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545 results for “Latin”
EduLifeDesks Archive: 2011 Latin School Project Week (254) DwCA
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Botanical Illustrations: Latin Botanical Illustrations
<p>Digitally restored by Daniel Salim, literature sources</p>
Authors reviewed and translated in a dataset of 24 Latin American magazines (1898-1959)
<p><span> </span></p> <p><span>This file shows 2 columns, one of authors reviewed and the other of authors translated, from 1898 to 1959 in a subset of 24 Latin American journals: </span><em><span>Claridad</span></em><span>, <em>Revista Azul, Revista Avance, Amauta, Martín Fierro, Cuba Contemporánea, Nosotros, Sur, Contemporáneos, Luz i sombra, La Biblioteca, Revista Proa, Instantáneas, Editorial Titikaka, Horizonte, Boletín Titiaka, La Habana Literaria, Alfar, Vanguardia, Revista Nueva, Marcha, Irradiador, La Nota, Film</em><span>. </span></span><span>It is based on a larger dataset from the ERC project ‘</span><span>Grant <em>Social Network of the Past. Mapping Hispanic and Lusophone Modernity (1898</em>–<em>1959)</em>, grant agreement 803860.</span></p>
FIGURE 1. Aongstroemia gayana. A in The genus Aongstroemia in Latin America (Dicranaceae, Bryophyta) with special reference to Aongstroemia gayana
FIGURE 1. Aongstroemia gayana. A, Dry plants; B, C, Wet plant; D, Leaves; E, Perichaetial leaves; F, Leaf apìces; G, Shoulder cells; H, Transverse sections of the leaf; I, Stem cross section. A, B, C, D, E= 1mm. F, G, H, I= 25 μm.
Dataset for: Country Image in Latin America: A Systematic Review of Research in Scopus, Web of Science, and SciELO
<p>Dataset for: Country Image in Latin America: A Systematic Review of Research in Scopus, Web of Science, and SciELO</p>
Examining the role of environmental, social and governance measures in enterprises' financial resilience: Evidence from Europe, the Middle East, and Latin America
<p>The dataset contains information on ESG and financial resilience indicators for the period 2010 to 2020 for European, Middle-East, and Latin American enterprises. </p>
Data and R code from: Fire-induced loss of the world's most biodiverse forests in Latin America
<p>Fire plays a dominant role in deforestation, particularly in the tropics, but the relative extent of transformations and influence of fire frequency on eventual forest loss remain unclear. Here we analyze the frequency of fire and its influence on post-fire forest trajectories between 2001-2018. We account for ~1.1% of Latin American forests burnt in 2002-2003 (8,465,850 ha). Although 40.1% of forests (3,393,250 ha) burned only once, by 2018~48% of the evergreen forests converted to other, primarily grass-dominated uses. While greater fire frequency yielded more transformation, our results reveal the staggering impact of even a single fire. Increasing fire frequency imposes greater risks of irreversible forest loss, transforming forests into ecosystems increasingly vulnerable to disturbance and degradation. Reversing this trend is indispensable to both mitigate and adapt to climate change globally. As climate change transforms fire regimes across the region, key actions are needed to conserve Latin American forests.</p>
FIGURE 1 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 1. Geographic distribution of Muscidae species: a) Biopyrellia sp. b) Mesembrina sp. c) Morellia spp. d) Neorypellia spp. e) Polietina spp. f) Sarcopromusca spp. g) Azelia spp. h) Drymeia spp. i) Micropotamia spp. j) Ophyra spp. k) Potamia spp. l) Brachygasterina spp.
FIGURE 4 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 4. Geographic distribution of Muscidae species: a) Graphomya spp. b) Hemichlora spp. c) Mydaea spp. d) Myospila spp. e) Scenetes spp. f) Scutellomusca spp. g) Agenamyia spp. h) Albertinella sp. i) Drepanocnemis spp. j) Limnophora spp. k) Lispe spp. l) Lispoides spp.
FIGURE 3 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 3. Geographic distribution of Muscidae species: a) Cyrtoneurina spp. b) Cyrtoneuropsis spp. c) Mulfordia spp. d) Neomuscina spp. e) Neomusciniopsis spp. f) Neurotrixa spp. g) Pseudoptilolepis spp. h) Chaetophaonia spp. i) Dolichophaonia spp. j) Helina spp. k) Phaonia spp. l) Souzalopesmyia spp.
FIGURE 6 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 6. Geographic distribution of Muscidae species: a) Neodexiopsis spp. b) Notoschoenomyza spp. c) Oxytonocera spp. d) Pentacricia spp. e) Pilispina spp. f) Plumispina spp. g) Reynoldsia spp. h) Schoenomyza spp. i) Schoenomyzina spp. j) Spathipheromyia spp. k) Stomopogon spp.
FIGURE 5 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 5. Geographic distribution of Muscidae species: a) Pachyceramyia spp. b) Rhabdotoptera spp. c) Spilogona spp. d) Syllimnophora spp. e) Tetramerinx spp. f) Thaumasiochaeta spp. g) Altimyia spp. h) Apsil spp. i) Bithoracochaeta spp. j) Coenosia spp. k) Cordiluroides spp. l) Insulamyia sp.
FIGURE 2 in Muscidae (Insecta: Diptera) of Latin America and the Caribbean: geographic distribution and check-list by country
FIGURE 2. Geographic distribution of Muscidae species: a) Callainireinwardtia sp. b) Chaetagenia sp. c) Correntosia spp. d) Dalcyella sp. e) Itatingamyia sp. f) Palpibracus spp. g) Philornis spp. h) Psilochaeta spp. i) Arthurella spp. j) Cariocamyia spp. k) Charadrella spp. l) Chortinus spp.
ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset
<p>The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script.</p> <p>If you use this upload, please cite:</p> <p>Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. <a href="https://doi.org/10.1109/ICDAR.2017.224">https://doi.org/10.1109/ICDAR.2017.224</a></p> <p>We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation.</p> <p>The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on <a href="https://clamm.irht.cnrs.fr/icdar-2017/">https://clamm.irht.cnrs.fr/icdar-2017/</a> in Nov. 2017.</p>
Aligned Latin-Myanmar Transliteration Dataset
<p>Aligned Latin-Myanmar Transliteration Dataset</p> <p> Chenchen Ding<br> Tue Nov 22 00:00:00 JST 2022</p> <p>* Introduction</p> <p>This data set is a further refined and annotated version of the data at<br> https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/western-myanmar-transliteration.zip</p> <p>The data set is developed by Chenchen Ding from NICT. The license is</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License<br> https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p>* Contents</p> <p>- data.txt : 42,736 segmented and aligned instances.</p> <p>* Format</p> <p>Each line contains a segmented transliteration pair in a format of</p> <p>[Latin segment 1] | [Latin segment 2] ... ||| [Myanmar segment 1] | [Myamar segment 2] | ...</p> <p>where the Latin-Myanmar pair has identical number of segments.</p> <p>* Annotation Guidelines</p> <p>- There is no insertion but only segmentation on the Latin side.<br> - A placeholder @ is inserted in the Myanmar side for unaligned Latin segments.</p> <p>- The consonant clusters at syllable onset are generally segmented and aligned to Myanmar basic letters<br> - The consonants at coda are generally aligned to the placeholder, unless they are absorbed by a rhyme with nasalization or glottal stop, or by an extra explicit killed-letter.<br> - Doubled consonant letters are generally segmented and treated as coda and onset of two neighboring syllables.</p> <p>- Myanmar rhymes are generally not segmented.<br> - The Myanmar letter A (0x1021) is unsegmented in the case of vowel-beginning words as no insertion on Latin side.</p> <p>The data can be directly used to train a sequence-labeling model for Myanmar Romanization.</p> <p>* Disclaimer</p> <p>[1] NICT bears no responsibility for the contents of the corpus and the lexicon and assumes no liability for any direct or indirect damage or loss whatsoever that may be incurred as a result of using the corpus or the lexicon.</p> <p>[2] If any copyright infringement or other problems are found in the corpus or the lexicon, please contact us at alt-info [at] khn [dot] nict [dot] go [dot] jp. We will review the issue and undertake appropriate measures when needed.</p> <p> </p>
Latin-American voice anti-spoofing dataset
<p>This dataset contains samples of spoof and real human voice with different accents from Latin-American countries.</p> <p> Table 1. Real samples distribution</p> <table> <tbody> <tr> <td><strong>Accent</strong></td> <td><strong>Gender</strong></td> <td><strong># Speakers</strong></td> <td><strong># Files</strong></td> <td><strong>Nomenclature</strong></td> </tr> <tr> <td>Colombian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>14</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2534</td> </tr> <tr> <td>2070</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>com</td> </tr> <tr> <td>cof</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Chilean</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>17</td> </tr> <tr> <td>12</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2487</td> </tr> <tr> <td>1602</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>clm</td> </tr> <tr> <td>clf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Peruvian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>20</td> </tr> <tr> <td>18</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>2917</td> </tr> <tr> <td>2529</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>pem</td> </tr> <tr> <td>pef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Venezuelan</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>10</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1754</td> </tr> <tr> <td>1463</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>vem</td> </tr> <tr> <td>vef</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Argentinian</td> <td> <table> <tbody> <tr> <td>Male</td> </tr> <tr> <td>Female</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>12</td> </tr> <tr> <td>30</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>1670</td> </tr> <tr> <td>3790</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>arm</td> </tr> <tr> <td>arf</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Total</td> <td> </td> <td>162</td> <td>22816</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>The bonafide samples were obtained from the following sources:</p> <ul> <li>Colombian accents: <a href="https://www.openslr.org/72/">https://www.openslr.org/72/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Chilean accents: <a href="https://www.openslr.org/71/">https://www.openslr.org/71/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Peruvian accents: <a href="https://www.openslr.org/73/">https://www.openslr.org/73/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Venezuelan accents: <a href="https://www.openslr.org/75/">https://www.openslr.org/75/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> <li>Argentinian accents: <a href="https://www.openslr.org/61/">https://www.openslr.org/61/</a> (<a href="https://www.openslr.org/resources/75/LICENSE">License</a>)</li> </ul> <p> </p> <p>The strategies used to generate the spoof samples:</p> <p> Table 2. Spoof Samples distribution</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>#Samples</strong></td> </tr> <tr> <td>StarGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>CycleGAN</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>Diffusion</td> <td>Voice conversion</td> <td>16000</td> </tr> <tr> <td>TTS</td> <td>Text-to-speech</td> <td>5000</td> </tr> <tr> <td>TTS-StarGAN</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> <tr> <td>TTS-Diff</td> <td>Text-to-speech / Voice conversion</td> <td>2500</td> </tr> </tbody> </table> <p> </p> <ul> <li><a href="https://arxiv.org/abs/1806.02169">StarGAN-VC: Non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks</a></li> <li><a href="https://ieeexplore.ieee.org/document/8553236">Cyclegan-VC: Non-parallel voice conversion using cycle-consistent adversarial networks</a></li> <li><a href="https://arxiv.org/abs/2109.13821">Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme</a></li> <li>TTS: Microsoft azure TTS</li> <li>TTS-VC: Microsoft azure TTS + StarGAN/Diff</li> </ul> <p> </p> <p> Table 3. Dataset overview</p> <table> <tbody> <tr> <td><strong>Audio Samples</strong></td> <td><strong>Human Speakers</strong></td> <td><strong>Spoofing algorithms</strong></td> <td><strong>Sampling rate</strong></td> </tr> <tr> <td> <table> <tbody> <tr> <td>Bonafide</td> <td>Spoof</td> </tr> <tr> <td>22816</td> <td>58000</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>Male</td> <td>Female</td> </tr> <tr> <td>78</td> <td>84</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td>VC</td> <td>TTS</td> <td>VC and TTS</td> </tr> <tr> <td>3</td> <td>1</td> <td>2</td> </tr> </tbody> </table> </td> <td>16kHz</td> </tr> </tbody> </table> <p> </p> <p>On the <em>protocol.txt</em> file is listed all the files with the following structure:</p> <p><em> Subject_id file_name – spoof_type Label</em></p> <p>Consider this line on protocol.txt file:<br> <em>arf_00295 StarGAN-arf _00295_01349969200-cof _03349 _0077577 - StarGAN spoof</em><br> The first part (arf_00295) represent the subject id, from which we can also identify the accent and the gender (see nomenclature column on Table 1). The file name identify the type of spoof following for the source audio file and the target file. StarGAN represents the type of spoof. According to the table 2, this method is a Voice Conversion algorithm. If the file is a bonafide sample, we replace the <em>spoof_type</em> with a dash (-). Finally at the end of the line we refer the kind of label of the file, in the example, the file corresponds to a spoof case.</p> <p>Each zip file contains 6 folders, each one holds a type of samples. For the voice conversion folders, there are 25 sub-folders that indicate the conversion between accents. For example, Argentina-Venezuela folder indicates that the source accent of the file is Argentinian and the target is Venezuelan accent. Inside the folder there are 64 sub-folders that represent the subjects used for the conversion. For instance, the folder arf_00295-vem_04310 means that the source is an Argentinean female and the target is a Venezuelan male (see Table 1 for nomenclature). In the case of a Text-to-Speech folder there are 5 sub-folders that represent the accents. A TTS-VC folder there are 2 sub-folders that represent the voice conversion strategy used. Inside there are other sub-folders for the different combinations of source and target accents.</p> <p>You can check the folder tree structure in the tree.txt file. Table 3 shows a summary of the resulting dataset.</p> <p> </p>
Data on U.S. geopolitical relations with Latin America
<p>GDELT part of the big data and access path;</p> <p>Data Mining Results;</p> <p>U.S. and Latin American countries data filtering results, etc.</p>
Climate change impact on vernacular and archaeological cultural heritage building materials in Europe and Latin America
<p>The analysis and interpretation of past climate data and simulations of climate models for future periods will allow us to study the impacts of climate change on cultural Heritage. The H2020 SCORE project (Sustainable COnservation and REstoration of built cultural heritage – 2020-2024) centres on two types of cultural heritage that differ by their geographical location and therefore their climatic conditions, as are vernacular cultural heritage in Europe (6 sites in Denmark, France, Italy and Spain) and archaeological sites in Latin America (2 sites in Mexico). All the cases study share a fundamental similarity in terms of the use of materials and construction techniques.</p> <p>One objective of the project is to quantify the impacts of continuous climate and pollution changes on building materials of cultural heritage under future IPCC socioeconomic scenarios with high and low mitigation measures at years 2030, 2050 and 2070, using peer-reviewed dose-response equations. We also focus on the degradation effects due to extreme events (heatwave, dry spells and extreme rainfall/flood) of each of the selected regions of our cases study. We apply these climatic conditions past and future) in different models, based on scientific literature, that allow estimate the weathering of the materials employed in the construction of cultural heritage buildings.</p> <p>Finally, we deliver preliminary results for a “cocktail of extreme events” based on the literature review and experiment in laboratory specifically designed to quantify the damages and degradation of building materials due to a realistic series of adverse climate and pollution events.</p> <p>We present here some results of future weathering (2081-2100) compared to near par (2001-2020) for different weathering process under the SSP5- RCP 8.5 scenario. The evolution of each process is different and it is different form one site to the other.</p> <p> </p>
Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America"
<p>Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America": dataset, truth table, prime implicants and list of populist presidents with sources supporting that they are populist</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1963
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1963.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
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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
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.