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35 results for “random fields”
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
3D S-wave velocity profiles with random fields
<p>This dataset contains 100,000 3D matrices representing S-wave velocity profiles with random fields. The spatial resolution corresponds to a grid of 9.6 x 9.6 x 9.6km with a spatial step of 300m (matrices are of size 33 x 33 x 33). Velocities go from 1,071 to 4,500m/s.</p> <p>Each profile contains a 1.8km-thick bottom layer at constant velocity 4500m/s. Above, the profile contains between 1 and 6 layers of random thickness. In each layer, the mean velocity is chosen uniformly in [1,785; 3,214m/s] and the coefficient of variation follows a Gaussian distribution N(0.2; 0.1). Then, a correlation length is chosen randomly among 1.5, 3, 4.5, 6 km in all three directions for each layer.</p> <p>The random fields' kernel has a von Karman correlation with a Hurst exponent of 0.1 and log-normal marginal distributions.</p>
Data from article: "Wide‑field magnetometry using nitrogen‑vacancy color centers with randomly oriented micro‑diamonds"
<p>This repository contains the dataset obtained from the CW-ODMR magnetic imaging experiment with nitrogen-vacancy (NV) centers using a custom-built wide-field setup.</p> <p><strong>Related publication: </strong></p> <p>Sengottuvel, S., Mrózek, M., Sawczak, M. <em>et al.</em> Wide-field magnetometry using nitrogen-vacancy color centers with randomly oriented micro-diamonds. <em>Sci Rep</em> <strong>12</strong>, 17997 (2022). <a href="https://doi.org/10.1038/s41598-022-22610-5">https://doi.org/10.1038/s41598-022-22610-5</a>.</p> <p><strong>Authors:</strong></p> <ul> <li>Saravanan Sengottuvel, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mariusz Mrózek, (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Mirosław Sawczak, (Szewalski Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Poland)</li> <li>Maciej J. Głowacki, (Gdańsk University of Technology, Poland)</li> <li>Mateusz Ficek, (Gdańsk University of Technology, Poland)</li> <li>Wojciech Gawlik (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> <li>Adam M. Wojciechowski (Institute of Physics, Jagiellonian University in Krakow, Poland)</li> </ul> <p><strong>Abstract: </strong></p> <p>Magnetometry with nitrogen-vacancy (NV) color centers in diamond has gained significant interest among researchers in recent years. Absolute knowledge of the three-dimensional orientation of the magnetic field is necessary for many applications. Conventional magnetometry measurements are usually performed with NV ensembles in a bulk diamond with a thin NV layer or a scanning probe in the form of a diamond tip, which requires a smooth sample surface and proximity of the probing device, often limiting the sensing capabilities. Our approach is to use micro- and nano-diamonds for wide-field detection and mapping of the magnetic field. In this study, we show that NV color centers in randomly oriented submicrometer-sized diamond powder deposited in a thin layer on a planar surface can be used to detect the magnetic field. Our work can be extended to irregular surfaces, which shows a promising path for nanodiamond-based photonic sensors.</p> <p><strong>Funding: </strong></p> <p>The research was carried out within the TEAM NET programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund, project POIR.04.04.00-00-1644/18. This research was funded in part by National Science Centre, Poland grant number 2020/39/I/ST3/02322<strong>. </strong></p> <p><strong>Description of the data: </strong></p> <p>The dataset consists of 24 individual data files labelled chronologically, starting from f0.fits to f24.fits. The data format is Flexible Image Transport System (FITS). Each FITS file consists of a header and 3-dimensional image data. The header contains the experimental parameters set during data acquisition, which may also be helpful for data analysis. The FITS file can be read using any software (e.g., MATLAB, Python) that supports the FITS file format.</p> <p><strong>An example header:</strong></p> <p> {'STARFREQ'} {[ 2700]} {' in MHz '}<br> {'STOPFREQ'} {[ 3000]} {' in MHz '}<br> {'STEPSIZE'} {[ 1.500000000000000]} {' Frequency interval '}<br> {'MWPOWER'} {[ 5]} {' in dBm '}<br> {'NSCANS'} {[ 5]} {' Total number of scan repetitions '}<br> {'EXPOSURE'} {[ 20]} {' in ms '}<br> {'FPS'} {[ 20]} {' no. of frames per second '}<br> {'LEDCURR'} {[ 0.990000000000000]} {' LED current in mA }<br> {'EXPTIME'} {[1.942112698000000e+02]} {' Measurement time in seconds '}<br> {'END' } {0×0 char } {0×0 char }</p> <p><strong>Table:</strong> Data file name and the associated current value set in the wire during the magnetic imaging measurement</p> <table> <thead> <tr> <th>File name</th> <th>Current value (mA)</th> <th>File name</th> <th>Current value (mA)</th> </tr> </thead> <tbody> <tr> <td>f0.fits</td> <td>0</td> <td>f13.fits</td> <td>-300</td> </tr> <tr> <td>f1.fits</td> <td>+50</td> <td>f14.fits</td> <td>-550</td> </tr> <tr> <td>f2.fits</td> <td>+100</td> <td>f15.fits</td> <td>-250</td> </tr> <tr> <td>f3.fits</td> <td>-50</td> <td>f16.fits</td> <td>+550</td> </tr> <tr> <td>f4.fits</td> <td>-100</td> <td>f17.fits</td> <td>+350</td> </tr> <tr> <td>f5.fits</td> <td>-150</td> <td>f18.fits</td> <td>+400</td> </tr> <tr> <td>f6.fits</td> <td>+250</td> <td>f19.fits</td> <td>-400</td> </tr> <tr> <td>f7.fits</td> <td>+450</td> <td>f20.fits</td> <td>-450</td> </tr> <tr> <td>f8.fits</td> <td>+600</td> <td>f21.fits</td> <td>-500</td> </tr> <tr> <td>f9.fits</td> <td>-200</td> <td>f22.fits</td> <td>+150</td> </tr> <tr> <td>f10.fits</td> <td>-350</td> <td>f23.fits</td> <td>+300</td> </tr> <tr> <td>f11.fits</td> <td>+200</td> <td>f24.fits</td> <td>-600</td> </tr> <tr> <td>f12.fits</td> <td>+500</td> <td> </td> <td> </td> </tr> </tbody> </table> <p>For more information on the data analysis methods and results, we recommend you to read the article.</p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Selective extinctions resulting from random habitat destruction lead to under‐estimates of local and regional biodiversity loss in a manipulative field experiment
<p>Land-use change is a significant cause of anthropogenic extinctions, which are likely to continue and accelerate as habitat conversion proceeds in most biomes. One way to understand the effects of habitat loss on biodiversity is through improved tools for predicting the number and identity of species losses in response to habitat loss. There are relatively few methods for predicting extinctions and even fewer opportunities for rigorously assessing the quality of these predictions. In this paper we address these issues by applying a new method based on rarefaction to predict species losses after random, but aggregated, habitat loss. We compare predictions from three rarefaction models, individual-based, sample-based, and spatially-clustered, to those derived from a commonly-used extinction estimation method, the Species-Area Relationship (SAR). We apply each method to a mesocosm experiment, in which we aim to predict species richness and extinctions of arthropods immediately following 50% habitat loss. While each model produced strikingly accurate predictions of species richness immediately after the habitat loss disturbance, each model significantly underestimated the number of extinctions occurring at both the local (within-mesocosm) and regional (treatment-wide) scales. Despite the stochastic nature of our small-scale, short-term, and randomly applied habitat loss experiment, we found surprisingly clear evidence for extinction selectivity, for example when abundant species with low extinction probabilities were extirpated following habitat loss. The important role played by selective extinction even in this contrived experimental system suggests that ecologically driven, trait-based extinctions play an equally important role to stochastic extinction, even when the disturbance itself has no clear selectivity. As a result, neutrally stochastic null models such as the SAR and rarefaction are likely to underestimate extinctions caused by habitat loss. Nevertheless, given the difficulty of predicting extinctions, null models provide useful benchmarks for conservation planning by providing minimum estimates and probabilities of species extinctions.</p>
Supporting data: Structure-based Markov random field model for representing evolutionary constraints on functional sites
<p>Supporting data for the paper:</p> <p>Chan-Seok Jeong, Dongsup Kim. Structure-based Markov random field model for representing evolutionary constraints on functional sites. Submitted. (2015)</p> <p>See 'README' for a description of the contents.</p>
Experimental realization of the 1D random field Ising model
<p>We have measured magnetic-field-induced avalanches in a square artificial spin ice array of interacting nanomagnets. Starting from the ground state ordered configuration, we imaged the individual nanomagnet moments after each successive application of an incrementally increasing field. The statistics of the evolution of the moment configuration show good agreement with the canonical one-dimensional random field Ising model. We extract information about the microscopic structure of the arrays from our macroscopic measurements of their collective behavior, demonstrating a process that could be applied to other systems exhibiting avalanches.</p>
Effect Of Pulsed Electromagnetic Field And Microwave Therapy On Pain And Physical Function In Older Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
<p><strong><span>Background and purpose:</span></strong><span> </span><span>Pulsed electromagnetic field (PEMF) therapy and microwaves (MW) are two electrotherapy modalities that have shown to improve pain and function in patients with knee osteoarthritis (KOA). Nevertheless, the effectiveness of these therapies is controversial due to diversity in the application parameters and treatment protocols in these patients. The objective is to compare the effectiveness of active PEMF versus MW, as well as sham PEMF, in addressing pain and improving functionality for treating KOA.</span></p> <p><strong><span>Methods:</span></strong><span> </span><span>Double-blind, placebo-controlled, randomized clinical trial. Participants diagnosed with KOA were assigned to an intervention combining an exercise program with active PEMF, MW, or sham PEMF, delivered in three weekly sessions over four weeks. The main outcomes were pain, reported on a 0–10 cm visual analogue scale (VAS), and functionality, as evaluated with the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire, and the Timed Up and Go test. The outcomes were measured at pre-intervention, immediately post-intervention, and one and four months after the intervention.</span></p>
Supplementary data to "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations"
<p>This dataset contains the data used to generate the results in the work "Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations" [1].</p> <p>processed_data.zip contains the data used for the figures shown in [1], while raw_data.zip contains the original simulation results without further processing.</p> <p>To get an overview of the organization of the directories and a description of the data we recommend the README files in the top- and subdirectories.</p> <p>[1] C. Krämer et al., Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations, <a href="https://doi.org/10.48550/arXiv.2403.05223">10.48550/arXiv.2403.05223</a>, 2024</p>
Wearable Pulsed Electromagnetic Fields Device in Knee Osteoarthritis: Double Blinded, Randomized Clinical Trial
ClinicalTrials.gov study NCT01877278. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Experimental realization of the 1D random field Ising model
Open the record for dataset details and reuse information.
Selective extinctions resulting from random habitat destruction lead to under‐estimates of local and regional biodiversity loss in a manipulative field experiment
Open the record for dataset details and reuse information.
Mesh biaxial specimen with random elasticity field
<p>Mesh biaxial specimen with random elasticity field</p>
Clustering Data for Passive Tracers in Random Velocity Fields
<p>Contains the post-processed model output. All detected clusters from each individual model run used to compile statistics are included as well as python scripts to manipulate cluster objects and create figures.</p>
Figure 3. Gaussian Markov random field skyride plots for 11 in Phylogeography of the Poecilimon luschani species group (Orthoptera, Tettigoniidae): a radiation strictly correlated with climatic transitions in the Pleistocene
Figure 3. Gaussian Markov random field skyride plots for 11 populations of the Poecilimon luschani species group. The median estimates are shown as a thick solid line, and the 95% highest posterior density limits are shown by the grey area. The background grey graphs show fluctuations in temperature during the respective periods of the Pleistocene. The upper and lower margins show the time in kiloyears (Kyr), the left margin shows the log of the effective population size (Nef), and the right margin the departure in temperature (ΔT in °C). Names of the glacial periods are indicated in the boxes at the bottom of the graphs. The background graph shows the temperature reconstruction of Vostok; modified from http://agwobserver.wordpress.com. A, İzmir (P. ledereri); B, Aydın (P. tuncayi); C, Kütahya (P. egrigozi); D, Balıkesir (P. helleri); E- Es¸en2 (P. l. chobanovi); F, Es¸en3 (P. l. chobanovi); G, Demre (P. l. luschani); H, Kalkan (P. l. luschani); I, Patara (P. l. luschani); J, Olympos (P. l. birandi); K, Bakırlıdag (P. l. birandi).
Random cohesion field
<p>This file contains a 2D random cohesion field used that can be used as an initial condition for the simulation of shear banding.</p>
Pivotal, Randomized, Open-label Study of Optune® (Tumor Treating Fields) Concomitant With RT & TMZ for the Treatment of Newly Diagnosed GBM
ClinicalTrials.gov study NCT04471844. IPD Sharing: Not stated. Countries: 11. Publications: 2.
Impacts of the 10 Steps for Healthy Feeding in Infants: a Randomized Field Trial
ClinicalTrials.gov study NCT00629629. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Community Based Management of Severe Malnutrition in Tribal Area of Melghat- Cluster Randomized Control Field Trial
ClinicalTrials.gov study NCT02671786. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Biological Meshes in Infected Fields: a Randomized Controlled Trial
ClinicalTrials.gov study NCT01594450. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.