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1,644 results for “italian”
Shear wave velocity profiles from Italian Seismic Microzonation project
<p>The dataset SMDB contains the shear wave velocity profiles from Italian Seismic Microzonation project (14,897 profiles).</p> <p>The records are:</p> <p>-survey id;</p> <p>-survey latitude and longitude in UTM33N coordinates;</p> <p>-survey type;</p> <p>-depth in meters;</p> <p>-shear wave velocity value (Vs) in m/s;</p> <p>-seismic microzonation (SM) cluster id.</p> <p>The file percentile_sigma_ln_vs.csv contain sigma lnvs in depth for the three percentiles 16,50,84.</p>
Fig. 2 in A New Locality of the Italian Wall Lizard Podarcis siculus (Rafinesque-Schmaltz, 1810) from Turkey
Fig. 2. Photo of the discovered specimen of Podarcis siculus hieroglyphicus. Photo: I. Mollov.
Fig. 1 in Eidophelus caucasicus (Lindemann, 1877) a bark beetle confirmed for the Italian fauna after almost 100 years (Coleoptera: Curculionidae, Scolytinae)
Fig. 1 –Eidophelus caucasicus (Lindemann, 1877) (1,72 mm).
Fig. 2 in First record of Aphaereta difficilis Nixon, 1939 from Italy with a key to the Italian species of the genus (Hymenoptera: Braconidae, Alysiinae)
Fig. 2 – Aphaereta difficilis Nixon, 1939, known country-level distribution.
Fig. 14 in New records of Adelidae from forested habitats of Calabria (South Italy) with an update of the Italian ckecklist (Lepidoptera: Adeloidea)
Fig. 14 – Male genitalia of Nematopogon swammerdamella, Cappiglione (microscope slide: CREA-0238).
data set related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study
<p>This record contains raw data related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study</p>
COMMONFARE: F2F Italian pilot questionnaire dataset
<p>Dataset of the PIE News project questionnaire in the Italian pilot managed by the BIN Italia partner.</p>
Italian XX-XXI Century Music
<p>A dataset on Italian music. The data consists of a bipartite network connecting artists to the bands they published a record with, in a given year. Bands have a number of attributes, including the number of records with a given genre they published, whether they published a record in a given year, and from which Italian region they originate. The dataset also includes two projections of the bipartite network into unipartite views: bands connected with each other if they share a significant number of artists, and artists connected with each other if they share a significant number of bands,</p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
Italian articles reporting protest to Agrivoltaics in Italy from 2021 to 2023
<p>The following lists present the articles gathered for an article on Social representations of Agrivoltaics in Italy, to be published on Energy Research and Social Science thanks to the collaboration of Mauro Sarrica, Alessandra Scognamiglio and Roberto Fasanelli.</p>
Dataset related to article: "Health risk assessment for dietary exposure to 3-monochloropropane-1,2-diol, 2-monochloropropane-1,2-diol, and glycidol for Italian consumers"
<p><span><span>Excel file containing food consumption data and concentration data in foodstuff used for exposure assessment<br></span></span></p>
Italian Covid-19 Retweet Network (2020-2022)
<p><strong>Description</strong></p> <p>This repository contains directed retweet interactions between anonymized Twitter (now X) users.</p> <p>Data is stored in five csv files, each one relating to a different phase of the pandemic in Italy.</p> <ul> <li>early covid (01/01/2020 – 08/03/2020): from Covid’s first tracing in Wuhan,<br>China, up to the first lockdown in Italy;</li> <li>pre vaccine (09/03/2020 – 31/10/2020): from the first Italian lockdown to the start of the vaccination campaign;</li> <li>early vaccine (01/11/2020 – 16/04/2021): the first months of the vaccination campaign;</li> <li>vaccine drive (17/04/2021 - 31/07/2021): the main phase of intensive vaccination;</li> <li>late vaccine (01/08/2021 – 31/12/2021): phase in which a significant portion<br>of the Italian population was fully vaccinated;</li> </ul> <p>CSV files are structured as follows: </p> <p><em>from,to,weight</em></p> <p> </p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong><br>This work is supported by (i) the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, ”SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); (ii) SoBigData.it which receives funding from the European Union –<br>NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: ”SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021; (iii) EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).</p>
FIG. 1 in Habitat preferences of Papilio alexanor Esper, [1800]: implications for habitat management in the Italian Maritime Alps
FIG. 1. — Papilio alexanor Esper,[1800].Photograph:Davide Piccoli.
Italian data for the SDGs
<p>This dataset shows normalized data related to indicators available on Istat related to Italian regions.</p>
Italian DBnary archive in original Lemon format
<p>The DBnary dataset is an extract of Wiktionary data from many language editions in RDF Format. Until July 1st 2017, the lexical data extracted from Wiktionary was modeled using the lemon vocabulary.</p> <p>This dataset contains the full archive of all DBnary dumps in Lemon format containing lexical information from Italian language edition, ranging from 27th August 2012 to 1st July 2017.</p> <p>After July 2017, DBnary data has been modeled using the ontolex model and will be available in another Zenodo entry.</p>
Data from: Fear of infection and the common good: COVID-19 and the first Italian lockdown
<p>The Excel file contains the data for the paper "Fear of infection and the common good: COVID-19 and the first Italian lockdown". This paper is currently under review. The original data came from the paper "Flesia L, Monaro M, Mazza C, Fietta V, Colicino E, Segatto B, et al. Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models. J Clin Med. 2020;9(10)."</p>
Database of the Italian disdrometer network (V04)
<p><span lang="EN-US">This is the Version 04 (V04) of the GID database that includes the data collected by the GID disdrometer network along the Italian peninsula. The main upgrade of V04 with respect to the V03 is the higher number of disdrometer data due to the presence of new sensors in the GID network (in the V03 there were 19 disdrometers, while in the V04 there are 23 disdrometers) and the availability of more years of measurements (i.e. with respect to V03 we added data until 31 December 2024). <span> </span></span></p> <p><span lang="EN-US">The disdrometers belong to several Italian institutions that are part of the Italian Group of Disdrometry (in Italian it reads: Gruppo Italiano Disdrometria, GID, </span><span><a href="https://www.gid-net.it/"><span lang="EN-US">https://www.gid-net.it/</span></a></span><span lang="EN-US">). </span></p> <p><span lang="EN-US">In particular, the GID database contains the 1-minute Drop Size Distributions (DSD) obtained from the data of the laser disdrometers of the GID network during rainy minutes. For each disdrometer, data are available up to 31 December 2024. The database is structured in 23 sub-folders (one for each disdrometer), the name of these subfolders is the disdrometer ID in five digits (for more information see </span><span><a href="https://www.gid-net.it/network/"><span lang="EN-US">https://www.gid-net.it/network/</span></a></span><span lang="EN-US">). In each of these folders, there is one .xlsx file for each year of measurement. The latter file reports the time and the DSDs collected by the selected disdrometer during a given year. Following there are the mean value of class diameter (in mm) and the diameter class width (in mm) used to compute the DSD for the two types of laser disdrometers available in the GID network: </span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">Thies Clima Laser Precipitation Monitor disdrometer (TC):</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.1875, 0.3125, 0.4375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875, 2.25, 2.75, 3.25, 3.75, <span> </span>4.25, 4.75, 5.25, 5.75, 6.25, 6.75, 7.25, 7.75, 9];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250, 0.250, 0.250, 0.500, 0.500, 0.500, 0.500, 0.500, <span> </span>0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 1.25];</span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">OTT Parsivel 2 disdrometer (P2)</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.062, 0.187, 0.312, 0.437, 0.562, 0.687, 0.812,0.937, 1.062, 1.187, 1.375, 1.625, 1.875, 2.125,2.375, 2.750, 3.250, 3.750, 4.250, 4.750, 5.500,6.500, 7.500, 8.500, 9.500, 11.00, 13.00, 15.00,17.00, 19.00, 21.50, 24.50];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250,0.250, 0.500, 0.500, 0.500, 0.500, 0.500, 1.000,1.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000,2.000, 2.000, 3.000, 3.000];</span></p> <p><span lang="EN-US">To obtain uniform and high-quality DSD, the same processing has been adopted to all the disdrometer raw data (i.e. the V02 of the GID processing). The description of V02 GID processing, along with more information on the GID database structure are available in the data paper "Database of the Italian disdrometer network" published on June 2023 in the Journal "Earth System Science Data" (DOI: </span><span><a href="https://doi.org/10.5194/essd-15-2417-2023"><span lang="EN-US">https://doi.org/10.5194/essd-15-2417-2023</span></a></span><span lang="EN-US">). For further information contact the GID team at <strong>gid.info@gid-net.it</strong>.</span></p>
Dataset supporting publication: "Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city"
<p>Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: <a href="https://zenodo.org/record/3901556">GEOFIT Zenodo</a>)</p> <p>Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).</p> <p>The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.</p>
Data from: Range reexpansion after long stasis: Italian otters (Lutra lutra) at their northern edge
<p><span>Species range shifts and expansion are subjects of primary research interest in the context of climate warming and biological invasions. Few studies have focused on reexpansion of species that suffered severe declines. Here, we focused on population recovery of Eurasian otters (<em>Lutra</em> <em>lutra</em>) in Italy, first detected in 2003 after a southward range contraction</span><span>. We modelled the rate of range expansion and occupancy at the northern expanding front </span><span>(central Italy)</span><span>, to gain insights into the progress of recovery and mechanisms of reexpansion. We performed a field survey in 2021, </span><span>which redefined the northern limit of distribution further north, in close proximity to the Gran Sasso National Park. Then we analyzed a time series (1985–2021) of distances of northernmost occurrences from the centre of the 1985 range. Using segmented regression, we were able to identify a prolonged stasis of the northern range edge and a simultaneous increase in occupancy from 0.151 to 0.4. A breakpoint was estimated in 2006, after which the range expanded northwards at an average rate of </span><span>5.48 km/year. From 2006 to 2021, the overall northward shift was about 80 km. Occupancy continued to increase until </span><span>2019</span><span> and abruptly declined in 2021. </span><span>These patterns suggest that the reexpansion of the range can be limited by low occupancy at the expanding front. As occupancy increases, long-distance dispersal increases and then range expands. The low occupancy at the current distribution limit of otters may reflect a higher anthropogenic pressure on northern habitats, which could slow down the reexpansion process.</span></p>
WRF-Noah/Alpine3D simulations for 2018-2021 snow seasons in Italian Central Apennines
<p>This dataset contains the results of two numerical simulations over Italian Central Apennines at 3 km resolution for the three snow seasons 2018/19, 2019/20 and 2020/21. The file wrf-noah_2018-2021.nc contains the WRF-Noah model output, while the file wrf-alpine3d_2018-2021.nc contains the WRF-Alpine3D model output.</p>
ScienceDex guides
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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.