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105 results for “fractals”
Earthquakes unveil the global-scale fractality of the lithosphere
<p>The repository contains the post-processed data required to produce the figures of the article entitled <i>"Earthquakes unveil the global-scale fractality of the lithosphere"</i> under consideration in Communications Earth & Environment.</p><p>Data are divided in directories, each corresponding to an article figure and numbered accordingly.</p>
Dataset for "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities"
<p>This dataset supports the research study "Predicting the electrical conductivity of partially saturated frozen porous media, a fractal model for wide ranges of temperatures and salinities" by H. L. Luo, D. Jougnot, A. Jost, J. D. Teng, A. Mendieta, G. Lin, and L. D. Thanh.<br> We provide the experimental data of electrical condutivity and unfrozen water saturation with different initial water saturations and salinities. Meanwhile, we also offer the matlab code for calculating the predicted values of electrical conductivity and apparent formation factor.</p> <p>Each file has its header, describing each column.</p> <p> </p>
data from "Fractal properties of isolines at varying altitude revealing different dominant geological processes on Earth"
<p>The file contains the data used to produce Fig.4 for the paper "Fractal properties of isolines at varying altitude revealing</p><p>different dominant geological processes on Earth", by Andrea Baldassarri, Marco Montuori, Olga Prieto-Ballesteros,</p><p>and Susanna C. Manrubia, Journal of Geophysical Research: PlanetsVolume 113, Issue E9, https://doi.org/10.1029/2007JE003066</p>
Fractal Analysis of Clouds in DYAMOND Summer Simulations (revised)
<p><strong>Data accompanying "<em>The Fractal Nature of Clouds in Global Storm-Resolving Models</em>", by H. M. Christensen and O. Driver, submitted to Geophysical Research Letters.</strong></p> <p> </p> <p><strong>Summary</strong></p> <p>We compute the fractal dimension of clouds in the DYAMOND Summer simulations: https://www.esiwace.eu/services/dyamond/summer<br> This is compared to the dimension computed using the Himawari 8 satellite.</p> <p>The simulations span 1 August--10 September 2016. We use data between 25<sup>o</sup>S-25<sup>o</sup>N, 80-200<sup>o</sup>E. A binary cloud field is defined for the model simulations using outgoing long wave radiation using a given threshold. For Himawari observations we use the derived Cloud Top Temperature product, with a consistent threshold: see paper for details. Any pixel with outgoing long wave radiation or cloud top temperature below these values is defined as 'cloudy'.</p> <p> </p> <p><strong>Available model and satellite derived data</strong></p> <p>[model identifier]_clouds_230.csv</p> <p>Contains sets of Area-Perimeter data couplets for each selected timestamp in the DYAMOND simulation indicated by [model identifier], using the 230 K cloud top temperature threshold.</p> <p>[model identifier]_dims_threshold.csv</p> <p>Contains the fractal dimension measured for each selected timestamp in the DYAMOND simulation indicated by [model identifier], as a function of threshold. This is the Area-Perimeter fractal dimension, <span class="math-tex">\(P \propto A^{D/2}\)</span>. This can be obtained as the gradient of the regression line through the logarithm of the data in the 'clouds' files, multiplied by two. Data are provided for the following thresholds: 200, 210, 220, 230, 240, 250, 260 K.</p> <p>Since the satellite fields are only available during daylight hours, we provide and analyse the data at 0200, 0300, and 0400 UTC for both satellite and model data (or the closest available timestamp to these times for each model).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>H.M.C. was funded by Natural Environment Research Council grant number NE/P018238/1.</p> <p>DYAMOND data management was provided by the German Climate Computing Center (DKRZ) and supported through the projects ESiWACE and ESiWACE2. The projects ESiWACE and ESiWACE2 have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 675191 and 823988. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project IDs bk1040 and bb1153.</p>
Fortran code used in 'A fractal model for effective excess charge density in variably saturated fractured rocks'
<p>This code is uploaded to support the research study 'A fractal model for effective excess charge density in variably saturated fractured rocks' by L. Guarracino and D. Jougnot (submitted to JGR: Solid Earth, 2021).</p> <p>Files:<br> a) Fortran source code (qvfrac.f) for estimating the effective excess charge density in fractured rocks. The calculation is based on model equations described in the research study.<br> b) Input data (network1.dat) to calculate the effective excess charge density for fracture network 1 described in Section 3 (Figure 5a).</p>
Fig. 3 in Spatial Variation Of The Land Snail Brephulopsis Cylindrica (Gastropoda, Pulmonata, Enidae): A Fractal Approach
Fig. 3. The dependence between intrapopulation variation (Mst) and the sample sites number in B. cylindrica: A — shell height (HS); B — shell width (WS); C — shell form index (FS) (95 % confidence interval is indicated by dotted lines).
Fig. 4 in Spatial Variation Of The Land Snail Brephulopsis Cylindrica (Gastropoda, Pulmonata, Enidae): A Fractal Approach
Fig. 4. Plots of the logarithms of semivariance versus logarithms of spatial scale (in meter) for morphometric shells traits of B. cylindrica of the studying population. D — fractal dimension value; R2 — determination coefF ficient): A — shell height (HS); B — shell width (WS); C — shell form index (FS).
Fig. 2 in Spatial Variation Of The Land Snail Brephulopsis Cylindrica (Gastropoda, Pulmonata, Enidae): A Fractal Approach
Fig. 2. Moran's index for morphometric shells traits of B. cylindrica of the studying population: A — shell height (HS); B — shell width (WS); C — shell form index (FS). Significant values of Moran's index indicated as solid circles.
Fig. 1 in Spatial Variation Of The Land Snail Brephulopsis Cylindrica (Gastropoda, Pulmonata, Enidae): A Fractal Approach
Fig. 1. The variation of morphometric shell traits in B. cylindrica from different sample sites: A — shell height (HS); B — shell width (WS); C — shell form index (FS).
Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space).
<p>Data set from Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space), and figures.</p>
Research data supporting "Fractal-like hierarchical organisation of bone begins at the nanoscale"
<p>Raw research data supporting: N. Reznikov et al., Science 360, eaao2189 (2018). DOI: 10.1126/science.aao2189</p>
Figure 4 in Fractal analysis of structural differences of otolith microrelief in closely related and distant Baikal ichthyotaxa
Figure 4. Initial images and multifractal spectra for crystalline surface of the sulcus acusticus of Baikal fish otoliths: T. baicalensis (a, b), L. leuciscus (c, d), L. kesslerii (e, f), and P. knerii (g, h).
Figure 1 in Fractal analysis of structural differences of otolith microrelief in closely related and distant Baikal ichthyotaxa
Figure 1. Scheme of sagittal otolith (T. baicalensis, L. kesslerii, and P. knerii) (a), photo and scheme of utricular otolith (L. leuciscus) (b).
Figure 5 in Fractal analysis of structural differences of otolith microrelief in closely related and distant Baikal ichthyotaxa
Figure 5. Multifractal spectra for crystalline surface of otolith of closely related and distant species (combined graph).
data for "Could we achieve the on-line Measurements of the Optical Fractal Dimensions of Black Carbon?"
Open the record for dataset details and reuse information.
Fig. 7 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 7. RW1/RW2 plot showing the neat separation of Krithe compressa from Krithe iniqua specimens. Deformation grids along RW1 (set at values of –0.2 and 0.2) are reported. A. Plot of RW1 against RW2 scores. B, C. Shell deformation at extreme values along RW1.
Fig. 4 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 4. Main morphological features of studied ostracods species. A. Krithe iniqua Abate, Barra, Aiello, and Bonaduce, 1993, right valve, transparence drawing from external view, sample 59, B.O.C. 2518, upper Pliocene, KI−29, sample 59. B. Krithe compressa (Seguenza, 1880), right valve, transparence drawing from external view, KC−29, sample 58, B.O.C. 2547, upper Pliocene.
Fig. 2 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 2. Krithe iniqua Abate, Barra, Aiello, and Bonaduce, 1993, right valves; transparence drawings from external view; sample 59; upper Pliocene. A. KI−01, B.O.C. 2490. B. KI−02, B.O.C. 2491. C. KI−03, B.O.C. 2492. D. KI−04, B.O.C. 2493. E. KI−05, B.O.C. 2494. F. KI−06, B.O.C. 2495. G. KI−07, B.O.C. 2496. H. KI−08, B.O.C. 2497. I. KI−09, B.O.C. 2498. J. KI−10, B.O.C. 2499. K. KI−11, B.O.C. 2500. I. KI−12, B.O.C. 2501. L. KI−13, B.O.C. 2502. M. KI−14, B.O.C. 2503. N. KI−15, B.O.C. 2504. O. KI−16, B.O.C. 2505. P. KI−17, B.O.C. 2506. Q. KI−18, B.O.C. 2507. R. KI−19, B.O.C. 2508. S. KI−20, B.O.C. 2509. T. KI−21, B.O.C. 2510. U. KI−22, B.O.C. 2511. V. KI−23, B.O.C. 2512. W. KI−24, B.O.C. 2513. Y. KI−25, B.O.C. 2514. Z. KI−26, B.O.C. 2515. AA. KI−27, B.O.C. 2516. BB. KI−28, B.O.C. 2517.
Fig. 9 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 9. Continuous shape variation in Krithe compressa valves drawn along RW 2. Deformation grids relate to specimen of the three different samples belonging to Krithe compressa from the highest (A) to the lowest (C) RW 2 scores (see Fig. 7). Deformation grid in B refers to undeformed shape. From the above, a valve from sample 58 (specimen KC 25), a specimen from sample 51 (KC 16), and a specimen from sample 50 (KC 1).
Fig. 5 in Fractal analysis of ostracod shell variability: A comparison with geometric and classic morphometrics
Fig. 5. The logarithm of number of pairs C of points with mutual distance smaller than R (̊m), as a function of log(R). Vertical dashed lines are the limits inside which the linear slope of log(C) on log(R) provides the best fitting to the data.
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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)
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.