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Research data supporting: "Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles"
<p>This repository contains the set of data shown in the paper <strong>"Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles"</strong>, published on The Journal of Physical Chemistry B (DOI:10.1021/acs.jpcb.2c08726).</p>
Supporting materials for analysis of global emissions from rivers and streams
<p>These are the raw files to run the analysis detailed here: https://github.com/rocher-ros/RiverMethaneFlux. Further instructions of use can be find in the code repository. </p> <p>The results of this analysis are a global gridded product of methane river concentrations and fluxes, which can be found in: https://doi.org/10.5281/zenodo.8108959.</p> <p>The journal article based on the results of this analysis is currently in press in Nature (https://doi.org/10.1038/s41586-023-06344-6)</p>
Supporting data for "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, digitized observation-summary tables, and Python scripts for reproducing the analysis results/ figures presented in "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information. </p>
Supporting data for "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning"
<p>This repository contains the datasets created and extracted for the paper:</p> <p>Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022.<br> "<strong>CoVEffect</strong>: Interactive System for Mining the <strong>Effects of SARS-CoV-2 Mutations and Variants</strong> Based on Deep Learning". (Available online at http://gmql.eu/coveffect)</p> <p>--------------------------------------------------------------------------------<br> LIST OF FILES WITH DESCRIPTION:<br> --------------------------------------------------------------------------------</p> <p>AdditionalFile1-effects-taxonomy:<br> Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy.</p> <p>AdditionalFile2-levels-taxonomy:<br> Descriptions of legal values for the 'Level' field.</p> <p>AdditionalFile3-training_dataset_target:<br> List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple).</p> <p>AdditionalFile4-validation_dataset_target:<br> List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile5-validation_dataset_highlighted:<br> Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow.</p> <p>AdditionalFile6-validation_dataset_prediction:<br> List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p> <p>AdditionalFile7-keywords_query_list:<br> Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'.</p> <p>AdditionalFile8-CORD-19_batch_dataset_metadata:<br> Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7.<br> These abstracts have been annotated by the prediction framework.</p> <p>AdditionalFile9-CORD-19_batch_dataset_prediction:<br> List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19.</p> <p>AdditionalFile10-test_dataset_target:<br> List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile11-test_dataset_prediction:<br> List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p>
Supporting data for manuscript "Chemical and visual characterisation of EGRIP glacial ice and cloudy bands within "
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Chemical and visual characterisation of EGRIP glacial ice and cloudy bands within" accepted for publication in "The Cryosphere" (https://doi.org/10.5194/tc-2022-250). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 48Ti, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file. These maps were obtained using a 20 µm square spot. Samples are referred to using the original identifier of the manuscript, e.g. S2 for sample 2 from 1367.05 m depth.</p>
Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"
<p><strong>Description</strong></p> <p>In this work, we use few-shot learning to segment the body and vein architecture of <em>P. trichocarpa</em> leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy and retain biologically realistic features. The leaf and vein segmentations are compared against a U-Net baseline model, and subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the "vein density" trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code, we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes.</p> <p><strong>Directories:</strong></p> <pre><code>Few-shot learning for p. trichocarpa leaf traits ├── data │ ├── genomes │ │ ├── Ptri_V4_Nisq1.[...].bed │ │ ├── Ptri_V4_Nisq1.[...].bim │ │ └── Ptri_V4_Nisq1.[...].fam │ ├── images │ │ └── *.jpeg │ ├── leaf_masks │ │ └── *.png │ ├── leaf_preds │ │ └── *.png │ ├── leaf_unet_preds │ │ └── *.png │ ├── results │ │ ├── digital_traits.tsv │ │ ├── gwas_results.csv │ │ ├── manual_traits.tsv │ │ ├── vein_density_blups.tsv │ │ └── vein_density_tps_adj.tsv │ ├── vein_bce_preds │ │ └── *.png │ ├── vein_bce_probs │ │ └── *.png │ ├── vein_fl_preds │ │ └── *.png │ ├── vein_fl_probs │ │ └── *.png │ ├── vein_masks │ │ └── *.png │ ├── vein_unet_bce_preds │ │ └── *.png │ ├── vein_unet_bce_probs │ │ └── *.png │ ├── vein_unet_fl_preds │ │ └── *.png │ ├── vein_unet_fl_probs │ │ └── *.png ├── figures │ └── *.png ├── logs │ ├── leaf_tracer_256.txt │ ├── leaf_unet_256.txt │ ├── vein_grower_bce_128.txt │ ├── vein_grower_fl_128.txt │ ├── vein_unet_bce_128.txt │ └── vein_unet_fl_128.txt ├── models │ ├── BuildCNN.py │ ├── BuildUNet.py │ ├── LeafTracer.py │ └── VeinGrower.py ├── notebooks │ ├── Figures.ipynb │ ├── GrowerInference.ipynb │ ├── GrowerTraining.ipynb │ ├── TracerInference.ipynb │ ├── TracerTraining.ipynb │ ├── UNetLeafSegmentation.ipynb │ └── UNetVeinSegmentation.ipynb ├── utils │ ├── GetLowestGPU.py │ ├── ImageLoader.py │ ├── LeafGenerator.py │ ├── ModelWrapperGenerator.py │ ├── TimeRemaining.py │ ├── TraceInitializer.py │ ├── UNetTileGenerator.py │ └── VeinGenerator.py └── weights ├── leaf_tracer_256_best_val_model.save ├── leaf_unet_256_best_val_model.save ├── vein_grower_bce_128_best_val_model.save ├── vein_grower_fl_128_best_val_model.save ├── vein_unet_bce_128_best_val_model.save └── vein_unet_fl_128_best_val_model.save </code></pre> <p><strong>Data:</strong></p> <p>The <code>data</code> folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, <code>C_1_1_2_bot.jpeg</code>, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs.</p> <ul> <li>The <code>images</code> folder includes the 2,906 high-resolution leaf scans taken in the field.</li> <li>The <code>leaf_masks</code> folder includes 50 ground truth segmentations used for training the leaf tracing algorithm.</li> <li>The <code>leaf_preds</code> folder includes the 2,906 predicted segmentations from the leaf tracing algorithm.</li> <li>The <code>leaf_unet_preds</code> folder includes the 2,906 predicted segmentations from the U-Net model for leaf segmentation.</li> <li>The <code>vein_masks</code> folder includes 8 ground truth segmentations used for training the vein growing algorithm.</li> <li>The <code>vein_*_preds</code> folder includes the 1,453 predicted segmentations from the vein growing algorithm, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_*_probs</code> folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_preds</code> folder includes the 1,453 predicted segmentations from the U-Net model for vein segmentation, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>vein_unet_*_probs</code> folder includes the 1,453 predicted probability maps from the U-Net model for vein segmentation before thresholding, where * specifies the loss function (bce: binary cross-entropy, fl: focal loss).</li> <li>The <code>genomes</code> folder includes the set of SNPs called against the v4 <em>P. trichocarpa</em> genome for 1,419 genotypes with a README file detailing the steps taken.</li> <li>The <code>results</code> folder includes <ul> <li>Raw values of the 68 predicted leaf traits in <code>digital_traits.tsv</code></li> <li>Manually measured values of petiole length and width in <code>manual_traits.tsv</code></li> <li>Thin plate spline (TPS) adjusted values of the vein density trait in <code>vein_density_tps_adj.tsv</code></li> <li>Best linear unbiased prediction (BLUP) adjusted values of the vein density trait in <code>vein_density_blups.tsv</code></li> <li>GWAS results for the vein density trait, including chromosome positions and corresponding P values, in <code>gwas_results.csv</code></li> </ul> </li> </ul> <p><strong>Figures:</strong></p> <p>The <code>figures</code> folder includes all figures and videos used in the manuscript. See <code>notebooks/Figures.ipynb</code> for the methods used to generate these figures.</p> <p><strong>Logs:</strong></p> <p>The <code>logs</code> folder includes logs of CNN convergence for the training and validation sets during model training for the leaf tracing CNN vein growing CNN, and U-Net models. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN).</p> <p><strong>Models:</strong></p> <p>The <code>models</code> folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time.</p> <ul> <li><code>BuildCNN.py</code> defines the CNN architecture for leaf tracing or vein growing, with user-specified input shape, output shape, layers, and output activation functions.</li> <li><code>BuildUNet.py</code> defines the U-Net architecture for leaf and vein segmentation, with user-specified input/output shape, layers, and output activation functions.</li> <li><code>LeafTracer.py</code> defines the leaf tracing algorithm at inference time.</li> <li><code>VeinGrower.py</code> defines the vein growing algorithm at inference time.</li> </ul> <p><strong>Notebooks:</strong></p> <p>The <code>notebooks</code> folder includes Jupyter notebooks used for model training, model inference, and figure generation.</p> <ul> <li><code>Figures.ipynb</code> is used to generate all of the manuscript figures.</li> <li><code>GrowerTraining.ipynb</code> is used to train the vein growing CNN.</li> <li><code>GrowerInference.ipynb</code> is used to apply the vein growing algorithm to the 1,453 leaf bottom images.</li> <li><code>TracerTraining.ipynb</code> is used to train the leaf tracing CNN.</li> <li><code>TracerInference.ipynb</code> is used to apply the leaf tracing algorithm to the 2,906 leaf top and bottom images.</li> <li><code>UNetLeafSegmentation.ipynb</code> is used to train and apply U-Net for leaf segmentation.</li> <li><code>UNetVeinSegmentation.ipynb</code> is used to train and apply U-Net for vein segmentation.</li> </ul> <p><strong>Utils:</strong></p> <p>The <code>utils</code> folder includes utility scripts implemented in Python that assist in model training and inference.</p> <ul> <li><code>ImageLoader.py</code> loads image/mask pairs for sampling training/validation tiles.</li> <li><code>LeafGenerator.py</code> generates inputs/outputs for the leaf tracing CNN.</li> <li><code>VeinGenerator.py</code> generates inputs/outputs for the vein growing CNN.</li> <li><code>UNetTileGenerator.py</code> generates inputs/outputs for the U-Net model.</li> <li><code>GetLowestGPU.py</code> identifies available GPUs using the <code>nvidia-smi</code> command and selects the one with lowest memory usage, if none available the device is set to CPU.</li> <li><code>ModelWrapperGenerator.py</code> wraps the PyTorch CNN and data loaders with similar functionality to the Keras Model class in TensorFlow (e.g., model.fit(...)).</li> <li><code>TimeRemaining.py</code> is used by the model wrapper to estimate remaining time left per epoch.</li> <li><code>TraceInitializer.py</code> is used by the tracing algorithm at inference time to initialize the leaf trace using automatic thresholding.</li> </ul> <p><strong>Weights:</strong></p> <p>The <code>weights</code> folder includes the CNN parameters from the epoch resulting in the best validation error. The file names include the model, loss function (bce: binary cross-entropy, fl: focal loss), and size of the input window for each method (e.g., 128 for the vein growing CNN). The weights are loaded into the CNN models for inference.</p> <p><strong>Citation:</strong></p> <pre><code>@article{ doi:10.34133/plantphenomics.0072, author = {John Lagergren and Mirko Pavicic and Hari B. Chhetri and Larry M. York and Doug Hyatt and David Kainer and Erica M. Rutter and Kevin Flores and Jack Bailey-Bale and Marie Klein and Gail Taylor and Daniel Jacobson and Jared Streich }, title = {Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa}, journal = {Plant Phenomics}, volume = {0}, number = {ja}, pages = {}, year = {}, doi = {10.34133/plantphenomics.0072}, URL = {https://spj.science.org/doi/abs/10.34133/plantphenomics.0072}, eprint = {https://spj.science.org/doi/pdf/10.34133/plantphenomics.0072}, }</code></pre>
Supporting Data Ferguson, Camenzind, et al., "Measurement-induced induced population switching", Phys. Rev. Research 5, 023028 (2023)
<p>This repository contains data for the publication "Measurement-induced population switching", Phys. Rev. Research 5, 023028 (2023) by Ferguson, Camenzind, <em>et al</em>.</p> <p><strong>Abstract</strong></p> <p>Quantum information processing is a key technology in the ongoing second quantum revolution, with a wide variety of hardware platforms competing toward its realization. An indispensable component of such hardware is a measurement device, i.e., a quantum detector that is used to determine the outcome of a computation. The act of measurement in quantum mechanics, however, is naturally invasive as the measurement apparatus becomes entangled with the system that it observes. This always leads to a disturbance in the observed system, a phenomenon called quantum measurement backaction, which should solely lead to the collapse of the quantum wave function and the physical realization of the measurement postulate of quantum mechanics. Here we demonstrate that backaction can fundamentally change the quantum system through the detection process. For quantum information processing, this means that the readout alters the system in such a way that a faulty measurement outcome is obtained. Specifically, we report a backaction-induced population switching, where the bare presence of weak, nonprojective measurements by an adjacent charge sensor inverts the electronic charge configuration of a semiconductor double quantum dot system. The transition region grows with measurement strength and is suppressed by temperature, in excellent agreement with our coherent quantum backaction model. Our result exposes backaction channels that appear at the interplay between the detector and the system environments, and opens new avenues for controlling and mitigating backaction effects in future quantum technologies.</p>
Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"
<p>This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.</p>
Data supporting Tutorial: EPI Distortion Correction
<p>This data support Tutorial: EPI Distortion Correction by Barbara Dymerska for the Weekend Course during the ISMRM 2023 session: Artifacts & Correction Strategies on Sunday, 04 June 2023:</p> <p>https://github.com/fil-physics/ISMRM2023_educational</p> <p> </p>
CMIP5 and CMIP6 post-processed AMOC and MLD data supporting Jesse et al. 2023
<p>These are the datasets supporting the paper "Why is CMIP6 projecting larger ocean dynamic sea level in the North Sea than CMIP5?" from Jesse et al. submitted to ERL.</p> <p>See the paper for more information about the data and GitHub for the code that generated the data: https://github.com/dlebars/CMIP_SeaLevel</p> <p>The Atlantic meridional overturning circulation (AMOC) is computed at two latitudes 26N and 35N from different CMIP variables:</p> <p>cmip5_amoc is computed from the variable "msftmyz".</p> <p>cmip6_amoc is computed from the variable "msftmz" or "msftyz" as indicated in the name of the file.</p> <p>cmip5_amoc_vo and cmip6_amoc_vo are computed from the meridional ocean velocity ("vo").</p> <p>For mixed layer depth the variable "mlotst" is used.</p> <p> </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>
Processed data supporting figures in Yang et al. 2023: Oceanic eddies induce a rapid formation of an internal wave continuum
<p>This data repository supports a manuscript by Luwei Yang, Roy Barkan, Kaushik Srinivasan, James C. McWilliams, Callum J. Shakespeare, and Angus H. Gibson, submitted to <em>Communications Earth & Environment</em>. This repository contains the processed data that support the figures in the manuscript. </p>
Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes
<p>Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes".</p> <p><strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: <a href="https://github.com/MrOlm/ZenodoREADME/blob/main/README.md">https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</a></strong></p>
CALIPR Framework Myelin Water Imaging Supporting Data
<p>Data supporting the publication:</p> <p><em><strong>The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity</strong></em></p> <p> </p>
Supporting Information Files
<p>The database contains supporting information files for the research paper entitled: "The uncertainty related to the inexactitude of prioritization based on consistent pairwise comparisons". The files contain the applied simulation algorithm scheme and all datasets generated during Monte Carlo simulations and meticulously examined in the published research paper.</p>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p>
Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.
<p>Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023. </p> <p>Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript. </p> <p>Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients. </p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Data supporting Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland
<p>Data supporting the paper:</p> <blockquote> <p>Land-Miller, H., A. Roos, M. Simon, R. Dietz, C. Sonne, S. Pedro, A. Rosing-Asvid, F. Rigét, and M. McKinney. 2023. Comparison of feeding niches between Arctic and northward moving sub-Arctic marine mammals in Greenland. Marine Ecology Progress Series.</p> </blockquote> <p>This data is in five files:</p> <p>1. <strong>greenland_marmam_metadata.csv</strong> contains metadata for all samples used in this project, including sample identifiers:</p> <ul> <li><em>Sample: </em>unique sample ID per individual animal</li> <li><em>Species</em></li> </ul> <p>and details of collection, including <em>Year, Location </em>(general area), <em>Lat, </em><em>Long, </em>and<em> </em><em>Date. </em>It also includes other data on the animal (<em>Sex, Age, Length</em>), when available, as well as the co-author who provided the sample to the project (<em>Sample sender</em>) and the tissues available/analyzed for each individual (<em>Tissues received</em>).</p> <p>2. <strong>all_sample_locations.csv</strong> includes latitude/longitude of each sample for mapping. Latitude and longitude are consistent with the full metadata file when coordinates were available, and estimated based on general sampling area (<em>Location </em>or <em>Area</em>) when not. The variable <em>estimate</em><strong> </strong>denotes samples for which coordinates were estimated.</p> <p>3. <strong>fatty_acids_greenland_marmams.csv </strong>contains fatty acid data for all samples. Variables <em>8:00</em> to <em>24:1n9</em> represent the proportion of each individual fatty acid, out of total fatty acids in that sample. Data are represented as whole number percents (i.e., 10 = 10% and all fatty acids sum to 100 for each sample). </p> <p>4. <strong>CNS_greenland_McGill.csv</strong> contains bulk stable isotope data for all samples analyzed at McGill. In addition to <em>Sample</em> and <em>Species</em>, this includes:</p> <ul> <li><em>treatment</em>: whether a sample was lipid-extracted (<em>LE</em>) or non-lipid-extracted (<em>nLE</em>) prior to analysis</li> <li><em>d15N</em>: stable isotope ratio δ<sup>15</sup>N</li> <li><em>d13C: </em>stable isotope ratio δ<sup>13</sup>C</li> <li><em>d34S: </em>stable isotope ratio δ<sup>34</sup>S</li> <li><em>perc.C: </em>mass percent of carbon in the sample</li> <li><em>perc.N: </em>mass percent of nitrogen in the sample</li> <li><em>perc.S: </em>mass percent of sulfur in the sample</li> <li><em>C.N.ratio: </em>mass ratio of carbon to nitrogen in the sample </li> </ul> <p>5. <strong>CN_greenland_nLE_copenhagen.csv</strong> contains stable isotope data for non-lipid-extracted samples analyzed at the University of Copenhagen for δ<sup>13</sup>C and δ<sup>15</sup>N. Variables <em>treatment</em>, <em>d13C</em>, and <em>d15N</em> are consistent with CNS_greenland_McGill.csv. </p>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.