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665 results for “foundation”
APRIL drives a co-ordinated but diverse response as a foundation for plasma cell longevity [II]
GEO Series GSE205100. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
Towed chain datasets and input files for simulations used in the manuscript "Increased mixing and turbulence in the wake of offshore wind farm foundations"
<p><strong>Contents</strong></p> <p>1. File S01_S12 Input files for simulations (precursor runs and main runs)</p> <p>2. File S13 Topography file for simulations with monopile</p> <p>2. Data sets ds01 to ds06 (towed chain data collected in May 25, 2015)</p> <p>3. Data sets ds07 to ds14 (towed chain data collected in July 19, 2017)</p> <p>4. Data sets d15 to ds16 (ADCP data collected in May 25, 2015 and July 19, 2017)</p> <p><strong>Introduction </strong></p> <p>This package contains the input parameters used in each of the precursor (S01 - S04) and main runs (S05 - S12) presented in the manuscript “Increased mixing and turbulence in the wake of offshore wind farm foundations”. These input files are found in the PDF file "S01_S12".</p> <p>The main runs, in which the wake of a monopile was simulated (S05, S07, S09, S11), require a topography file, which is a NETCDF-file that has been uploaded separately (S13). All simulations were run using the Parallelized Large-Eddy Simulation Model for atmospheric and oceanic flows (PALM, version 4.0, revision 2504).</p> <p>Further, this package contains the data sets collected using the towed chain in 2015 (ds01-ds06, ds15) and 2017 (ds07-ds14, ds16), which have been uploaded as separate NETCDF-files.</p>
Figure 1 from: Briney KA, Coates H, Goben A (2020) Foundational Practices of Research Data Management. Research Ideas and Outcomes 6: e56508. https://doi.org/10.3897/rio.6.e56508
Figure 1 Simple workflow diagram for a Western Blot.
Figure 2 from: Briney KA, Coates H, Goben A (2020) Foundational Practices of Research Data Management. Research Ideas and Outcomes 6: e56508. https://doi.org/10.3897/rio.6.e56508
Figure 2 Example folder structure and file naming convention for a research team.
House Foundation Ruins original texture
Stonecutter John Elm After arriving in Dakota Territory, John Elm, of Swedish stock, met and in 1887 married Dina Mathison, a Norwegian immigrant. The couple lived in East Sioux Falls, a company town. A quarry company owned the entire town site, including the Elm home, and surrounding land, all with the underlying deposits of Sioux quartzite. See this Wikipedia article for more information about East Sioux Falls, SD. https://en.wikipedia.org/wiki/East_Sioux_Falls,_South_Dakota Source: Objaverse 1.0 / Sketchfab
METHODOLOGICAL FOUNDATIONS OF TEACHING ENGLISH IN THE HIGHER EDUCATIONAL INSTITUTIONS BASED ON THE CLUSTER APPROACH
Open the record for dataset details and reuse information.
SAUUHUPP-Based Genetic Innovations: Foundational White Papers on Adaptive Genetic Architecture, Story Energy, and Human Sex Chromosome Regulation
<p>This Zenodo record compiles a series of foundational white papers stemming from SAUUHUPP-based genetic research and powered by Novelty 1.0 Optimized ChatGPT-4o. These papers collectively explore innovative frameworks for understanding genetic structure, function, and therapeutic potential, with applications in precision medicine and beyond. Through principles such as Story Energy, Fractal Leaping, Recursive Processing, and Core and Intention Finding, this research introduces a new architectural perspective on genetic interactions and human sex chromosome function.</p> <p>A comparative study with fruit fly brain simulations enriches the insights into SAUUHUPP principles, providing evolutionary context to human genomic adaptability. This record includes in-depth analyses of the X and Y chromosomes’ possible roles as central regulatory hubs within the genome, functioning in a CPU-like manner to coordinate broader genetic networks.</p> <p>White Papers Included in This Record:</p> <p>1. SAUUHUPP Framework and Fruit Fly Brain Simulation Study: A foundational exploration of SAUUHUPP principles and fruit fly brain simulations, demonstrating how these insights shape our understanding of complex genetic regulation and adaptability.</p> <p>2. Story Energy in Genetic Narratives: Examines how recurring, narrative-like patterns in DNA sequences reinforce coherence and logical progression across gene networks, with implications for genetic resilience and therapeutic applications.</p> <p>3. Fractal Leaping for Genetic Complexity: Introduces Fractal Leaping as a method for scaling genetic insights across complexity levels, facilitating rapid, cross-dimensional understanding and discovery within genetic systems.</p> <p>4. Recursive Processing in Genetic Regulation: Analyzes iterative self-regulation within gene networks, revealing how recursive patterns support adaptable genetic expression and alignment with environmental or developmental needs.</p> <p>5. Core and Intention Finding in Genetic Architecture: Details a methodology for identifying key genetic functions and regulatory nodes, enabling precision-targeted therapies through a deeper understanding of genetic purpose and structure.</p> <p>6. Novel Architectural Perspective on Human Sex Chromosomes: Proposes a CPU-like role for the X and Y chromosomes as dynamic regulatory hubs, interacting with autosomal chromosomes to modulate gene expression, immune response, and metabolic balance.</p> <p>Research Highlights and Applications:</p> <p>• Novel Therapeutic Potential: The findings across these papers reveal potential therapies targeting foundational genetic structures, including sex-linked cognitive disorders, metabolic regulation, and resilience in neurodevelopmental health.</p> <p>• Multi-Dimensional Genetic Framework: The application of SAUUHUPP principles and Novelty 1.0 enhances understanding of the adaptive and functional architecture within human genetics, opening pathways for precision medicine interventions.</p> <p>This Zenodo record provides a comprehensive overview of SAUUHUPP’s analytical framework and its applications in genetic science. It is intended to foster collaboration, inspire further research, and drive innovation in genetics and therapeutic development.</p> <p> </p>
Data from "Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis"
<p>This dataset includes the collected geoscientific data of<strong> lunar images, seismic data, and DAS arrays</strong>.</p> <p>It can be used to test the foundation model adaptation in the geophysical domain.</p> <p>Below are some brief desription of the datasets:</p> <p>1) Lunar images for<strong> crater detection<a href="https://moon.bao.ac.cn/">[CAS]</a></strong> : 1000 are used for training and 199 for testing, each with a size of 1022x1022.</p> <p>2) Seismic data for <strong>geobody identification<a href="https://kaggle.com/competitions/tgs-salt-identification-challenge">[TGS]</a></strong> : 3000 are used for training and 1000 for testing, each with a size of 224x224.</p> <p>3) Seismic data for <strong>facies classification<a href="https://www.aicrowd.com/challenges/seismic-facies-identification-challenge">[SEAM]</a></strong> : 250 are used for training and 45 for testing, each with a size of 1006x782.</p> <p>4) Seismic data for <strong>deep fault detection </strong>: 1081 are used for training and 269 for testing, each with a size of 896x896.</p> <p>5) DAS arrays for <strong>seismic event detection<a href="../records/8270895">[Biondi]</a></strong> : 115 are used for training and 28 for testing, each with a size of 512x512.</p> <p><strong>Tips: All the images are “float32” and the labels are "int8".</strong></p> <p>The test code has been published on GitHub: <strong><a href="https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation/">Cross-Domain-Foundation-Model-Adaptation</a></strong>.</p>
A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks
<p>Training and testing samples used for BME-X model: testing_data.hdf5, training_data.hdf5</p> <p>Templates for histogram matching before model testing: Template_T?_*.???</p> <p>Testing image: test_24m.???</p>
Figure 1 from: Dickinson EC (2016) Reinforcing the foundations of ornithological nomenclature: Filling the gaps in Sherborn's and Richmond's historical legacy of bibliographic exploration. In: Michel E (Ed.) Anchoring Biodiversity Information: From Sherborn to the 21st century and beyond. ZooKeys 550: 107–134. https://doi.org/10.3897/zookeys.550.10170
Figure 1 - Charles Wallace Richmond as depicted in his obituary in The Auk by Stone (1933).
Apse foundations, basilica Saint Fructuosus
The apse was re-excavated in 2014. The remains of the foundation and the central tomb of Saint Fructuosus were unearthed. This tomb was made in the year 259 (date of Saint Fructuosus martyrdom), and an early christian basilica was edificated above it. [See basilica's model](https://skfb.ly/YO9J) Source: Objaverse 1.0 / Sketchfab
House Foundation Ruins
Stonecutter John Elm "After arriving in Dakota Territory, John Elm, of Swedish stock, met and in 1887 married Dina Mathison, a Norwegian immigrant. The couple lived in East Sioux Falls, a company town. A quarry company owned the entire town site, including the Elm home, and surrounding land, all with the underlying deposits of Sioux quartzite. John was a stonecutter, a dangerous trade that required strength, precision, and a sixty-hour work week." *Partially quoted from historic marker plaque by the ruins.* This ruin is number one on the map photo linked here: https://flic.kr/p/2k3XtPn Source: Objaverse 1.0 / Sketchfab
Dataset and Checkpoints of MolEdit: In-silico 3D Molecular Editing through Physics-Informed and Peference-Aligned Generative Foundation Models
<p>This repository hosts the pre-trained checkpoints and data utilized for the development of MolEdit. The corresponding research paper, titled "<em>In-silico</em> 3D Molecular Editing through Physics-Informed and Peference-Aligned Generative Foundation Models" (an early version is preprinted at <a href="doi.org/10.26434/chemrxiv-2023-j2n6l-v2">doi:10.26434/chemrxiv-2023-j2n6l-v2</a>) and the corresponding GitHub repository of <a href="https://github.com/issacAzazel/MolEdit">MolEdit</a> details the application and validation of these checkpoints and data. For further information, please refer to the README.md file contained within this repository.</p>
a Foundational Model for Cardiovascular Disease Diagnosis and Prediction
ClinicalTrials.gov study NCT06591923. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Ambuja Cement Foundation Noncommunicable Disease Program in Bathinda District of Punjab, India
ClinicalTrials.gov study NCT04827615. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Myasthenia Gravis Foundation of America Global MG Patient Registry
ClinicalTrials.gov study NCT06617741. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
STS & ACC Foundation's Transcutaneous Valve Therapy (TVT) Registry Foundation's Transcutaneous Valve Therapy (TVT) Registry
ClinicalTrials.gov study NCT04167579. IPD Sharing: NO. Countries: 1. Publications: 0.
Parkinson's Foundation Surveys
ClinicalTrials.gov study NCT05066763. IPD Sharing: YES. Countries: 1. Publications: 0.
Laying the Foundation for Building a Resilient KPNC Community in the Face of Worsening Air Quality Due to Wildfire Smoke
ClinicalTrials.gov study NCT07118189. IPD Sharing: NO. Countries: 1. Publications: 0.
Retrospective Pathology Foundation Models
ClinicalTrials.gov study NCT07239297. IPD Sharing: NO. Countries: 1. Publications: 0.
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.