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1,644 results for “Italian”
Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network
<p><strong>Data Set </strong></p> <p>Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s. </p> <p>Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation.</p> <p>The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times. </p> <p>We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).</p> <p>The final double-difference catalog includes 33,982 events occurring between 24<sup>th</sup> of August 2016 and 18<sup>th</sup> of January 2018.</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul> <p> </p> <p> </p> <p> </p> <p><br> </p>
For our world without sound. The opportunistic debitage in the Italian context: a methodological evaluation of the lithic assemblages of Pirro Nord, Cà Belvedere di Montepoggiolo, Ciota Ciara cave and Riparo Tagliente.
<p>Raw data concerning the technological analysis of both the experimentation and archaeological collections.</p>
Twitter Dataset for "Will You Take the Knee? Italian Twitter Echo Chambers' Genesis During EURO 2020"
<p>Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.</p>
FraneItalia: a catalog of recent Italian landslides
<p><strong>FraneItalia</strong> is a geo-referenced catalog of recent landslides affecting the Italian territory. The catalog has been <strong>developed consulting online news sources from 2010 </strong><strong>onward</strong> and it includes both fatal landslide events and events that did not produce physical harm to people. Landslide events are classified considering two numerosity categories and three consequence categories.</p> <p>NUMEROSITY CATEGORIES</p> <ul> <li><strong>Single Landslide Events</strong> (<strong>SLE</strong>), for records only reporting one landslide;</li> <li><strong>Areal Landslide Events</strong> (<strong>ALE</strong>), for records referring to multiple landslides triggered by the same cause in the same geographic area.</li> </ul> <p>Note: the ALE category has been introduced to simplify collection of the landslide records for the numerous cases when many landslides are mentioned together in the news.</p> <p>CONSEQUENCE CATEGORIES</p> <ul> <li><strong>Very severe consequences</strong> (<strong>C1</strong>), for events with victims and/or missing people;</li> <li><strong>Severe consequences</strong> (<strong>C2</strong>), for events with injured persons and/or evacuations;</li> <li><strong>Minor consequences</strong> (<strong>C3</strong>), for events that did not cause physical harm to people.</li> </ul> <p>Note: the consequence classification is based on the severity of the effects to human life not considering other consequence measures (e.g., economic loss, environmental damage)</p> <p>INFORMATION on each recorded landslide event includes:</p> <ul> <li>data on the location of the event [compulsory entries]</li> <li>day of occurrence of the landslide(s) [compulsory entry]</li> <li>duration of the landslide event [if applicable]</li> <li>number of landslides for ALE [compulsory entry]</li> <li>classes of accuracy of the spatial and temporal data [compulsory entries]</li> <li>source(s) of information [compulsory entries]</li> <li>landslide characteristics [optional entries]</li> <li>phase of activity [optional entry]</li> <li>details on the consequences [optional entries]</li> </ul>
RiceFloodIT: Water Management in the Italian Rice Paddies Estimated from MODIS data
<p>This repository includes two datasets used in Ranghetti et al. (2018) and Ranghetti & Boschetti (2022) to analyse the magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period in the Italian rice district: methods used to generate these data from MODIS remote sensing imagery are described in these papers.</p> <ul> <li><strong>ffavg_2021.csv</strong>: this dataset includes values of yearly FF<sub>avg</sub> (averaged Flooding Fraction) at pixel level. Each record represent the FF<sub>avg</sub> value of a specific pixel in a specific year. <ul> <li><strong>x</strong> and <strong>y</strong> identifies the latitude and longitude of each record (in UTM32 coordinates);</li> <li><strong>subdistrict</strong> represent the sub-district ID of each pixel ("A" to "G");</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ff</strong> is the FFavg value (range 0-1);</li> <li><strong>count</strong> is the number of MODIS images used to generate each FF<sub>avg</sub> aggregated value.</li> </ul> </li> <li><strong>ws_2021.csv</strong>: this dataset includes values of WS (proportion of Water-Seeded rice surface) at sub-district and district levels. <ul> <li><strong>subdistrict</strong> represent the sub-district ID of each record ("A" to "G", plus "all" which identifies values aggregated at district level);</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ws</strong> is the WS value (range 0-1);</li> <li><strong>count</strong> is the number of pixels used to generate each WS aggregated record.</li> </ul> </li> </ul> <p>Current data version (2021.01) includes estimated values in the period 2000-2021.</p> <p>References:</p> <p>Ranghetti, Luigi, Elisa Cardarelli, Mirco Boschetti, Lorenzo Busetto and Mauro Fasola. 2018. “Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies.” <em>Remote Sensing</em> 10 (3). doi:<a href="https://doi.org/10.3390/rs10030416">10.3390/rs10030416</a>.</p> <p>Ranghetti, Luigi and Mirco Boschetti. 2022. “Updated trends of water management practice in the Italian rice paddies from remotely sensed imagery.” <em>European Journal of Remote Sensing</em> 55 (1), pp. 1-9. doi:<a href="https://doi.org/10.1080/22797254.2021.2002726">10.1080/22797254.2021.2002726</a>.</p>
Dataset to "Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo"
<p>This record contains pre-processed data of a ten-and-a-half-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasProcessed_YYYY-MM-DD.dat correspond to the prepared data into a form for data analysis and processing as presented in “Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo”, accepted for publication in journal energy and buildings (<a href="https://doi.org/10.1016/j.enbuild.2022.111896">https://doi.org/10.1016/j.enbuild.2022.111896</a>).</p> <p>Each file, format MeasProcessed _YYYY-MM-DD.dat, corresponds to the daily registered data monitored every minute.</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuration, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
Polifonia Corpus - Books Module Metadata - Italian Language (Full)
<p>We release the Metadata of the Books module of the Polifonia Textual Corpus. According to the availability from the source origin, the Metadata may include the URL from which a text of the Books corpus is accessible, along with the title, the author, the year of publication, and the publisher. Metadata allows for a complete reconstruction of the corpus as we cannot make the actual texts available because they are subject to heterogeneous licensing.</p> <p>Full description at <a href="http://github.com/polifonia-project/Polifonia-Corpus">https://github.com/polifonia-project/Polifonia-Corpus</a></p>
The Italian Music Dataset
<p><strong>Overview</strong></p> <p>The dataset is built by exploiting the Spotify and SoundCloud APIs. It is composed of over 14,500 different songs of both famous and less famous Italian musicians. Each song in the dataset is identified by its Spotify id and its title. Tracks' metadata include also lemmatized and POS-tagged lyrics and, in the most of cases, ten musical features directly gathered from Spotify. Musical features include acousticness (float), danceability (float), duration_ms (int), energy (float), instrumentalness (float), liveness (float), loudness (float), speechiness (float), tempo (float) and valence (float). All features range from 0.0 to 1.0 except for loudness that typically ranges between -60 and 0 db, the tempo that represents beats per minute (BPM) and the duration that represents the track in milliseconds. For further information refer to the Spotify's documentation at <a href="https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/">Spotify Documentation</a></p> <p>For further information regarding the dataset and the related project visit <a href="https://bit.ly/2MUUwEx">SoBigData Catalogue</a></p>
Ontolex-lemon and TIAD versions of Apertium Spanish-Italian dictionary
<p>OntoLex-lemon and TSV conversion of Apertium Bidix. For more details, see <a href="https://www.aclweb.org/anthology/2020.lrec-1.401/">https://www.aclweb.org/anthology/2020.lrec-1.401/</a></p> <p>Authors of the original data:</p> (c) 2007--2008 Prompsit Language Engineering S.L. (http://www.prompsit.com) (c) 2008 Universitat d'Alacant, Grup Transducens (http://transducens.dlsi.ua.es)
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Photonics4All Bookmark Chip (Italian)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How light makes computers and phones smaller and faster?</p> <p>Did you know that we use light to fabricate the electronic chips in computers and mobile phones? Recent developments in photolithography where light is used to control where conductive metal is placed on the chips - have enabled us to put more transistors than there are people on earth! Transistors are responsible for controlling the path of electricity/information through a chip. These technological developments have led to improving the speed, size and energy consumption of our chips, making them smaller and more efficient.</p> <p> </p>
Photonics4All Bookmark Bubble (Italian)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> Why do soap bubbles have colour?<br> <br> Light reflects off both the inner and outer surfaces of a soap bubble. As the bubble dries out it changes thickness and the light waves reflecting off both surfaces have to travel different distances. White light is made up of all different colours – or waves of different lengths and - when light waves meet – or overlap - they create different colours. Because reflected light travels different distances due to the different film thicknesses we see iridescence in soap bubbles. This phenomenon is used in photonics to provide anti-reflection coating on your glasses for example.</p> <p> </p>
Photonics4All Bookmark Needle (Italian)
<p>The purpose of this bookmarks for our project Photonics4all is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light replace a needle?<br> We no longer need to use a needle to monitor the level of oxygen in your blood! We can use light emitting diodes (LEDs) attached to the top of your finger - and a light detector underneath to measure the amount of light passing through your finger. As Hemoglobin - the proteins in red blood cells which carry oxygen - absorb light we can determine whether you have enough oxygen in your blood. More advanced devices can also monitor your heart-rate and blood pressure. We'll even be using light to measure your blood-sugar level in the future. All thanks to Photonics!</p>
Photonics4All Bookmark LED (Italian)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can Light Emitting Diodes (LEDs) transform local food production?<br> <br> Because LEDs emit pure and specific colours they can be used to make plants grow faster and larger. LEDs can replace sunlight or costly greenhouse lamps to grow crops in cold climates or during off-season periods. Growing food locally reduces the need for long-distance transport and lessens the environmental impact used to produce the food. All thanks to Photonics!</p> <p> </p>
Photonics4All Bookmark Crime (Italian)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light help solve crimes?<br> <br> Light sources used to illuminate crime scenes help investigators solve crimes. Light is used by forensic detectives to help locate evidence such as latent fingerprints, bodily fluids, hair and fibres, bruises, wound patterns, shoe and foot imprints, gunshot residues or drug traces. All of these clues fluoresce - or glow brightly - under selectively coloured light. Photography is also vital to help collect and store this evidence. </p> <p>All thanks to Photonics! </p>
e-ITALICA (enhanced ITAlian rainfall-induced LandslIdes CAtalogue)
<p>The enhanced ITAlian rainfall-induced LandslIdes CAtalogue (e-ITALICA) currently lists 6312 records with information on rainfall-induced landslides that occurred over the Italian territory between January 1996 and December 2021. Information on rainfall-induced landslides has a high accuracy on their spatial and temporal location. e-ITALICA includes the triggering rainfall conditions associated with the landslides and the coordinates of the representative rain gauges. Moreover, details on elevation, slope, and land cover are also included.</p>
Speech/text alignments for Italian end-to-end TTS
<p>Here are 146 chapters of several audiobooks from Librivox (33:22:19.176) read by 33 italian speakers:</p> <ul> <li>19 Females<br>#LC (Lisa Caputo): 18 chapters - 8643 utterances> 07:51:25.991<br>#EG (Enrica Giampieretti): 16 chapters - 8473 utterances> 07:04:47.044<br>#MT (Mariateresa): 7 chapters - 2735 utterances> 02:57:0.487<br>#MR (Mariarosa): 2 chapters - 1150 utterances> 00:55:59.825<br>#FA (Fabiola) 2 chapters - 324 utterances> 00:13:36.635<br>#NI (Nicole Grassi) 2 chapters - 396 utterances> 00:19:26.222<br>#SP (Simona Pagliari) 7 chapters - 557 utterances> 00:26:58.920<br>#MM (Marzia Marianera) 2 chapters - 523 utterances> 00:28:18.365<br>#ANGE (Angelina) 2 chapters - 246 utterances> 00:15:45.223<br>#LAURA (Laura) 2 chapters - 899 utterances> 00:38:33.592<br>#FG (Filippo Gioachin): 4 chapters - 1358 utterances> 00:58:59.194<br>#GIOEMILY (?) 2 chapters - 743 utterances> 00:33:26.872<br>#CAPI (Silvia di Simone) 1 chapters - 482 utterances> 00:29:16.553<br>#ALLIE (Allie Cingi) 2 chapters - 838 utterances> 00:50:31.221<br>#CAIMMA () 1 chapters - 328 utterances> 00:19:26.483<br>#DOLCINEA () 1 chapters - 393 utterances> 00:19:19.275<br>#MGT (Maria Grazia Tundo) 1 chapters - 290 utterances> 00:10:41.887<br>#FR (Francesca Roma) 2 chapters - 246 utterances> 00:16:16.611<br>#PETULA () 3 chapters - 251 utterances> 00:16:27.894</li> <li>14 Males:<br>#RF (Riccardo Fasol): 3 chapters - 961 utterances> 01:03:59.329<br>#RC (Roberto Confini): 3 chapters - 1238 utterances> 00:42:2.761<br>#SB (Sergio Baldelli): 2 chapters - 910 utterances> 00:54:57.145<br>#DA (Daniele) 2 chapters - 420 utterances> 00:24:36.827<br>#RECL (Renzo Clerico) 3 chapters - 778 utterances> 00:37:31.304<br>#STRALF (?) 3 chapters - 1392 utterances> 00:51:23.847<br>#PAOLO (?) 2 chapters - 484 utterances> 00:34:3.345<br>#PIER (?) 1 chapters - 320 utterances> 00:14:10.860<br>#AB (Andrea Briglia) - 31 chapters - 863 utterances> 00:40:26.526<br>#SIRJOE (Sergio Bersanetti) 2 chapters - 626 utterances> 00:31:39.754<br>#KIUKKO (Luigi Chiaro) 1 chapters - 368 utterances> 00:17:48.204<br>#AZ (Francesco Montana) 1 chapters - 287 utterances> 00:14:39.814<br>#BM (Beniamino Massimo) 1 chapters - 415 utterances> 00:16:6.842<br>#ML (Mirko Lamberti) 1 chapters - 252 utterances> 00:12:27.594<br><br></li> <li>and a dictionnary of 14969 words with aligned phones</li> </ul> <p>Sources:</p> <ul> <li>Audios are from <a href="https://librivox.org/">Librivox</a></li> <li>Aligned original texts are from diverses sources including <a href="https://www.intratext.com">Intratext</a>, <a href="https://it.wikisource.org">wikisource</a>, <a href="https://www.pirandelloweb.com">pirandelloweb</a>, etc</li> </ul> <p>Each .wav file (sampled at 22050Hz) corresponds to one entire chapter. The format of the filenames is:<br>{author's acronym}_{book's acronym}_{reader's acronym}_{volume's number}_{chapter's number}</p> <p>The IT.csv file gives text (or sometimes, phones) and signal alignments for utterances in 4 fields separated by '|': {filename}|{start_ms}|{end_ms}|{text or phonetic content}. Most utterances are separated by at least a pause of 400ms (exceptionally less when phonation exceeds 11s). The intervals [start_ms:end_ms] comprise leading and trailing silences of 130ms (since wavs are entire chapters, these silences are "true" ambient silences).</p> <p>When phonetic alignment has been performed, 1 additional field has been added: {aligned phones}. Each input character or phone has a corresponding aligned phone. Note that all aligned utterances start and end with an aligned silence of 130ms. The set of aligned phones comprises:</p> <ul> <li>The set of input phones</li> <li>The silence: '__'</li> <li>The symbol '_' for silent characters, e.g. "occhi" is aligned with 'o^1 k: _ _ i'</li> </ul> <p>Text is in UTF8. '«»','—', '~','""','()','[]' are respectively used for speaking quotes, turn switches, three dots, quoted expression, aside quotes, notes. Because of rare occurrences, 'ö' has been transcribed as 'oe'. Paragraphs (two consecutive carriage returns in the original text) are cued by a special character '§'. It usually ends an utterance but could be used within an utterance if its associated pause is too short.</p> <p>Part of text under clear emphasis is surrounded by "#"</p> <p>When available, phonetic content is given per word in curly brackets '{}'. We use 39 phonetic symbols:</p> <ul> <li><strong>oral vowels</strong>: a (f<strong>a</strong>), e (v<strong>e</strong>), e^ (<strong>e</strong>d), i (r<strong>iz</strong>), u (t<strong>u</strong>), o (un<strong>o</strong>), o^ (c<strong>o</strong>n)</li> <li><strong>loan vowels & diphtongs: </strong>a&i and x^ (t<strong>i</strong>m<strong>er</strong>),</li> <li><strong>semi-vowels</strong>: h (g<strong>h</strong>etto.), w (q<strong>u</strong>el), j (va<strong>j</strong>)</li> <li><strong>consonants</strong>: p (vespa), t (<strong>t</strong>u), k (<strong>c</strong>alde), b (<strong>b</strong>uon), d (<strong>d</strong>isse), g (<strong>g</strong>razie), f (<strong>f</strong>ame), s (<strong>s</strong>auna) , s^ (<strong>sc</strong>ia), v (<strong>v</strong>erde), z (ro<strong>s</strong>a), z^ (<strong>j</strong>udo), r (<strong>r</strong>izo), l (<strong>l</strong>etto), l^ (e<strong>gl</strong>i), m (<strong>m</strong>apo), n (<strong>n</strong>uda), n~ (pu<strong>gn</strong>i)</li> <li><strong>long/double consonants are suffxed by ":",</strong> e.g. p: (zu<strong>pp</strong>a)</li> <li><strong>primary stress</strong> if any is noted "1" and appended to the vowel, e.g. a1 g a p e (agape)</li> </ul>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</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.