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352 results for “data enrichment”
Data from: Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon
<p>DNA sequence alignments used for phylogenetic analyses in Bratzel et al. (2022) Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (Bromeliaceae). Taxon.</p>
Data archive: Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Christina E. Mueller, Tomas Lundälv, Fleur C. van Duyl, Jasper M. de Goeij, Jack J. Middelburg<span> </span>(In press) <strong>Niche overlap between a cold-water coral and an associated sponge for isotopically-enriched particulate food sources</strong>. PLOS ONE</p>
Data and figures for Characterization of 30 ^{76}Ge enriched Broad Energy Ge detectors for GERDA Phase II
<p>Data and figures for Characterization of 30 <sup>76</sup>Ge enriched Broad Energy Ge detectors for GERDA Phase II</p>
Extended data of the article "Lockbox enrichment facilitates manipulative and cognitive activities for mice": Supplementary Figure
<p><span>Figure and results of the distance traveled in the Free Exploratory Paradigm, Open Field Test, and Elevated Plus Maze Test during habituation are shown</span>.</p>
Linked collectors and determiners for: Combining target enrichment and Sanger sequencing data to clarify the systematics of the diverse Neotropical butterfly subtribe Euptychiina (Nymphalidae, Satyrinae).
Natural history specimen data linked to collectors and determiners held within, "Combining target enrichment and Sanger sequencing data to clarify the systematics of the diverse Neotropical butterfly subtribe Euptychiina (Nymphalidae, Satyrinae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba">https://bionomia.net/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba">https://gbif.org/dataset/bfb878f3-8a74-46d3-a104-36485c32aaba</a>. Formatted as a Frictionless Data package.
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Enriched Data of Wind Farms (EDWin)
<p>EDWin (Enriched Data of Wind Farms) is a dataset developed to provide information about global wind farms. The dataset is based on OpenStreetMap (OSM) data and has been enriched with additional variables obtained from various databases. The dataset includes two separate data sets, one for global turbines and one for wind farms. As of September 2022, this dataset contains the most recent information available.</p> <p>The datasets have the following structures:</p> <p><strong>Wind Turbine data </strong></p> <p>The data for wind turbines includes 359,947 entries and 12 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Key value of the data point</td> </tr> <tr> <td>lon</td> <td>Longitude of the location</td> </tr> <tr> <td>lat</td> <td>Latitude of the location</td> </tr> <tr> <td>country</td> <td>Country where the turbine is located</td> </tr> <tr> <td>continent</td> <td>Continent where the turbine is located</td> </tr> <tr> <td>land cover</td> <td>The type of land on which the turbine is located</td> </tr> <tr> <td>landform</td> <td>The physical features of the land on which the turbine is located</td> </tr> <tr> <td>elevation</td> <td>The altitude of the turbine</td> </tr> <tr> <td>turbine spacing</td> <td>The distance between turbines in the wind farm</td> </tr> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> </p> <p><strong>Wind Farm data </strong></p> <p>The data for wind farms includes 20,608 entries and 11 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>lon</td> <td>Longitude of the location (center of the wind farm)</td> </tr> <tr> <td>lat</td> <td>Latitude of the location (center of the wind farm)</td> </tr> <tr> <td>country</td> <td>Country where the wind farm is located</td> </tr> <tr> <td>continent</td> <td>Continent where the wind farm is located</td> </tr> <tr> <td>land cover</td> <td>The modal value of the land cover for the turbines in the wind farm</td> </tr> <tr> <td>landform</td> <td>The average value of the landform for the turbines in the wind farm</td> </tr> <tr> <td>elevation</td> <td>The average elevation of the turbines in the wind farm</td> </tr> <tr> <td>turbine spacing</td> <td>The average turbine spacing for the turbines in the wind farm</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> Note that the data for "Country", "Continent", "Land Cover", "Landform", "Elevation" and "Turbine spacing" were collected turbine-specific and later added to the wind farm dataset in an aggregated form. For the categorical variables, the modulus of the respective turbine values was taken, and for numerical variables, the average was calculated. The two variables, number of turbines (i.e. wind farm size) and wind farm shape (i.e. a rough shape of the wind farm), were obtained from the wind farms data and added to the turbine dataset.<br> </p> <p><strong>Sources</strong></p> <p>[1] Open street map. <a href="https://openstreetmap.org/">https://openstreetmap.org/</a>. [Online] Accessed: 2022-10-02.</p> <p>[2] Cutler J. Cleveland, Christopher Morris, Dictionary of Energy (Second Edition), Elsevier, 2015, Pages 638-655, ISBN 9780080968117</p> <p><a href="https://doi.org/10.1016/B978-0-08-096811-7.50023-8">https://doi.org/10.1016/B978-0-08-096811-7.50023-8</a>.</p> <p>[4]<em> </em>Dunnett, S., Sorichetta, A., Taylor, G. <em>et al.</em> Harmonised global datasets of wind and solar farm locations and power. <em>Sci Data</em> <strong>7</strong>, 130 (2020).</p> <p><a href="https://doi.org/10.1038/s41597-020-0469-8">https://doi.org/10.1038/s41597-020-0469-8</a></p> <p>[5] Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers-Collection 2. Remote Sensing 2020, 12 Volume 108, 1044. <a href="https://doi.org/10.3390/rs12061044">doi:10.3390/rs12061044</a></p> <p>[6] Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., & Albano, C. M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619">e0143619</a></p> <p>[7] Global Multi-resolution Terrain Elevation Data 2010 courtesy of the U.S. Geological Survey</p>
Enriched OpenAlex Data for Universidad de Antioquia
<p>Advanced user API output from http://impactu.colav.co for the Institutional Profile "Universidad de Antioquia"</p>
Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
<p>A fundamental neuroscience topic is the link between the brain's molecular, cellular and cytoarchitectonic properties and structural connectivity (SC). Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the Matrix Inversion and Subset Selection (MISS) algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell type densities, demonstrating surprisingly strong prediction accuracy of cell types in general and particular cells like oligodendrocytes. We found evidence of a strong distance-dependency in the cell-connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia are salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.</p>
Data from: Water regime and nitrogen enrichment facilitate the encroachment of woody plants at various developmental stages in freshwater marshes
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Matlab example for Local Enrichment Analysis (LEA) analysis with real data
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Data from: Enriching the ant tree of life: enhanced UCE bait set for genome-scale phylogenetics of ants and other Hymenoptera
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Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
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Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
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Data and code from: Species interactions amplify functional group responses to elevated CO2 and N enrichment in a 24-year grassland experiment
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Data from: Bivalve shells reflect 15N enrichment in a fertilizer-dominated estuary
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Data from: Peatland fungal community responses to nutrient enrichment: a story beyond nitrogen
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Data from: Water controls the divergent responses of terrestrial plant photosynthesis under nitrogen enrichment
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Data and code for: Spatial cell type enrichment predicts mouse brain connectivity
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Root carbon/nitrogen data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
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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.