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7,974 results for “V1”
Abb. V1-V6 in Zur Chorologie und Faunistik der Tagfalter in den Ost- und Südalpen 1. Tagfalter (Papilionoidea) aus der Sammlung von Herbert Meier † sowie Daten aus den Sammlungen des Entomologischen Forschungsmuseums EFMEA in Feldkirch
Abb. V1-V6: (V1) Vor Sonnenaufgang (auch Titel eines Romans von Gerhard Hauptmann). Grosswalsertal, Hochlicht 2600 m; (V2) Rheintal. Blick von der Hohen Kugel bei Götzis, 1645 m. Aus der Ebene ragt der tektonisch den Westalpen zuzurechnende Kummenberg, ein Inselhorst, heraus. Postglazial verfüllte der Rhein und seine Zubringer die Talfurche mit bis zu 500 m mächtigen Sedimentschichten. © Aistleitner 1988; (V3) Bregenzerwald. Blick vom Zitterklapfen (siehe weiter unten) nach Norden. Im Vordergrund die Berge der Flyschzone (Annalper Joch), in der Bildmitte die Kanisfluh, 2044 m, aufgebaut aus Jurakalken (Quintnerkalk) und Mergeln des westalpinen Helvetikums, links hinten ist der Bodensee im Dunst zu erahnen. © Aistleitner 1974; (V4a und V4b) Kleinwalsertal. Allgäuer Alpen, Hoher Ifen, 2230 m, geologisch den Westalpen (Helvetikum) zuzurechnen; markant sind die schräg liegende Gipfelplatte und die mächtigen Wandfluhen aus Schrattenkalk, einem hellen Riffschuttkalk. Das Kleinwalsertal erfährt eine geologische und formenreiche Dreiteilung durch seinen Anteil am westalpinen Helvetikum und dem Penninischen Flysch und andererseits dem Oberostalpin. © Aistleitner 1997; (V5) Kleinwalsertal. Gottesackerplateau NE des Ifens, eines der grössten Kare und Karstphänomene der topografischen Ostalpen. © Aistleitner 1997.
Generative deep learning for hydrological forecasting: CVAE-75 basins from CANOPEX_v1
<p>Data associated with https://doi.org/10.1016/j.jhydrol.2023.130498</p>
Synthetic dataset for end-to-end Relation Extraction of relationships between Organisms and Natural-Products with Vicuna-13b-v1.5
<p>A new synthetic dataset (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products.</p><p>The new dataset was generated using <a href="https://huggingface.co/lmsys/vicuna-13b-v1.5">Vicuna-13b-v1.5</a>, derived from LLaMA 2. Like the model, the produced synthetic data are also submitted to the License of the model used for generation, see the original <a href="https://github.com/facebookresearch/llama/blob/main/LICENSE">LLaMA 2 license</a>.</p><p>The new dataset was created based on the top-1000 (per biological kingdom) LOTUS literature references extracted with the <a href="https://github.com/idiap/gme-sampler">GME-sampler</a>.</p><p>The dataset contains 10,405 items in the training set and 547 items in the validation set.</p><p>The dataset was generated using the same protocol as described in the <a href="https://github.com/idiap/gme-sampler">article</a>.</p>
SeaFlow data v1: High-resolution abundance, size and biomass of small phytoplankton measured by flow-cytometry
<p>SeaFlow is an underway flow cytometer designed to continuously monitor the optical properties of the smallest phytoplankton from a ship's flow-through seawater system. It collects high-resolution data, generating the equivalent of 1 sample every 3 minutes or every 1 km (for a ship moving at 10 knots).</p> <p>The dataset provides measurements of cell abundance, cell size (equivalent spherical diameter) and carbon biomass for small phytoplankton populations: the cyanobacteria Prochlorococcus, Synechococcus, Crocosphaera, and small eukaryotic phytoplankton (<5 μm ESD). Data processing followed the methods outlined in <a href="https://doi.org/10.1038/s41597-019-0292-2">Ribalet et al. (2019)</a>. For more information, visit the <a href="https://seaflow.netlify.app/">SeaFlow website</a>.</p> <p><strong>New in version 1.6 </strong>The updated dataset includes flow cytometric measurements from 89 cruises, spanning nearly 14,000 hours of observations across 130,000 km of the surface oceans.</p>
Arctic Moisture Intrusion Dataset v1 1/2 (1979-1999)
<p>Moisture intrusion tracking algorithm output (1/2) from 1979-1999. Output contains binary files with associated ID numbers of moisture intrusion events and assoicated ERA5 total column water vapor and northward water vapor flux. </p> <p>Dataset 2/2 - 10.5281/zenodo.13984122</p>
BIO4AFRICA_Small-scale HTC batch unit_Dataset1_261124_v1
<p><span>Hydrochar Characterization Database: This database reflects the characterization of hydrochar produced at IHE Delft for Water Education within the framework of the Bio4Africa project. It includes the experimental operational conditions, the types of biomass used, the mass yield of the product, and the properties of the solid and liquid outputs of the process.</span></p> <p><span><span>Machine learning database: This is an analysis ready database to be used by user who are interested in predicting the properties of hydrochar using machine learning approaches. The database combines the experimental results from IHE and the experimental results from the literature. The data are standardized in the same manner, allowing the users to directly use it without any required edits.</span></span></p>
Fórmulas de Nutrição Parenteral disponíveis no Brasil (v1 - Novembro de 2024)
<p>O dataset contém <strong>37 registros</strong>. Aqui está a descrição corrigida:</p> <p> </p> <h3>Descrição:</h3> <p>Este dataset documenta as características técnicas de diferentes soluções nutricionais parenterais disponíveis no Brasil em novembro de 2024. Ele contém especificações detalhadas de produtos, incluindo composição nutricional, parâmetros físico-químicos e propriedades relacionadas à administração clínica.</p> <p>O dataset foi construído pela extração das bulas do fabricante, auxilada por chatbot (GPT 4.0).</p> <h3>Estrutura do Dataset:</h3> <ul> <li><strong>Formato:</strong> Arquivo Excel</li> <li><strong>Aba Principal:</strong> "Planilha1"</li> <li><strong>Número de Colunas:</strong> 76</li> <li><strong>Número de Registros:</strong> 37</li> </ul> <h3>Variáveis Principais:</h3> <ol> <li><strong>Nome:</strong> Nome do produto nutricional</li> <li><strong>Laboratório:</strong> Fabricante responsável pelo produto</li> <li><strong>Via:</strong> Método de administração (Central ou Periférica).</li> <li><strong>Bolsa (mL):</strong> Volume total do produto por bolsa.</li> <li><strong>Calorias:</strong> Valor calórico total fornecido pelo produto.</li> <li><strong>Calorias Não Proteicas:</strong> Energia derivada de componentes não proteicos.</li> <li><strong>Densidade Calórica:</strong> Calorias por mL do produto.</li> <li><strong>pH:</strong> Medida de acidez ou alcalinidade.</li> <li><strong>Osmolaridade (mosmol/L):</strong> Concentração osmótica do produto.</li> <li><strong>Osmolalidade (mosm/kg de água):</strong> Concentração osmótica ajustada à massa de água.</li> <li><strong>Eletrólitos</strong></li> <li><strong>Nitrogenio e aminoacidograma</strong></li> <li><strong>Sais minerais</strong></li> <li><strong>Macronutrientes:</strong> Glicose monoidratada, Glicose anidra, Gordura (com especificação do tipo de emulsão, e.g., soja, óleo de peixe, óleo de oliva).</li> <li><strong>Excipientes:</strong> Detalhes dos excipientes como fosfolipídios de ovo, glicerol, entre outros.</li> </ol> <h4>Informações Clínicas:</h4> <ul> <li><strong>Tipo de Emulsão Lipídica:</strong> Exemplo: Soja.</li> <li><strong>Componentes Secundários:</strong> Quantidades de triglicerídeos de cadeia média (TCM), óleos, entre outros.</li> </ul> <h3>Aplicações do Dataset:</h3> <ol> <li><strong>Pesquisas Clínicas:</strong> Avaliação de eficácia e segurança de soluções parenterais.</li> <li><strong>Modelagem e Simulação:</strong> Criação de modelos de suporte nutricional baseados na composição.</li> <li><strong>Ensino e Treinamento:</strong> Uso em treinamentos sobre terapia nutricional.</li> <li><strong>Comparação de Produtos:</strong> Benchmarking entre diferentes marcas e formulações.</li> </ol> <h3>Observações:</h3> <ul> <li>O dataset contém 37 registros únicos.</li> <li>Todas as células estão preenchidas com valores numéricos ou descritivos para análise quantitativa e qualitativa.</li> <li>Não há registros em branco.</li> </ul> <p> </p>
Agricultural Residue Burning Emissions 2019 v1
<p>The data provides the agricultural residue burning emission estimates over 11 agroclimatic zones of Madhya Pradesh, India, for the 2019 rabi season. It also includes supplementary information used in the emission estimation for the reader's reference. </p> <p>References:</p> <p>1. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3)https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html </p> <p>2. Gupta, P. K.; Sahai, S.; Singh, N.; Dixit, C. K.; Singh, D. P.; Sharma, C.; Tiwari, M. K.; Gupta, R. K.; Garg, S. C. Residue Burning in Rice-Wheat Cropping System: Causes and Implications. Curr. Sci. 2004, 87 (12).</p> <p>3. Jain, N.; Bhatia, A.; Pathak, H. Emission of Air Pollutants from Crop Residue Burning in India. Aerosol Air Qual. Res. 2014, 14 (1).<a href="https://doi.org/10.4209/aaqr.2013.01.0031"> https://doi.org/10.4209/aaqr.2013.01.0031</a>.</p> <p>4. Venkatramanan, V.; Shah, S.; Rai, A. K.; Prasad, R. Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India. Front. Energy Res. 2021, 8.<a href="https://doi.org/10.3389/fenrg.2020.614212"> https://doi.org/10.3389/fenrg.2020.614212</a>.</p> <p>5. Van Der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.; Morton, D. C.; Defries, R. S.; Jin, Y.; Van Leeuwen, T. T. Global Fire Emissions and the Contribution of Deforestation, Savanna, Forest, Agricultural, and Peat Fires (1997-2009). Atmos. Chem. Phys. 2010, 10 (23). <a href="https://doi.org/10.5194/acp-10-11707-2010">https://doi.org/10.5194/acp-10-11707-2010</a>.</p>
InnoRate_Feedback_on_InnoRate's_pilots_Dataset11_2021.11.25_v1
<p>This dataset has been collected in the context of monitoring and assessing the pilot operation of the technology assessment and rating platform of the InnoRate project (H2020 GA 821518). It represents the outcome of an online survey targeting pilot participants, including the stakeholder groups of innovators, innovation intermediaries, and investors. To deploy the online survey, we used the EUSurvey tool.</p>
InnoRate_Social_media_statistics_Dataset14_2021.12.28_v1
<p>This dataset contains the final statistics of (i) the social media accounts (Facebook, Twitter, LinkedIn) and (ii) the InnoRate web portal, which both have been created in the frame of the InnoRate Project (H2020 GA 821518).</p>
InnoRate_Multi-component web-based survey_Dataset4_2021.12.28_v1
<p>This dataset has been collected by deploying an online survey to gather insight and analyse the needs and preferences of potential users and stakeholders of the InnoRate Platform of the InnoRate Project (H2020 GA 821518). We have gathered responses from 3 main stakeholder groups, namely innovators, investors and innovation intermediaries. The surveys for each stakeholder group (.docx format), along with the respective anonymised version of their responses (.xlsx format) are uploaded on Zenodo.</p>
InnoRate_Data_collected_from_dissemination_events_Dataset15_2021.12.28_v1
<p>This dataset contains the aggregate data of the dissemination activities and events that were performed in the frame of the InnoRate Project (H2020 GA 821518). These activities and events have mostly been focused on promoting the InnoRate platform and services, its pilot rounds, the benefits for each user group, the matchmaking and investment readiness events to InnoRate’s stakeholders.</p>
Annual mean TROPOMI-derived ground-level NO2 mixing ratio (2019 - North America v1)
<p>Annual mean ground-level NO2 mixing ratio for 2019 inferred from the TROPOMI satellite instrument over North America at 0.025x0.03125 degree resolution. Included is 2019 annual mean and 1.5 year mean spanning July 2018 – December 2019.</p> <p><strong>Reference:</strong></p> <p>Cooper, M.J., R.V. Martin, C.A. McLinden, and J.R. Brook (2020), Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument, Env. Res. Lett., DOI:10.1088/1748-9326/aba3a5</p>
Reims Trematodes MSP Database V1
<p>MALDI TOF Bruker MSP database for Trematoda.</p> <p>Described in "MALDI-TOF : A new tool for the identification of <em>Schistosoma</em> cercariae and detection of hybrids" (publication pending) and Huguenin, Antoine, et al. 2019. « MALDI-TOF mass spectrometry: a new tool for rapid identification of cercariae (Trematoda, Digenea) ». <em>Parasite</em> 26 (8): 11‑13. <a href="https://doi.org/10.1051/parasite/2019011">https://doi.org/10.1051/parasite/2019011</a>.</p> <p> </p>
HCFMRP COVID-19 & LID (v1)
<p>Dataset designed to characterize lung interstitial diseases (LID) and COVID-19 on chest X-ray. For the composition of the dataset, frontal chest X-ray of patients from the Ribeirão Preto Medical School of University of São Paulo (Brazil) were classified into three groups: healthy (382 images), with LID (308 images), and with COVID-19 (189 images).</p> <p>All images were analyzed by a thoracic radiologist and COVID-19 diagnosis was confirmed by RT-PCR.</p> <p>For each of the groups, three types of files are available, all anonymized: the frontal view of the chest X-ray in DICOM format; the original images converted to PNG format; and the same images in 3-channel PNG format (RGB).</p> <p>The following directory and file structure is presented:</p> <p> <strong>1-Normal:</strong> for healthy cases</p> <ul> <li> <strong>normal-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong>normal-png:</strong> image files converted to PNG</li> <li> <strong>normal-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p> <strong>2-LID:</strong> for LID cases</p> <ul> <li> <strong>lid-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong> lid-png:</strong> image files converted to PNG</li> <li> <strong>lid-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p> <strong>3-COVID:</strong> for COVID-19 cases</p> <ul> <li> <strong>covid-dcm-anonymized:</strong> anonymized DICOM files</li> <li> <strong>covid-png:</strong> image files converted to PNG</li> <li> <strong>covid-png-RGB:</strong> image files converted to 3-channel PNG</li> </ul> <p>For more information, contact us: https://mainlab.fmrp.usp.br/</p>
Genome and annotation files for Blumeria graminis f. sp. tritici isolate ISR_7 (genome assembly: Bgt_ISR7_genome_v1_4)
<p>Genome and annotation files for Blumeria graminis f. sp. tritici isolate ISR_7 (genome assembly: Bgt_ISR7_genome_v1_4)</p>
Source data for analysis of super-enhancer interactomes v1
<p><strong>Super-enhancer interactomes from single-cells link clustering and transcription</strong></p> <p>Derek Le, Antonina Hafner, Sadhana Gaddam, Kevin Wang, Alistair Boettiger</p> <p>This Zenodo repository contains data files associated with our analysis.</p> <p>Software for processing the data is available in the associated github repository: https://github.com/BoettigerLab/SEclustering-2024</p> <p>Additional information can be found in the associated manuscript, currently in preparation -- once it is posted on BioRxiv, it will be linked here. </p> <p>This deposition currently includes<br>1) SuperEnhancerLoci.xlsx - a master data table linking the genomic sequence barcode data from the corrected tables (described below) to the corresponding super-enhancer and their genomic coordinates in mm10.<br>2) Corrected_Data_Tables_by_FOV.zip -- contains drift corrected and chromatically corrected x,y,z coordinates and cellular barcode data to track cell type and coordinate barcode data to identify genomic sequences. <br>3) Processed_Seq_Data.zip -- re-processed sequencing based data used in this study.<br>4) FOF-CT_Spot_tables.zip -- draft versions of the 4DN FOF-CT formatted data-standard spot tables. See data format description here: https://fish-omics-format.readthedocs.io/en/latest/ <br>5) Probe_Sequences.zip -- fasta files, bed files, and codebook tables for the RNA and DNA probe sequences used in this study.</p>
OpenAlex Topic Classification v1 Model Artifacts and Training Data
<p>This is all data used to train the topic classification model and also the model artifacts to deploy the model. Please see the github repo for more information:</p> <p>https://github.com/ourresearch/openalex-topic-classification</p>
Dataset for UNEP-v1
<p>Dataset for the paper: <strong>General-purpose machine-learned potential for 16 elemental metals and their alloys</strong></p> <p><strong>UNEP</strong>-v1 = version 1 of <strong>U</strong>nified <strong>N</strong>euro<strong>E</strong>volution <strong>P</strong>otential as implemented in GPUMD (https://gpumd.org/)</p> <ul> <li>INCAR: Input file of VASP for single-point DFT calculations</li> <li>nep.in: Input file of GPUMD for NEP training</li> <li>UNEP-v1-main.txt: The main UNEP-v1 model used in the paper</li> <li>ensemble-nep-models.zip: The ensemble of eight UNEP-v1 models</li> <li>trainset.zip: Training data <ul> <li>1-component: all the 1-component training data in extended XYZ format</li> <li>2-component: all the 2-component training data in extended XYZ format</li> <li>combined-train-xyz: all the training data in a single extended XYZ file</li> </ul> </li> <li>testset.zip: Test data for Fig 2 and Figs. S1-S17</li> </ul>
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
<p>Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.</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.