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31 results for “2D materials”
Materials for 2d representation of the HathiTrust Library
<p>Materials to create the LargeVis visualization online at http://creatingdata.us/datasets/hathi-features/, and described in <em>Benjamin Schmidt, "Stable random projection: lightweight, general-purpose dimensionality reduction for digitized libraries," Journal of Cultural Analytics. October 3, 2018.</em></p> <p>Two items. First, `hathi_pca.bin`: a binary file with 100-dimensional representations of the complete Hathi Trust Extended Features set. These began as 1280-dimensional SRP features, and were reduced to 100 dimensions using a PCA transformation matrix derived using a random sample of the full 13 million book set. Vectors were reduced to unit length before PCA, but not afterwords; this means that in general, their length gives some sense of much information was lost in the PCA representation. This can be read using the code at https://github.com/bmschmidt/pySRP, or anything that reads word2vec formatted vectors. Includes HathiTrust identifiers.</p> <p>Second, `hathi.tsv.gz`: a row oriented set containing a variety of metadata fields for each set, including (as 'x' and 'y') the coordinates of a 2-d LargeVis visualization. This is the immediate input to the visualization at ttp://creatingdata.us/datasets/hathi-features/. Columns should be relatively straightforward; they are derived from the HathiTrust MARC records, which can be accessed through Hathi's public API. Classification codes ('lc1') are using the Library of Congress classification; they represent the subclass (generally two characters, though it can be one or three). The first character alone represents the LC class and can be useful for coloring high-level overviews.</p> <p>These two files can be merged through the Hathi Trust identifier present in both.</p> <p> </p>
Replication Data for: Probing magnetism in 2D materials at the nanoscale with single spin microscopy
<p>Data repository for: <strong>Probing magnetism in 2D materials at the nanoscale with single spin microscopy</strong></p> <p><em>Data description.pdf </em>describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>MzFromBNV.m</em>, <em>kvalues.m</em>, <em>NVZeemanShiftFromMagnetizedSampleEdge.m</em> are Matlab code files used to transform and fit the data.</p> <p> </p>
2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware
<p>This dataset contains the raw data used for the publication:</p> <p><strong>2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware</strong></p> <p>By Lei Tong<sup>1</sup>, Zhuiri Peng<sup>1</sup>, Runfeng Lin<sup>1</sup>, Zheng Li<sup>1</sup>, Yilun Wang<sup>1</sup>, Xinyu Huang<sup>1</sup>, Kan-Hao Xue<sup>1</sup>, Hangyu Xu<sup>2</sup>, Feng Liu<sup>3</sup>, Hui Xia<sup>2</sup>, Peng Wang<sup>2</sup>, Mingsheng Xu<sup>4</sup>, Wei Xiong<sup>1</sup>, Weida Hu<sup>2,</sup>*, Jianbin Xu<sup>5</sup>, Xinliang Zhang<sup>1</sup>, Lei Ye<sup>1,</sup>*, Xiangshui Miao<sup>1</sup></p> <p>Detailed descriptions for each file can be found in "Dataset description.docx".</p>
Hyperspectral 2D fan-beam X-ray CT dataset of 5 materials
<p>Hyperspectral X-ray CT dataset acquired at the DTU 3D imaging center. The phantom consists of 5 materials: Aluminium (10 mm) and PVC (7.8 mm) in solid blocks. Sugar, H2O2, and H2O in circular glass containers.</p> <p>3D array with dimension: 128 x 370 x 258 < channel, angle, horizontal ></p> <p> </p> <p>Detector parameters:</p> <p>Number of detector pixels: 258 (concatenated from 2 detector modules with 128 pixels each and 2 pixel interpolated across a gap between detectors)</p> <p>Pixel size: 0.077 cm</p> <p>Sep=0.153 Pixels' gap length (cm)</p> <p>det_space=(ndet)*pixel_size+Sep # physical width of detector in cm (pixels*pixel_size), including the gap</p> <p> </p> <p>Acquisition Parameters</p> <p>360 # Angular span of projections in degrees</p> <p>370 # Number of projections. note: last projection taken is not a duplicate of the first projection. At angle: 360/370 degrees from first projection.</p> <p>115.0 # Source-Detector distance in cm</p> <p>0 # Vertical source shift from perfect placement</p> <p>0 # Vertical detector shift from perfect placement</p> <p>57.5 # Source-AxisOfRotation distance in cm</p> <p> </p> <p>rot_axis_x = 0 # x-position offset of AxisOfRotation</p> <p>rot_axis_y = 0 # y-position offset of AxisOfRotation</p>
Supplementary Material: Computational Study of Quasi-2D Liquid State in Free Standing Platinum, Silver, Gold, and Copper Monolayers
<p>Supplementary files for <em>Condensed Matter</em> <strong>2016</strong>, <em>1</em>(1), 1; doi:10.3390/condmat1010001; http://www.mdpi.com/ 2410-3896/1/1/1.</p> <p>Captions:</p> <p><strong>Video S1.</strong> (Pt 2400 K 5 ps) 5 ps Molecular Dynamics Movie of Pt Freestanding Monolayer at 2400 K. </p> <p><strong>Video S2.</strong> (Ag 1050K 6 ps) 6 ps Molecular Dynamics Movie of Ag Freestanding Monolayer at 1050 K.<br /> <br /> <strong>Video S3.</strong> (Au 1600K 4ps) 4 ps Molecular Dynamics Movie of Au Freestanding Monolayer at 1600 K.<br /> <br /> <strong>Video S4.</strong> (Cu 1400K 3ps) 3 ps Molecular Dynamics Movie of Cu Freestanding Monolayer at 1400 K. </p>
Hyperspectral photoluminescence and reflectance microscopy of 2D materials
<h2>Description of Uploaded Raw Data and Programs for Recreating Figures</h2><h3>Raw Data</h3><p>The raw data in this dataset is primarily in ".sif" binary format, which is used in the creation of Figures 2, 3, 4, and Supplementary Information (SI) Figure 2 in the paper. The ".sif" files contain spectrum data. The data for Figure 3 also includes focal data provided as .png and intensity line-cuts in .csv files.</p><p>A Python program, "load_sif.py", is included in the dataset to read and process these ".sif" files.</p><h3>Software and Programs</h3><p>The figures in the paper were generated using Python programs, which are included in the dataset. These programs are:</p><p>for Figure 2:<i> RClf_calibration.py </i></p><p>for Figure 3: <i>knife_edge_measurement.py </i>and <i>plot_intensity_profile_imageJ.py </i></p><p>for Figure 4 as well as SI Figure 1: <i>PL_linefocus_2color.py, PL_fit_image.py, PL_line_fit.py, RC_linefocus_2color.py </i>and<i> RC_line.py </i></p><p>for SI Figure 2: <i>BG_spectum_PL.py </i>and<i> Ref_spectum_RC.py </i></p><h3>Steps to Recreate Figures</h3><p>Download the zipped folder for each figure. The Python programs are using the ".sif", ".png", and ".csv" files from the downloaded folder.</p><p>Please ensure you have the appropriate software to run these Python programs and handle the provided file formats.</p>
Database on available 2D materials
<p>This deliverable is a database of exfoliable three-dimensional (3D) layered materials available for 2D-PRINTABLE, and the corresponding two-dimensional (2D) materials produced by project partners by means of various exfoliation methods in liquid media, including liquid-phase exfoliation method (LPE), electrochemical exfoliation (EE) and chemical exfoliation (CE). Exfoliable 3D layered materials are those synthesized and currently available at VSCHT facilities, while LPE-produced 2D materials are those produced by BeD, UKa, TCD TUD and VSCHT. The database includes the main specifications for exfoliable 3D layered materials, including their (physical) form (e.g., powder/crystal and corresponding dimension), stoichiometry and doping, as well as the material amount that can be supplied within the consortium. For 2D materials, the database reports the references to public documents (e.g., paper in international peer-reviewed journal or public repositories) showing material characterizations. </p> <p>This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101135196. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements
<p>Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements</p> <p>DOI: 10.1021/acsami.3c19254</p> <p><span><span>I. Brotons-Alcázar, Jason. S. Terreblanche, S. Giménez-Santamarina, G. M. Gutiérrez-Finol, K. S. Ryder, A. Forment-Aliaga, E. Coronado, <em>ACS Appl. Mater. Interfaces</em> <strong>2024</strong>, <em>16</em>, 19711.</span> </span></p>
Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles. Small 2024, 2406599
<p>Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles</p> <p>N. V. Vassilyeva, A. Forment-Aliaga, E. Coronado, <em>Small</em> <strong>2024</strong>, e2406599.</p> <p>doi: 10.1002/smll.202406599</p>
2D Sound Navigation - Tutorial Materials
<p>Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.</p>
The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data
<pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>
Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2
<p>This data set contains all resources for the research project "<span>Programmable nonlinear optical neuromorphic computing with bare 2D material MoS2" (published in Nature Communications (2024)).</span></p>
Label-free imaging of DNA interactions with 2D materials
<p>Raw images and data analysis related to the manuscript entitled "Label-free imaging of DNA interactions with 2D materials"</p>
Dataset for Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions
<p>The dataset contains all relevant data and figures regarding the Figure 4, S4 and S5 of manuscript "Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure4a.csv file corresponds to data used to plot graphs from Figure4a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Supplemental materials to "A quasi-2D model of convectively coupled vortices"
<p>math_derivation_note: A hand-written note of key mathematical steps, mostly about section 4 and Appendix C. </p> <p>quasi-2D model.zip: The package of the quasi-2D model code.</p> <p>postprocess_code_quasi2D.zip: The package of the postprocessing codes and intermediate files (.mat) related to the quasi-2D simulations.</p> <p>postprocess_code_CM1.zip: The package of the postprocessing codes and intermediate files (.mat) related to the CM1 simulation.</p> <p>Group_dh.avi: The Group-dh experiments with varying convective intermittency (dh/H). The first, second, and third column shows Group-dh-1, Ref, and Group-dh-2. Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l</em>=30 km) normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_dL.avi: The Group-<em>l</em> experiments with varying convective filter length <em>l.</em> The first, second, and third column shows Ref (<em>l</em>=30 km), Group-<em>l</em>-1 (<em>l</em>=45 km), and Group-<em>l</em>-2 (<em>l</em>=60 km). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_fE.avi: The Group-fE experiments with varying Coriolis parameter f and Ekman number E<em>.</em> They differ in the strength of the rotational flow. The first, second, and third column shows Group-fE-1 (f=1e-5 1/s), Ref (f=1e-4 1/s), and Group-fE-2 (f=2e-4 1/s). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_eta.avi: The Group-eta experiments with varying mesoscale feedback parameter eta<em>.</em> They differ in the strength of the mesoscale feedback. The first, second, and third column shows Group-eta-1 (eta=0), Group-eta-2 (eta=1.2), and Group-eta-3 (eta=1.4). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu) if you have any questions!</p>
Data: set for "Low-voltage 2D materials-based printed field-effect transistors for integrated digital and analog electronics on paper"
<p>The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.</p>
Bromination of 2D materials [doi: 10.1088/1361-6528/ad1201]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1088/1361-6528/ad1201]</p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>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 TiO<sub>2</sub>-WO<sub>3</sub> 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.</p> <p><strong>Authors and funding:</strong></p> <p>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.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>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.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· 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).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>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 <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> 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 <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> 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.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> 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.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, 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.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> 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 TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Reshaping Foramen Magnum Research. Analyzing foramen magnum variation in modern humans using 2D osteometry and 3D geometric morphometrics – A master thesis summary and research review. Supplementary Materials.
<p>This supplementary materials document refers to: <em>Göldner, D., 2024. Reshaping Foramen Magnum Research. Analyzing foramen magnum variation in modern humans using 2D osteometry and 3D geometric morphometrics – A master thesis summary and research review. Mitteilungen der Berliner Gesellschaft für Anthropologie, Ethnologie und Urgeschichte 44 (2023).</em></p> <p> </p> <p> </p>
Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM
<p>This is the raw data for the manuscript:</p> <p>Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM</p> <p> </p> <p>A readme file containing all descriptions can be found in the main folder. </p> <p> </p> <p>Abstract:</p> <p>Quantitative diffraction contrast analysis with defined diffraction vectors is a wellestablished method in TEM for studying defects in crystalline materials. A comparable transmission techniques is however not available in the more widely used SEM platforms. In this work, we transfer the aperture-based dark-field imaging method from the TEM to the SEM, thus enabling quantitative diffraction contrast studies at lower voltages in SEM. This is achieved in STEM mode by inserting a custom-made aperture between the sample and the STEM detector and centering the hole on a desired reflection. To select individual reflections for dark-field imaging, we use our Low Energy Nanodiffraction (LEND) setup [Schweizer et al., Ultramicroscopy 213, 112956 (2020)], which captures transmission diffraction patterns from a fluorescent screen positioned below the sample. The aperture-based dark-field STEM method is particularly useful for studying extended defects in 2D materials, where (i) stronger diffraction at the lower voltages used in SEM is advantageous, but at the same time (ii) two-beam conditions cannot be established, making quantitative diffraction contrast analysis with standard bright-field and annular dark-field detectors impossible. We demonstrate the method by studying basal plane dislocations in bilayer graphene, which have attracted considerable research interest due to their exceptional structural and electronic properties. Direct comparison of results obtained on identical dislocations by the established TEM method and by the new aperture-based dark-field STEM method in SEM shows that a reliable Burgers vector analysis is possible by applying the wellknown g·b=0 invisibility criterion. We further use the LEND setup to acquire 4D-STEM data and show that the virtual dark-field images match well with those in aperturebased dark-field STEM images for reliable Burgers vector analysis.</p> <p> </p>
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.