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4,725 results for “Normalization”
Intermuscular Coherence in Normal Adults: Variability and Changes with Age
<p><strong>Dataset underlying the paper "Intermuscular Coherence in Normal Adults: Variability and Changes with Age", published in PLOS ONE.</strong></p> <p><strong>Abstract: </strong>We investigated beta-band intermuscular coherence (IMC) in 92 healthy adults stratified by decade of age, and analysed variability between and within subjects. In the dominant upper limb, IMC was estimated between extensor digitorum communis and first dorsal interosseous as well as between flexor digitorum superficialis and first dorsal interosseous. In the ipsilateral lower limb, IMC was measured between medial gastrocnemius and extensor digitorum brevis as well as between tibialis anterior and extensor digitorum brevis. Age-related changes in IMC were analysed with age as a continuous variable or binned by decade. Intrasession variance of IMC was examined by dividing sessions into pairs of epochs and comparing coherence estimates between these pairs. Eight volunteers returned for a further session after one year, allowing us to compare intrasession and intersession variance. We found no age-related changes in IMC amplitude across almost six decades of age, allowing us to collate data from all ages into an aggregate normative dataset. Interindividual variability ranged over two orders of magnitude. Intrasession variance was significantly greater than expected from statistical variability alone, and intersession variance was even larger. Potential contributors include fluctuations in task performance, differences in electrode montage and short-term random variation in central coupling. These factors require further exploration and, where possible, minimisation. This study provides evidence that coherence is remarkably robust to senescent changes in the nervous system and provides a large normative dataset for future applications of IMC as a biomarker in disease states.</p>
PERCEPÇÕES DE GESTANTES E PROFISSIONAIS DA SAÚDE SOBRE PARTO NORMAL: UM ESTUDO COM GRUPOS OPERATIVOS
<p>Introdução: O Brasil é um dos países que mais realiza cesarianas e as taxas dessa cirurgia vem aumentando ano após ano. A cultura do medo da dor e a catastrofização do parto normal são fatores que permeiam o imaginário das mulheres e influenciam na escolha da via de parto, fazendo com que optem pela realização de uma cesariana. Objetivo: Conhecer as percepções, expectativas conhecimentos e sentimentos demonstrados por gestantes e profissionais de saúde com relação à possibilidade de que a sua via de parto seja o parto normal. Metodologia: Estudo de corte transversal descritivo com abordagem qualitativa no qual foram incluídas participantes do projeto "Escolha Consciente da Via de Parto" no período de 2021 a julho de 2023. Projeto em parceria entre um grupo de estudos da Universidade Federal de Uberlândia (GESTAR-UFU) e a Superintendência Regional de Saúde de Uberlândia, com realização de atividades de ações em saúde na modalidade de grupo operativo desenvolvidos na Macrorregião Triângulo Norte. Resultados: A partir da nuvem de palavras identificou-se que o vocábulo "medo" foi mencionado 72 vezes, "dor" 42 vezes, "ansiedade" 25 vezes e "insegurança" 15 vezes. Na análise de similitude o elemento lexical central é o "medo" e há conexidade considerável com a palavra "não", que está relacionada aos relatos das mulheres sobre temer não conseguir ter um parto normal. Conclusão: Fatores como o medo da dor, medo de não conseguir, ansiedade, ausência de conhecimento sobre o processo de trabalho de parto, postura dos profissionais envolvidos na atenção à saúde da mulher, além de fatores socioculturais influenciam na escolha da via de parto das mulheres.</p>
NMDA receptors in visual cortex are necessary for normal visuomotor integration and skill learning
<p>Raw data and Matlab code to generate the figures of the publication "NMDA receptors in visual cortex are necessary for normal visuomotor integration and skill learning" <a href="https://doi.org/10.7554/eLife.71476">https://doi.org/10.7554/eLife.71476</a>. Run generate_NKO_figures.m to produce all figures in the publication. </p>
Western Indian Ocean coral and fish normalized site richness collected between 1991 to 2000
<p><strong>Aim</strong>: Strong social-ecological trade-offs between resource extraction and protection have created challenges for large, protected area management in natural-resource-dependent countries. Therefore, <span>local governments and </span>community conservation activities <span>are becoming common and need information about </span>low <span>environmental</span> exposure <span>and high biodiversity</span> for <span>planning localized </span>conservation<span> activities</span>.</p> <p><strong>Location</strong>: the western Indian Ocean</p> <p><strong>Methods</strong>: <span>C</span>oral reef sites <span>were</span> evaluate<span>d for</span> local scale <span>environmental and species richness to elucidate local patterns in spatial </span>heterogeneity. Local coral and fish taxonomic richness were normalized to partially account for <span>common and heterogeneous </span>disturbances to coral cover and fish biomass<span>. Residuals were evaluated for patterns of local diversity with geography, environmental stress, and by machine learning to evaluate the </span>relationship <span>with </span>21 specific environmental variables<span>.</span></p> <p><strong>Results</strong>: <span>High</span> variability <span>in richness </span>was <span>found at similar latitudes where richness was high</span>. <span>R</span>elationships with <span>specific </span>environmental and human influences variables were <span>complex and </span>spatially heterogeneous. Expected large-scale biogeographic variables <span>influenced</span> richness but variability and environmental influences were highly <span>specific and </span>localized. Among the environmental and human influence variables examined, ~ 8 variables contributed 8 to 25% of the variance <span>to the richness of both coral and fishes</span>.</p> <p><strong>Main conclusions</strong>: <span>Decisions to focus s</span>mall-scale conservation <span>on locally biodiverse locations </span>could contribute to species persistence <span>by planning for local heterogeneity</span> i<span>n</span> richness<span> and stress.</span> From this specific data set, sites in the Pemba Channel between the Tanzanian mainland and Pemba Island, and northern Mozambique and Madagascar<span> fit these characteristics</span>.</p>
Data used to evaluate normalization approaces
<p>Data used in "Normalization of direct citations in publication-level networks: Evaluation of six approaches". </p> <p>Related code found in https://github.com/petersjogarde/papers/tree/main/normalization_dc_evaluation. </p>
Size normalizing planktonic Foraminifera abundance in the water column
<p>Data and R code for the paper <strong>Size normalizing planktonic Foraminifera abundance in the water column</strong> (<a href="https://doi.org/10.1002/lom3.10637">https://doi.org/10.1002/lom3.10637</a>) by Sonia Chaabane, Thibault de Garidel-Thoron, Xavier Giraud, Julie Meilland, Geert-Jan A. Brummer, Lukas Jonkers, P. Graham Mortyn, Mattia Greco, Nicolas Casajus, Olivier Sulpis, Michal Kucera, Azumi Kuroyanagi, Hélène Howa, Gregory Beaugrand, Ralf Schiebel</p> <p> </p> <p>The codes serve to generate a new normalization approach for estimating the abundance of planktonic Foraminifera (ind/m³) within the specified collection size fraction range. Data utilized in this study are sourced from the FORCIS database, containing records collected from the global ocean at various depths spanning the past century. A cumulative distribution across size fractions is identified and modeled using a Michaelis-Menten function. This modeling results in multiplication factors enabling the normalization of one fraction to any other size fraction equal to or larger than 100 µm. The resultant size normalization model is then tested across various depths and compared against a previous size normalization solution.</p> <p>Scripts written by Sonia Chaabane.</p> <p>DATA SOURCES</p> <ul> <li>FORCIS database: <a href="https://doi.org/10.5281/zenodo.7390791" target="_new">https://doi.org/10.5281/zenodo.7390791</a></li> </ul> <p>DATA</p> <ol> <li>data_raw_from_excel.RDS</li> </ol> <p>CODES</p> <ol> <li>Code 1_Prepare the data.R: Reads the data and prepares it for analysis.</li> <li>Code 2_Data-model_training.R: Analyzes the data and builds the model.</li> <li>Code 2_MM_confidence interval_all oceans_depths_seasons.R: Analyzes the data and computes confidence intervals across all oceans, depths, and seasons.</li> <li>Code 3_Validation.R: Compares actual vs. estimated number concentrations.</li> <li>Code 4_Test with berger scheme.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme.</li> <li>Code 5_Cross validation_Retailleau et al.R: Applies the FORCIS number concentration-size correction scheme on an independent dataset.</li> <li>Code 6_Retailleau et al. using berger approach.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme from an independent dataset.</li> <li>function.R: Additional functions used in the analysis.</li> </ol> <p> </p>
Normalized water permeability of hydrate-bearing sediments
<p>This dataset include some normalized water permeability, hydrate saturation, porosity and the water saturation of hydrate-bearing sediments </p>
AI4PROFHEALTH - Automatic Silver Gazetteer for Named Entity Recognition and Normalization
<p>This dataset comprises a professions gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. </p> <p>A silver gazetteer for mention classification and normalization is created combining the predictions of automatic Named Entity Recognition models and normalization using Entity Linking to three controlled vocabularies SNOMED CT, NCBI and ESCO. The sources are 265,025 different documents, where 249,538 correspond to <a href="https://zenodo.org/records/4707104">MESINESP2 Corpora</a> and 15,487 to clinical cases from open clinical journals. From them, 5,682,000 mentions are extracted and 4,909,966 (86.42%) are normalized to any of the ontologies: SNOMED CT (4,909,966) for diseases, symptoms, drugs, locations, occupations, procedures and species; ESCO (215,140) for occupations; and NCBI (1,469,256) for species.</p> <p>The repository contains a .tsv file with the following columns:</p> <ul> <li><strong><code>filenameid</code></strong>: A unique identifier combining the file name and mention span within the text. This ensures each extracted mention is uniquely traceable. Example: biblio-1000005#239#256 refers to a mention spanning characters 239–256 in the file with the name biblio-1000005.</li> <li> <p><strong><code>span</code></strong>: The specific text span (mention) extracted from the document, representing a term or phrase identified in the dataset. Example: centro oncológico.</p> </li> <li> <p><strong><code>source</code></strong>: The origin of the document, indicating the corpus from which the mention was extracted. Possible values: mesinesp2, clinical_cases.</p> </li> <li> <p><strong><code>filename</code></strong>: The name of the file from which the mention was extracted. Example: biblio-1000005.</p> </li> <li> <p><strong><code>mention_class</code></strong>: Categories or semantic tags assigned to the mention, describing its type or context in the text. Example: ['ENFERMEDAD', 'SINTOMA'].</p> </li> <li> <p><strong><code>codes_esco</code></strong>: The normalized ontology codes from the European Skills, Competences, Qualifications, and Occupations (ESCO) vocabulary for the identified mention (if applicable). This field may be empty if no ESCO mapping exists. Example: 30629002.</p> </li> <li> <p><strong><code>terms_esco</code></strong>: The human-readable terms from the ESCO ontology corresponding to the <code>codes_esco</code>. Example: ['responsable de recursos', 'director de recursos', 'directora de recursos'].</p> </li> <li> <p><strong><code>codes_ncbi</code></strong>: The normalized ontology codes from the NCBI Taxonomy vocabulary for species (if applicable). This field may be empty if no NCBI mapping exists.</p> </li> <li> <p><strong><code>terms_ncbi</code></strong>: The human-readable terms from the NCBI Taxonomy vocabulary corresponding to the <code>codes_ncbi</code>. Example: ['Lacandoniaceae', 'Pandanaceae R.Br., 1810', 'Pandanaceae', 'Familia'].</p> </li> <li> <p><strong><code>codes_sct</code></strong>: The normalized ontology codes from SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) vocabulary for diseases, symptoms, drugs, locations, occupations, procedures, and species (if applicable). Example: 22232009.</p> </li> <li> <p><strong><code>terms_sct</code></strong>: The human-readable terms from the SNOMED CT ontology corresponding to the <code>codes_sct</code>. Example: ['adjudicador de regulaciones del seguro nacional'].</p> </li> <li> <p><strong><code>sct_sem_tag</code></strong>: The semantic category tag assigned by SNOMED CT to describe the general classification of the mention. Example: environment.</p> </li> </ul> <p> </p> <p><strong>Suggestion</strong>: If you load the dataset using python, it is recommended to read the columns containing lists as follows</p> <div> <blockquote> <div>import ast</div> <div>df["mention_class"] = df["mention_class"].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)</div> </blockquote> </div> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong>. This means you are free to:</p> <ul> <li>Share: Copy and redistribute the material in any medium or format.</li> <li>Adapt: Remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p><strong>Attribution Requirement</strong>: Please credit the dataset creators appropriately, provide a link to the license, and indicate if changes were made.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li> <p><a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> </li> <li> <p><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> </li> </ul>
CZU normal cropped dataset
<p>The CZU dataset collected from the Czech University of Life Sciences, Prague (CZU) campus in 2022, includes 386 images of European beech (Fagus sylvatica L.), Large-leaved linden (Tilia platyphyllos), Norway Maple (Acer platanoides), and Scots pine (Pinus sylvaltica L.). The dataset comprises multiple angles of tree stems, with at least ten photographs taken per tree to capture the bark structure and patterns. In this dataset we removed the anomaly region from the images, kept the rest of the image in the original form, and named these datasets normal cropped.</p>
CZU combination dataset (exact cropped and normal cropped)
<p>The CZU dataset, collected from the Czech University of Life Sciences, Prague (CZU) campus in 2022, consists of 772 images in the exact cropped dataset and 386 images in the normal cropped dataset. To create the combination dataset, both of these were merged. The dataset focuses on European beech (Fagus sylvatica L.), large-leaved linden (Tilia platyphyllos), Norway maple (Acer platanoides), and Scots pine (Pinus sylvestris L.). It includes photographs of tree stems taken from multiple angles, with a minimum of ten images per tree, highlighting the bark structure and patterns.</p>
Normalized subject indexing data of K10plus library union catalog
<p>This dataset contains <strong>normalized subject indexing data of K10plus library union catalog</strong>. It includes links between bibliographic records in K10plus and concepts (subjects or classes) from controlled vocabularies:</p> <ul> <li>kxp-subjects.tsv.gz: TSV format</li> <li>kxp-subjects.nt.gz: RDF format (in form of NTriples)</li> <li>vocabularies.json: information about vocabularies</li> <li>stats.json: statistics (number of records, subjects per vocabulary etc.)</li> </ul> <p>The dataset is based on a K10plus database dump at 2024-12-02.</p> <p><strong>K10plus</strong></p> <p>K10plus is a union catalog of German libraries, run by library service centers BSZ and VZG since 2019. The catalog contains bibliographic data of the majority of academic libraries in Germany. Bibliographic records in K10plus are uniquely identified by a PPN identifier.</p> <p>Several APIs exist to retrieve more data for a record via its PPN, e.g. link into K10plus OPAC:</p> <p>https://opac.k10plus.de/PPNSET?PPN={PPN}</p> <p>Retrieve full record in MARC/XML format:</p> <p>https://unapi.k10plus.de/?format=marcxml&id=opac-de-627:ppn:{PPN}</p> <p>Get formatted citation for display:</p> <p>https://ws.gbv.de/suggest/csl2?citationstyle=ieee&language=en&database=opac-de-627&query=pica.ppn=${PPN}</p> <p>APIs to look up more data from a notation or identifier of a vocabulary can be found in <a href="https://bartoc.org/">https://bartoc.org/</a>. For instance BK class 58.55 can be retrieved via DANTE API:</p> <p>https://api.dante.gbv.de/data?uri=http%3A%2F%2Furi.gbv.de%2Fterminology%2Fbk%2F58.55</p> <p>See vocabularies.json for mapping of vocabulary symbol to BARTOC URI and additional information.</p> <p><strong>Statistics</strong><br>See stats.json for number of records, links, triples and subjects per vocabulary.</p> <p><strong>TSV</strong></p> <p>The .tsv file contains three tab-separated columns:</p> <ol> <li>Bibliographic record identifier (PPN)</li> <li>Vocabulary symbol</li> <li>Notation or identifier in the vocabulary</li> </ol> <p>An example:</p> <p>010000011 bk 58.55<br>010000011 gnd 4036582-7</p> <p>Record 010000011 is indexed with class 58.55 from Basic Classification and with authority record 4036582-7 from Integrated authority file.</p> <p><strong>RDF</strong></p> <p>The NTriples file contains the same information as given in TSV file but identifiers are mapped to URIs. An example:</p> <p><http://uri.gbv.de/document/opac-de-627:ppn:010000011> <http://purl.org/dc/terms/subject> <http://d-nb.info/gnd/4036582-7> .<br><http://uri.gbv.de/document/opac-de-627:ppn:010000011> <http://purl.org/dc/terms/subject> <http://uri.gbv.de/terminology/bk/58.55> .</p> <p><strong>Changelog</strong></p> <ul> <li>2024-12-11: new dump based on K10plus title records extracted by a slightly different workflow.</li> <li>2024-04-05: new dump from end of February 2024. Total number of records decreased by merging of duplicates.</li> <li>2024-01-11: New dump from end of 2023. Added fivs and fivr classification</li> <li>2023-11-01: New dump from end of September.</li> <li>2023-05-07: New dump. Number of records slightly reduced because K10plus cleaned up duplicate records.</li> <li>2023-04-13: New dump, added stats.json</li> <li>2023-01-20: New dump</li> <li>2022-09-11: New dump, fixed PPN URIs and broken UTF-8 encoding</li> <li>2022-08-24: Fixed GND URIs, added LCC and KAB (<a href="https://doi.org/10.5281/zenodo.7018350">https://doi.org/10.5281/zenodo.7018350</a>)</li> <li>2022-08-24: First version (<a href="https://doi.org/10.5281/zenodo.7016626">https://doi.org/10.5281/zenodo.7016626</a>)</li> </ul> <p><strong>License and provenance</strong></p> <p>All data is public domain but references are welcome. See <a href="https://coli-conc.gbv.de/">https://coli-conc.gbv.de/</a> for related projects and documentation.</p> <p>This dataset has been created with public scripts from git repository <a href="https://github.com/gbv/k10plus-subjects">https://github.com/gbv/k10plus-subjects</a>.</p>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</p>
AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Dataset paper under review.</strong></p> <p><strong>Contact Ricardo Dalagnol (ricds@hotmail.com) for more information.</strong></p> <p> </p> <p><strong>Data:</strong> AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</p> <p><strong>Scale factor</strong>: 10000</p> <p><em>obs: the no_samples layer does not have scale.</em></p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in March 2000)</p> <p><strong>Spatial resolution:</strong> 0.009107388 degree equivalent to ~1 km</p> <p><strong>Temporal resolution:</strong> Monthly</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details summary (more detailed explanation in the paper):</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering (1) a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product; and (2) backward and forward scattering. For the nadir (NAD) product, the nadir normalization was taken. For the anisotropy (ANI) product, we calculated the backward minus forward surfaces for each layer, resulting in the ANI product.</li> <li>The daily data were aggregated into monthly composites by the pixel’s median.</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files - one per year. NAD are the nadir-normalized products and ANI the anisotropy. Inside zip files there are raster files with ".tif" format, one per month window.</li> <li>The filename syntax is "maiac_southamerica_month_PRODUCT_YYYY_MM_LAYER_latlon.tif", where YYYY is the year (e.g. 2000), MM is the month with two digits (e.g. 03 for March), PRODUCT is either nadir or anisotropy, and LAYER can be bands 1-8, EVI and NDVI.</li> <li>There is also the number of samples (no_samples) layers for each date, which can be used to filter composites with a minimum desirable number of daily observations. </li> </ul> <p> </p> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p> </p> <p><strong>Acknowledgements:</strong> R.D. was supported by Sao Paulo Research Foundation (FAPESP) grants 2015/22987-7 and 2019/21662-8. FHW was supported by FAPESP grant 2015/50484-0. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). The funders had no role in the study design, data collection and analysis, including the decision to publish or prepare the manuscript. We thank the MODIS MAIAC team from NASA for providing the freely available MODIS (MAIAC) daily dataset.</p> <p> </p> <p><strong>Auxiliary data of AnisoVeg:</strong></p> <ul> <li>Backscattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6040299">https://doi.org/10.5281/zenodo.6040299</a> and <a href="https://doi.org/10.5281/zenodo.6040790">https://doi.org/10.5281/zenodo.6040790</a></li> <li>Forward scattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6048784">https://doi.org/10.5281/zenodo.6048784</a> and <a href="https://doi.org/10.5281/zenodo.6048793">https://doi.org/10.5281/zenodo.6048793</a></li> </ul> <p> </p> <p><strong>Layers available at Google Earth Engine (GEE):</strong></p> <ul> <li>EVI Anisotropy: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy</a></li> <li>EVI Nadir: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir</a></li> </ul> <p>Obs: these require a (free) google earth engine account.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of hundreds of hours of coding starting in 2016 with the first author PhD work and then into his Postdoc, and many more hundreds hours of data processing. Data is free to use, but if you use this dataset, please cite the dataset paper or this repository (while paper is under review). Invitation for collaboration are welcomed.</p> <ul> </ul> <p> </p> <p><strong>While the paper is under review, for use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
ECOCARDIOGRAMA NORMAL
<p>Conceptos básicos y avanzados del ecocardiograma normal. </p>
Data for 'Normalization procedure for obtaining the local density of states from high-bias scanning tunneling spectroscopy'
<p>This folder contains all the raw data needed to generate the figures in the paper '<em>Normalization procedure for obtaining the local density of states from high-bias scanning tunneling spectroscopy.</em>' The data are seperated by the figures in which they appear, with a text folder in each folder that contains any relevant additional information. </p>
Patch clamp dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
Trafficking dataset for: A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2
<p><span>Many genes, including <em>KCNH2</em>, contain </span><span>'hotspot' domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of <em>KCNH2, </em>a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death<em>. </em>Forty-two percent of <em>KCNH2</em> exon 2 variants caused at least 50 % reduction in protein trafficking and 65% of these trafficking defective variants exerted a dominant-negative effect when co-expressed with a WT <em>KCNH2</em> allele as assessed using a calibrated patch clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this 'hotspot' domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.</span></p>
Analogue Models for comparing and testing the relationship between inverted normal faults and pure thrusting during the positive tectonic inversion
<p>This dataset contains a series of Analogue Models for comparing and testing positive tectonic inversion mechanisms and their newly formed structures . Furthermore, it includes 2-D seismic reflection profiles that can be compared with the models presented here. Finally, examples of natural cases that show tectonic inversion processes are included. Both, seismic lines and photos are located on a segment of Andean forearc, specifically, in the Domeyko Cordillera and the Preandean Basins, northern Chile.</p>
Bangla Text Normalization Benchmark Dataset
<p>This is a small benchmark dataset for Bangla Text Normalization. </p>
Normalized linear counts from NanoString autoimmune profiling panel and summary of statistical analyses
<p>Though dependent on genetic anomalies, clinical manifestations of the human autoimmune disease systemic lupus erythematosus (lupus) can be triggered by environmental exposures including inhalation toxicants such as crystalline silica dust (cSiO<sub>2</sub>), tobacco smoke, and ambient air particles. Prednisone, a glucocorticoid (GC), is a keystone therapy for managing lupus flaring and progression, however, long-term use is associated with many adverse side effects. Here, we characterized the dose-dependent immunomodulation and toxicity of prednisone in a preclinical model that emulates onset and progression of cSiO<sub>2</sub>-triggered lupus. Two cohorts of 6-wk-old female NZBWF1 mice were fed either control AIN-93G diet or one of three AIN-93G diets containing prednisone at 5, 15, or 50 mg/kg diet which span human equivalent oral doses (HED) currently considered to be low (PL; 5 mg/d HED), moderate (PM; 14 mg/d HED), or high (PH; 46 mg/d HED), respectively. At 8 wk of age, mice were intranasally instilled with either saline vehicle or 1 mg cSiO<sub>2</sub> once weekly for 4 wk. The experimental plan was to 1) terminate one cohort of mice (n=8/group) 14 wk after the last cSiO<sub>2</sub> instillation for pathology and autoimmunity assessment and 2) to maintain a second cohort (n=9/group) to monitor glomerulonephritis development and survival. Mean blood concentrations of prednisone's chief active metabolite, prednisolone, in mice fed PL, PM, and PH diets were 27, 105, 151 ng/ml, respectively, which are consistent with levels observed in human blood ≤ 12 h after single bolus treatments with equivalent prednisone doses. Results from the first cohort revealed that consumption of PM but not PL diet significantly reduced cSiO<sub>2</sub>-induced pulmonary ectopic lymphoid structure formation, nuclear-specific AAb production, and inflammation/autoimmune gene expression in the lung, splenomegaly, and glomerulonephritis in the kidney. Relative to GC-associated toxicity, PM but not PL diet elicited muscle wasting, but these diets did not affect bone density or cause glucosuria. Importantly, neither PM nor PL diet influenced latency of cSiO<sub>2</sub>-accelerated death. PH-fed mice in both cohorts displayed robust GC-associated toxicity including body weight loss, reduced muscle mass, and hyperglycemia 7 wk after the final cSiO<sub>2</sub> instillation requiring their early removal from the study. Taken together, our results demonstrate that while moderate doses of prednisone can reduce certain pathological endpoints of cSiO<sub>2</sub>-induced autoimmunity in lupus-prone mice, these ameliorative effects come with unwanted GC toxicity and, crucially, none of these three doses extended survival time.</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.