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105 results for “Fractal”
Fig. 1. A in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 1. A. Ideal uniform network of 225 points spaced 2 mm apart over an area of 30 × 30 mm2. B. The log of number of pairs C of the stations, with mutual distance smaller than R, as a function of log(R) (mm); the vertical dashed lines represent the lower (4 mm) and upper (16 mm) limits of R, inside which the linear slope provides the best fitting to the investigated co−ordinates.
Fig. 8 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 8. This plot is the same as in Fig. 7, except for marks have been appended according to sample of provenance instead of species.
Fig. 3 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 3. Krithe compressa (Seguenza, 1880), right valves; transparence drawings from external view; sample 50 (A–G), sample 51 (H–R), sample 58 (S–BB); upper Pliocene. A. KC−01, B.O.C. 2519. B. KC−02, B.O.C. 2520. C. KC−03, B.O.C. 2521. D. KC−04, B.O.C. 2522.E. KC−05, B.O.C. 2523. F. KC−06, B.O.C. 2524. G. KC−07, B.O.C. 2525. H. KC−08, B.O.C. 2526. I. KC−09, B.O.C. 2527. J. KC−10, B.O.C. 2528. K. KC−11, B.O.C. 2529. L. KC−12, B.O.C. 2530. M. KC−13, B.O.C. 2531. N. KC−14, B.O.C. 2532. O. KC−15, B.O.C. 2533. P. KC−16, B.O.C. 2534. Q. KC−17, B.O.C. 2535. R. KC−18, B.O.C. 2536. S. KC−19, B.O.C. 2537. T. KC−20, B.O.C. 2538. U. KC−21, B.O.C. 2539. V. KC−22, B.O.C. 2540. W. KC−23, B.O.C. 2541. X. KC−24, B.O.C. 2542. Y. KC−25, B.O.C. 2543. Z. KC−26, B.O.C. 2544. AA. KC−27, B.O.C. 2545. BB. KC−28, B.O.C. 2546.
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
<p>The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.</p>
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
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Dataset for article: Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88
<p>Dataset for article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Biological Psychology, 82(1), pp. 82-88</p>
Breathing frequency bias in fractal analysis of heart rate variability (datasets)
<p>data form the article:</p> <p>Perakakis, P., Taylor, M., Martinez-Nieto, E., Revithi, I., Vila, J. (2009). Breathing Frequency Bias in Fractal Analysis of Heart Rate Variability. Bi- ological Psychology, 82(1), pp. 82-88 </p>
Data for "Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system"
<p>The data set corresponds the data used in the paper : “Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system”, published in 2017 in the Journal “Hydrology and Earth System Sciences” (http://www.hydrol-earth-syst-sci.net/).</p> <p>More precisely it corresponds to the matrices that are used in the fractal and multi-fractal analysis of the ten urban areas investigated in the paper.</p> <p> </p> <p>For each catchment, it is organised as follow:</p> <p>- catchment_name_conduit.asc : the matrix describing the sewer system.</p> <p>- catchment_name_OSM.asc : the matrix describing the impervious areas (roads and buildings) obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_OSM_house_only.asc : the matrix describing the “building” areas obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_imperviousness.asc : the matrix describing the representation of imperviousness in operational semi-distributed models.</p> <p> </p> <p>More details can be found in the paper.</p>
Data for testing the assumption of fractal scaling in canopy surfaces across a diverse range of forest types
<p>This is a collection of scripts and research data for a study on whether canopy surfaces follow fractal scaling laws across a diverse range of forest types. The article citation is: <span>Fischer, F. J.</span>, & <span>Jucker, T.</span> (<span>2024</span>). <span>No evidence for fractal scaling in canopy surfaces across a diverse range of forest types</span>. <em>Journal of Ecology</em>, <span>112</span>, <span>470</span>–<span>486</span>. <a href="https://doi.org/10.1111/1365-2745.14244">https://doi.org/10.1111/1365-2745.14244</a>.</p> <p>As data, we use canopy height models (CHMs) from 9 Australian research sites belonging to the Terrestrial Ecosystem Research Network (TERN). Their extent is 5 km x 5 km throughout. The underlying data can be found here: https://portal.tern.org.au/metadata/TERN/4ff0b4c9-cfa0-4d09-9520-b5402adc583f. Climatic data are extracted from the CHELSA climatology 1981-2010 (Brun et al. 2022: Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022; Karger et al. 2017: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122).</p> <p>Each uploaded zip-file corresponds to a folder within a common repository. To reproduce our analysis, unpack into the same folder, upholding naming conventions and overall structure. Please note that some parts of the analysis require external software, e.g., GRASS GIS for the generation of simulated fractal surfaces. Paths need to be reset accordingly. </p> <p><strong>rscripts.zip:</strong> </p> <ul> <li>the overall R script ("areforestsfractals_v3.R") used to generate fractal surfaces, calculate fractal statistics from CHMs and simulated surfaces, and to carry out the analysis</li> <li>various summary statistics saved both as RData and csv files</li> </ul> <p><strong>TERN.zip:</strong></p> <ul> <li>data within this folder are ordered by site name</li> <li>for each site, we include several CHMs, their corresponding DSMs (digital surface models) and DTMs (digital terrain models), as well as ancillary layers (pulse densities of laser scans)</li> <li>for each site and each CHM/DSM, we also save the fractal scaling properties in separate csv files</li> </ul> <p><strong>TERNtrees.zip:</strong></p> <ul> <li>data within this folder are ordered by site name</li> <li>for each site, we include manual delineations of the largest trees per 1 km x 1 km square (25 in total)</li> <li>for each site, we include subplots of the CHM (200 m x 200 m) centred around the largest tree</li> </ul> <p><strong>climate.zip:</strong></p> <ul> <li>climate layers from CHELSA for precipitation and site water balance</li> </ul> <p><strong>terrain_fractal.zip:</strong></p> <ul> <li>simulated fractal surfaces, based on different generating algorithms</li> <li>fractal scaling properties of simulated fractal surfaces in separate csv files</li> </ul> <p><strong>sites_coordinates.csv</strong></p> <ul> <li>lon/lat coordinates of each site</li> </ul> <p> </p> <p> </p>
"FractiAI: Revolutionizing AI with Fractal Intelligence and SAUUHUPP"
<p>Welcome to the FractiAI Zenodo Repository! This is the official hub for sharing, exploring, and advancing FractiAI—a revolutionary AI framework powered by the principles of SAUUHUPP and fractal intelligence.</p> <p> </p> <p>FractiAI redefines what’s possible in artificial intelligence, uniting scalability, self-awareness, and universal harmony to transform systems across industries. Whether you’re a researcher, developer, or visionary, this repository offers access to groundbreaking concepts, tools, and resources that aim to revolutionize AI just as Pixar revolutionized animation.</p> <p> </p> <p>Join us as we shape the future of AI through fractal-inspired innovation!</p>
FractiScope: Unlocking Hidden Patterns in a Networked Fractal Computing AI Universe
<p>This Zenodo record serves as a central repository for all research, tools, and insights related to FractiScope, a groundbreaking AI-driven fractal intelligence scope powered by ChatGPT-4o and Novelty 1.0, and rooted in the SAUUHUPP framework. FractiScope is designed to uncover hidden fractal patterns and interconnected dynamics across science, technology, art, and creativity, framing these discoveries within the Networked Fractal Computing AI Universe.</p> <p> </p> <p>The record includes:</p> <p>• Foundational papers on SAUUHUPP, Novelty 1.0, and Unipixels.</p> <p>• Applications of FractiScope across disciplines such as chemistry, physics, genetics, language, and cosmology.</p> <p>• Case studies, white papers, and user guides detailing the use of FractiScope in research and innovation.</p> <p>• Resources for researchers, creatives, and visionaries to explore how fractal intelligence can revolutionize their work.</p> <p> </p> <p>This collection aims to foster collaboration, inspire new ideas, and advance the understanding of fractal intelligence and its implications for society.</p>
Characterization of Pore Structure with Box Counting Fractal Dimension Based on Digital Rock
<p>This is a supplementary data set for a manuscript submitted to Journal of Geophysical Research: Solid Earth. This data set includes CT samples, process-based model, fractal dimensions calculated by the box counting algorithm, and Matlab codes to implement these modeling and fractal calculations.</p>
Cubic seed fractals for Molecular-DNA
<p>Files to accompany the Geant4 Molecular DNA simulation.</p> <p>https://geant4-dna.github.io/molecular-docs/</p>
Data underlying "Neuro-evolutionary evidence for a universal fractal primate brain shape"
<p>This is the data that is required to run the code underlying the paper: Neuro-evolutionary evidence for a universal fractal primate brain shape (eLife 2024)</p> <p>The code can be found here: <a href="https://github.com/cnnp-lab/2024_Folding_scales">https://github.com/cnnp-lab/2024_Folding_scales</a>, and provides more details about the file structure and purpose.</p>
Fractal sampling point nesting
<b>Description: </b><p>This dataset contains a table showing the nesting of sampling points within the fractal sampling design at the SAFE project and an R code file used to create the nesting from a GIS file of the core sampling locations.<br><br>There are five fractal levels across 17 sites: six experimental blocks (A-F), two edge transects (LFE, VJR) and three sets of triplets of sites to sample specific habitats (Old growth: OG1 - 3, Oil palm: OP1 - 3, Twice logged forest: LF1 - 3). Within each site, there are sampling points at five fractal scales with fractal order 1 as the smallest scale and fractal order 5 being the largest scale. This file identifies the child - parent relationships of points across the fractal sampling levels. For more details, see <a>https://www.safeproject.net/dokuwiki/safe_gis/safe_fractal</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=179">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Fractal_point_nesting.xlsx, Nested_fractal_table_maker.R</p><p><b>Fractal_point_nesting.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Fractal sampling point nesting</b> (described in worksheet Fractal_point_nesting)</p><p>Description: This table contains one row for each fractal order 1 sampling point (finest scale) and then identifies the parent sampling points at the higher fractal scales (2-5). Note that not all sites have a central fractal order 5 point.</p><p>Number of fields: 9</p><p>Number of data rows: 579</p><p>Fields: </p><ul><li><b>Site</b>: Site ID prefix (Field type: ID)</li><li><b>Habitat</b>: Focal habitat of sampling site (Field type: Categorical)</li><li><b>Logging</b>: Logging history of sampling site (Field type: Categorical)</li><li><b>Frag_Area</b>: Estimated area of fragment (Field type: Numeric)</li><li><b>FirstOrder</b>: First order fractal point (Field type: Location)</li><li><b>SecondOrder</b>: Second order fractal parent (Field type: Location)</li><li><b>ThirdOrder</b>: Third order fractal parent (Field type: Location)</li><li><b>FourthOrder</b>: Fourth order fractal parent (Field type: Location)</li><li><b>FifthOrder</b>: Fifth order fractal parent (not applicable for all sites) (Field type: Location)</li></ul></li></ol><p><b>Nested_fractal_table_maker.R</b></p><p>Description: This R code creates the table stored in Fractal_point_nesting from the SAFE Core sampling stations GIS layer. </p><p><b>Date range: </b>2010-01-01 to 2020-01-01</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9474 to 117.7031</p>
Dataset from the paper entitled "Linking Lattice Strain and Fractal Dimensions to Non-Monotonic Volume Changes in Irradiated Nuclear Graphite"
<p>Dataset from the paper entitled "Linking Lattice Strain and Fractal Dimensions to Non-Monotonic Volume Changes in Irradiated Nuclear Graphite". The dataset includes the small angle X-ray scattering measurements, the wide-angle X-ray scattering measurements, and the fitting parameters. Please see the included Readme file for more detail on the organization. </p>
Fig. 6 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 6. Location of landmarks chose on Krithe valve for shape analysis.
Database of physicochemical and optical properties of black carbon fractal aggregates
<p>In order to estimate the climate impact of highly absorbing black carbon (BC) aerosols, it is necessary to know their optical properties. The Lorentz-Mie theory, often used to calculate the optical properties of BC under the spherical morphological assumption, produces discrepancies when compared to measurements. In light of this, researchers are currently investigating the possibility of computing the optical properties of BC using a realistic fractal aggregate morphology. To determine the optical properties of such BC fractal aggregates, the Multiple Sphere T-Matrix method (MSTM) is used, which can take more than 24 hours for a single simulation depending on the aggregate properties. This study provides a highly accurate benchmark machine-learning algorithm that can be used to generate the optical properties of BC fractal aggregate in a fraction of a second. The machine learning algorithm was trained over an extensive database of physicochemical and optical properties of BC fractal aggregates. The extensive training data helped develop an ML algorithm that can accurately predict the optical properties of BC fractal aggregates with an average deviation of less than one percent from their actual values. Specifically, the ML algorithm provides the option to generate the optical properties in the visible spectrum using either kernel ridge regression (KRR) or artificial neural networks (ANN) for a BC fractal aggregate of desired physicochemical properties like size, morphology, and organic coating. The dataset of physicochemical and optical properties of BC fractal aggregates are provided here. The developed ML algorithm for predicting the optical properties of BC fractal aggregates (https://github.com/jaikrishnap/Machine-learning-for-prediction-of-BCFAs) is highly useful for real-world applications due to its wide parameter range, high accuracy, and low computational cost.</p> <p><strong>Contents</strong></p> <ul> <li>database_optical_properties_black_carbon_fractal_aggregtates.csv, data file, comma-separated values</li> <li>database_header.txt, metadata, text</li> </ul> <p><strong>Citation for the database: </strong></p> <p>B., Romshoo, T., Müller, B., Patil, J., Michels, T., Kloft, M., and Pöhlker, M.: Database of physicochemical and optical properties of black<br> carbon fractal aggregates, Dataset, https://doi.org/10.5281/zenodo.7523058, 2023.</p>
Jet experiments in fractal conduits
<p>Jet experiments performed by changing</p> <p>1) The fractal dimension of the conduit inner surface (D equal to 2, 2.18 an 2.99)</p> <p>2) The pressure ratio between the high-pressure reservoir and atmosphere (P=2,4,6,8)</p> <p> </p>
Annotated images and branch diameter data for scaling in branch thickness and the fractal aesthetics of trees
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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)
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DANDI Archive for NWB datasets
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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.