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Dataset results
91 results for “Annealing”
Annealing_22
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Annealing_21
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Annealing_10
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Annealing_23
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_1
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_4
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_3
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_2
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_4
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_1
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_Annealing_2
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal faces".
<p>Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal facets".</p> <p>David John1,2, Zdeněk Remeš1, Radim Novák1, Štěpán Remeš1, Jakub Volf1,3,4, Oleg Babčenko1, Egor Ukraintsev5, Bohuslav Rezek5, and Maksym Buryi3</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10/112, 162 00, Prague, Czech Republic<br>2 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University, Břehová 7, 115019, Prague, Czech Republic<br>3 Institute of Plasma Physics of the Czech Academy of Sciences, U Slovanky 2525/1a, 182 00, Prague, Czech Republic <br>4 Department of Inorganic Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28, Czech Republic<br>5 Faculty of Electrical Engineering of the Czech Technical University, Technická 2, 160 00, Prague, Czech Republic</p> <p> </p> <p>Dataset description:</p> <p>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00000 AFM data<br>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00003 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00001 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00003 AFM data<br>afm zn AFM data<br>data phase shift fit phase shift data<br>grafy phase shift fit phase shift data<br>ZnO faces optical images</p>
Supplemental image data for "Mechanical annealing and memories in a disordered solid"
<p>This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.</p>
Dataset for Dynamic Analysis of Quantum Annealing Programs
<p>Quantum software engineering is emerging as a relevant field as it deals with the challenges of producing the new quantum software, whose adoption is increasing progressively. One of those challenges is how quantum software is migrated, how it operates in combination with classical software, or how it should be maintained. In this context this research focuses on reverse engineering of quantum annealing software to facilitates its integration in hybrid software systems. Quantum annealing software has gained a certain market penetration, demonstrating a good performance for optimization problems. While there are some preliminary reverse engineering techniques for gate-based quantum software, there is no reverse engineering techniques to discover the underlying optimization problem definitions (Hamiltonians functions to be minimized). Problem definitions are, in turn, dynamically defined through classical software, and can evolve over time, which make it difficult its accurate comprehension and abstract representation. Thereby, this paper presents a dynamic analysis technique for D-Wave (python) programs for reversing Hamiltonians expressions, that are additionally represented according to the Knowledge Discovery Metamodel. Due to the usage of this standard, the reversed Hamiltonians can be represented in combination with other parts of classical-quantum software systems. In order to facilitates its adoption, the proposed technique has been empirically validated through a case study with 27 D-Wave programs that demonstrates the effectiveness and efficiency. This dataset includes measures derived from that case study.</p>
small EBSD map from annealed ferritic steel
<p>There are two files in this dataset: </p> <p>1 - "DX54_biaxial_ref_small.h5" containing a small Electron Backscatter Diffraction map</p> <p>2 - "Fe_2866_2866_ver3.tif" containing a simulated EBSD from alpha-Fe on a stereographic projection</p> <p> </p> <p><strong>1 - "DX54_biaxial_ref_small.h5"</strong></p> <p>A small Electron Backscatter Diffraction dataset obtained from an annealed ferritic steel and used for algorithm development and testing. The data were collected on a Zeiss Merlin SEM at 20keV using a Bruker eFlash HR (1st generation) . The crystal orientations were determined through the Bruker eSprit software. The EBSD map consists of 64 by 48 points at a step size of ~0.5 microns, and with EBSD patterns recorded at 800 by 576 pixels (ie 2x2 binning). Axis systems and Euler angle conventions used are detailed in <a href="https://dx.doi.org/10.1016/j.matchar.2016.04.008">'Crystal orientations and EBSD — Or which way is up?'</a></p> <p>The dataset is given in an HDF5 file format. You can learn more about this format from <a href="https://www.hdfgroup.org">the HDF Group</a> and download some tools including HDFView which will allow you to explore the file structure. You could also take a look at the discussion given by <a href="https://doi.org/10.1186/2193-9772-3-4">Jackson et al (2014)</a> of archival data formats for EBSD.</p> <p>This map was used by the <a href="http://users.ox.ac.uk/~ajw/">Oxford Micromechnics Group</a> in development of tools for Multivariate Statistical Analysis of EBSD and TKD Datasets. You can read details of this method in the open access <a href="https://arxiv.org/abs/1806.02087">preprint on arXiv</a>. </p> <p> </p> <p><strong>2 - "Fe_2866_2866_ver3.tif" </strong></p> <p>This image file shows dynamical diffraction theory simulation results for alpha Fe undertaken using Bruker Dynamics software tool. The Kikuchi band intensities are presented on a stereographic projection.</p> <p>This was used as the 'Master Pattern' from which a simulated EBSD pattern library was constructed and used in a template matching approach to index characteristic patterns generated from a multivariate statistical analysis of EBSD data described above.</p> <p> </p> <p>These outputs were generated as part of a project on <em>Micro-mechanical modelling techniques for forming texture, non-proportionality and failure in auto materials</em> funded by the Engineering and Physical Sciences Research Council in the UK (grant number EP/I021043/1)</p> <p> </p>
Nanocrystalline Flash Annealed Nickel Oxide for Large Area Perovskite Solar Cells
<p>Dataset supporting the manuscript "Nanocrystalline Flash Annealed Nickel Oxide for Large Area Perovskite Solar Cells" published in Advanced Science (DOI:10.1002/advs.202302549)</p> <p>Datasets are named according to the corresponding figures, with data related to each panel named according to the panel. Further complementary information about the data can be found in the README files.</p>
Dataset for: Quantum cascade lasers with discrete and non equidistant extended tuning tailored by simulated annealing
<p>Dataset used for article 10.1364/OE.27.026701.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.