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3,478 results for “Italy”
Dataset on Weather-related disasters in agriculture in Italy - WDA
<h1><strong>Abstract</strong></h1> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Pontrandolfi A, Alilla R, De Natale F, Nuti R, Parisse B, Pepe AG, Dataset on Weather-related Disasters in Agriculture (WDA) in Italy 2005–2021, Data in Brief <br><a href="https://doi.org/10.1016/j.dib.2025.111323">https://doi.org/10.1016/j.dib.2025.111323</a></p> <p>The database on Weather-related disasters in agriculture (WDA) is a part of the cloud storage which hosts the materials of the <a href="https://agrometeo.crea.gov.it/">Observatory for agricultural meteorology and climatology</a> of the Research Center for Agriculture and Environment belonging to the Council for Agricultural Research and Economics (CREA). The Observatory website has a specific section devoted to <a href="https://agrometeo.crea.gov.it/dati-e-analisi__trashed/rischio-meteorologico-in-agricoltura/">weather-related risk in agriculture</a>.</p> <p>A specific relational SQL database has been created fo data entry information from the official decrees of WDA declaration in Italy.</p> <p>From this relational SQL database, a <strong>dataset </strong>of WDA has been extracted for the period from 2005 to 2021 and here published</p> <p>The WDA dataset aims to make available useful data for weather-related risk assessment and analysis in the Italian agricultural sector.</p> <h2>Attached content:</h2> <ul> <li>pdf file "A_Description_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "DiscoveryMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>xlsx file "StructuralMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> </ul>
Catalog of NE Italy earthquakes Mw with related velocimetric time series
<p>Mw catalog (xlsx format) of earthquakes occurred in Norheastern Italy from 2016 to 2023; the catalog reports estimations for:</p> <ul> <li>ML (Bragato and Tento, 2005);</li> <li>Mw calculated from SA (Moratto et al., 2017);</li> <li>Mw calculated from MT (Moment Tensor; Saraò et al., 2021);</li> <li>The tgz file with the corrected velocimetric waveforms (SAC fomat with P and S arrival times used for the locations and units in m/s); tgz file can be found in Waveforms.tgz. EVDP SAC header is expressed in meters.</li> </ul> <p>Continuous raw time series can be dowloaded from Oasis website (Priolo et al., 2015).</p> <p> </p>
N Italy Zooarchaeological Dataset
<p>This dataset contains zooarchaeological data relevant to cattle, sheep/goat, and pigs from lowland northern Italy, and an associated R analysis and visualisation script. This dataset supports the journal article:<br>A. Trentacoste, A. Nieto-Espinet, S. Guimarães Chiarelli, and Valenzuela-Lamas. (2023). Systems change: Investigating climatic and environmental impacts on livestock production in lowland Italy between the Bronze Age and Late Antiquity (c. 1700 BC - AD 700). <em>Quaternary International </em>662–663:26-36.<em> </em><a href="https://doi.org/10.1016/j.quaint.2022.11.005">https://doi.org/10.1016/j.quaint.2022.11.005</a></p> <p>The majority of the data were collected under the auspices of the ERC-Starting Grant ZooMWest – Zooarchaeology and Mobility in the Western Mediterranean: Husbandry production from the Late Bronze Age to the Late Antiquity (award number 716298), funded by the European Research Council Agency (ERCEA) under the direction of Sílvia Valenzuela-Lamas (2017–2022). This work built on previous data collection undertaken for Trentacoste's (2014) PhD thesis. The dataset was also expanded with support from a Gerda Henkel Stifling Scholarship (AZ 44/F/20) awarded to A. Trentacoste.</p> <p>The chronological timespan of the dataset is between the Middle Bronze Age and Late Antiquity (c. 1700 BC - AD 700). For details on the methodology underlying the creation of the dataset see Trentacoste et al. (2018) and Trentacoste et al. (2021).</p> <p>Zooarchaeological data were collected from published sources (see references file), with the exception of some data for the sites of Spina, Vidulis and Aquileia. Metadata for these sites were available in the published literature, but individual data were collected from the archive papers of Italian zooarchaeologist Alfredo Reidel (1925–2014). We are grateful to Francesco Boschin (Università degli Studi di Siena) for access to the archive.</p> <p>The dataset includes:</p> <ul> <li>Raw biometric data for post-cranial bones for cattle, sheep/goat, pigs, and wild boar on a specimen level. Measurement abbreviations follow Von den Driesch (1976) and Davis (1996; only humerus HT and HTC). File: NItaly_Livestock_Metric_Data.csv</li> <li>NISP (Number of Identified Specimens) data for site phases with over 100 identified cattle/sheep/goat/pig specimens. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv</li> <li>Location coordinates and information on environmental context: mean, min, and/or max values for a 5km radius for sites with NISP data. Elevation information was taken from Shuttle Radar Topography Mission (SRTM) terrain data from the U.S. Geological Survey (90m resolution; Jarvis et al., 2008). Precipitation data were from World Clim 2.1 (average monthly climate data for 1970–2000, 30 arc-sec; Fick and Hijmans, 2017), and solar irradiance data were from Global Solar Atlas 2.0 (9 arc-sec; developed and operated by Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP); https://globalsolaratlas.info). Soil characteristics were derived from LUCAS topsoil data (500m; Ballabio et al., 2016): clay, silt, sand, and coarse fragments content (%), bulk density, and Available Water Capacity (AWC) for the topsoil fine earth fraction. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv</li> <li>Bibliographic information for each assemblage with indication of whether NISP and/or biometric data was used. File: NItaly_Livestock_References.csv</li> <li>R script file for the analyses and visualisations in the above journal article. File: NItaly_Livestock_SysChange_Script.R</li> </ul> <p>If you re-use this data, please cite this dataset and the associated journal articles as relevant.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
FULFILL dataset round 1 Italy
<table> <tbody> <tr> <td>This dataset and codebook correspond to the initial round of survey data gathered in Italy in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. <br><br>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</td> </tr> </tbody> </table>
FULFILL dataset - diet policy acceptability - health information provision Italy
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from three countries: France, Italy, and Latvia, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from each country, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes the randomised provision of information on the health-risks associated with meat consumption. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if the information provision impacted people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Italy
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset round 2 Italy
<p>This dataset and codebook correspond to the second round of survey data gathered in Italy in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</p>
LTER-Italy site Lake Iseo figure
<p>Geographical representation of the LTER-Italy site Lake Iseo (LTER_EU_IT_102) - DEIMS-ID <a href="https://deims.org/0667dab1-f857-45a1-b01b-4261e6a499bd">https://deims.org/0667dab1-f857-45a1-b01b-4261e6a499bd</a></p>
LTER-Italy site Saldur River Catchment figure
<p>Geographical representation of the LTER-Italy site Saldur River Catchment (LTER_EU_IT_099) - DEIMS-ID <a href="https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1">https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1</a></p>
LTER-Italy site Mar Piccolo of Taranto figure
<p>Geographical representation of the LTER-Italy site Mar Piccolo of Taranto (LTER_EU_IT_095) - DEIMS-ID <a href="https://deims.org/ede24c6e-9cf2-4cf8-8bf7-36ba327403b4">https://deims.org/ede24c6e-9cf2-4cf8-8bf7-36ba327403b4</a></p>
LTER-Italy site Lago Braies figure
<p>Geographical representation of the LTER-Italy site Lago Braies (LTER_EU_IT_092) - DEIMS-ID <a href="https://deims.org/c54a2c21-2079-400d-b169-5e2de8dfdf06">https://deims.org/c54a2c21-2079-400d-b169-5e2de8dfdf06</a></p>
LTER-Italy site Lago di Tovel figure
<p>Geographical representation of the LTER-Italy site Lago di Tovel (LTER_EU_IT_090) - DEIMS-ID <a href="https://deims.org/f3146959-ae18-4b4e-a9be-16634b0b530a">https://deims.org/f3146959-ae18-4b4e-a9be-16634b0b530a</a></p>
LTER-Italy site Lago Paione Superiore figure
<p>Geographical representation of the LTER-Italy site Lago Paione Superiore (LTER_EU_IT_089) - DEIMS-ID <a href="https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313">https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313</a></p>
LTER-Italy site Lago Paione Inferiore figure
<p>Geographical representation of the LTER-Italy site Lago Paione Inferiore (LTER_EU_IT_088) - DEIMS-ID <a href="https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f">https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f</a></p>
LTER-Italy site Valbona figure
<p>Geographical representation of the LTER-Italy site Valbona (LTER_EU_IT_085) - DEIMS-ID <a href="https://deims.org/2b587e26-4550-4841-a032-ab3c93ced8a0">https://deims.org/2b587e26-4550-4841-a032-ab3c93ced8a0</a></p>
LTER-Italy site Monumento Naturale Torre Flavia (Roma) figure
<p>Geographical representation of the LTER-Italy site Monumento Naturale Torre Flavia (Roma) (LTER_EU_IT_083) - DEIMS-ID <a href="https://deims.org/e618c7ca-2b92-46cb-9156-d87336c5a81f">https://deims.org/e618c7ca-2b92-46cb-9156-d87336c5a81f</a></p>
LTER-Italy site Foce Trigno-Marina di Petacciato (Campobasso) figure
<p>Geographical representation of the LTER-Italy site Foce Trigno-Marina di Petacciato (Campobasso) (LTER_EU_IT_081) - DEIMS-ID <a href="https://deims.org/1835cda2-b56d-400a-b413-ab5c74086dc5">https://deims.org/1835cda2-b56d-400a-b413-ab5c74086dc5</a></p>
LTER-Italy site Foce Saccione-Bonifica Ramitelli (Campobasso) figure
<p>Geographical representation of the LTER-Italy site Foce Saccione-Bonifica Ramitelli (Campobasso) (LTER_EU_IT_080) - DEIMS-ID <a href="https://deims.org/088fe3af-c5bb-4cc8-b479-fe1ea6d5be80">https://deims.org/088fe3af-c5bb-4cc8-b479-fe1ea6d5be80</a></p>
LTER-Italy site Saldur river figure
<p>Geographical representation of the LTER-Italy site Saldur river (LTER_EU_IT_100) - DEIMS-ID <a href="https://deims.org/7f479263-8f0b-447e-a33d-e08723c86184">https://deims.org/7f479263-8f0b-447e-a33d-e08723c86184</a></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.
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OpenNeuro
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