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Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Electron backscatter patterns from Nickel acquired with varying camera gain
<p>Ten electron backscatter diffraction (EBSD) datasets from a recrystallized, polycrystalline sample of nickel. The datasets, each comprising 29 800 patterns, were collected from the same region of interest (ROI) with EBSD camera gains varying from 0 dB to 24 dB (camera maximum). A backscattered electron image of the ROI is also included.</p> <p>The data was acquired in order to study denoising of EBSD patterns, more specifically the increased signal-to-noise ratio obtained after principal component analysis followed by dimensionality reduction, as presented in H W Ånes, J Hjelen, B E Sørensen, A T J van Helvoort, K Marthinsen "Processing and indexing of electron backscatter patterns using open-source software," IOP Conf. Ser.:Mater. Sci. Eng. (2019), doi:<a href="https://doi.org/10.1088/1757-899X/891/1/012002">10.1088/1757-899X/891/1/012002</a>. This conference paper is part of the proceedings of EMAS 2019 - 16th European Workshop on Modern Developments and Applications in Microbeam Analysis held in Trondheim, Norway. However, the data is released with the hope that it can be used to compare the performance of denoising methods in general.</p> <p>The datasets, Pattern.dat, are stored in the NORDIF (binary) format, with the top-left pixel in the top-left pattern as the first byte, and the bottom-right pixel in the bottom-right pattern as the last byte. They can be opened in for example the open-source Python package kikuchipy (https://github.com/pyxem/kikuchipy). Assuming Python 3.7 or above and the package is installed, the patterns in scan 1 can be read and plotted with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load('/path/to/nickel_scan_gain/scan1_gain0db/Pattern.dat') s.plot()</code></pre> <p> </p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Femtosecond electron diffraction data of CsPbBr3 nanocrystals
<p>Femtosecond electron diffraction data of CsPbBr3 nanocrystals, acquired at the Fritz Haber Institute in Berlin. All measurements are performed at room temperature.</p>
Electronic Supplementary for: Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines
<p>This zipped repository accompanies the paper, "Refining patterns of melt with forward stratigraphic models on stable Pleistocene coastlines". Within the file are two sub-folders. "Model_Input" and "SELEN". "Model_Input" contains the parameters needed to re-run the model runs described in the manuscript in the model environement "OpenFlowSuit" (Beicip Franlab). The "SELEN" sub-folder contains three additional sub-folders: "Background", "Full", and "G2A5". Each of these sub-folders has three .csv files containing the GIA driven sea level curve under three different mantel viscosities, MV1, MV2, and MV3.</p>
Dyall dz, tz, and qz basis sets for relativistic electronic structure calculations
<p>This archive contains the Dyall basis sets for relativistic atomic and molecular electronic structure calculations. They are given in the format required by the DIRAC program (see <a href="http://diracprogram.org">diracprogram.org</a>), which is essentially a list of the exponents for each angular momentum for each element. The basis sets are of double-, triple-, and quadruple-zeta quality. For each quality, there are three basis set types: valence (v<em>N</em>z), core-valence (cv<em>N</em>z) and all-electron (ae<em>N</em>z). These basis sets include correlating functions for the relevant shells (valence, valence+outer core, all shells). In addition, for each of these basis sets there is another set that contains diffuse functions for the s, p, and d elements, optimized for the anion or extrapolated from neigboring elements where the anion is unbound or weakly bound. These sets are labeled av<em>N</em>z, acv<em>N</em>z, and aae<em>N</em>z. References for the basis sets are included in the basis set files.</p> <p>The archive files containing descriptions and recommendations for each basis set, as well as SCF coefficients and lists of exponents, are available <a href="https://doi.org/10.5281/zenodo.7606546">here</a>.</p>
Electron Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a positron passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the positrons with energy ranging from 20 to 350 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</p>
Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019
<p>An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).</p>
Electronic Supplement / Data Archive for "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response"
<p>These files provide supplemental data to accompany the paper "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response" submitted to AGU journal <em>Space Weather</em>, with manuscript number 2022SW003410. Details are provided in the file <strong>ReadMe_DataArchive.pdf</strong>.<br> </p>
Photoinduced Electron Transfer in Multicomponent Truxene- Quinoxaline Metal−Organic Frameworks
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>ARACAT_WP4_20200825_ULEI_03_60min_MUF77_OME_100K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MUF7_OME – </strong>NC-MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_OME – </strong>MUF-7_dbc-dpq-OMe MOF, <strong>MUF7_dpq – </strong>MUF-7_dbc-dpq MOF<strong> MUF77_paq – </strong>MUF-7_dbc-paq MOF</li> <li>_100K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Raw data from Cao et al. (2023) "Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water"
<p>Measured and fitted data from square-wave voltammetry (SWV) in DMSO for electron exchange capacities (EECs) of pyrogenic natural organic matter (pyDOM) and natural organic matter (NOM) standards. </p> <p>From Cao, H., A. S. Pavitt, J. M. Hudson, P. G. Tratnyek, and W. Xu. 2023. Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water. Environ. Sci. Proc. Impacts: ASAP. [10.1039/d3em00009e]</p> <p>The manuscript reports electron accepting capacity (EAC), electron donating capacity (EDC), and electron exchange capacities (EECs) measured with a new method involving square-wave voltammetry in an aprotic solvent (dimethyl sulfoxide, DMSO). The measurement method, fitting of peak areas, and conversion of peak areas to EECs are described in the main text and supporting information of the manuscript.</p> <p>Here we provide the original measured data, baseline corrected data used in the peak fitting, and fitted peak area data that were used to obtain the final EEC values. The data are provided in one .xlsx file that contains multiple tabs: (i) a table of contents, (ii) a summary of the final fitting results, and (iii) tabs numbered R1-R40 containing raw measured data for each pyDOM/NOM sample.</p> <p>The data provided here should be sufficient to replicate and verify all of the analysis described in the manuscript. If you use these data, please cite this Zenodo record (DOI 10.5281/zenodo.7747020) and the original manuscript (DOI: 10.1039/d3em00009e).</p>
Dataset for 'Zinc hybrid sintering for printed transient sensors and wireless electronics'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Zinc hybrid sintering for printed transient sensors and wireless electronics”.</p> <p>This work aims to study and develop a method for the efficient sintering of printed zinc metal, with the aim to facilitate the fabrication of biodegradable electronics by additive manufacturing. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste, and present opportunities for the fabrication of novel bioresorbable medical devices that can harmlessly degrade in the body and eliminate the need for re-operation. The method that is presented in this publication combines electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. Several sensors are shown as demonstrators (temperature, strain, pressure). The data that was collected in the frame of this work is present in this repository. It relates to both the study of the process introduced above as well as the characterization of the demonstrators. More information about the contents of the dataset is present in the included README files.</p>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
Universal Chalcidoidea Database World Wide Web electronic publication. http://www.nhm.ac.uk/chalcidoids. hash://sha256/562fc5f7ac62b0dba9952e267dc839ab16be3efcf149b98a8d77f6e88bce4f53 hash://md5/de2883bc9f8b1e79c96f5d00d650f596
<p>This repository contains an archival copy of the Universal Chalcidoidea Database by J.S. Noyes in their original Paradox Database (https://en.wikipedia.org/wiki/Paradox_%28database%29) file format. </p> <p>Files were provided by J.S. Noyes in period 2023-03/2023-04 and gave consent to publish the data under CC0.</p> <p>For getting started, please read the 00-Instructions.pdf first. Then, suggest to take a look at a flowchart in 01-Flowchart.pdf and the Table Structure in 02-TableStructure.pdf.</p> <p><strong>Citation</strong><br> On use of this data, please follow academic tradition and cite the data using: </p> <p>Noyes, J.S. March 2019. Universal Chalcidoidea Database. World Wide Web electronic publication. http://www.nhm.ac.uk/chalcidoids. hash://sha256/562fc5f7ac62b0dba9952e267dc839ab16be3efcf149b98a8d77f6e88bce4f53 hash://md5/de2883bc9f8b1e79c96f5d00d650f596 .</p> <p><strong>Signed Content </strong></p> <p>Using Preston [1,2], the UCD content was packaged and their provenance was signed.</p> <pre><code>preston history\ --anchor hash://sha256/562fc5f7ac62b0dba9952e267dc839ab16be3efcf149b98a8d77f6e88bce4f53\ --remote https://raw.githubusercontent.com/jhpoelen/ucd/main/data\ --remote https://zenodo.org/record/7864604/files\ --remote https://softwareheritage.org\ --remote https://linker.bio </code></pre> <p>yielded:</p> <pre><code><hash://sha256/562fc5f7ac62b0dba9952e267dc839ab16be3efcf149b98a8d77f6e88bce4f53> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/dc3f137ed7e456dd964545527cfff3acc3c1655baeaebecb6d07cdf3e1bbd549> . <hash://sha256/dc3f137ed7e456dd964545527cfff3acc3c1655baeaebecb6d07cdf3e1bbd549> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/298581b34133b518f251f4321f1920488afd923f3308e45b9c1d169da0e16b5b> . <hash://sha256/298581b34133b518f251f4321f1920488afd923f3308e45b9c1d169da0e16b5b> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/ec1760dc83dfb17df003ef5e626b965dd4403850bc58286ac59c7ef3a447e063> . <hash://sha256/ec1760dc83dfb17df003ef5e626b965dd4403850bc58286ac59c7ef3a447e063> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/eb416c97bf52a36b31ece2b47431a6a4a9bda7f52b9bc8ccb92f91f5c1bdf268> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/eb416c97bf52a36b31ece2b47431a6a4a9bda7f52b9bc8ccb92f91f5c1bdf268> . </code></pre> <p>This archive can be cloned using:</p> <pre><code>preston clone\ --anchor hash://sha256/562fc5f7ac62b0dba9952e267dc839ab16be3efcf149b98a8d77f6e88bce4f53\ --remote https://raw.githubusercontent.com/jhpoelen/ucd/main/data\ --remote https://zenodo.org/record/7864604/files\ --remote https://softwareheritage.org\ --remote https://linker.bio </code></pre> <p>or alternatively, by downloading a zip archive from :</p> <p>https://github.com/jhpoelen/ucd/archive/42a5815a2c50e396f1e34e9f73e0b13fcc67c44f.zip</p> <p><br> <strong>References</strong></p> <p>[1] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132</p> <p>[2] Elliott, M. J., Poelen, J. H., & Fortes, J. (2023, accepted with minor revisions). Signed Citations: Making Persistent and Verifiable Citations of Digital Scientific Content. https://doi.org/10.31222/osf.io/wycjn<br> </p>
Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: Preliminary results on loess from around the world
<p>Dataset for publication</p> <p><strong>Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: </strong></p> <p><strong>Preliminary results on loess from around the world</strong></p> <p> </p> <p>Quantitative provenance analysis studies are instrumental in understanding the tectonic and climatic processes that shape the earth’s landscape. Although the most abundant mineral in the sedimentary system is quartz, almost all studies in provenance analysis investigate accessory minerals. Quartz crystals contain a vast number of point defects, intrinsic or due to impurities. For a signal to be an accurate indicator of provenance one needs to show that it is either dose independent or reaches a quantifiable steady state characteristic of the source rock. For signals used by trapped charge dating methods (optically stimulated luminescence (OSL) and electron spin resonance (ESR)), the latter option is the feasible one. By using quartz samples collected from the Chinese Loess Plateau (Luochuan loess-paleosol section), we show that the laboratory and natural dose response curves of E`<sub>1</sub> and and peroxy electron spin resonance signals of quartz (as defined later) overlap and reach a steady state for doses over about 1000 Gy. For E’<sub>1</sub> signals we attribute this steady state to reaching an equilibrium state between diamagnetic oxygen vacancies (the oxygen deficiency centre (ODC), Si=Si<em>)</em> and paramagnetic oxygen vacancies (E’<sub>1</sub>). For sedimentary quartz irradiated naturally or artificially in this dose range we show a strong linear relationship with zero intercept between E’<sub>1</sub> and peroxy signals for samples worldwide, supporting the hypothesis that these defects are Frenkel pairs. Further, we show significant correlations between the optically stimulated (OSL) sensitivity and the above two mentioned ESR signals. The very strong correlations (Pearson`s r ˃0.9) between E’<sub>1</sub>, peroxy and OSL sensitivity remain valid after the samples have been heated for 15 min to 350 ˚C for E’<sub>1</sub> to reach its maximum value, believed to be a result of the conversion of diamagnetic oxygen vacancies to E’<sub>1</sub>, clearly suggesting a relationship between OSL sensitivity and oxygen vacancies in general. Samples collected from different loess sites around the world can be distinguished based on both these OSL and ESR properties. An empirical increase in OSL sensitivity as well as oxygen related defect concentrations is observed in areas where the source material has components with older detrital zircon U-Pb ages, inferring a positive correlation between OSL sensitivity, as well as the signal intensity for E<sub>1</sub>` and peroxy defects and the age of the source rocks.</p>
Background optimization of powder electron diffraction to implement e-PDF technique and study the local structure of iron oxide nanocrystals
<p>The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as Pair Distribution Function (PDF) (also known as Reduced Density Function) profiles derived from background corrected powder electron diffraction patterns. Due to the strong coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.</p>
Supporting Information for "The First GECAM Observation Results on Terrestrial Gamma-ray Flashes and Terrestrial Electron Beams"
<p><strong>Additional Supporting Information</strong></p> <ol> <li>GECAM_TGF_Catalog.xls</li> <li>GECAM_TEB_Catalog.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_CPD.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_GRD.xls</li> <li>Fig1AC_UT2021-07-05T07-45-41.783530_Sim.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_CPD.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_GRD.xls</li> <li>Fig1BD_UT2021-04-26T12-16-34.637228_Sim.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_CPD.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_GRD.xls</li> <li>Fig4A_UT2021-02-01T02-09-25.691512_Sim.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_CPD.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_GRD.xls</li> <li>Fig4B_UT2021-07-10T21-19-04.519543_Sim.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_CPD.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_GRD.xls</li> <li>Fig4C_UT2022-01-22T22-24-49.664579_Sim.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_CPD.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_GRD.xls</li> <li>Fig4D_UT2021-03-07T19-13-49.995485_Sim.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_CPD.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_GRD.xls</li> <li>Fig4E_UT2021-03-29T06-56-37.831848_Sim.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_CPD.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_GRD.xls</li> <li>Fig4F_UT2021-08-14T09-54-29.177203_Sim.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_CPD.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_GRD.xls</li> <li>Fig4G_UT2021-08-16T17-02-27.908009_Sim.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_CPD.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_GRD.xls</li> <li>Fig4H_UT2022-03-29T08-56-28.599361_Sim.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_CPD.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_GRD.xls</li> <li>Fig5C_UT2021-09-11T18-34-40.551997_Sim.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_CPD.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_GRD.xls</li> <li>Fig5D_UT2021-07-10T01-46-36.709997_Sim.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_CPD.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_GRD.xls</li> <li>Fig5A_UT2021-10-27T22-49-33.082008_Sim.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_CPD.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_GRD.xls</li> <li>Fig5B_UT2022-07-26T00-16-13.728010_Sim.xls</li> <li>Fig5EF_WWLLN_Lightning.txt</li> <li>GLD360data_forTGFUTC2021-02-22T00-17-18.034461.xlsx</li> <li>GLD360data_forTGFUTC2021-03-07T19-13-49.995436.xlsx</li> <li>GLD360data_forTGFUTC2021-03-25T09-48-08.785508.xlsx</li> <li>GLD360data_forTGFUTC2021-03-29T06-56-37.830006.xlsx</li> <li>GLD360data_forTGFUTC2021-04-17T20-10-34.446509.xlsx</li> <li>GLD360data_forTGFUTC2021-04-25T23-07-27.616005.xlsx</li> <li>GLD360data_forTGFUTC2021-04-29T18-12-43.227007.xlsx</li> <li>GLD360data_forTGFUTC2021-05-09T19-50-01.720689.xlsx</li> <li>GLD360data_forTGFUTC2021-05-10T21-38-43.498955.xlsx</li> <li>GLD360data_forTGFUTC2021-05-10T21-43-27.914962.xlsx</li> <li>GLD360data_forTGFUTC2021-05-12T09-58-08.470159.xlsx</li> <li>GLD360data_forTGFUTC2021-05-15T08-38-22.505997.xlsx</li> <li>GLD360data_forTGFUTC2021-05-16T08-43-35.339273.xlsx</li> <li>GLD360data_forTGFUTC2021-06-20T15-37-51.777130.xlsx</li> <li>GLD360data_forTGFUTC2021-06-21T22-38-57.377719.xlsx</li> <li>GLD360data_forTGFUTC2021-07-22T23-38-31.513009.xlsx</li> <li>GLD360data_forTGFUTC2021-08-16T15-11-40.193070.xlsx</li> <li>GLD360data_forTGFUTC2021-09-24T13-55-59.153000.xlsx</li> <li>GLD360data_forTGFUTC2021-10-05T10-16-04.302001.xlsx</li> <li>GLD360data_forTGFUTC2021-11-09T03-10-44.188748.xlsx</li> <li>GLD360data_forTGFUTC2021-12-04T01-37-23.893950.xlsx</li> <li>GLD360data_forTGFUTC2021-12-06T12-15-46.564243.xlsx</li> <li>GLD360data_forTGFUTC2021-12-12T21-41-33.038999.xlsx</li> <li>GLD360data_forTGFUTC2021-12-13T23-34-18.149995.xlsx</li> <li>GLD360data_forTGFUTC2021-12-22T19-36-38.765547.xlsx</li> <li>GLD360data_forTGFUTC2021-12-28T03-16-31.018224.xlsx</li> <li>GLD360data_forTGFUTC2022-02-16T15-26-20.379956.xlsx</li> <li>GLD360data_forTGFUTC2022-03-09T04-37-21.765997.xlsx</li> <li>GLD360data_forTGFUTC2022-03-11T04-56-30.604005.xlsx</li> <li>GLD360data_forTGFUTC2022-03-17T23-01-55.158520.xlsx</li> <li>GLD360data_forTGFUTC2022-03-26T20-48-39.098469.xlsx</li> <li>GLD360data_forTGFUTC2022-03-27T19-13-33.058448.xlsx</li> <li>GLD360data_forTGFUTC2022-03-30T19-35-55.714452.xlsx</li> <li>GLD360data_forTGFUTC2022-04-20T20-47-17.811510.xlsx</li> <li>GLD360data_forTGFUTC2022-05-03T03-38-25.725991.xlsx</li> <li>GLD360data_forTGFUTC2022-05-13T20-32-32.157110.xlsx</li> <li>GLD360data_forTGFUTC2022-05-13T20-36-38.126223.xlsx</li> <li>GLD360data_forTGFUTC2022-06-15T18-06-04.110702.xlsx</li> <li>GLD360data_forTGFUTC2022-06-24T10-42-13.680445.xlsx</li> <li>GLD360data_forTGFUTC2022-06-25T09-09-44.289205.xlsx</li> <li>GLD360data_forTGFUTC2022-07-20T20-59-28.784931.xlsx</li> </ol> <p> </p> <p><strong>Data </strong><strong>D</strong><strong>escription</strong></p> <p>We have uploaded 86 data files. These are:</p> <ol> <li>The list of 147 TGFs observed by GECAM from December 10, 2020 until August 31, 2022. The file includes information about a) the UTC time of observation, b) the longitude, latitude and altitude of the GECAM position, c) the duration calculated by the Bayesian Block algorithm, d) the number of net counts, e) the hardness ratio (energy limitation 200 keV), f) the CPD/GRD counts ratio. These data were used to produce Figure 1 and Figure 2.</li> <li>The list of 2 typical TEBs and 2 TEB-like events observed by GECAM from December 10, 2020 until August 31, 2022. The file includes information about a) the UTC time of observation, b) the longitude, latitude and altitude of the GECAM position, c) the duration calculated by the Bayesian Block algorithm, d) the CPD/GRD counts ratio, e) the longitude and latitude of the northern and sourthern magnetic footpoint. These data were used to produce Figure 1 and Figure 2.</li> <li>The CPD data of a cosmic-ray event. The CPD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure S1A&S1C.</li> <li>The GRD data of a cosmic-ray event. The GRD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure S1A&S1C.</li> <li>The SimEvt data of a cosmic-ray event. The CPD data include: a) the relative time to reference time (UT 2021-07-05T07:45:41.783530), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure S1A&S1C.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure S1B&S1D.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure S1B&S1D.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-04-26T12:16:34.637228), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure S1B&S1D.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3A.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3A.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-02-01T02:09:25.691512), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3A.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3B.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3B.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T21:19:04.519543), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3B.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3C.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3C.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-01-22T22:24:49.664579), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3C.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3D.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3D.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-07T19:13:49.995485), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3D.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3E.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3E.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-03-29T06:56:37.831848), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3E.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3F.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3F.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-14T09:54:29.177203), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3F.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3G.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3G.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2021-08-16T17:02:27.908009), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3G.</li> <li>The CPD data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 3H.</li> <li>The GRD data of a TGF event. The GRD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 3H.</li> <li>The SimEvt data of a TGF event. The CPD data include: a) the relative time to reference time (UT 2022-03-29T08:56:28.599361), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 3H.</li> <li>The CPD data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4C.</li> <li>The GRD data of a TEB-like event. The GRD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4C.</li> <li>The SimEvt data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-09-11T18:34:40.551997), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4C.</li> <li>The CPD data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4D.</li> <li>The GRD data of a TEB-like event. The GRD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4D.</li> <li>The SimEvt data of a TEB-like event. The CPD data include: a) the relative time to reference time (UT 2021-07-10T01:46:36.709997), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4D.</li> <li>The CPD data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4A.</li> <li>The GRD data of a typical TEB event. The GRD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4A.</li> <li>The SimEvt data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2021-10-27T22:49:33.082008), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4A.</li> <li>The CPD data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. CPD01 to CPD08). Data are used in Figure 4B.</li> <li>The GRD data of a typical TEB event. The GRD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) the deposited energy (keV), c) the event type, d) the time type, e) the detector ID (i.e. GRD01 to GRD25). Data are used in Figure 4B.</li> <li>The SimEvt data of a typical TEB event. The CPD data include: a) the relative time to reference time (UT 2022-07-26T00:16:13.728010), b) The Simultaneous Events Number (SimEvtNum). Data are used in Figure 4B.</li> <li>The specific WWLLN data of the TEB-like event UT 2021-09-11T18:34:40.551997. The WWLLN data include: a) WWLLN Lighning UT Time, b) WWLLN Lighning UNIX Time, c) WWLLN Lighning Longitude (deg) , d) WWLLN Lighning Latitude (deg) , e) WWLLN Lighning Energy (J) , f) WWLLN Lighning Energy Error (J). Data are used in Figure 4E&4F.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-02-22T00:17:18.034461 +/- 1 minute. The GECAM-B nadir (129.7E, 10.9N) of this TGF is located in the east Asia region (EAR, 77E-138E, 13S-30N). The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg) , c) Lighning Peak Current (kA) , d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-07T19:13:49.995436 +/- 1 minute. The GECAM-B nadir (92.2E, 4.7N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-25T09:48:08.785508 +/- 1 minute. The GECAM-B nadir (101.4E, 3.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-03-29T06:56:37.830006 +/- 1 minute. The GECAM-B nadir (105.0E, 2.4S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-04-17T20:10:34.446509 +/- 1 minute. The GECAM-B nadir (131.0E, 2.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>51) The specific GLD360 data near GECAM TGF UT 2021-04-25T23:07:27.616005 +/- 1 minute. The GECAM-B nadir (117.1E, 29.0N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>52) The specific GLD360 data near GECAM TGF UT 2021-04-29T18:12:43.227007 +/- 1 minute. The GECAM-B nadir (77.9E, 5.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-09T19:50:01.720689 +/- 1 minute. The GECAM-B nadir (119.4E, 15.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-10T21:38:43.498955 +/- 1 minute. The GECAM-B nadir (105.2E, 5.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-10T21:43:27.914962 +/- 1 minute. The GECAM-B nadir (119.5E, 3.5S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-12T09:58:08.470159 +/- 1 minute. The GECAM-B nadir (122.8E, 12.9N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-15T08:38:22.505997 +/- 1 minute. The GECAM-B nadir (115.3E, 10.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-05-16T08:43:35.339273 +/- 1 minute. The GECAM-B nadir (103.6E, 8.5N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-06-20T15:37:51.777130 +/- 1 minute. The GECAM-B nadir (128.0E, 21.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-06-21T22:38:57.377719 +/- 1 minute. The GECAM-B nadir (124.4E, 13.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-07-22T23:38:31.513009 +/- 1 minute. The GECAM-B nadir (117.2E, 16.3N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-08-16T15:11:40.193070 +/- 1 minute. The GECAM-B nadir (126.9E, 28.8N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-09-24T13:55:59.153000 +/- 1 minute. The GECAM-B nadir (131.1E, 5.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-10-05T10:16:04.302001 +/- 1 minute. The GECAM-B nadir (115.3E, 10.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-11-09T03:10:44.188748 +/- 1 minute. The GECAM-B nadir (114.8E, 6.6N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-04T01:37:23.893950 +/- 1 minute. The GECAM-B nadir (126.7E, 10.5S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-06T12:15:46.564243 +/- 1 minute. The GECAM-B nadir (128.9E, 10.6N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-12T21:41:33.038999 +/- 1 minute. The GECAM-B nadir (119.0E, 11.2S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-13T23:34:18.149995 +/- 1 minute. The GECAM-B nadir (117.0E, 7.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-22T19:36:38.765547 +/- 1 minute. The GECAM-B nadir (104.2E, 3.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2021-12-28T03:16:31.018224 +/- 1 minute. The GECAM-B nadir (117.7E, 3.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-02-16T15:26:20.379956 +/- 1 minute. The GECAM-B nadir (102.0E, 3.6S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-09T04:37:21.765997 +/- 1 minute. The GECAM-B nadir (109.5E, 5.3S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-11T04:56:30.604005 +/- 1 minute. The GECAM-B nadir (114.3E, 8.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-17T23:01:55.158520 +/- 1 minute. The GECAM-B nadir (120.4E, 9.7S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-26T20:48:39.098469 +/- 1 minute. The GECAM-B nadir (111.5E, 3.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-27T19:13:33.058448 +/- 1 minute. The GECAM-B nadir (113.7E, 5.0S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-03-30T19:35:55.714452 +/- 1 minute. The GECAM-B nadir (102.1E, 4.0N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-04-20T20:47:17.811510 +/- 1 minute. The GECAM-B nadir (104.9E, 1.8S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-05-03T03:38:25.725991 +/- 1 minute. The GECAM-B nadir (109.0E, 11.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>81) The specific GLD360 data near GECAM TGF UT 2022-05-13T20:32:32.157110 +/- 1 minute. The GECAM-B nadir (115.8E, 0.1N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-05-13T20:36:38.126223 +/- 1 minute. The GECAM-B nadir (128.2E, 7.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-15T18:06:04.110702 +/- 1 minute. The GECAM-B nadir (109.7E, 10.9S) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-24T10:42:13.680445 +/- 1 minute. The GECAM-B nadir (116.8E, 10.4N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-06-25T09:09:44.289205 +/- 1 minute. The GECAM-B nadir (127.3E, 13.2N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> <li>The specific GLD360 data near GECAM TGF UT 2022-07-20T20:59:28.784931 +/- 1 minute. The GECAM-B nadir (97.4E, 22.8N) of this TGF is located in the EAR. The GLD360 data include: a) GLD360 Lighning UT Time, b) GLD360 Lighning Longitude (deg) and Latitude (deg), c) Lighning Peak Current (kA), d) Intracloud (IC) Lightning (Cloud=1) or Cloud-to-Ground Lighning (Cloud=0). Data are used in Figure 1B & 1C & 1D.</li> </ol>
Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors
<p>Raw dataset for "<strong>Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors</strong>" by T.Zhang, T.B.Britton.</p> <ul> <li>ArXiv: https://doi.org/10.48550/arXiv.2306.14167</li> </ul> <p>An excel file with metadata of the patterns is included. </p> <p> </p> <p>Details will be updated after acceptance.</p> <p>Processing with the proposed methodology in the paper above requires the AstroEBSD toolbox in MATLAB. This is available on GitHub at https://zenodo.org/record/8078806</p>
Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator
<p>Drift velocity and longitudinal diffusion for the manuscript:</p> <p>Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator</p> <p>https://arxiv.org/abs/2303.13963</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.