href="../doi/10.5281/zenodo.10639552">10.5281/zenodo.10639552</a></p> <p>The content available at the above provided URL will reproduce the results as documented in the first paper's submission. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno">https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno</a> represent the developmental versions and might have undergone modifications since the paper's publication.</p> <p><strong>Maintainer of this repository: </strong></p> <p>Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>; <a href="https://orcid.org/0000-0002-3255-1005">https://orcid.org/0000-0002-3255-1005</a>) </p> <p><strong>Published paper:</strong></p> <p>Armando Falcucci, Simona Arrighi, Vincenzo Spagnolo, Matteo Rossini, Owen Higgins, Brunella Muttillo, Ivan Martini, Jacopo Crezzini, Francesco Boschin, Annamaria Ronchitelli, Adriana Moroni. A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy. <em>Scientific Reports</em>, 14: 12783. doi:10.1038/s41598-024-59896-6 (2024)</p> <p><strong>Abstract:</strong></p> <p>The Aurignacian is the first European technocomplex assigned to Homo sapiens recognized across a wide geographic extent. Although archaeologists have identified marked chrono-cultural shifts within the Aurignacian mostly by examining the techno-typological variations of stone and osseous tools, unraveling the underlying processes driving these changes remains a significant scientific challenge. Scholars have, for instance, hypothesized that the Campanian Ignimbrite (CI) super-eruption and the climatic deterioration associated with the onset of Heinrich Event 4 had a substantial impact on European foraging groups. The technological shift from the Protoaurignacian to the Early Aurignacian is regarded as an archaeological manifestation of adaptation to changing environments. However, some of the most crucial regions and stratigraphic sequences for testing these scenarios have been overlooked. In this study, we delve into the high-resolution stratigraphic sequence of Grotta di Castelcivita in southern Italy. Here, the Uluzzian is followed by three Aurignacian layers, sealed by the eruptive units of the CI. Employing a comprehensive range of quantitative methods—encompassing attribute analysis, 3D model analysis, and geometric morphometrics—we demonstrate that the key technological feature commonly associated with the Early Aurignacian developed well before the deposition of the CI tephra. Our study provides thus the first direct evidence that the volcanic super-eruption played no role in this cultural process. Furthermore, we show that local paleo-environmental proxies do not correlate with the identified patterns of cultural continuity and discontinuity. Consequently, we propose alternative research paths to explore the role of demography and regional trajectories in the development of the Upper Paleolithic.</p> <p><strong>Keywords:</strong></p> <p>Early Upper Paleolithic; Italy; Aurignacian; lithic technology; geometric morphometrics; 3D model analysis; cultural evolution; human-environment interaction; open science.</p> <p><strong>Overview of contents and how to reproduce:</strong></p> <p>Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the complete dataset, the core dataset, and 2D outline coordinates utilized for the geometric morphometrics study. To replicate the results, download the entire repository and employ <code>Castelcivita-Aur-Techno.Rproj</code> and open the folder <code>script</code>, following the numbered folder structure. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here.</p> <p><strong>Licenses:</strong></p> <p>Code: <strong>MIT </strong>(<a href="http://opensource.org/licenses/MIT">http://opensource.org/licenses/MIT),</a> copyright holder: Armando Falcucci (2024).</p> <p><strong>Data and intellectual work:</strong> Creative Commons Attribution 4.0 International License (<a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).</p>
Exploration of historical mining site - Mezzano iron mines (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)
<p>Exploration of the historical mining site of Mezzano (San Bartolomeo, Cavargna Valley, Italy, 06/01/2024)</p> <p>- Main ore minerals: pyrite, chalcopyrite, siderite, aragonite</p> <p>- Provisional References:</p> <ul> <li>https://www.valcavargna.org/luoghi_di_interesse/miniere-di-mezzano/#:~:text=Le%20miniere%20di%20Mezzano&text=A%20partire%20dagli%20ultimi%20anni,Fratelli%20Campioni%20l'anno%20seguente.</li> <li>https://www.isprambiente.gov.it/it/attivita/museo/regioni/musei/miniera-di-mezzano</li> <li>https://www.valcavargna.org/tradizioni_popolari/vecchi-mestieri/siderurgia/</li> <li>https://www.research.unipd.it/handle/11577/3465257</li> <li>http://www.cmalpilepontine.it/cmvlarcer/zf/index.php/servizi-aggiuntivi/index/index/idtesto/13</li> </ul>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Circulation type classifications for surface temperature and precipitation optimized for Italy
<p>The four files are two couple of files for two circulation type classifications (pct9 and san9) optimized for Italy, in order to stratify precipitation and surface temperature respectively.</p> <p>"pct9.cla" and "san.cla" are the circulation type daily series between 1979 and 2015 computed on mean sea level pressure (MSLP) and geopotential height at 500 hPa (500HGT) respectively. Meteorological fields are extracted by the NCEP-NCAR Reanalysis 2 dataset.</p> <p>"pct-nc.txt" and "san9-nc.txt" are the centroid values of MSLP and 500HGT respectively, computed on 9 classes over a spatial domain of 7 X 7 grid points across Italy.</p> <p>These files are created through the COST733 software package (DOI: 10.1002/joc.3920). </p> <p>The pct9 and san9 classifications were selected as the best performing for the stratifacation of precipitation and surface temperature respectively across Italian peninsula, through a sensitivity analysis detailed in a specific study (DOI: 10.1002/joc.5219). In summary several circulation type classifications were computed with different classification methods, number of types and classification variables (i.e. predictands). Then such classifications were compared through the use of proper statistical indexes in order to assess the stratification of the ground-level precipitation and the surface air temperature across Italian peninsula.</p> <p>These two classifications could be evaluated also for other meteorological or environmental variables.</p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2017
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p> <p> </p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p>
STORM Project: monitoring environment conditions at Baths of Diocletian site (Rome, Italy). Dataset 2018 - 2019
<p>This dataset was created by the Engineering Ingegneria Informatica S.p.A. through a set of prototypes based on Libelium Waspmote for collecting the following parameters: </p> <ul> <li>Climate parameters (Temperature, Relative Humidity, Barometric Pressure, Luminosity, Wind direction/speed and Rainfull) using a Libelim PlugAndSense Agricolture Pro;</li> <li>Environmental Parameters (Monoxide Carbon, Oxigen, Air Polluction, Volatile Organic Compounds VOC, Carbon Dioxide, Nitric Dioxide , Hydrogen Sulfide, Sulfure Dioxide and Particle Matter PM 1, 2.5 and 10) using two nodes: PlugAndSense Smart Cities Pro and Waspmote with gases sensor board;</li> <li>Acoustic Noise Sensor and Vibrations with accelerometer, using a prototype based on Libelium Waspmote.</li> </ul> <p>The data produced by the sensors were acquired and sent to the Meshlium (mini-pc linux based) which automatically saved and sent to the STORM Platform. The dataset is composed of the data obtained from February 2018 to March 2019.</p> <p>STORM (Safeguarding Cultural Heritage through Technical and Organisational Resources Management) is a HORIZON 2020 funded European Union Cultural Heritage project that aims at the protection of Cultural Heritage through a combination of technical and organizational resources (http://www.storm-project.eu).</p>
A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)
<p><strong>We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. <br> Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.</strong></p>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
Inventory of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) in May 2023
<p>The dataset contains 49103 landslides, that were manually mapped by visual inspection of pre- and post-event satellite images in an area of 8981 km2. Such images are acquired by PlanetScope satellites (<a href="https://www.planet.com/">https://www.planet.com/</a>) and are provided under an academic license; 3-m resolution multiband tiles are used.</p> <p>Pre-event imagery refers to the Monthly Global Basemap products provided by Planet, the April 2023 Basemap was used. Post-event images were acquired between 22 May and beginning of June 2023. The cloud-free image closer to the event was used and multi-temporal frames were checked in selected areas (e.g., due to the presence of shadows or unclear images). Images are accessed through the Planet QGIS Plugin.</p> <p>This dataset supersedes version 1, since it represents its update; major changes include:</p> <ul> <li>mapping over a wider area (8981 vs 5764 km2);</li> <li>check on the landslides mapped in version 1 located on flat slopes (lower than 5°); removal of polygons associated with river erosion and not due to gravity movements</li> </ul> <p> </p> <p>NOTES ON VERSION 1</p> <p>landslides were manually mapped at a scale of 1:5.000 by a single operator in a time interval of 5 weeks following the rainfall event; the inventory (version 1.0) was completed on 28 June 2023. Please note that data did not undergo any kind of validation.</p> <p>Data are provided in shapefile format (coordinate system WGS84 UTM 32N) and in kml format.</p> <p>The main dataset is the “Emilia landslides” shp/kml file; the “area” shapefile refers to the investigated area; the “riverbank and agricultural fields” files include polygons that were mapped but refer either to river courses having high discharge in the post-event images, or to color changes probably due to farming activities or the evolution of agricultural fields. The “riverbank and agricultural fields” elements should not refer to slope movements, and usage of these data is not recommended, unless a validation is made.</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.