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2,031 results for “Transformation”
The Measurement and Prediction of Phase Transformation Kinetics in a Nuclear Steel During Weld-Like Thermal Cycles: supporting data
<p>The Excel files contain the raw dilatometry data for the FGHAZ and CGHAZ experiments, with the spreadsheet used to calculate the modified offset also included. </p> <p>The .tif files are the raw 2D images acquired during synchrotron X-ray diffraction experiments. The sample-detector distance was approximately 1400 mm. Please see the publication for further details (or contact the corresponding author). </p>
The Toybox Dataset of Visual Object Transformations-Part 2: Households
<p>We introduce a new video dataset called Toybox for computer vision research. Videos in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. This is part two of the three-part dataset. Part two contains videos of household objects: ball, cup, mug, and spoon. Part one and three include toy animals and vehicles, respectively.</p>
The Toybox Dataset of Visual Object Transformations-Part 1: Animals
<p>We introduce a new video dataset called Toybox for computer vision research. Videos in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. This is part one of the three-part dataset. Part one contains videos of toy animals: cat, duck, giraffe, and horse. Part two and three include households and vehicles, respectively.</p>
Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems
<p>Data set belonging to the IEEE Trans. Instr. Meas. paper with DOI: <a href="https://doi.org/10.1109/TIM.2018.2879171">10.1109/TIM.2018.2879171</a></p> <p>G. Rietveld, E. Mohns, E. Houtzager, H. Badura, and D. Hoogenboom,<br> <em>Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems</em></p> <p>This project has received funding from the European Metrology Programme for Innovation and Research co-financed by the Participating States and in part by the European Union’s Horizon 2020 Research and Innovation Programme.</p>
Data package for paper "Transformer models for astrophysical time series and the GRB prompt-afterglow relation"
<p>This is a data package accompanying the paper "Transformer models for astrophysical time series and the GRB<br>prompt-afterglow relation". The code used to acquire the data is in the "data" folder. The code used to analyse the data is in the "analysis" folder.</p> <p>DOI paper: <a href="https://doi.org/10.1093/rasti/rzae026">10.1093/rasti/rzae026</a></p>
Source data of Waning snowfields have transformed into hotspots of greening within the alpine zone
<p>Source data of the figures and extended data figures of the paper entitled </p> <p><strong><span>Waning snowfields have transformed into hotspots of greening within the alpine zone</span></strong></p>
Fitted Parameters of Power Transformation for rainfall in Brazil
<h1>Power Transformation: Correcting bias in MIROC6 rainfall</h1> <p>The Climate System Monitoring and Modeling Laboratory (LAMMOC), part of Fluminense Federal University (UFF) has tried forecasting rainfall over Brazil using one of the most known process-based models: MIROC6.</p> <p>Unlike other regions of Earch, MIROC6 resulted in unacceptable bias, leading the team to explore bias correction methods that best suit this specific use case.</p> <p>Power Transformation (PT) was suggested as it was used in different research papers.</p> <p>The first netCDF file (PT_all_grid.nc) hosts parameters of PT after fitting it to rainfall data for the period 1961 - 2014.</p> <p>Those parameters were used to correct MIROC6 forcasted rainfall over the period 2015-2100 for each of the main Shared Socio-economic Pathways (SSP).</p> <p>Both raw and corrected rainfall data can be extracted via ssp_corr_245.nc, ssp_corr_370.nc and ssp_corr_585.nc for each of ssp2-4.5, ssp3-7.0 and ssp5-8.5 respectively.</p> <p>Observed Rainfall data was obtained from https://doi.org/10.1002/joc.7731.</p> <p>Raw MIROC6 data was generated within the LAMMOC lab.</p> <p>The associated GitHub repository hosts a usage guideline as well as relevant information.</p>
High-resolution chain transform fault bathymetry from the PI-LAB experiment
<p>Bathymetry data from PI-LAB, MGL1602 (<a href="https://doi.org/10.1029/2018JB015982">https://doi.org/10.1029/2018JB015982</a>).</p> <p>Format: Lat/Lon/Depth [m]</p> <p>For reference, please cite: Harmon, N., Rychert, C., Agius, M., Tharimena, S., Le Bas, T., Kendall, J. M., & Constable, S. (2018). Marine geophysical investigation of the Chain Fracture Zone in the equatorial Atlantic from the PI‐LAB experiment. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>(12), 11-016</p>
Dataset: Asymptotic-state prediction for fast flavor transformation in neutron star mergers
<p>These data accompany the paper "Asymptotic-state predictions for fast flavor transformations in neutron star mergers" by S. Richers, J. Froustey, S. Ghosh, F. Foucart, and J. Gomez. The data are sorted into three directories:</p> <p>In the datasets, spacetime components are generally ordered as:<br>0 = x<br>1 = y<br>2 = z<br>3 = t</p> <p>Neutrino/antineutrino indices are ordered as:<br>0 = neutrino<br>1 = antineutrino</p> <p>Flavor indices are ordered as:<br>0 = e<br>1 = mu<br>2 = tau</p> <p>#==========#<br># Emu_data #<br>#==========#<br>Contains four files, each containing the initial and final results of simulations of neutrino quantum kinetics.</p> <p>M1NuLib-2016_5ms_rl0.h5: extracted from refinement level 0 of a 1.2Msun-1.2Msun NS merger simulation at 5ms after merger (https://doi.org/10.1103/PhysRevD.94.123016).<br>M1NuLib_3ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 3ms after merger (https://arxiv.org/abs/2407.15989)<br>M1NuLib_7ms_rl1.h5: extracted from refinement level 1 of a 1.3Msun-1.4Msun NS merger at 7ms after merger (https://arxiv.org/abs/2407.15989)<br>random: randomly generated initial conditions as described in the manuscript.</p> <p>Contents:</p> <p>F4_initial(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>The initial number density four-flux of each species in units of 1/cm^3.</p> <p>F4_final(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>The final number density four-flux of each species in units of 1/cm^3, averaged in time following the procedure indicated in the accompanying paper.</p> <p>F4_final_stddev(1|ccm):<br>[simulation index, 4 spacetime components, 2 neutrino/antineutrino, 3 flavors]<br>Not used in the manuscript. The standard deviation from the distribution of F4 that the final answer is averaged over.</p> <p>directorynames:<br>[simulation index]<br>A string for each simulation index indicating the directory the calculation corresponds to. Used in debugging.</p> <p>growthRate(1|s):<br>[simulation index]<br>Not used in the manuscript. Measured growth rate of the flavor off-diagonal components of the number density in s^-1. N_offdiag ~ e^(w t), where w is the growth rate and t is the time.</p> <p>nf:<br>scalar<br>Assumed number of flavors. Should be 3 for all datasets included here.</p> <p>xplot:<br>[simulation index, time index]<br>t/t_saturation. Used in creating Figure 2 of the manuscript.</p> <p>y0plot:<br>[simulation index, time index]<br>N_ee(t) / N_ee(0). Used in creating Figure 2 of the manuscript.</p> <p>y1plot:<br>[simulation index, time index]<br>N_offdiag_mag(t) / Ntot. Used in creating Figure 2 of the manuscript.</p> <p><br>#===========#<br># SpEC_data #<br>#===========#<br>Contains snapshots of grid quantities extracted from neutron star merger simulations. The file M1NuLib_2016_5ms_rl0.h5 contains data from the 1.2Msun-1.2Msun simulation of https://doi.org/10.1103/PhysRevD.94.123016. All others contain data from the 1.3Msun-1.4Msun M1-NuLib simulation of https://doi.org/10.48550/arXiv.2407.15989. The (3ms, 5ms, 7ms) part of the filename indicates how much time after merger the snapshot was taken. The (rl0, rl1, rl2, rl3) part of the filename indicates which refinement level the data are extracted from. NaNs in the data indicate that that cell was covered by a finer refinement level in the simulation. Radiation quantities are Lorentz transformed into an orthonormal tetrad comoving with the background fluid, and are rotated such that the net ELN flux is in the z direction. Each dataset contains:</p> <p>J_{e,a,x}(erg|ccm) - energy density of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if erg/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>fn_{e,a,x}(1|ccm) - number flux of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}, where "x" contains the sum of all four heavy species, in units if 1/cm^3. Indexed by direction and spatial position of the grid cell: [xyz, i,j,k]</p> <p>minerbo_Z{e,a,x} - the Z parameter of the maximum entropy closure at each point for {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless.</p> <p>n_{e,a,x}(1|ccm) - number diensity of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos} in units of 1/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>{x,y,z}(cm) - coordinates of the centers of each grid cell in units of cm. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>rho(g|ccm) - background comoving matter density in units of g/cm^3. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>T(MeV) - background comoving matter temperature in units of MeV. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>Ye - background electron fraction (dimensionless). Indexed by spatial position of the grid cell: [i,j,k]</p> <p>fluxfac_{e,a,x} - flux factor of {electron neutrinos, electron anti-neutrinos, heavy lepton neutrinos}. Dimensionless.</p> <p>crossing_discriminant - a crossing exists if this number is larger than 0. Computed using the following, based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005. Indexed by spatial position of the grid cell: [i,j,k]<br>a = gamma**2 + alpha**2<br>b = -2. * gamma * eta<br>c = eta**2 - alpha**2<br>discriminant = (b**2 - 4.*a*c) / (2.*a)**2<br>hasCrossing = (discriminant >= 0)</p> <p>deltaCrossingAngle - width of the ELN crossing based on Equation 16 in https://doi.org/10.1103/PhysRevD.106.083005 in units of radians. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>{nue,anue}_absrate(1|s) - absorption rate for {electron neutrinos, electron antineutrinos} in units if 1/s. Indexed by spatial position of the grid cell: [i,j,k]</p> <p>g{xx,yy,zz} - diagonal components of the metric tensor (dimensionless). Indexed by spatial position of the grid cell: [i,j,k]</p> <p><br>#===========#<br># ML_models #<br>#===========#<br>Contains the ML models used in the accompanying paper. In addition, there is an example script that uses the Rhea code to generate the relevant databases. Running the script requires the Rhea directory to be in the Python path. The Rhea code contains example Python and C++ code to use a trained model.</p> <p>Expected Rhea code:<br>Snapshot at 10.5281/zenodo.13675320<br>https://github.com/srichers/Rhea commit 68b7dc69d8fa33c7f5de1d8bec0790496f282d80</p> <p> </p>
Microscopic mechanism of the L1_2–D0_19 phase transformation in a Co-base single crystal superalloy
<p>This record contains datasets related to the publication:</p> <p>N. Karpstein et al., Microscopic mechanism of the L1<sub>2</sub>-D0<sub>19</sub> phase transformation in a Co-base single crystal superalloy, Acta Materialia (2024), <a href="https://doi.org/10.1016/j.actamat.2024.120416" target="_blank" rel="noopener">doi:10.1016/j.actamat.2024.120416</a>.</p> <p>A readme file containing descriptions of datatypes can be found in the main folder.</p> <p> </p> <p>Abstract:</p> <p>In γ′-strengthened superalloys based on the Co-Al-W system, the stability of the γ′ phase is often limited, as it is found to transform into other phases such as B2 and D0<sub>19</sub> after long-term annealing. To explore the details behind the annealing-induced transformation from the metastable L1<sub>2</sub>-γ′ phase to the thermodynamically stable D0<sub>19</sub>-χ phase in the single-crystalline Co-base superalloy ERBOCo-VF60 (Co<sub>79.8</sub>Al<sub>8.9</sub>W<sub>9.0</sub>Ta<sub>2.3</sub> in at.%), scanning transmission electron microscopy and atom probe tomography are employed. Due to the structural similarity between the L1<sub>2</sub> and D0<sub>19</sub> structures, coherent plate-shaped χ precipitates are formed in the γ/γ′ microstructure parallel to {111} planes through a shear-based transformation mechanism. While the χ phase precipitates slowly during isothermal aging, its formation can be significantly accelerated locally by coating the superalloy with a Cr-rich layer, which indirectly stabilizes the χ phase as revealed by thermodynamic calculations. This procedure allowed us to obtain an intermediate (incomplete) state of χ-phase formation in terms of both crystal structure and composition. By characterizing the partial dislocations at the tip of growing χ precipitates, the microscopic details of the shear-based cubic-to-hexagonal transformation from L1<sub>2</sub> to D0<sub>19</sub> are uncovered. The compositional aspect of the transformation involves a significant diffusion-mediated enrichment of W in the χ phase, accompanied by a simultaneous W depletion in the γ′ phase, leading to its transformation to the γ phase.</p>
Transformative Change Assessment TCA Corpus Data Package
<p>The data used in the TCA Corpus DMR.</p>
Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image
<p>Leveraging Digital Transformation for Effective Issue Management as a Strategy to Strengthen Corporate Image by Wiwi Lestari, Adhi Murti, Nisrin Husna</p>
Convolutional transformer wave functions
<p>Ground state and dynamics data for the manuscript "Convolutional transformer wave functions".</p> <p> </p> <p>The ground state data (Fig.2 in the manuscript) is provided for the 10x10 J1J2 Heisenberg model with J2/J1=0.5.</p> <p>The dynamics data (Fig.3 in the manuscript) is the evolution of observables with Jt=1e-3 in our simulations and Jt=2e-3 in "CNN_Schmitt".</p>
Supplementary data for the manuscript: Image2SMILES: Transformer-based Molecular Optical Recognition Engine
<p>This is the supplementary data for the manuscript: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758c6469df4169bf45744">Image2SMILES: Transformer-based Molecular Optical Recognition Engine</a></p> <p>It contains pairs of image-string, generated from 1M SMILES strings. These strings were randomly chosen from PubChem database.<br> It was prepared using the code, published at <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a></p> <p>To unpack do:<br> <em>tar xvf subset_1M.tar.xz && tar xvf subset_1M_dump.tar.gz && rm subset_1M_dump.tar.gz</em></p> <p>You'll get the following data:</p> <ul> <li>subset_1M.smi - list of 1M source SMILES</li> <li>subset_1M_dump - directory with images </li> <li>subset_1M_result.csv - list of pairs FGSMILES - pathcode, first 3 chars of pathcode are corresponding subdirs in subset_1M_dump</li> <li>subset_1M_fails.csv - list of failed molecules from subset_1M.smi</li> <li>subset_1M_grpcounter.lst - list of counted groups, used in this generation</li> </ul> <p>You can generate your own data using <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a> </p>
Dataset for electromagnetic holding force of split core transformer
<p>Dataset for electromagnetic holding force of split core transformer grasping mechanism. Dataset is separated in two documents with "coil_test1" containing data from a single test at a primary AC current of 1000 A. "coil_test2" contain eight test samples at different secondary currents denoted by 1-8. </p>
Data from: Evolution transforms pushed waves into pulled waves
<p>Understanding the dynamics of biological invasions is crucial for managing numerous phenomena, from invasive species to tumours. While the Allee effect (where individuals in low-density populations suffer lowered fitness) is known to influence both the ecological and evolutionary dynamics of an invasion, the possibility that an invader's susceptibility to the Allee effect might itself evolve has received little attention. Since invasion fronts are regions of perpetually low population density, selection should be expected to favour vanguard invaders that are resistant to Allee effects. This may not only cause invasions to accelerate over time, but, by mitigating the Allee effects experienced by the vanguard, also make the invasion transition from a pushed wave, propelled by dispersal from behind the invasion front, to a pulled wave, driven instead by the invasion vanguard. To examine this possibility, we construct an individual-based model in which a trait that governs resistance to the Allee effect is allowed to evolve during an invasion. We find that vanguard invaders evolve resistance to the Allee effect, causing invasions to accelerate. This results in invasions transforming from pushed waves to pulled waves, an outcome with consequences for invasion speed, population genetic structure, and other emergent behaviours. These findings underscore the importance of accounting for evolution in invasion forecasts, and suggest that evolution has the capacity to fundamentally alter invasion dynamics.</p>
Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system
<p>Dataset of the publication “Theoretical analysis and simulations of two-dimensional Fourier transform spectroscopy performed on exciton-polaritons of a quantum-well microcavity system“, H. Rose, J. Paul, J. K. Wahlstrand, A. Bristow, and T. Meier, Proceedings of the SPIE 11684, 1168414 (2021) ( <a href="https://doi.org/10.1117/12.2576696">https://doi.org/10.1117/12.2576696</a> ). The zip file includes the data on which the plots shown in figure 2 are based.</p>
The Ubiquitous Creeping Segments on Oceanic Transform Faults (supplementary)
<p>The supplementary material to <em><strong>The ubiquitous creeping segments on oceanic transform faults, Pengcheng Shi, Meng (Matt) Wei, Robert A. Pockalny, 2021</strong></em>.</p> <p>Link to <a href="https://github.com/shipengcheng1230/OTF2021/tree/v1.0.1">GitHub</a>.</p>
Innovation Systems & Policy Insight 2_2021 Steering towards transformative change
<p><strong>New Monitoring Evaluation & Learning tools for improving innovation projects transformative potential</strong><br> To achieve systemic impacts, innovation projects require reflexive steering to adapt to new insights or<br> changing circumstances. In MOTION and PROPORTION, AIT researchers collaborated closely with practitioners<br> working on sustainable agriculture and innovative land use management practices. The AIT team<br> supported their work through tools that improve the transformative potential of their innovation activities<br> and helped develop their capacities to promote and steer their project towards systemic and transformative<br> impacts.</p>
Tracing the imprint of river runoff on Arctic water mass transformation [dataset]
<p>This dataset contains the underlying data for the manuscript Lambert et al., Tracing the imprint of river runoff<br> variability on Arctic water mass transformation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Both files contain variables with the general notation:<br> S..., which are the cumulative salt fluxes;<br> S..2, which are the salinity-transformation fluxes;<br> T..., which are the cumulative heat fluxes; and<br> T..2, which are the temperature-transformation fluxes.</p> <p>-----------------------------------<br> In the file crfdata.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> emp: evaporation-precipitation, small en neglected in the manuscript<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> fsiso/ftiso: isopycnal diffusion of salt/heat<br> fsdia/ftdia: diapycnal diffusion of salt/heat<br> sec: advection across the collective Arctic gateways</p> <p>Each variable is of size [4,12,nS] or [4,12,nT] where nS is the number of salinity bins, equal to the length of variable S<br> and nT is the number of temperature bins, equal to the length of variable T</p> <p>The first dimension is ordered as follows:<br> 0: delta_s, the equilibrium response to a 30% increase in total Arctic river runoff<br> 1: tau_s, the e-folding time scale of this response in months<br> 2: std, the standard deviation of the control value<br> 3: ctrl, the average control value</p> <p>The second dimension indicates the calendar month</p> <p>-----------------------------------------<br> In the file pp2.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> adv: advection across the collective Arctic gateways<br> dif: total isopycnal + diapyncal diffusion</p> <p>Each variable is of size [2,nS] or [2,nT]</p> <p>The first dimension is:<br> 0: explained model variance between 0 and 1<br> 1: explained model variance where correlations with p>.05 equal NaN</p>
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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.