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96 results for “field methods”
Datasets to "Compressible test-field method and its application to shear dynamos"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Compressible test-field method and its application to shear dynamos" by M. J. Kapyla, M. Rheinhardt, & A. Brandenburg (Astrophys. J., in press, arXiv:2106.01107). If anything turns out to be incomplete, please email maarit.kapyla@aalto.fi or brandenb@nordita.org. </pre>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </p>
Field Line Resonances estimated using Machine Learning methods
<p>This data set contains the machine learning input matrix (composed by 1D Fourier cross-spectra) + additional information, for the Classification algorithm implemented in Foldes et al. (Automatic Detection of Field Line Resonance Frequencies in the Earth’s Plasmasphere, 2023) for the pair of station Tartu-Birzai (TAR-BRZ).</p> <p>Each file contains the following header at line 1. Columns are:</p> <p>- P(f0)-P(f211): Cross-phase value per frequency bin</p> <p>- YEAR</p> <p>- DOY (Day Of Year)</p> <p>- HOUR</p> <p>- ToD_flag: "Umbra", "Penumbra", 'Light'</p> <p>- L: McIllwain parameter</p> <p>- stat_tag: "tarbrz"</p> <p>- Kp</p> <p>- Kp_w_05d: Kp index weighted on a 12hrs time window</p> <p>- Kp_w_10d: Kp index weighted on a 24hrs time window</p> <p>- Kp_w_15d: Kp index weighted on a 36hrs time window</p> <p>- Kp_w_20d: Kp index weighted on a 2-day time window</p> <p>- Kp_w_25d: Kp index weighted on a 2.5-day time window</p> <p>- Kp_w_30d: Kp index weighted on a 3-day time window</p> <p>- Kp_m_05d: Kp index max on a 12hrs time window</p> <p>- Kp_m_10d: Kp index max on a 24hrs time window</p> <p>- Kp_m_15d: Kp index max on a 36hrs time window</p> <p>- Kp_m_20d: Kp index max on a 2-day time window</p> <p>- Kp_m_25d: Kp index max on a 2.5-day time window</p> <p>- Kp_m_30d: Kp index max on a 3-day time window</p> <p>- DST</p> <p>- DST_m_05d: DST index min on a 12hrs time window</p> <p>- DST_m_10d: DST index min on a 24hrs time window</p> <p>- DST_m_15d: DST index min on a 36hrs time window</p> <p>- DST_m_20d: DST index min on a 2-day time window</p> <p>- DST_m_25d: DST index min on a 2.5-day time window</p> <p>- DST_m_30d: DST index min on a 3-day time window</p> <p>- F107: F10.7 solar activity proxy</p> <p>- EField: Earth Electric co-rotation field</p> <p>- f(mHz): FLR frequency in mHz</p> <p>- df(mHz): Uncertainty on the validated frequency</p> <p>- class: 0 for "NoFreq", 1 for "Freq" and 2 for "PBL"</p>
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
MESSENGER magnetic field data with Mercury's magnetic main field removed through application of the Chapman-Miller method
<p>The MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) spacecraft followed a highly elliptical orbit about Mercury. Therefore, attenuation with radial distance of the dipole and higher order terms of Mercury’s core-generated, steady main field led to MESSENGER’s low-noise, triaxial ring-core fluxgate magnetometer registering magnetic field variations of several hundred nanoteslas. These variations swamp Mercury’s significantly smaller time-varying induction signal. Generally, the steady main field of a planetary body can be removed using a model derived through spherical harmonic analysis. However, MESSENGER’s highly eccentric orbit with near-polar perihermian leads to models of Mercury’s magnetic main field that are inadequately characterised for this purpose. Instead, novel application of the Chapman-Miller method, a geophysical processing technique, better models and removes Mercury’s magnetic main field from MESSENGER data. Three-component magnetic field time series sampled at 10 s intervals were downloaded from NASA’s Planetary Data System (Korth and Anderson, 2016) and processed by applying the Chapman-Miller method to 20 pairs of MESSENGER orbits, yielding 40 events of 256 data points per magnetic component that provide a basis for studying electromagnetic induction in Mercury’s deep crust and mantle.</p>
Fig. 3 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 3. Mean activity counts for before and afer pheromone exposure (lef), and the mean afer/before activity ratio (right), for un-irradiated (0 Gy = −) and irradiated (300 Gy = +) Epiphyas postvittana males. Error bars are 95% confidence limits for each mean. An afer/before ratio of 1 (marked) indicates an equal level of activity before and afer pheromone exposure.
Fig. 2 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 2. Measured activity of male Epiphyas postvittana as summed counts at 30 s intervals in a locomotor activity monitor, before and afer pheromone stimulus of irradiated and non-irradiated moths.
Fig. 1 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 1. An illustration of the modified Locomotion Activity Monitor set-up used for the bioassays in this study. Synthetic pheromone was puffed (2 s) from upwind to stimulate LBAM males into wing fanning. Each time a male moved through the beam array it was counted as 1 observation. Comparisons were made between pre-pheromone exposure activity and post-pheromone exposure activity for each treatment.
Fig. 4 in Tools and Methods to Assess Field Performance Locomotion activity meter for quality assessment of mass-reared sterile male moths (Lepidoptera)
Fig. 4. Mean activity counts for before and afer pheromone exposure (lef), and the mean afer/before activity ratio (right), un-irradiated (0 Gy) and irradiated (300 Gy) Epiphyas postvittana males exposed to 1 of 4 levels of temperature shock (0, 1, 2, 4 h at 30 °C). Error bars are 95% confidence limits for each mean. An afer/before ratio of 1 (marked) indicates an equal level of activity before and afer pheromone exposure.
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI.
<p><strong>Dataset description: </strong>Accompanying data for manuscript “<a href="https://www.biorxiv.org/content/early/2018/01/10/245738">A scalable method to improve gray matter segmentation at ultra high field MRI</a>” written by Omer Faruk Gulban, Marian Schneider, Ingo Marquardt, Roy Haast, Federico De Martino.</p> <p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335">Published in PLOS One, June 6, 2018</a>.</p> <p>The dataset consist of 7 Tesla MRI anatomical images of living human brains (whole brain; 0.7mm isotropic resolution; T1 weighted, T2* weighted, proton density weighted MPRAGE images; inversion 1, inversion 2, T1, uni, MP2RAGE images; Multi-echo 3D GRE) and hand labeled cortical gray matter images (for further details see <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198335#sec012">section 4.1 of our manuscript</a>).</p> <p>Folder structure is organized according to Brain Imaging Data Structure (BIDS). Further details can be found the README files.</p> <p><strong>Citation</strong></p> <p>Please cite the following paper together with this dataset doi:</p> <ul> <li>Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to improve gray matter segmentation at ultra high field MRI. <em>PLOS ONE</em>, <em>13</em>(6), e0198335. http://doi.org/10.1371/journal.pone.0198335</li> </ul> <p><br> Bibtex format:</p> <p>```<br> @article{Gulban2018,<br> author = {Gulban, Omer Faruk and Schneider, Marian and Marquardt, Ingo and Haast, Roy A. M. and {De Martino}, Federico},<br> doi = {10.1371/journal.pone.0198335},<br> editor = {Pham, Dzung},<br> issn = {1932-6203},<br> journal = {PLOS ONE},<br> month = {jun},<br> number = {6},<br> pages = {e0198335},<br> title = {{A scalable method to improve gray matter segmentation at ultra high field MRI}},<br> url = {http://dx.plos.org/10.1371/journal.pone.0198335},<br> volume = {13},<br> year = {2018}<br> }<br> <br> ```</p>
Calculation of RF sheath properties from surface wave-fields: a post-processing method
<p>The accompanying files contain digital data for figures in the article "Calculation of RF sheath properties from surface wave-fields: a post-processing method" by J.R. Myra and H. Kohno, submitted to the journal Plasma Physics and Controlled Fusion.</p> <p><br> Abstract:</p> <p>In ion cyclotron range of frequency (ICRF) experiments in fusion research devices, radio frequency (RF) sheaths form where plasma, strong RF wave fields and material surfaces coexist. These RF sheaths affect plasma material interactions such as sputtering and localized power deposition, as well as the global RF wave fields themselves. RF sheaths may be modeled by employing a sheath boundary condition (BC) in place of the more customary conducting wall BC; however, there are still many ICRF computer codes that do not implement the sheath BC. In this paper we present a method for post-processing results obtained with the conducting wall BC. The post-processing method produces results that are equivalent to those that would have been obtained with the RF sheath BC, under certain assumptions. The post-processing method is also useful for verification of sheath BC implementations and as a guide to interpretation and understanding of the role of RF sheaths and their interactions with the waves that drive them.</p> <p> </p>
Figure 1 in Identification of blood meals in field collected Culex pipiens, Anopheles sacharovi and Culex tritaeniorhynchus (Diptera: Culicidae) using the ELISA method
Figure 1. Sampling localities of Anopheles sacharovi, Culex pipiens, Culex tritaeniorhynchus populations (1. Huzurkent, 2. Düziçi, 3. Akhisar, 4. Dalaman, 5. Gelendost, 6. Selçuk, 7. Karataş, 8. Eşme, 9. Türkoğlu, 10. Dörtyol, 11. Kırıkhan, 12. Manavgat, 13. Afyon, 14. Tarsus, 15. Kadirli, 16. Aydın, 17. Kozan, 18. Sandıklı, 19. Dinar, 20. Uşak, 21. Ceyhan, 22. Antalya, 23. Tuzla 24. İzmir, 25. Söke, 26. Kuşadası, 27. Akköy). Red stars indicate locations where Cx. pipiens was sampled, the green diamond shape indicates locations of Cx. tritaeniorhynchus and the blue pins indicate the locations of An. sacharovi.
Figure 2 in Identification of blood meals in field collected Culex pipiens, Anopheles sacharovi and Culex tritaeniorhynchus (Diptera: Culicidae) using the ELISA method
Figure 2. Percentage distributions of single and multiple host meal choices for three mosquito species collected in the Aegean and Mediterranean regions.
Figure 5 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 5. Principal component analysis of the samples collected from two sites, alfalfa field and boundary zone based on nine soil properties.
Figure 4 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 4. Seasonal changes in total precipitation and mean monthly temperature in Kopaida valley during the period April 2021 – March 2022.
Figure 3 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece
Figure 3 depicts the monthly changes of the mean soil moisture of all three soil depths and the instant soil temperature at the sampling time at 10 cm depth. It is obvious that these two parameters altered identically in the two fields and only small differences can be detected, e.g. the rise in soil moisture in July in the alfalfa field due to the application of irrigation water.
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.