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1,644 results for “italian”
Supplementary Online Content to "Alterity marking and enhancing accessibility in lexical borrowing: Meta-information techniques in the use of incipient anglicisms in French and Italian"
<ul> <li>Overview of newspaper articles analysed in Sections 4 and 5</li> <li>Sources of the newspaper articles</li> <li>Sample analyses of the use of meta-information techniques in F2 and I3</li> </ul>
TA-EG italian Translation Questionnaire Dataset
<p>This file contains the raw data of TA-EG questionnaire single item information and the comments provided for each item </p>
Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)
<p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA daily gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period <strong>1981-2022</strong>. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in <strong>csv </strong>format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the <strong>ERA5 cell polygons </strong>covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:</p> <ul> <li>Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). <em>Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA)</em> [Data set]. Zenodo. <a href="http://10.5281/zenodo.6868944">https://doi.org/10.5281/zenodo.6868944</a> </li> </ul> <p>Further details on methods applied for data processing are available in:</p> <ul> <li>Parisse B., Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em>, Data in Brief, 46 (2023), 108843, <a href="https://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</li> </ul> <p>The MADIA daily dataset will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders:</p> <ol> <li>csv_data: daily time series for each year from 1981 to 2022 in csv format</li> <li>metadata: discovery and description metadata</li> <li>shp_data: a complementary vector layer with the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
Contrasting impacts of climate change on protection forests of the Italian Alps: Supporting Data
<p><strong>Input files</strong> for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study.</p> <p>The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at <a href="https://ites-fe.ethz.ch/openaccess/">https://ites-fe.ethz.ch/openaccess/</a>. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at <a href="https://doi.pangaea.de/10.1594/PANGAEA.924502">https://doi.pangaea.de/10.1594/PANGAEA.924502</a> while the CHELSA climate data for future scenarios are available at <a href="https://www.chelsa-climate.org">https://www.chelsa-climate.org</a>.</p> <p>If interested in using this dataset for a research study or a project, please contact <a href="https://www.marco-mina.com">Marco Mina</a></p> <p>-----------------------------------------------------------------------</p> <p>Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) <strong>Contrasting impacts of climate change on protection forests of the Italian Alps</strong>. <em>Frontiers in Forests and Global Change</em>, 6, 2023 <em> </em><a href="https://doi.org/10.1111/gcb.16197">https://doi.org/</a><a href="https://doi.org/10.3389/ffgc.2023.1240235">10.3389/ffgc.2023.1240235</a></p> <p>ABSTRACT.</p> <p>Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.</p>
Partial collection of LynxDocuments of the Italian Copyright legislation
<p>The dataset is organized as LynxDocuments and is designed to annotate Italian copyright law. Compliant with the Lynx framework for legal document management, this dataset is particularly relevant for legal informatics and semantic web technologies. It employs various RDF vocabularies and ontologies to ensure structured and machine-readable annotations.</p> <p>Key Features:</p> <p>1. Lynx Compliant Structure: The dataset is tagged as `lkg:LynxAnnotation`, aligning it with the Lynx ontology. This makes it easily integrable into broader legal informatics platforms that use the Lynx standard.</p> <p>2. Content Scope: The dataset includes detailed legal provisions found within the `nif:anchorOf` and `nif:annotationUnit` fields. These provisions elaborate on the categories of creative works protected under Italian copyright law, such as literature, music, arts, and software.</p> <p>3. Legal Context: Specific references to existing legal frameworks, including the "Convenzione di Berna" and "legge 20 giugno 1978, n. 399," are provided, thereby adding context to the legal provisions.</p> <p>4. Types of Creative Works: The dataset goes beyond general categories to specify various forms of protectable works, including collective works and software.</p> <p>5. Semantic Annotations: Annotations are ontology-based, primarily using labels like `schema1:CreativeWork`. These semantic tags aid in automated reasoning and facilitate data integration across multiple legal documents.</p> <p>6. Metadata: The dataset includes metadata fields like `ns1:taConfidence` for confidence scores and `ns1:taAnnotatorsRef` for annotator references, adding an additional layer of credibility and accountability.</p>
Dataset and description of an EnergyPLAN model of the Italian energy system in 2021
<p>This document describes an EnergyPLAN model of the Italian energy systems for the year 2021.</p> <p>The dataset, consisting of the EnergyPLAN input files necessary to run the simulation, is also provided.</p> <p>See the EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
Italian Word2Vec models
<p>Italian Word2Vec models trained from scratch on a dataset composed of:</p> <p>- <strong>wiki</strong>: a dump of Italian Wikipedia (as of December 15, 2022), comprising 25,548,651 sentences and 526,640,982 words (3.2 GB of raw text);<br> - <strong>webz</strong>: a dataset of Italian news (159,226 documents) from the webz.io platform, crawled in October 2015, containing 44,041,823 sentences and 44,544,385 words (244 MB);<br> - a dataset of 5,510 Italian news articles from the newspaper ModenaToday (<strong>MT</strong>) or 15,115 documents from the Italian version of Reuters (<strong>RCV2</strong>).</p> <p><strong>w2v_wiki_wbz_mt_20_epochs.zip</strong>: Word2Vec model trained on the dataset consisting of wiki, webz, and MT for 20 epochs</p> <p><strong>w2v_wiki_wbz_mt_50_epochs.zip</strong>: Word2Vec model trained on the dataset consisting of wiki, webz, and MT for 50 epochs</p> <p><strong>w2v_wiki_wbz_reut_20_epochs.zip</strong>: Word2Vec model trained on the dataset consisting of wiki, webz, and RCV2 for 20 epochs</p> <p><strong>w2v_wiki_wbz_reut_50_epochs.zip</strong>: Word2Vec model trained on the dataset consisting of wiki, webz, and RCV2 for 50 epochs</p>
Italian FastText models
<p>Italian FastText models trained from scratch on a dataset composed of:</p> <p>- <strong>wiki</strong>: a dump of Italian Wikipedia (as of December 15, 2022), comprising 25,548,651 sentences and 526,640,982 words (3.2 GB of raw text);<br> - <strong>webz</strong>: a dataset of Italian news (159,226 documents) from the webz.io platform, crawled in October 2015, containing 44,041,823 sentences and 44,544,385 words (244 MB);<br> - a dataset of 5,510 Italian news articles from the newspaper ModenaToday (<strong>MT</strong>) or 15,115 documents from the Italian version of Reuters (<strong>RCV2</strong>).</p> <p><strong>ft_wiki_wbz_mt_20_epochs</strong>: FastText model trained on the dataset consisting of wiki, webz, and MT for 20 epochs</p> <p><strong>ft_wiki_wbz_mt_50_epochs</strong>: FastText model trained on the dataset consisting of wiki, webz, and MT for 50 epochs</p> <p><strong>ft_wiki_wbz_reut_20_epochs</strong>: FastText model trained on the dataset consisting of wiki, webz, and RCV2 for 20 epochs</p> <p><strong>ft_wiki_wbz_reut_50_epochs</strong>: FastText model trained on the dataset consisting of wiki, webz, and RCV2 for 50 epochs</p>
Italian GloVe models
<p>Italian GloVe models trained from scratch on a dataset composed of:</p> <p>- <strong>wiki</strong>: a dump of Italian Wikipedia (as of December 15, 2022), comprising 25,548,651 sentences and 526,640,982 words (3.2 GB of raw text);<br> - <strong>webz</strong>: a dataset of Italian news (159,226 documents) from the webz.io platform, crawled in October 2015, containing 44,041,823 sentences and 44,544,385 words (244 MB);<br> - a dataset of 5,510 Italian news articles from the newspaper ModenaToday (<strong>MT</strong>) or 15,115 documents from the Italian version of Reuters (<strong>RCV2</strong>).</p> <p><strong>glv_wiki_wbz_mt_20_epochs.zip</strong>: GloVe model trained on the dataset consisting of wiki, webz, and MT for 20 epochs</p> <p><strong>glv_wiki_wbz_mt_50_epochs.zip</strong>: GloVe model trained on the dataset consisting of wiki, webz, and MT for 50 epochs</p> <p><strong>glv_wiki_wbz_reut_20_epochs.zip</strong>: GloVe model trained on the dataset consisting of wiki, webz, and RCV2 for 20 epochs</p> <p><strong>glv_wiki_wbz_reut_50_epochs.zip</strong>: GloVe model trained on the dataset consisting of wiki, webz, and RCV2 for 50 epochs</p>
Italian Gender Bias Dataset
<p>Dataset described in the paper Badaloni S., Rodà A., Scant M., "An Italian dataset for the analysis of gender stereotypes in textual documents". Accepted for publication in the Proc. of the AIxIA Conference, Rome 6-9 November 2023.</p>
10 Centesimi (Italian, 1894)
10 Centesimi, 1894, marked "BI", 32 million mintings. Copper, 10g, diameter 30mm, thickness 2mm. Front: "10 CENESIMI", Back: Head of King Umberto I of Italy, text: "RE D'ITALIA", "UMBERTO I", also name of the coin designer written small: "SPERANZA". https://de.ucoin.net/coin/italy-10-centesimi-1893-1894/?tid=58633 Another photogrammetry test with a bit too few images at the head side of the coin. Not a good coin for 3d scanning anyways, as it is really flat. Setup: OpenScanner (www.openscan.eu) in a light tent with no artificial lightsource, Sony Alpha 77 II, 3 chunks with 206 RAW images total processed in lightroom, Agisoft Metashape Standard. Txture editing in photoshop, model smoothing in Blender. Source: Objaverse 1.0 / Sketchfab
Assessment of Usability and Preliminary Effectiveness of a Digital Sleep Aid in an Italian Sample
ClinicalTrials.gov study NCT06926348. IPD Sharing: NO. Countries: 1. Publications: 30.
Collaborative Italian-American Clinical Trial of Nutritional Supplements and Age-related Cataract (CTNS)
ClinicalTrials.gov study NCT00309387. IPD Sharing: Not stated. Countries: 1. Publications: 4.
IPF Italian Observational Study (FIBRONET) in Idiopathic Pulmonary Fibrosis
ClinicalTrials.gov study NCT02803580. IPD Sharing: Not stated. Countries: 1. Publications: 1.
An Italian Study of the Efficacy of Atomoxetine in the Treatment of Children and Adolescents With Attention-Deficit/Hyperactivity Disorder (ADHD) and Comorbid Oppositional Defiant Disorder (ODD).
ClinicalTrials.gov study NCT00192023. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Italian Study on the Right Hemicolectomy: How the Lymphadenectomy is Performed
ClinicalTrials.gov study NCT05943951. IPD Sharing: NO. Countries: 1. Publications: 1.
ONCOTIPNET: Multicenter Study on Risk Factors and Outcome in Cancer Pediatric Patients Admitted in Italian PICUs
ClinicalTrials.gov study NCT04581655. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Speech and Language Interventions for Italian People With PPA
ClinicalTrials.gov study NCT06739967. IPD Sharing: NO. Countries: 1. Publications: 7.
Data from: Range reexpansion after long stasis: Italian otters (Lutra lutra) at their northern edge
Open the record for dataset details and reuse information.
Data from: What shapes the genetic diversity of the <em>Alnus cordata</em> species across its Italian native range? informing conservation strategies
Open the record for dataset details and reuse information.
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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.