Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'

3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423

opencc-by-4.0Mar 2024View details →

Dataset for Exciton Fine Structure in 2D Perovskites: The Out‐of‐Plane Excitonic State

This dataset includes raw photoluminescence and transmission spectra acquired at zero and high magnetic field (identified by the folder name). Samples of different origins have been identified and the folder name includes the institution where the sample was synthetized. In the filename, one can find the information concerning the excitation power used, the wavelength of the excitation laser, the acquisition time, the degree read out on the half-wavelength plate. PL _ 5K_0.5uW_511.5nm_spot8 _aquisition time _ number of aquisitions _ X.csv   R _5K_ 1uW_511.5nm _ aquisition time _ number of aquisitions _spot8 _ X.csv   X: degree on the half-wave plate   Temperature:5K Excitation power: x uW Spot y - spot on a sample Grating center: zzz nm

opencc-by-4.0May 2024View details →

Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'

This is the supplemental data for the manuscript titled ‘Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’ submitted to Ultramicroscopy. Motivation: Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts. Authors and funding: The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG. <pre> </pre> Dataset description: We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data. training_data.zip contains the training data. In the 3D case, the subfolder mask contains the ground truth segmentation masks; the subfolder reconstruction contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder HAADF contains the 2D-projection images. In both cases, the subfolder json contains the annotation. Each file within the json folder provides for each image or reconstruction the following information: ·        aggregat_no: image id, the number of the corresponding image file ·        particle_position_x: list of particle position x-coordinates in nm ·        particle_position_y: list of particle position y-coordinates in nm ·        particle_position_z: list of particle position z-coordinates in nm ·        particle_radius: list of volume equivalent particle radii in nm ·        particle_type: list of particle types, 1: TiO2, 2: WO3 ·        particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron ·        rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation) ·        deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation). ·        cluster_index: list of cluster indices for each particle ·        initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged ·        fractal_dimension: the intended fractal dimension of the aggregate ·        fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities) ·        fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities) ·        fractal_prefactor: fractal prefactor ·        mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles) ·        mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles) ·        mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume) ·        mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight) ·        particle_1_rho: density of TiO2 used for the calculations ·        particle_1_size_mean: mean TiO2 radius ·        particle_1_size_min: smallest TiO2 radius ·        particle_1_size_max: largest TiO2 radius ·        particle_1_size_std: standard deviation of TiO2 radii ·        particle_1_clustersize: average TiO2 cluster size ·        particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters) ·        particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters ·        particle_2_rho: density of WO3 used for the calculations ·        particle_2_size_mean: mean WO3 radius ·        particle_2_size_min: smallest WO3 radius ·        particle_2_size_max: largest WO3 radius ·        particle_2_size_std: standard deviation of WO3 radii ·        particle_2_clustersize: average WO3 cluster size ·        particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters) ·        particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters ·        number_of_primary_particles: number of particles within the aggregate ·        gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities) ·        gyration_radius_weighted: gyration radius of the aggregate (including particle densities) ·        mean_coordination: mean total coordination number (particle contacts) ·        mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material) ·        mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material) ·        material_1: the name of the first material (TiO2) ·        material_2: the name of the second material (WO3) ·        radius_equiv: list of area equivalent particle radii (in projection) in nm ·        k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0) ·        polygons: list of polygons that surround the particle (COCO annotation) ·        bboxes: list of particle bounding boxes ·        aggregate_size: projected area of the aggregate translated into the radius of a circle in nm ·        n_pix: number of pixel per image in horizontal and vertical direction (squared images) ·        pixel_size: pixel size in nm ·        image_size: image size in nm ·        add_poisson_noise: 1 if poisson noise was added, 0 otherwise ·        frame_time: simulated frame time (required for poisson noise) ·        dwell_time: dwell time per pixel (required for poisson noise) ·        beam_current: beam current (required for poisson noise) ·        electrons_per_pixel: number of electrons per pixel ·        dose: electron dose in electrons per Å2 ·        add_scan_noise: 1 if scan noise was added, 0 otherwise ·        beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise) ·        scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise) ·        add_focus_dependence: 1 if a focus effect is included, 0 otherwise ·        add_partial_coating: 1 if some TiO2 particles were coated by a thin WO3 film, 0 otherwise. ·        data_format: data format of the images, e.g. uint8 For 3D reconstructions, the following information is added: ·        add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise. ·        add_projection_noise: 1 if noise was added to projection angles, 0 otherwise. ·        N_SIRT: number of SIRT iterations for the 3D-reconstruction. ·        proj_angles: list of angles used for the projection directions of the tilt series in rad. For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training. For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script. The zip archive models.zip includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file 2D_projection/logs/fit/20240209-095952/iter_60000.pth. These weights can be loaded with the jupyter-notebook 2D_projection_prediction.ipynb. Furthermore, a configuration file, which is required by the notebooks, is stored as 2D_projection/logs/fit 20240209-095952/config_file.py. In the 3D case, network weights are stored in 3D_reconstruction/model/weights_best.h5. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook 3D_reconstruction_prediction.ipynb. The zip archive experiment_measurement.zip includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN. The zip archive experiment_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs. The zip archive simulation_measurement.zip includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks. The zip archive simulation_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process. In the zip archive code.zip, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed. ·        2D_projection_training.py: This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox. ·        3D_reconstruction_training.py: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package. ·        2D_projection_prediction.ipynb: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data. ·        3D_reconstruction_prediction.ipynb: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data. ·        Evaluation_experiment.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations. ·        Evaluation_simulation.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations. ·        particle_detection_functions.py: This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments. ·        ASTRA_CM_plot_results.m: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions. ·        ASTRA_CM_particle_detection.m: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.   There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening. The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.

opencc-by-4.0Jul 2021View details →

Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis

Here are the waveform data used in the Geophysical Journal International paper entitled "Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis". The dataset is used for the reader who wants to reproduce the result in the paper.

opencc-by-4.0Dec 2022View details →

Dataset for "Band structures and Z2 invariant of 2D transition metal dichalcogenides from fully relativistic Dirac--Kohn--Sham theory using Gaussian-type orbitals"

Two-dimensional (2D) materials exhibit a wide range of remarkable phenomena, many of which owe their existence to the relativistic spin--orbit coupling (SOC). To understand and predict properties of materials containing heavy elements, such as the transition metal dichalcogenides (TMDs), full account of relativistic effects is mandatory in first-principles calculations. We present an all-electron method based on the four-component Dirac Hamiltonian and Gaussian-type orbitals (GTOs) that overcomes complications associated with linear dependencies and ill-conditioned matrices arising when diffuse functions are included in the basis. Until now, there has been no systematic study of the convergence of GTO basis sets for periodic solids neither at the nonrelativistic nor the relativistic level. Here, we provide such a study of relativistic band structures of the 2D TMDs in the hexagonal (2H), tetragonal (1T), and distorted tetragonal (1T') structural phases while focusing on SOC-driven properties (the Rashba splitting and the $\mathbb{Z}_2$ topological invariant). We demonstrate that our approach is valid even if large basis sets with multiple basis functions corresponding to each valence atomic orbital (denoted triple- and quadruple-$\zeta$) are used in the relativistic regime. The method does not require the use of pseudopotentials and provides access to all electronic states within the same framework, paving the way for direct studies of material properties that depend heavily on the electron density near atomic nuclei where relativistic effects and SOC are strongest, such as the parameters of the spin Hamiltonian.

opencc-by-4.0Jan 2023View details →

Datasets for benchmarking RNA 2D structure prediction algorithms.

Datasets for benchmarking ML approaches in RNA 2D structure prediction task.

opencc-by-4.0Jun 2023View details →

Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148

Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a> The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>. MoS2 4D-STEM dataset: <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> C60/MoS2 4D-STEM dataset: <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul>

opencc-by-4.0May 2023View details →

High-speed TIRF and 2D super-resolution structured illumination microscopy with large field of view based on fiber optic components

Super-resolved structured illumination microscopy (SR-SIM) is among the most flexible, fast, and least perturbing fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-built instruments are easily able to deliver two-fold resolution enhancement at video-rate frame rates, but the cost of the instruments is still relatively high, and the physical size of the instruments based on the implementation of their optics is still rather large. Here, we present our latest results towards realizing a new generation of compact, cost-efficient, and high-speed SR-SIM instruments. Tight integration of the fiber-based structured illumination microscope capable of multi-color 2D- and TIRF-SIM imaging, allows us to demonstrate SR-SIM with a field of view of up to 150 × 150 μm2 and imaging rates of up to 44 Hz while maintaining highest spatiotemporal resolution of less than 100 nm. We discuss the overall integration of optics, electronics, and software that allowed us to achieve this, and then present the fiberSIM imaging capabilities by visualizing the intracellular structure of rat liver sinusoidal endothelial cells, in particular by resolving the structure of their trans-cellular nanopores called fenestrations.

opencc-by-4.0Dec 2023View details →

Controlling propagation velocity in Al/Ni reactive multilayer systems by periodic 2D surface structuring

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2021View details →

Imaging the northeast lobe of the Sudbury Structure through 2D and 2.5D visco-acoustic full-waveform inversion

Seismic reflection profile LN182 - Northeast lobe of the Sudbury Structure. The LN182 transect comprises over 1,300 receivers (single wireless vertical-component 5-Hz geophones) and over 1,500 vibroseis sources. A linear upsweep of 5-120 Hz was generated by four vibroseis trucks forming the vibroseis source system.

opencc-by-4.0Oct 2024View details →

Reconstructing the 2D Material Structure from Diffraction Pattern with Physical-Sensitive Deep Learning

Dataset, saved Models' parameters, and test set

opennotspecifiedFeb 2022View details →

Fig. 3. Described key 2D in Monoterpene indole alkaloids from Vinca minor L. (Apocynaceae): Identification of new structural scaffold for treatment of Alzheimer's disease

Fig. 3. Described key 2D NMR correlations of revision of previously assigned positions in molecule 17 (COSY interaction – red bond, HMBC interaction – blue arrow). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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Data from: Catalytic self-folding of 2D structures through cascading magnet reactions

While thousands of proteins involved in development of the human body are capable of self-assembling in a distributed manner from merely 20 types of amino acid, macroscopic products that can be assembled spontaneously from `alive' components remains an aspiration in engineering. To attain such a mechanism, a major challenge lies in understanding which attributes from the bio-molecular realm must be leveraged at the macro-scale. Inspired by protein folding, we present a centimetre-size 1D tile chain whose self-folding processes are directed by structure-embedded magnetic interactions, which can theoretically self-assemble into convex 2D structures of any size or shape without the aid of a global `controller'. Each tile holds two magnets contained in paths designed to control their interactions. Once initiated by a magnetic unit (termed Catalyst), the chain self-reconfigures by consuming magnetic potential energy stored between magnet pairs, until the final 2D structure is reached at an energetic minimum. Both simulation and experimental results are presented to illustrate the method's efficacy on chains of arbitrary length. Results demonstrate the promise of a physically implemented, bottom-up, and scalable self-assembly method for novel 2D structure manufacturing, bridging the bio-molecular and mechanical realms.

opencc-zeroJul 2019View details →

opennotspecifiedFeb 2022View details →

Fig. 2. Key 2D in Isolation and structural elucidation of bioactive obovatol dimeric neolignans from the bark of Magnolia officinalis var. biloba

Fig. 2. Key 2D correlations of compounds 1 and 4.

opennotspecifiedJul 2021View details →

Fig. 4. Selected 2D in Structurally diverse polyprenylated acylphloroglucinols from Hypericum uralum Buch.-Ham. ex D. Don

Fig. 4. Selected 2D NMR correlations of 2.

opennotspecifiedJul 2021View details →

Fig. 2. Selected 2D in Structurally diverse polyprenylated acylphloroglucinols from Hypericum uralum Buch.-Ham. ex D. Don

Fig. 2. Selected 2D NMR correlations of 1.