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36 results for “Canto I”
O Canto da SereIA: inovação ou ilusão tecnológica?
<p>CAMPAGNOLLI, J. N. L.; CARVALHO, L. L. de; ATHAYDE, C. C. de O.; FARIAS, F. D. C. de. O canto da SereIA: inovação ou ilusão tecnológica?. <strong>Revista Tecnológica de Administração</strong>, Rio de Janeiro, v. 1, n. 3, p. 283-290, 2024. Disponível em: https://periodicos.fgv.br/reta/article/view/91840</p>
Ecomorphology of Bos remains, Los Castillejos (Fuente de Cantos, Badajoz)
<p>Ecomorphological (following De Gusta & Vrba, 2003; 2005) and Cortical measurements of Bos remains, excavated from Los Castillejos (Fuente de Cantos, Badajoz). The remains include the first phalanx (FA1), second phalanx (FA2), talus (TA), humerus (HU), radius (RA) and femur (FE). </p> <p> </p> <p>- De Gusta & Vrba, 2003. A method for inferring paleohabitats from the functional morphology of bovid astragali.</p> <p>- De Gusta & Vrba, 2005. Methods for inferring paleohabitats from the functional morphology of bovid phalanges.</p>
Lectura Dantis - Inferno. Canto 5 (Francesca da Rimini)
<p>Lettura parlata (non recitata) del canto V dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p> <p>CC BY-NC-ND 4.0</p>
Lectura Dantis - Inferno: Canto 26 (Ulisse) - edited
<p>Lettura parlata (non recitata) del canto XXVI dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 1, video per lettura e ascolto simultanei
<p>Lettura parlata (non recitata) con sottotitoli del canto I dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 10 (Farinata degli Uberti)
<p>Lettura parlata (non recitata) del canto X dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 26 (Ulisse), video per lettura e ascolto simultanei
<p>Lettura parlata (non recitata) con sottotitoli del canto XXVI dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 15 (Brunetto Latini)
<p>Lettura parlata (non recitata) del canto XV dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 4 (limbo), video per lettura e ascolto simultanei
<p>Lettura parlata (non recitata) con sottotitoli del canto IV dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 10 (Farinata degli Uberti), video per lettura e ascolto simultanei
<p>Lettura parlata (non recitata) con sottotitoli del canto X dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
Lectura Dantis - Inferno: Canto 3 (Caronte), video per lettura e ascolto simultanei
<p>Lettura parlata (non recitata) con sottotitoli del canto III dell'Inferno di Dante Alighieri, registrata per accompagnare la video lecture "Lectura Dantis - Inferno" del semestre invernale 2021/22.</p>
PHI-Canto approved curation sessions: December 2022
<p>Approved curation sessions from the <a href="https://github.com/PHI-base/canto">PHI-Canto</a> curation tool, as of 13 December 2022. PHI-Canto is used to curate literature on pathogen–host interactions, and supplies data to <a href="http://www.phi-base.org/">PHI-base</a>, the Pathogen­–Host Interactions Database.</p> <p>The curated data is exported in JSON format, and contained in a single JSON object. The object's keys are the identifiers for individual curation sessions, where each curation session corresponds to one publication. The export contains the raw data exported by PHI-Canto: no further processing has been applied.</p> <p>There is a JSON Schema file included that describes the data fields used in the export file.</p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
Lectura Dantis: Inferno - RVL 05, podcast, commento del canto 10 (versione italiana)
<p>Il canto politico dell'Inferno, passo a passo.</p> <p>licenza del podcast: Prof. Dr. Dina De Rentiis CC BY-NC-ND 4.0</p> <p>L'edizione citata: http://www.carocci.it/index.php?option=com_carocci&task=schedalibro&Itemid=72&isbn=9788843085149</p> <p>Musica (royalty free): https://youtu.be/0GmPkWB9IA4 e https://youtu.be/oQFbU3-SDz0</p>
Lectura Dantis: Inferno - RVL 06, podcast, il viaggio di Dante e il rapporto fra autori nel canto 15 (versione italiana)
<p>Seconda visione d'insieme partendo dal canto 15 (Brunetto Latini).</p> <p>Licenza del podcast: Prof. Dr. Dina De Rentiis CC BY-NC-ND 4.0</p> <p>L'edizione citata: http://www.carocci.it/index.php?option=com_carocci&task=schedalibro&Itemid=72&isbn=9788843085149</p> <p>Musica (royalty free): https://youtu.be/0GmPkWB9IA4 e https://youtu.be/oQFbU3-SDz0</p>
Lectura Dantis: Inferno - RVL 07, podcast, il viaggio di Ulisse, l'attenzione e i rapporti umani nel canto 26, seconda parte (versione italiana)
<p>Nuova lettura del canto, seconda parte.</p> <p>Licenza del podcast: Prof. Dr. Dina De Rentiis CC BY-NC-ND 4.0</p> <p>L'edizione citata: http://www.carocci.it/index.php?option=com_carocci&task=schedalibro&Itemid=72&isbn=9788843085149</p> <p>Musica (royalty free): https://youtu.be/0GmPkWB9IA4 e https://youtu.be/oQFbU3-SDz0</p>
Lectura Dantis: Inferno - RVL 07, podcast, il viaggio di Ulisse, l'attenzione e i rapporti umani nel canto 26, prima parte (versione italiana)
<p>Nuova lettura del canto, prima parte.</p> <p>Licenza del podcast: Prof. Dr. Dina De Rentiis CC BY-NC-ND 4.0</p> <p>L'edizione citata: http://www.carocci.it/index.php?option=com_carocci&task=schedalibro&Itemid=72&isbn=9788843085149</p> <p>Musica (royalty free): https://youtu.be/0GmPkWB9IA4 e https://youtu.be/oQFbU3-SDz0</p>
xeno-canto America: xeno canto America - DwCA latest
xeno-canto America is a community database of shared bird sounds from the Americas. <p></p>https://www.xeno-canto.org/ . This resource is set to refresh regularly.
Sixteen birdsong datasets from xeno canto website.
<p>We collected birdsong data on the https://xeno-canto.org/ platform. The datasets include 7 orders, 9 families, 15 genera and 16 species, and a total of 271 audios.</p>
FIGURA 5 in Descrição Do Canto De Anúncio E Dimorfismo Sexual Em Proceratophrys Concavitympanum Giaretta, Bernarde & Kokubum
FIGURA 5: Macho de Proceratophrys concavitympanum (MZUFV 9548, CRC = 43,84) em vida encontrado em Aripuanã, Mato Grosso, Brasil.
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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)
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DANDI Archive for NWB datasets
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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.