Showing 9,501 - 9,520 results of 20,442 for search '(functional OR function) data analysis', query time: 0.39s Refine Results
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    Status of Flora on Gochang Ungok and Gochang Incheon River Estuarine Protected Wetland Areas by Hyeongcheol Lee, Chang-Su Lee, Sanghun Lee

    Published 2024-12-01
    “…Wetland ecosystem is rapidly changing due to human activities and climate change, leading to concerns about biodiversity loss and ecosystem function degradation. Study on the flora of wetlands is essential for conservation and sustainable management. …”
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    Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida by Inacio T. Bueno, Carlos A. Silva, Caio Hamamura, Victoria M. Donovan, Ajay Sharma, Jiangxiao Qiu, Jinyi Xia, Kody M. Brock, Monique B. Schlickmann, Jeff W. Atkins, Denis R. Valle, Jason Vogel, Andres Susaeta, Mauro A. Karasinski, Carine Klauberg

    Published 2025-07-01
    “…We combined Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with synthetic aperture radar (SAR) and passive optical satellite imagery to model GEDI AGBD as a function of image-derived data, enabling predictions across the study area and producing continuous AGBD maps. …”
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  6. 9506

    Galar - a large multi-label video capsule endoscopy dataset by Maxime Le Floch, Fabian Wolf, Lucian McIntyre, Christoph Weinert, Albrecht Palm, Konrad Volk, Paul Herzog, Sophie Helene Kirk, Jonas L. Steinhäuser, Catrein Stopp, Mark Enrik Geissler, Moritz Herzog, Stefan Sulk, Jakob Nikolas Kather, Alexander Meining, Alexander Hann, Steffen Palm, Jochen Hampe, Nora Herzog, Franz Brinkmann

    Published 2025-05-01
    “…Galar consists of 80 videos, culminating in 3,513,539 annotated frames covering functional, anatomical, and pathological aspects and introducing a selection of 29 distinct labels. …”
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  7. 9507

    A dataset on potentially groundwater-dependent vegetation in the Sierra Nevada Protected Area (Southern Spain) and its underlying NDVI-derived ecohydrological attributesZENODO by Javier Cabello, Montserrat Escudero-Clares, Sergio Martos-Rosillo, J. Jesús Casas, Juanma Cintas, Thomas Zakaluk, María J. Salinas-Bonillo

    Published 2025-08-01
    “…All spatial layers are projected in ETRS89 / UTM Zone 30N (EPSG: 25830) and are ready for visualization and analysis in standard GIS platforms. Partial validation of the classification was performed using spring location data and the distribution of hygrophilous plant species from official conservation databases. …”
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    Pricing Strategy versus Heterogeneous Shopping Behavior under Market Price Dispersion by Francisco Álvarez, José-Manuel Rey, Raúl G. Sanchis

    Published 2016-01-01
    “…We analyze the seller’s solution with respect to different exogenous perturbations of parametric and functional inputs. For that purpose, we produce synthetic price data using the family of Generalized Error Distributions that includes normal and quasiuniform distributions as particular cases, and we also generate consumers’ shopping data from different behavioral assumptions. …”
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    Exploring the common genetic basis of metabolic syndrome-related diseases and chronic kidney disease: insights from extensive genome-wide cross-trait analyses by Yu Yin, Chenkai Zhao, Yibo Hua, Fei Yang, Dandan Qiu, Jiasheng Yan, Xiaodong Jin

    Published 2025-08-01
    “…Methods We performed a cross-trait pleiotropy analysis using summary-level GWAS data from ten MetS-related diseases and CKD obtained from the IEU database to detect shared pleiotropic loci and genes. …”
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  19. 9519

    Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications by Ying Li, Yanwu Yang, Yuchu Chen, Chenfei Ye, Ting Ma

    Published 2025-01-01
    “…Self-supervised learning (SSL) models offer a transformative approach for mapping dependencies in functional neuroimaging data. Leveraging the intrinsic organization of brain signals for comprehensive feature extraction, these models enable the analysis of critical neurofunctional features within a clinically relevant framework, overcoming challenges related to data heterogeneity and the scarcity of labeled data. …”
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