Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
34
datasets available to search
ShareScore release 0.9.0
Dataset results
34 results for “fractal analysis”
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>
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).
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.
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.
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>
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.
Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them
<p>Despite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the Higuchi’s fractal dimension (HFD) and the Hurst exponent (H). We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining <em>in vivo</em> recordings and computational approaches.</p> <p>In this supplementary data set we made available: a) the metadata file “metadata.dat”; b) the baseline signals file “baseline_data.RData”; c) the R script “fractal_analysis.R” to calculate the H and HFD of the baseline data and produce the log-log plots. Explanatory comments are profusely present throughout the script and in the metadata file.</p>
A group of images used for fractal analysis in Grading Evaluation of Marbling in Wagyu Beef Using Fractal Analysis.
Open the record for dataset details and reuse information.
Figure 2 in Fractal analysis of structural differences of otolith microrelief in closely related and distant Baikal ichthyotaxa
Figure 2. Otoliths of T. baicalensis (a), L. leuciscus (b), L. kesslerii (c), and P. knerii (d): macroforms.
Figure 4 from: Musarella CM, Cano-Ortiz A, Pinar Fuentes JC, Navas-Urena J, Pinto Gomes CJ, Quinto-Canas R, Cano E, Spampinato G (2018) Similarity analysis between species of the genus Quercus L. (Fagaceae) in southern Italy based on the fractal dimension. PhytoKeys 113: 79-95. https://doi.org/10.3897/phytokeys.113.30330
Figure 4 Value of the medians for each homogeneous group. Fractal dimensions (mean values) of the studied species where Quercusilexsubsp.ilex and Quercussuber have an FD < 1.6 and the marcescent Quercus has a FD > 1.6.
ScienceDex guides
Understand access before you commit
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.