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677 results for “Inversion”
Double inverse nanotapers for efficient light coupling to integrated photonic devices - Open Data
<p>Available data for the manuscript "Double inverse nanotapers for efficient light coupling to integrated photonic devices"</p>
Current Full-Waveform Inversion of the Return Stroke Channel based on Single-Station Electric Field Observations
<p>In manuscript entitled "Current Full-Waveform inversion of the Return Stroke Channel Based on Single-Station Electric Field Oberbations", the data of rocket-triggered flash o901 was obtained during the SHATLE was used. The data of our results are including in the Data- for- figrue-x.fig. These files can be opened later. The data supports the aforementioned manuscript and can bue used freely for scientific purposed with appropriate citation.</p>
PROPTI - An Generalised Inverse Modelling Framework - Data Set
<p><strong>Contents</strong></p> <p>Data set supplementary to the conference paper "<a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">PROPTI - A Generalised Inverse Modelling Framework</a>", presented at the <em>3rd European Symposium on Fire Safety Science</em>, ESFSS 2018 in Nancy, France.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis script to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 260 MB.</p>
Database for Research Projects to Solve the Inverse Heat Conduction Problem
<p>To achieve the optimal performance of an object to be heat treated, it is necessary to know the exact value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHCP), which cannot be solved by direct numerical methods. There are numerous techniques used to solve the IHCP based on heuristic search algorithms having very high computational demand. As another approach, it would be possible to use machine-learning methods for the same purpose, which are capable of giving prompt estimations about the main characteristics of the HTC function. As known, a key requirement for all successful machine-learning projects is the availability of high quality training data. In this case, the amount of real-world measurements is far from satisfactory because of the high cost of these tests. As an alternative, it is possible to generate the necessary databases using simulations. This paper presents a novel model for random HTC function generation based on control points and advanced smoothing techniques. As an additional step, a GPU accelerated finite-element method was used to simulate the cooling process resulting in the required temporary data records. These datasets make it possible for researchers to develop and test their IHCP solver algorithms.</p>
Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"
<p>This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.</p>
An Alkyne Linchpin Strategy for Drug:Pharmacophore Conjugation: Experimental and Computational Realization of a meta-selective Inverse Sonogashira Coupling
<p>This folder contains the output log files of the study of Pd-catalysed alkynylation</p> <p>entitled "A Linchpin Approach to Access Drug-Pharmacophore Conjugate by Inverse Sonogashira at meta-Position: Experimental and Computational Exploration"</p> <p>The folder structure is organised as below:</p> <p>/0_sm/: starting materials for the reactions</p> <p>/1_alkynylation_of_1b/: alkynylation reaction using substrate 1b, including regioconvergence studies</p> <p>/2_alkynylation_of_1b_aa_ligand/: alkynylation (C-H activation and 1,2-migratory insertion) reaction using MPAA ligand instead of acetate</p> <p>/3_alkynylation_of_1b_copper/: alkynylation involving Cu(OAc)2 additive</p> <p>/4_arene_site_selectivity_ortho_para/: site selectivity studies for C-H activation</p> <p>/5_alternative_oxidative_addition_TSs/: oxidative addition TSs that all have higher activation barriers that 1,2-migratory insertion TSs</p> <p>/6_ethynyltrimethylsilane_1c/: alkynylation reaction using substrate 1c, including regioconvergence studies</p> <p>/7_bromoethynylbenzene_1d/: alkynylation reaction using substrate 1d, including regioconvergence studies</p> <p>/8_other_substrates/: alkynylation reaction using </p> <p> - substrates 1e-1h (TIPS-, TBDMS-, TES-alkynyl bromide and siloxy-substituted alkynyl bromide), </p> <p> - arene substrates 4, 5, 12 having different substituents</p> <p> - substrates for products 17-19 with different DG tether lengths</p> <p>including regioconvergence studies</p>
waveform data of the Empirical Green's Functions (EGFs) and earthquakes for the manuscript "Unraveling the Mantle Dynamics in Central Asia with Full Waveform Inversion Tomography"
<p>The dataset uploaded contains the original ASDF files and the SAC files (with '_sac') converted from ASDF</p> <p>For the ASDF files:</p> <p>The ASDF data file could be read through python module pyasdf and obspy after decompressing. </p> <p>The earthquake information could be accessed by the following command</p> <p>import pyasdf</p> <p>ds=pyasdf.ASDFDataSet(ASDFfile, mode='r')</p> <p>event=ds.events[0].preferred_origin()</p> <p>print(event)</p> <p>the waveforms coul dbe accessed through</p> <p>stream=ds.waveforms[net.station].raw_recording</p>
Fig. 4 in Species and shape diversification are inversely correlated among gobies and cardinalfishes (Teleostei: Gobiiformes)
Fig. 4 Phylomorphospace for Gobiiformes. The phylogeny is superimposed on a plot of PC1 vs. PC2, with points color coded as indicated on the figure. Pseudamia is labeled to distinguish it from the other Apogonidae because the phylogeny indicates that it forms a lineage
Result files for boundary inversion of Sherrill et al. (2024)
<p>This repository of text files contains major results displayed in figures in Sherrill, Johnson, and Jackson (2024, submitted) manuscript titled “Locating Boundaries Between Locked and Creeping Regions at Nankai and Cascadia Subduction Zones” submitted to Journal of Geophysical Research — Solid Earth.</p>
Temperature dependent inverse spin Hall effect in Co/Pt spintronic emitters
<div> <div>Data, figure and code for the publication “Temperature dependent inverse spin Hall effect in Co/Pt spintronic emitters”. See README.md for file descriptions.</div> </div>
data for comparison of inverse methods
<p>Data and code related to Bayesian calibration methods for climate models: a comparison of traditional and non-traditional approaches</p>
Debris Flow Dataset for Debris Flow Velocity Inversion based on Farneback Optical Flow
<p>A velocity inventory of large-scale debris flow flume experimental data, published by USGS (Logan, 2018), was generated using the Debris Flow Velocity Inversion Method based on optical flow model (Farnebäck<span>, 2003</span>). This dataset includes raw data from three debris flow experiments conducted in 2007, 2015, and 2017. Each dataset corresponds to three relevant results: perspective transformation, optical flow analysis, and front position detection.</p>
Supplements for Inferring Long-Term Tectonic Uplift Patterns from Bayesian Inversion of Fluvially-Incised Landscapes paper
<p><strong>Data and File Organization:</strong></p> <ol> <li><strong>Natural Landscapes (DEM):</strong> <ul> <li>Look for <code>.tif</code> files containing DEMs of natural landscapes. These files are in latitude-longitude coordinates; convert them to UTM if needed.</li> </ul> </li> <li><strong>Synthetic Landscapes (DEM):</strong> <ul> <li>DEM files for synthetic landscapes, ready for use in inversion schemes, are labeled with a <code>syn_</code> prefix.</li> </ul> </li> <li><strong>Climatic Data:</strong> <ul> <li>Climatic data for the Himalayas is available in <code>climate_data_him.zip</code>.</li> </ul> </li> </ol> <p><strong>Running the Code:</strong></p> <ol> <li> <p><strong>Loading DEMs:</strong></p> <ul> <li>Use the <code>loadDEM</code> package to load your DEM file.</li> <li>Specify <code>Z0</code> and <code>A0</code> values, then plot the landscape and <code>basinID</code> for reference.</li> </ul> </li> <li> <p><strong>Identifying Basins of Interest:</strong></p> <ul> <li>Determine which <code>basinID</code>s are of interest, then save them as forward objects. The functions for this process are available within the relevant packages.</li> </ul> </li> <li> <p><strong>Loading the Forward Model:</strong></p> <ul> <li>Load the forward model from the saved file using the appropriate function in the <code>frd</code> package.</li> <li>Choose the number of knots and specify if you prefer a 1D or 2D inversion.</li> </ul> </li> <li> <p><strong>Running and Plotting Inversion Results:</strong></p> <ul> <li>After running the inversion, view results in <code>inversion.step</code>.</li> <li>Plot these results using the plotting functions in the <code>frdplotting</code> package.</li> </ul> </li> </ol> <p> </p> <p> </p> <p><strong>Setup and Installation:</strong></p> <ul> <li>Install the package <code>scabbard</code> with: <div> <div> </div> <div><code>pip install pyscabbard </code></div> </div> </li> <li>All other Python dependencies are standard and can be installed via <code>pip</code> or <code>conda</code> as needed.</li> </ul>
Crustal Structure of the Yunnan, China Region Revealed by Adjoint Inversion of Frequency-dependent Traveltimes
<p>These files are observed waveforms used in the paper entitled 'Crustal Structure of the Yunnan, China Region Revealed by Adjoint Inversion of Frequency-dependent Traveltimes' by Wei et al. (2024).</p>
Data from: Multiple large inversions and breakpoint rewiring of gene expression in the evolution of the fire ant social supergene
Supergenes consist of co-adapted loci that segregate together and are associated with adaptive traits. In the fire ant Solenopsis invicta, two 'social' supergene variants regulate differences in colony queen number and other traits. Suppressed recombination in this system is maintained, in part, by a >9 Mb inversion, but the supergene is larger. Has the supergene in S. invicta undergone multiple large inversions? The initial gene content of the inverted allele of a supergene would be the same as that of the wild-type allele. So, how did the inversion increase in frequency? To address these questions, we cloned one extreme breakpoint in the fire ant supergene. In doing so, we found a second large (>800 Kb) rearrangement. Furthermore, we determined the temporal order of the two big inversions based on the translocation pattern of a third small fragment. Because the S. invicta supergene lacks evolutionary strata, our finding of multiple inversions may support an introgression model of the supergene. Finally, we showed that one of the inversions swapped the promoter of a breakpoint-adjacent gene, which might have conferred a selective advantage relative to the non-inverted allele. Our findings provide a rare example of gene alterations arising directly from an inversion event.
A Deep Learning Approach to the Forward Prediction and Inverse Design of Plasmonic Metasurface Structural Color - Raw Data
<p>Reflection spectra of PDMS - Al nanorod metamaterials were collected using LUMERICAL FDTD simulations. PDMS material properties were defined using a refractive index of 1.41 and Al material properties were defined using frequency selective permittivities from the handbook of Palik. A total of 4620 structures were simulated, sweeping the following dimension parameters:</p> <ul> <li>Aluminium thickness (t)</li> <li>Pillar height (h)</li> <li>Pillar diameter (d)</li> </ul> <p>Reflectance spectra were converted into CIE 1931 chromaticity values (x,y). This dataset is comprehensive and allows for the development of deep learning models for the forward and inverse design of the given metamaterial structure as detailed in the associated manuscript. The associated manuscript and supporting documentation provide extensive details of data collection and processing methods.</p> <p> </p>
Convolutional-neural-network-based reflection full-waveform inversion
<p>The data is used by the paper "Convolutional-neural-network-based reflection 1 full-waveform inversion"</p>
LM_MM_Inverse_Rig
<p>The shared data directory that accompanies the paper...</p>
Supporting Data for "Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties" Manuscript
<p>Training data and xTB calculated properties of sampled structures from the generative active learning experiments reported in the manuscript.</p>
Cascading rupture process of the 2021 Maduo, China earthquake revealed by the joint inversion of seismic and geodetic data
<p>The coseismic Sentinel-1 line-of-sight (LOS) displacements, strong-motion and teleseismic waveforms used in the joint inversion of the source rupture process for the 2021 Maduo, China earthquake are included in this repository.</p>
ScienceDex guides
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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.