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665 results for “foundation”
GEOLAB Project FROSPER: FROst heaving soils-Solar Panel foundations interaction in cold European Regions: an experimental study
<p>The objective of the proposed project is to investigate the<strong> resilience of solar panels foundations in cold climates</strong>. The efficiency of solar panels is overall better in cold climates, when the direct sunlight is available, due to the lower temperature-induced dispersion. For this reason, large solar fields tend to be installed in cold regions, such as northern Europe or Canada. A solar field with <i>e.g.</i>, 10 MW, requires a great number of solar panels (~6000) whose foundations are generally<strong> steel piles</strong> driven into the soil down to a depth of 2÷5 m below the ground surface. In the cold regions, the shallow layers of soil are periodically subjected to <strong>freezing thus to the frost-heaving phenomena</strong>. The latter can increase the <strong>risk of uplift failure mechanism of the pile foundation</strong> compromising the exercise of the entire solar panel row.</p><p>This project aims to investigate the interaction between saturated soils and solar panel foundations under frozen conditions in <strong>scaled centrifuge models</strong>. The steel piles will be at first driven into the saturated soil sample, then a set of the <strong>freezing-thawing cycles</strong> will be reproduced in the centrifuge. <strong>Possible practical interventions to reduce the soil frost-heaving effects</strong> will also be explored, as the use of protective insulating mantel at the ground surface all around the head of the pile.</p><p>This project will represent a breakthrough in the understanding of the solar panel foundations behaviour, allowing a deeper insight into possible uplift failure mechanisms. The motivation of the study lies in the reduction of the costs and in the resilience improvement of critical infrastructures for the generation of energy in the EU territories. </p>
Data from: Co-restoring keystone predators and foundation species to recover a coastal wetland
<p>Provided here are the following datasets for the paper entitled "Co-restoring keystone predators and foundation species to recover a coastal wetland" upcoming in the Journal of Applied Ecology. </p> <p>1 Proximity to existing Scylla burrows.xlsx This file contains all data for the paper's section "Effects of proximity to existing Scylla burrows on Scirpus restoration" </p> <p>2 Scylla reintroduction.xlsx This file contains all data for the paper's section "Effects of Scylla reintroduction on Scirpus restoration" </p> <p>3 Consumptive and non-consumptive effects.xlsx This file contains all data for the paper's section "Consumptive and non-consumptive effects of Scylla" </p> <p>4 Opening size and food items.xlsx This file contains all data for Fig. S1 and Table S1 in the Supporting Information </p> <p>The metadata for interpreting these data sets are included in the Excel files. </p>
Roman palace foundation | Sremska Mitrovica
Source: Objaverse 1.0 / Sketchfab
O'Donnell Site foundations
Source: Objaverse 1.0 / Sketchfab
Facilitation cascades across space: monitoring estuarine foundation species from satellites to the microscope
<p>Estuaries are productive ecosystems that are vulnerable to human impacts and environmental disturbances. Estuaries are often inhabited by foundation species that facilitate other organisms by creating and modifying habitats. However, spatiotemporal variability and underpinning drivers of facilitation from estuarine foundation species are poorly studied, especially in the context of monitoring and conservation. Here, we combined analyses of satellite, drone, and camera images, close-up field sampling of quadrats and individuals, and laboratory microscopy, to quantify co-occurrences between the cockle <em>Austrovenus stutchburyi</em> and green macroalga <em>Ulva</em> spp., and their associated invertebrate communities, across spatial resolutions and environmental gradients in a New Zealand estuary. We found a higher abundance of <em>Ulva</em> in winter and a higher abundance of <em>Austrovenus</em> at a site near the ocean. Furthermore, <em>Ulva</em> was, across space and time, often attached to <em>Austrovenus</em>, either live or dead shells, suggesting a hierarchy between a primary (cockle) and secondary (macroalgae) foundation species. Finally, <em>Ulva</em> provided a better habitat for mobile invertebrates than <em>Austrovenus</em>, and thereby increased estuarine diversity through a density-dependent facilitation cascade, that also was consistent in space and time. Overall, our study showed that facilitation cascades can be indirectly inferred and monitored through remote sensing of foundation species using supplementary imaging methods, and that integration of data across scales allows researchers to better understand drivers and consequences of facilitation from foundation species. These insights may aid the development of efficient monitoring strategies and ultimately improve management of estuarine ecosystems.</p>
FractiScope Research Project Foundational Papers
<p>Welcome to the official Zenodo repository for the FractiScope Research Project Foundational Papers. Here, we archive groundbreaking research that explores and validates the principles of fractal intelligence, recursive dynamics, and the SAUUHUPP framework as the foundation of a Networked Fractal Computing AI Universe.</p> <p> </p> <p>These papers provide essential insights into the fractal architecture of reality, offering new paradigms for science, technology, and humanity. From theoretical explorations to empirical validations, our work demonstrates the transformative power of fractal intelligence across domains such as neural networks, climate modeling, genomics, and universal harmony metrics.</p> <p> </p> <p>We invite researchers, innovators, and visionaries to join this growing community, collaborate on pioneering studies, and contribute to a deeper understanding of our fractalized universe.</p> <p> </p> <p>Explore. Discover. Connect.</p> <p>For more information, visit <a href="https://github.com/AiwonA1/FractiAI">FractiAI GitHub</a> or contact us at info@fractiai.com.</p>
Data Set from Quasi-static Cyclic Tests of Scoured 2×3 Pile-Group Foundations for Bridge Structures
<p>This data set is from Quasi-static cyclic tests of scoured 2×3 pile-group foundations for bridge structures, as a data paper submitted to Earthquake Spectra (Submission ID: EQS-24-0190)</p>
Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub
<p>This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. </p> <h3>Dataset Composition:</h3> <ul> <li><strong>Sample Folder:</strong> Contains the input data utilized for testing the model’s functionality.</li> <li><strong>Reference Folder:</strong> Contains the corresponding output provided by the original model contributor.</li> <li><strong>Test.yml File:</strong> This file includes the original contributor’s test setup, which has been accepted by the MHub team.</li> </ul> <h3>Sample Data Source:</h3> <p>The sample images used in this dataset are sourced from public datasets available through the <strong><a href="https://datacommons.cancer.gov/repository/imaging-data-commons" target="_blank" rel="noopener">Imaging Data Commons (IDC)</a></strong>, a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.</p> <h3>Purpose and Utility:</h3> <p>The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.</p> <p>This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.</p> <h3>About MHub:</h3> <p>MHub (<a href="https://mhub.ai/" target="_new" rel="noopener">mhub.ai</a>) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.</p> <p>For more information on the platform and its capabilities, visit <a href="https://mhub.ai/" target="_blank" rel="noopener">mhub.ai</a>.</p>
Host genetics, phenotype and geography structure the microbiome of a foundational seaweed
<p>Interactions between hosts and their microbiota are critical to the functioning and resilience of eukaryotic macro-organisms. Critically, for hosts that play foundational roles in communities, understanding what drives these interactions is essential for informing restoration and conservation of entire ecosystems. Here, we investigated the relative influence of host traits and the surrounding environment on microbial communities associated with the foundational seaweed Phyllospora comosa. We collected data on 16 morphological and functional phenotypic traits, host genetics (using 354 Single Nucleotide Polymorphisms) and surface-associated microbial communities (using 16S rRNA gene amplicon sequencing) from 160 individuals sampled from eight sites spanning Phyllospora's entire latitudinal distribution (1300 km). Combined, these factors explained 54% of the overall variation in Phyllospora's associated microbial community structure, much of which was related to the local environment (~32%). We found that putative "core" microbial taxa (i.e. present on all Phyllospora individuals sampled) exhibited slightly higher association with host traits when compared to "variable" taxa (not present on all individuals). We identified several key genetic loci and phenotypic traits in Phyllospora that were strongly related to multiple microbial amplicon sequence variants, including taxa with known associations to seaweed defense, disease and tissue degradation. This information on how host-associated microbial communities vary with host traits and the environment enhances our current understanding of holobionts and how they are structured. Such understanding can be used to inform management strategies of these important and vulnerable habitats.</p>
Apache Software Foundation Incubator Project Sustainability Dataset
<p>The data set contains ASF incubator projects data (updated in June 2021).</p>
MFD-2-TM-de: German Moral Foundation Dictionary 2 by Translation Matrix
<p>This dataset was created for the research paper: "Reiter-Haas, M., Kopeinik, S., & Lex, E. (2021, May). Studying moral-based differences in the framing of political tweets. In <em>Proceedings of the International AAAI Conference on Web and Social Media</em> (Vol. 15, pp. 1085-1089).". Available at <a href="https://ojs.aaai.org/index.php/ICWSM/article/view/18135/17938">https://ojs.aaai.org/index.php/ICWSM/article/view/18135/17938</a></p> <p>The translation were created using a translation matrix of positive and negative valence words. The dictionary was used to measure effects in the embedding space for which it is suitable. However, it is to note that the dictionary does not work in a classical sense, as it contains duplicates (e.g., words of the opposite poles). While this was also the case in the original dictionary (e.g., for "wound"), the effect has become much more pronounced in the translated version.</p> <p>Format:</p> <ul> <li>Lines starting with % denote a new section that the following words belong to (e.g., care.vice for harm).</li> <li>Otherwise, one word per line.</li> </ul> <p>For implementation details, refer to the <a href="https://github.com/socialcomplab/icwsm21-framing">GitHub Repo</a></p> <p> </p>
SpectralGPT: The first remote sensing foundation model customized for spectral data
<p>SpectralGPT is the first purpose-built foundation model designed explicitly for spectral RS data. It considers unique characteristics of spectral data, i.e., spatial-spectral coupling and spectral sequentiality, in the MAE framework with a simple yet effective 3D GPT network.</p> <p>We will gradually release the trained models (SpectralGPT, SpectralGPT+), the new benchmark dataset (SegMunich) for the downstream task of semantic segmentation, original code, and implementation instructions.</p>
FIGURE 13 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURE 13. Alice Wells and Laurence Mound walking hand by hand in Tidbinbilla National Park, Canberra, Australia, 2011 (Photo: Adriano Cavalleri).
FIGURES 5–7 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURES 5–7. (5) Xaniothrips xantes, described by Laurence Mound as one of his most exciting discoveries. Laurence Mound in various parts of the world 6–7: (6) In Linz, Austria, with Hermann Priesner, 1966; (7) In Ibadan, Nigeria, during his employment for the Federal Department of Agricultural Research, 1960.
FIGURES 8–12 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURES 8–12. Laurence Mound in various parts of the world. (8) In Piracicaba, Brazil with Roberto Zucchi and Renata Monteiro, 1996; (9) In Beltsville, USA with Sueo Nakahara and Cheryle O'Donnell, 2015; (10) In Kunming, China with Xia Wang, Zhaohong Wang, Hongrui Zhang, Alice Wells, Lihong Dang, Shimeng Zhang and Luke Watson (from left to right) during the XIth International Symposium on Thysanoptera and Tospoviruses, 2019; (11) Online on YouTube with Elison Lima advising new thrips students during the I Brazilian Symposium of Thysanoptera, 2020; (12) In ANIC, Canberra, Australia, being interviewed by Desley Tree, 2023.
Рис. 13. ГнёзΑа скаΛьных гоΛубей Columba rupestris, основаниями которых сΛужиΛи старые гнёзΑа птиц этого виΑа. 1 — Δазовский район, мост через кΛюч ЗвёзΑочка, 25.05.2022, фото В.П. Шохрина; 2 — Октябрьский район, разваΛины в окрестностях сеΛа Чернятино, 23.04.2022, фото À.В. Коробова Fig. 13. Nests of hill pigeons Columba rupestris with old nests of the same species as foundations. 1 — Lazovsky District, bridge over Zvezdochka Spring, 25.05.2022. Photo by V.P. Shokhrin; 2 — Oktyabrsky District, ruins in the vicinity of the village of Chernyatino, 23.04.2022. Photo by D.V. Korobov in About breeding biology of the hill pigeon Columba rupestris in Primorsky Krai
Рис. 13. ГнёзΑа скаΛьных гоΛубей Columba rupestris, основаниями которых сΛужиΛи старые гнёзΑа птиц этого виΑа. 1 — Δазовский район, мост через кΛюч ЗвёзΑочка, 25.05.2022, фото В.П. Шохрина; 2 — Октябрьский район, разваΛины в окрестностях сеΛа Чернятино, 23.04.2022, фото À.В. Коробова Fig. 13. Nests of hill pigeons Columba rupestris with old nests of the same species as foundations. 1 — Lazovsky District, bridge over Zvezdochka Spring, 25.05.2022. Photo by V.P. Shokhrin; 2 — Oktyabrsky District, ruins in the vicinity of the village of Chernyatino, 23.04.2022. Photo by D.V. Korobov
Verus: A Practical Foundation for Systems Verification -- Artifact -- Set 2 Screen Capture
<p>A screen capture of the experiment in Set 2 of the artifact for the SOSP24 conditionally accepted paper "Verus: A Practical Foundation for Systems Verification": the experiment was performed on an Amazon AWS c5.4xlarge machine running Windows.</p>
How the past creates the present: reviewing the foundational roles of colonialism and systemic racism and their modern manifestations in paleontology
<p><span><span>This talk was delivered by Dr. Pedro Monarrez on March 26th, 2024. </span></span></p> <p><span><span>Dr. Monarrez is the Recruitment, Outreach, Diversity, Equity, Inclusion (RODEI) coordinator in the Department of Earth, Planetary, and Space Sciences at UCLA. His research investigates the biological processes that drive evolutionary patterns of marine organisms across geologic time and the role that the environment has played in these processes. He also studies the variation and reconciliation of local and regional expressions of global macroevolutionary patterns and perturbations, such as mass extinctions. </span></span></p> <p><span><span>This talk was hosted by the Ethical Open Science research coordination network For more information visit: </span><span><a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbTBmTEsxNkotcDdpQ1N2azVBcXBKM1E5YVZLQXxBQ3Jtc0ttV2NQb29UUVcxRnRkSkFCWXRMNEc2ZXdaeHpES0s5ZzZDbUdiMHNsLTd3QWc0c0FNZl9oODAtNkFWUlJZY3JnZlpBaFhxdVhzeXdsV3JBYWRMRlpsbTFFTjdKNHNhekVuaW9SeWpsSVl5OEhoeDZTYw&q=https%3A%2F%2Feos-rcn.github.io%2Fweb%2Fhome&v=DB-87ZbdSnE" target="_blank" rel="nofollow noopener">https://eos-rcn.github.io/web/home</a></span></span></p> <div></div> <div> <div> <div></div> </div> </div>
NextVir: Enabling Classification of Tumor-Causing Viruses with Genomic Foundation Models
<p>These are a collection of 150bp reads synthesized using ART. Viral reference genomes were downloaded using iCAV, and the primary assemblies of GRCh38.p14 were used for the human reference.</p>
FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting
<div>Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.</div> <div> </div> <div>This dataset comprises three folders: the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.</div> <div> </div> <div>FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.</div>
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.