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824 results for “spanish”
FIG. 7 in A comprehensive analysis of Morales Agacino entomological expeditions in Spanish Sahara 1941-1946, with an updated checklist of collection sites and collected insect species (Insecta Polyneoptera, Hymenoptera, Coleoptera Carabidae and Tenebrionidae)
FIG. 7. — Bolivaremia domenechi Morales Agacino, 1949: A, original drawing by Morales Agacino (1949); B, specimen observed in the North of Boujdour (26°22'N, 13°58'W; 50 m), dorsal view; C, specimen observed at the Sebkhat Tah (27°39'N, 12°55'W; 25 m), side view. Photos: Annie Garcin.
Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. Supplementary data.
<p>Original data linked to the publication Spanish melon landraces: revealing useful diversity by genomic, morphologic, and metabolomic analysis. It includes, Supp. Table 1: Genomic data; Supp. Table 2: Characterization data; Supp. Table 3: sugar and acids data; Supp Table Supp. Table 4: Voaltile organic compounds data; Supp Table 5: Germplasm details; Supp. table 6: Cromatographic parameters</p>
SocialDisNER corpus: gold standard annotations for detection of disease mentions in Spanish tweets
<p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <p>Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. <a href="https://aclanthology.org/2022.smm4h-1.48">The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora</a>. In <em>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task</em>, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics.</p> <pre><code class="language-json">@inproceedings{gasco2022socialdisner, title = "The {S}ocial{D}is{NER} shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora", author = "Gasco S{\'a}nchez, Luis and Estrada Zavala, Darryl and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Miranda-Escalada, Antonio and Krallinger, Martin", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.48", pages = "182--189" }</code></pre> <p> </p> <p><strong>Introduction:</strong><br> The <strong>SocialDisNER corpus</strong> of the SMM4H 2022 – Task 10 task focus on the recognition of disease mentions in tweets written in Spanish after selecting primarily<strong><em> first-hand experience of diseases</em></strong> and other health-relevant content (from patient associations, professional healthcare institutions, and through followers of patient association accounts of a <em>diversity of pathologies</em> including rare diseases, mental health, cancer, etc..).</p> <p><strong>SocialDisNER Gold Standard</strong></p> <p>The Gold Standard corpus was manually annotated by medical experts following the <a href="https://doi.org/10.5281/zenodo.6983041">SMM4H-SocialDisNER guidelines</a>. These guidelines were adapted from previous efforts used to annotate patient clinical records and medical literature. It covers rules for annotating <strong>mentions of diseases</strong> in health-related tweets in Spanish,</p> <p>The training set consists of 5000 tweets written in Spanish and the validation set consists of 2500 tweets written in Spanish. Both sets have been manually annotated by healthcare professionals. The test dataset contains 23430 tweets, although only 2000 will be used to evaluate the systems participating in the task (the rest is background set). We don't plan to publish the test set, but if you want you can test your system from <a href="https://codalab.lisn.upsaclay.fr/competitions/3531">SocialDisNER Codalab</a>.</p> <p><strong>SocialDisNER Large Scale Corpus</strong></p> <p>The large-scale data contains mentions automatically extracted from a set of 85000 tweets. Separate datasets are shown for each entity including diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm, and persons.</p> <p><strong>SocialDisNER co-mention networks</strong></p> <p>We have computed a co-occurrence matrix of the extracted diseases, as well as several co-mention matrices between the disease mentions and the rest of the entities in the large-scale corpora.</p> <p> </p> <p><strong>File structure:</strong></p> <p>The structure of the corpus is: </p> <ul> <li><strong>SocialDisNER_Data:</strong> <ul> <li>training-validation-data folder <ul> <li><strong><em>train-valid-txt-files</em></strong>: folder with training and validation text files. One text file per tweet, the file name corresponds to the tweet id. One sub-directory per corpus split (train and valid). The files named <em>ids_dev_set.txt</em> and<em> ids_train_set.txt </em>contain the list of file identifiers for each of the data splits (validation and train).</li> <li><strong><em>mentions.tsv</em></strong>: This file contains the manually annotated disease mentions. The file has the following fields: <ul> <li><em>tweets_id</em>: This is the id of the tweet, using Twitter API you can query the content of the tweet.</li> <li><em>Begin</em>: This is the position in the tweet where the annotation was found.</li> <li><em>End</em>: This is the position of the last character of the annotation in the tweet.</li> <li><em>Type: </em>This is the type of entity found, in our case "ENFERMEDAD".</li> <li><em>Extraction</em>: This is the literal extraction, in other words, the fragment of text which refers to the annotation. </li> </ul> </li> </ul> </li> <li>test-data folder: <ul> <li><strong>test-data-txt-files</strong>: folder with test text files. One file per tweet, the file name corresponds to the tweet id. The folder contains 23430 tweets to be used as test set of the task. Of them, 2000 will be used to evaluate the participating systems.</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>SocialDisNER_LargeScale_additionaldata:</strong> <ul> <li>socialdisner_diseases: <ul> <li><strong>tweets_txt:</strong> Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li><strong>diseases_mentions.tsv</strong>: This file contains the automatically annotated disease mentions from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_ENTITY: Each folder with this naming convention contains the following data structure. Corpora have been generated with mentions of diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm and persons <ul> <li><strong>tweets_txt:</strong> Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li><strong>ENTITY_mentions.tsv</strong>: This file contains the automatically annotated mentions of type “ENTITY” from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_networks: This folder contains tsv files containing the co-mention matrices between the diseases and the rest of the entities of the large-scale socialdisner data. Each file follows the following naming convention: <ul> <li><strong>socialdisner_disease-ENTITY_net.tsv</strong><em>: </em>The tsv file contains a series of columns and rows corresponding to the mentions used for building the matrix. Each column is separated by “;”. The type of each mention is identified by the label in parentheses of each title. The count represents the number of times that mention x and mention y were found in the same tweet of the large-scale dataset.</li> <li><strong>socialdiser_disease_net.tsv</strong>: This tsv file contains the array of socialdisner-disease large-scale corpus co-mentions separated by ";". This file can be loaded into NetworkX to perform disease co-morbidity analysis on the socialdisner-disease large-scale data.</li> </ul> </li> </ul> </li> </ul> <p><em>Note: In previous versions of the dataset the order of the columns in the mentions.tsv file was not in the correct order. From this version onwards the order is correct and adequate to send the predictions of the task.</em></p> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/socialdisner/">https://temu.bsc.es/socialdisner/</a></p> <p><strong>Summary statistics:</strong></p> <table> <caption>Manually annotated data</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">Training set</th> <th scope="col">Development set</th> </tr> </thead> <tbody> <tr> <th scope="row"># tweets</th> <td>5000</td> <td>2500</td> </tr> <tr> <th scope="row"># characters</th> <td>1253431</td> <td>516768</td> </tr> <tr> <th scope="row"># tokens</th> <td>211555</td> <td>84478</td> </tr> <tr> <th scope="row">Avg. char / tweet</th> <td>250.69</td> <td>206.71</td> </tr> <tr> <th scope="row">Avg. tok. / tweet</th> <td>42.31</td> <td>33.79</td> </tr> <tr> <th scope="row"># mentions</th> <td>15173</td> <td>4252</td> </tr> <tr> <th scope="row"># unique mentions</th> <td>4407</td> <td>1413</td> </tr> </tbody> </table> <p> </p> <table> <caption>Large-scale annotated data (Silver Standard)</caption> <tbody> <tr> <td> </td> <td><em>Socialdisner-diseases</em></td> <td><em>Socialdisner-pharma</em></td> <td><em>Socialdisner-morphology_neoplasms</em></td> <td><em>Socialdisner-symptoms</em></td> <td><em>Socialdisner-professions</em></td> <td><em>Socialdisner-Procedures</em></td> <td><em>Socialdisnerv-Person</em></td> <td><em>Socialdisner-Species</em></td> </tr> <tr> <td><strong># tweets</strong></td> <td>85077</td> <td>1759</td> <td>8518</td> <td>12624</td> <td>15831</td> <td>11462</td> <td>41033</td> <td>12118</td> </tr> <tr> <td><strong># characters</strong></td> <td>19920670</td> <td>435141</td> <td>2082574</td> <td>3023784</td> <td>4063114</td> <td>2873791</td> <td>10273278</td> <td>2933925</td> </tr> <tr> <td><strong># tokens</strong></td> <td>3236411</td> <td>68269</td> <td>332539</td> <td>521503</td> <td>660071</td> <td>467059</td> <td>1689479</td> <td>486249</td> </tr> <tr> <td><strong>Avg. char / tweet</strong></td> <td>234.15</td> <td>247.38</td> <td>244.49</td> <td>239.53</td> <td>256.66</td> <td>250.72</td> <td>250.37</td> <td>242.11</td> </tr> <tr> <td><strong>Avg. tok. / tweet</strong></td> <td>38.04</td> <td>38.81</td> <td>39.04</td> <td>41.31</td> <td>41.69</td> <td>40.75</td> <td>41.17</td> <td>40.13</td> </tr> <tr> <td><strong># mentions</strong></td> <td>116260</td> <td>1029</td> <td>8943</td> <td>12896</td> <td>18590</td> <td>10080</td> <td>58007</td> <td>14014</td> </tr> <tr> <td><strong># unique mentions</strong></td> <td>16034</td> <td>530</td> <td>541</td> <td>6991</td> <td>3667</td> <td>3841</td> <td>3446</td> <td>1676</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p>Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter <a href="https://twitter.com/en/tos">Terms of Service</a>, <a href="https://twitter.com/en/privacy">Privacy Policy</a>, <a href="https://developer.twitter.com/en/developer-terms/agreement">Developer Agreement</a>, and <a href="https://developer.twitter.com/en/developer-terms/policy">Developer Policy</a>.</p> <p> </p> <p> </p>
SSHOC Training Material Video 1 - What is the Ethnic and Migrant Minorities Survey Registry? - training video in English, French and Spanish
<p>A generic video, like the one produced about the SSH Marketplace, clearly and succinctly introducing and presenting the EMM Survey Registry. Would serve as an introductory video to train any new user on what the EMM Survey Registry is and who this tool is for. Would also present all available resources about the Registry (so introducing the full set of training materials available about the Registry)</p> <p>This has been produced in English, French, Spanish</p>
Data from the stochastic flood study of the Area of Special Flood Risk (ARPSI in Spanish) of Zamora, Spain.
<p>The published data are part of the stochastic flood study of the city of Zamora, where the different uncertainties affecting the hydraulic model are considered in order to obtain a series of stochastic maps with important implications for flood risk management.</p> <p>HEC-RAS 2D has been used for the flood study and Python has been used to automate the stochastic analyses and modeling.</p> <p>The data corresponds to:<br> - Model inputs: the basic files to generate the model, the hydraulic model of HEC-RAS 2D and all the data related to the stochastic sampling of the procedure are considered as inputs.<br> - Model Outputs: Outputs are considered to be the results obtained in the different phases of the stochastic analysis (convergence maps, sensitivity maps and stochastic maps).</p>
Food Allergy Severity Score Diagram (Spanish)
<p>The Food Allergy Severity Score (FASS) is an instrument developed and validated to score the severity of allergic reactions elicited by foods. FASS has three formats that can be mapped to each other consistently: two ordinal scores with 3 (oFASS-3) or 5 grades (oFASS-5), and a numerical score (nFASS). The development and validation are reported in the manuscript of Fernández-Rivas et al. published in Allergy (https://doi.org/10.1111/all.15165). </p>
SSHOC Training Material Video 3 - Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer - training video in English, French and Spanish
<p>Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer</p> <p>A training video targeting COVID-19 survey producers to entice contributions to the COVID-19 collection of the EMM Survey Registry<br> <strong>Target Audience for the video</strong>: Survey producers (academic and non-academic) of COVID-19 surveys with EMM respondents</p>
SSHOC Training Material Video 2 - How to exploit the Ethnic and Migrant Minorities Survey Registry as an academic and non-academic user - training video in English, French and Spanish
<p>A training video about how to search for and learn about surveys using the EMM Survey Registry, both as an academic and non-academic user.</p> <p>This video has been produced in English, French, Spanish</p>
Fig. 2 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 2 Endocardium. Mixed infhammatoru and fibrinous exudate associated to a bacteriah cohonu (white arrow). H&E staininc (×200). Scale-bar: 40 μm
Fig. 3 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 3 Endocardium. Fibrinous and neutrophihic exudation accompanied sith intense fibrovascuhar reactivitu (bottom richt). H&E staininc (×400). Scale-bar: 40 μm
Fig. 4 in First description of Bartonella koehlerae infection in a Spanish dog with infective endocarditis
Fig. 4 Evohutionaru rehationships of taxa. The evohutionaru historu sas inferred usinc the Neichbor-Joininc method. The optimah tree is drasn to scahe, sith branch hencths (next to the branches) in the same units as those of the evohutionaru distances used to infer the phuhocenetic tree. The evohutionaru distances sere computed usinc the Maximum Composite Likehihood method and are in the units of the number of base substitutions per site
Judicial Decisions, Backlash and Secessionism: The Spanish Constitutional Court and Catalonia
<p>This package containts replication instructions for the analysis in “Judicial Decisions, Backlash and Secessionism: The Spanish Constitutional Court and Catalonia” by Agustin Casas, Federico Curci, and Antoni-Italo de Moragas in the Economic Journal. Please begin with the detailed Readme file and let us know if you have doubts.</p> <p>The paper <span>study the effects of judicial decisions </span><span>on sensitive issues on political attitudes. In 2010, the Spanish Constitutional Court</span><br><span>partially overruled the new Catalan Constitution–the</span> <span>Estatut</span><span>–that granted further </span><span>decentralization.</span> The main result is that <span>the ruling increased support for </span><span>independence by 5 percentage points. Please take a look at the paper to see the mechanisms and additional results.</span></p>
Spanish Congress parliamentary records (1977-2024)
<p>The goal of this data collection has been to create a unified and easily accessible database with all the published documents of El Congreso De Los Diputados in raw text format. It contains more than 111.000 documents, including the diary of sessions of the Spanish Congress, as well as that of the Spanish Senate. A dataset of these characteristics is created for researchers to analyze various aspects of Spanish politics and legislation. This includes studying the law-making process, comparing it to other countries, and evaluating the effectiveness of policies. The dataset also promotes transparency and allows for research using Natural Language Processing techniques.</p>
Fig. 1 in Distribution Of "Spanish Slug" Arion Lusitanicus Auct. Non Mabille 1868 (Or Arion Vulgaris Moquin-Tandon, 1855) (Gastropoda: Arionidae) In Latvia
Fig. 1. Confirmed localities of Arion lusitanicus auct. non J. Mabille 1868 in Latvia during 2009 - 2016 (map drawn by M. Nitcis). Full name of places are given in Table 1.
Fig. 2 in Records of Solenosthedium bilunatum (Lefèbvre, 1827) on the Italian island of Ustica and the Spanish island of Ibiza (Hemiptera: Heteroptera: Scutelleridae).
Fig. 2.- Adult of Solenosthedium bilunatum (Lefèbvre, 1827), Sant Antoni de Portmany, Ibiza, Spain. (Photo: Juan Pérez).
Fig. 1 in How many threatened lice are there? An approximation to the red list of the Spanish Phthiraptera
Fig. 1. Felicola (Lorisicola) isidoroi. Adult male, habitus. This is the holotype of the species and is deposited in the collection of the Museo Nacional de Ciencias Naturales (CSIC) in Madrid, Spain. Photography by Jean-Claude Stahl (Te Papa Tongarewa Museum, Wellington, New Zealand).
Data for Spanish versión of The App Behavior Change Scale (ABACUS-Es)
<p>Data for the paper Spanish versión of The App Behavior Change Scale (ABACUS-Es)</p>
Oficio de Hipotecas de Girona. A dataset of Spanish notarial deeds (18th Century) for Handwritten Text Recognition and Layout Analysis of historical documents.
<p>This dataset is a subset of 596 documents from the <em>Registre d'Hipoteques de Girona</em> of 1769 collection, guarded by the <a href="http://xac.gencat.cat/ca/llista_arxius_comarcals/girones/"><em>Arxiu Històric de Girona</em></a>. This collection, is composed by hundreds of thousands of notarial deeds from the XVIII-XIX century (1768-1862). Sales, redemption of censuses, inheritance and matrimonial chapters are among the most common documentary typologies in the collection.</p> <p>This dataset is composed of more than 23700 text lines written by a single hand, covering more that 50 different topics (documentary typologies) and a vocabulary of more than 2400 different words. The documents are transcribed using the so-called diplomatic criteria. Additionally, transcripts were tagged with <br> extra enriching/complementary information (e.g. expansion of the abbreviations, hyphen marks, etc.). Along with the transcripts the layout of the document is detected and recorded. Pages have been labeled using six different layout regions.</p> <p>The images along with their respective ground-truth was compiled in PAGE compliant XML format<br> by the <a href="http://www2.udg.edu/tabid/11296/Default.aspx"><em>Centre de Recerca d'Història Rural</em></a> and the HTR group of the <a href="https://www.prhlt.upv.es">Pattern Recognition and Human Language Technologies Research Center</a>.</p>
Spanish 3B words Word2Vec Embeddings
<p>Ready to use gensim Word2Vec embedding models for the Spanish language. Models are created using a window of +/- 5 words, discarding those words with less than 5 instances and creating a vector of 400 dimensions for each word. The text used to create the embeddings has been recovered from news, Wikipedia, the Spanish BOE, web crawling and open literary sources. The used text has a total of 3.257.329.900 words and 18.852.481.207 characters.</p> <p>We support two types of models: Gensim full models (complete_model.zip) and KeyedVectors (keyed_vectors.zip). You can check the differences between them in the following URL: <a href="https://radimrehurek.com/gensim/models/keyedvectors.html">https://radimrehurek.com/gensim/models/keyedvectors.html</a></p> <p>To load the full model use: model = Word2Vec.load("complete.model")<br> To load the KeyedVectors use: word_vectors = KeyedVectors.load('complete.kv', mmap='r')</p> <p>More info about the models can be found in: <a href="https://github.com/aitoralmeida/spanish_word2vec">https://github.com/aitoralmeida/spanish_word2vec</a></p>
Corpus of Spanish Word-in-Noise Confusions
<p>The dataset represents a large-scale corpus of noise-induced robust misperceptions in Spanish. The corpus contains 3235 consistent misperceptions, selected for the corpus if at least 6 listeners reported the same response from a group of 15 listeners. The dataset consists of a metadata table, separate audio waveforms for the speech and noise signals that led to each confusion, and masker waveforms.</p> <p>The corpus was described in the following journal article: http://dx.doi.org/10.1121/1.4905877 </p>
ScienceDex guides
Understand access before you commit
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.