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201 results for “portuguese”
Fig. 9 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 9. Patterns of toe arrangement on the fourth feet of species of Batillipes Richters, 1909 (see text for details; toe 1 is the most cephalically). A. Group A (B. lusitanus sp. nov.). B. Group A with very short middle toes (B. pennaki Marcus, 1946). C. Group B, subgroup B1 (B. brasiliensis Santos, da Rocha, Gomes Jr. & Fontoura, 2017). D. Group B, subgroup B2 (B. algharbensis sp. nov.). E. Group C (B. acuticauda Menechella, Bulnes & Cazzaniga, 2015). Scale bars = 10 µm.
Fig. 7 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 7. Batillipes lusitanus sp. nov. A. Posterior portion of the body, showing the dorsal sculpture. B. Caudal apparatus, showing details of cuticular pillars. C. SEM photo showing the dorsal (white arrowhead) and ventral (black arrowhead) aspect of the cuticle. Pillars are also visible (black arrow). Scale bars = 10 µm.
Fig. 8 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 8. Batillipes phreaticus Renaud-Debyser, 1959. A–C. Portuguese specimens. A. Anterior region of the body. B. Posterior region of the body, black arrow indicates fourth leg sensory organ. C. Detail of the fourth feet. D. Detail of the fourth feet showing the toe arrangement pattern of a specimen from Scotland in Pollock's collection. Scale bars = 10 µm.
Fig. 5 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 5. Batillipes lusitanus sp. nov. A–B, D. Holotype, ♀. A–B. Detail of the head. A. Dorsal view, showing internal cirrus (black arrowhead), lateral cirrus (asterisk), secondary clava (white arrowhead). B. Ventral view, showing primary clava (white arrowhead), external cirrus (black arrowhead). C. Detail of body lateral projections of a paratype (slide C.IX-46) (black arrowheads); D. Detail of leg sensory organ on leg IV. Scale bars = 10 µm.
Fig. 6 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 6. Batillipes lusitanus sp. nov. A. Fourth leg of a paratype (slide C.IX-46) with details of toes (1–6), note the shape of the ventro-lateral projection between legs III–IV (black arrowhead). B. Fourth leg of a paratype (slide C.IX-1), showing the rigid process on the distal extreme (black arrowhead). C. Female gonopore (white arrowhead) and anus (black arrowhead). D. Male gonopore (white arrowhead) and anus (black arrowhead). Scale bars = 10 µm.
Fig. 2 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 2. Batillipes algharbensis sp. nov. A. Schematic drawing, dorsal view. B. Habitus of the holotype, ♀, dorsal view. Scale bars = 10 µm.
Fig. 4 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 4. Batillipes lusitanus sp. nov. A. Schematic drawing, dorsal view. B. Habitus of the holotype, ♀, ventral view. Scale bars = 50 µm.
Fig. 3 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 3. Batillipes algharbensis sp. nov., holotype, ♀. A–B. Detail of the head. A. Dorsal view, showing median cirrus (black arrowhead), internal cirri (white arrowheads), lateral cirri (asterisks). B. Ventral view, showing primary clavae (asterisks), secondary clavae (black arrowheads), external cirri (white arrowheads). C–E. Body. C. Ventral view, showing ventro-lateral projections (black arrowheads). D. Anterior portion with details of leg spine I (black arrowhead), note the secondary clavae (white arrowhead). E. Posterior portion with details of leg sensory organ on leg IV (black arrowhead). F. Fourth leg with details of toes (1–6). Scale bars = 10 µm.
Fig. 1 in Batillipes (Tardigrada, Arthrotardigrada) from the Portuguese coast with the description of two new species and a new dichotomous key for all species
Fig. 1. Localities of the sampling sites along the Portuguese coast. 1. Afife Beach, 2. Torreira Beach. 3. Vieira Beach. 4. Baleal Sul Beach. 5. Portinho da Arrábida Beach. 6. Vasco da Gama Beach. 7. Meia-Praia Beach. 8. Ilha de Tavira Beach.
Portuguese results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2022 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2021 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2020 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2019 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2018 - Direcção-Geral de Alimentação e Veterinária</p> <p>MOPER_2017 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2016 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2015 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2014 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2013 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2012 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p>MOPER_2011 - Instituto Nacional de Investigação Agrária e Veterinária</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Dataset of suicidal ideation texts in Brazilian Portuguese - Boamente System
<p>We obtained non-clinical texts from tweets (user posts of the online social network Twitter). To find suicide-related tweets, we used the Twitter API to download tweets in a personalized way based on search terms associated with suicide. After different experiments to retrieve relevant texts, 5699 tweets were collected in May 2021. Each downloaded tweet had user-specific information (for example, user ID, timestamp, language, location, number of likes, etc.). Still, we kept only the post content (suicide-related texts) and discarded the additional data. Therefore, all texts were anonymized. </p><p>After data collection, three psychologists were invited to perform the data annotation, in which they individually labeled each tweet. To avoid bias in the annotation process, we selected psychologists with different psychological approaches, namely cognitive behavioral theory, psychoanalytic theory, and humanistic theory. Professionals had to classify each tweet as negative for suicidal ideation (annotated as 0), or positive for suicidal ideation (annotated as 1). </p><p>All tweets with at least one divergence between psychologists (n = 1513) were excluded, resulting in a dataset with 4186 instances. 398 duplicate tweets were excluded. The final dataset consists of 2691 instances labeled negative and 1097 labeled positive.</p><p> </p>
A Dataset of Polarities and Emotions from Brazilian Portuguese Play Store Reviews
<p>User reviews play a crucial role in shaping consumer perceptions and guiding decision-making processes in the digital marketplace. With the rise of mobile applications, platforms like the Google Play Store serve as hubs for users to express their opinions and experiences with various apps and services. Understanding the polarities and emotions conveyed in these reviews provides valuable insights for developers, marketers, and researchers alike.</p> <p>The dataset consists of user reviews collected from the "Trending" section of the Google Play Store in May 2023. A total of 300 reviews were gathered for each of the top 10 most downloaded applications during this period. Each review in the dataset has been meticulously labeled for polarity, categorizing sentiments as positive, negative, or neutral, and emotion, encompassing a range of emotional responses such as happiness, sadness, surprise, fear, disgust and anger.</p> <p>Additionally, it's worth noting that this dataset underwent a rigorous annotation process. Three annotators independently classified the reviews for polarity and emotion. Afterward, they reconciled any discrepancies through discussion and arrived at a consensus for the final annotations. This ensures a high level of accuracy and reliability in the labeling process, providing researchers and practitioners with trustworthy data for analysis and decision-making.</p> <p>It's important to highlight that all reviews in this dataset are in Brazilian Portuguese, reflecting the specific linguistic and cultural nuances of the Brazilian market. By leveraging this dataset, stakeholders gain access to a robust resource for exploring user sentiment and emotion within the context of popular mobile applications in Brazil.</p>
Multidecadal satellite-derived Portuguese Burn Severity Atlas (1984 -2022)
<p>The <strong>Portuguese burn severity atlas (1984 -2022)</strong> provides satellite-derived burn severity estimates of each historical fire with its start and end dates recorded and equal to or larger than<strong>100 ha</strong> for fires from 1984 to 2022 in Portugal. The fire perimeters are provided by the Instituto da Conservação da Natureza e das Florestas (ICNF) (https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af). <strong>Landsat </strong>imagery is applied for the creation of this atlas. The burn severity indices applied within this atlas are differenced Normalized Burn Ratio (<strong>dNBR</strong>), Relative differenced Normalized Burn Ratio (<strong>RdNBR</strong>), Relative Burn Ratio (<strong>RBR</strong>), and index combining dNBR with enhanced vegetation index (<strong>dNBR-EVI</strong>).</p> <p>Since 1984 to 2022, the total burned area recorded in Portugal is 4.85 million ha. Only <strong>valid fires</strong>, which are fires equal to or larger than 100 ha with known dates, were considered for the burn severity estimates. The total area of valid fires is 3.29 million ha. The <strong>Portuguese Burn Severity Atlas</strong> provides estimates for 3.17 million ha, accounting for 65% of all fires and <strong>96% of valid fires</strong>.</p> <p>The <strong>Portuguese burn severity atlas </strong>is organized in subfolders, each entitled as the corresponding year and containing shapefile, maps and a table with details on pairs of images used for burn severity estimates. Within each subfolder, the following data are stored:</p> <ul> <li> the annual fires’ perimeters shapefile (.dbf, .prj, .shp, .shx)</li> <li>dNBR map (.tiff)</li> <li>RdNBR map (.tiff)</li> <li>RBR map (.tiff)</li> <li>dNBR-EVI map (.tiff)</li> <li>confidence map: average “SUITABILITY in each pixel within the area of the fire (.tiff)</li> <li>comma separated value (.csv) file containing details on the pair of images used for burn severity estimates (year, ID, iteration number, pre-fire time lag(day), pre-fire cloud%, pre-fire suitability (%), post-fire time lag (day), post-fire cloud%, post-fire suitability(%), confidence in the iteration(%), area with dNBR estimation(ha), area of fire (ha), fire i number (to refer in GEE code), dNBR offset value, RdNBR offset value, RBR offset value, and dNBR-EVI offset value).</li> </ul> <p>To the best of our knowledge, <strong>Portuguese Burn Severity Atlas </strong>is the first open access atlas providing burn severity estimates of historical fires for an entire European country, in this case, <strong>Portugal</strong>, with 38 years of coverage. Target audience who can benefit from this atlas can be policymakers at national levels, field managers, project managers, and agency and academic researchers.</p> <p><strong>***</strong> The second version of this atlas provides updated fire data and burn severity estimates of fires from 1984 to 2000 with corrected dates area equal or larger than 100 ha. Moreover, for 2012, aside from burn severity estimates provided via imagery from Terra abroad Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat7-derived maps are included. </p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA
<p>This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger.</p> <p>The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness.</p> <p>The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.</p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Pretraining Datasets raw audios from CORAA
<p>This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech.</p> <p>The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.</p>
English-Portuguese Dictionary of Verbal Collocations
<p>This is the json-LD version of the data of Tagnin's <strong>English-Portuguese Dictionary of Verbal Collocation</strong>s, which is available as a proof-of-concept interactive resource at <a href="https://mangalamresearch.shinyapps.io/EnglishPortugueseVerbalCollocations/" target="_blank" rel="nofollow noopener noreferrer">https://mangalamresearch.shinyapps.io/EnglishPortugueseVerbalCollocations/</a> .</p> <p>This is still work in progress; comments and suggestions are most welcome. Contact me at seotagni@gmail.com.</p> <p>The conversion of the dictionary data and its display in the form of a digital dictionary were made possible thanks Ligeia Lugli, Tilak Balavijayan and the NEH-funded project <em>Democratizing Digital Lexicography</em> (HAA-290402-23).</p> <p>For more information about the criteria adopted in the dictionary, please go to the Word file included in this repository.</p> <p> </p>
Fig. 1 in Second Portuguese record of Polytoxus siculus (A. Costa, 1842) (Hemiptera: Reduviidae: Saicinae)
Fig. 1- Male specimen of Polytoxus siculus (A. Costa, 1842) collected in Laborim de Baixo (Vila Nova de Gaia) on 28/09/2023 (MHNCUP-ART- 41176).
Fig. 1 in Braga, third Portuguese district for Yponomeuta evonymella (Linnaeus, 1758) (Lepidoptera: Yponomeutidae)
Fig. 1.- The specimen of Yponomeuta evonymella (Linnaeus, 1758) collected in Cambeses (Barcelos, Portugal).
Map 1 in New Portuguese records of Oedemera (Oncomera) femoralis Olivier, 1803 (Coleoptera, Oedemeridae).
Map 1.- Iberian distribution of Oedemera (Oncomera) femoralis Olivier, 1803, with the previously known Portuguese district and Spanish provinces (in grey), the new Portuguese district (in blue), and the MGRS 10x10 km squares of the collecting sites in Portugal (new sites in orange, previous site in green).
IL-6 and TNF-α salivary levels according to the periodontal status in pregnant Portuguese women
<p>The aim of this study was to evaluate the expression levels of interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) in pregnant women with different periodontal status at Garcia de Orta Hospital (Almada, Portugal).</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.