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zenodo44/100

Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'

Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.

opencc-by-4.0Aug 2022View details →

zenodo44/100

Datasets and supplemental information accompanying scANANSE

This repository contains supplementary files from the paper: "scANANSE: gene regulatory network and motif analysis of single-cell clusters" as well as several accompanying datasets. The supplementary files:  -        Install_Rstudio.pdf -        AnanseScanpy_equivalent.pdf The pre-processed Seurat object and the two pre-processed Scanpy objects can be used to run the scANANSE pipeline with: -        preprocessed_PBMC.Rds -        rna_PBMC.h5ad -        atac_PBMC.h5ad Additional raw data, used to construct the pre-processed objects, supplemented from:  https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz, https://cf.10xgenomics.com/samples/cell-arc/1.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz.tbi, https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seura

opencc-by-4.0Dec 2022View details →

zenodo44/100

The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0

Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059 At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued. Lambert Schomaker - February 2023 _______________________________________________________________________________ The ICDAR 2003 Informal Competition for the Recognition of On-line Words:                The Unipen-ICROW-03 benchmark set                 Version 0.0 Lambert Schomaker / International Unipen Foundation The ICROW suite of test files for the recognition of isolated on-line free-style (handprint, mixed and cursive) words has been composed. Different tablets, nationalities and languages are involved. Only the ASCII set is used within word labels. The set contains:    13119 written words      884 unique lexical word entries       72 writers  Language: Dutch, English, Italian. Nationalities: Dutch, Irish, Italian, + mixed The benchmark test is a good estimator for  "walk-up" recognition performance. [Note: some of the writers (NIC-Pc95*.dat set) are present in the UNIPEN R01/V07 distribution, but the actual words are unseen  outside of the Int. Unipen Foundation.] Please note the Copyright notice in the  accompanying file 'Copyright' Wed Jul 16 21:20:10 CEST 2003 Lambert Schomaker --------------------------------------------------------------------------- Instructions for the ICDAR 2003 informal competition for the recognition of on-line words. 1 - unpack the .tgz file 2 - use the UNIPEN files as input for your recognizer. 3 - report, for each writer, a file <writer-id>.res   Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res Format of the .res file. No XML for this moment: simplicity does it. We assume that the recognizer is able to produce a top-10 list of likely words, sorted from most likely to least likely. The output for each word is on a single line. The correct target word is in the first column. <targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp> Example with two words: summertime   slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature Aberdeen     Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide 4 - pack the  *.res files in a .tgz or .zip file and send them     to schomaker@ai.rug.nl     All *.dat files need to be processed. LS.

opencc-by-4.0Jul 2003View details →

zenodo44/100

IRIS preprocessed data used in paper "Multi variables time series information bottleneck"

Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre. Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a> The selected data was labeled using these definitions: QS: Quiet Sun AR: Active Regions of the Sun FL: Flare A time series is labeled QS when every single time step refer to a quiet sun activity. When a given time series is partially composed of flaring events, the global time series is labeled as FL. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å). Each sample is given a 'position' which is a list of length 4: position[1] is a string that gives the name of the event position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info : Type: .npz Size: 11.89GB *** Key: 'data_TR_QS' ndarray data of length 2467 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_FL' ndarray data of length 1055 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_QS' ndarray data of length 325 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_FL' ndarray data of length 714 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_QS' ndarray data of length 1428 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_AR' ndarray data of length 792 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_FL' ndarray data of length 356 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR' ndarray data of length 4564 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL' ndarray data of length 2081 containing np.ndarray of shapes ['various', 240] *** Key: 'data_TE' ndarray data of length 2576 containing np.ndarray of shapes ['various', 240]   *** Key: 'position_TR_QS' ndarray data of length 2467 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_FL' ndarray data of length 1055 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_QS' ndarray data of length 325 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_FL' ndarray data of length 714 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_QS' ndarray data of length 1428 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_AR' ndarray data of length 792 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_FL' ndarray data of length 356 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR' ndarray data of length 4564 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL' ndarray data of length 2081 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE' ndarray data of length 2576 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}

opencc-by-4.0Jan 2023View details →

zenodo44/100

Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity

The data present in this repository reflect intermediate and processed data presented in the manuscript, Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity. This manuscript is still under review; as such, this page will be updated upon publication.   Manuscript Abstract: Evolutionary perspectives on the deployment of immune factors following infection have been shaped by studies on a limited number of biomedical model systems with a heavy emphasis on vertebrate species. Though their contributions to contemporary immunology cannot be understated, a broader phylogenetic perspective is needed to understand the evolution of immune systems across Metazoa. In our study, we leverage differential gene expression analyses to identify genes implicated in the antiviral immune response of the acorn worm hemichordate, Saccoglossus kowalevskii, and place them in the context of immunity evolution within deuterostomes – the animal clade composed of chordates, hemichordates, and echinoderms. Following acute exposure to the synthetic viral dsRNA analog, poly(I:C), we show that S. kowalevskii responds by regulating the transcription of genes associated with canonical innate immunity signaling pathways (e.g., NF-κB and IRF signaling) and metabolic processes (e.g., lipid metabolism), as well as many genes without clear evidence of orthology with those of model species. Aggregated across all experimental time point contrasts, we identify 423 genes that are differentially expressed in response to poly(I:C). We also identify 147 genes with altered temporal patterns of expression in response to immune challenge. By characterizing the molecular toolkit involved in hemichordate antiviral immunity, our findings provide vital evolutionary context for understanding the origins of immune systems within Deuterostomia.   Repository contents: ### Processed Data ### <ul> <li>Full_DESeq2_matrix.csv --> DESeq2 results for each contrast (e.g., 2hpi treatment vs. control)</li> <li>MaSigPro.Clusters.csv --> Mean expression for each gene placed within a pDEG cluster</li> <li>MaSigPro.SigGenes.TreatmentvsControl.Robj --> T.fit() R-object output from MaSigPro pipeline. This can be opened in R using the load() function.</li> </ul> ### Homology Assessment ### <ul> <li>Orthofinder.tar.gz --> OrthoFinder results</li> <li>Skowalevskii_Genome_Annotation.SPHuman_and_HOG.csv --> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-Human" and "HOG" output described in the main text of the paper</li> <li>Skowalevskii_Genome_Annotation.SPPANTHER.csv --> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-SwissProt" output described in the main text of the paper</li> </ul> ### Functional Annotation ### <ul> <li>Skow.HMMER_Pfam.domtblout.tsv --> Pfam annotation of the Skow1.1 genome assembly in HMMER's domblout format</li> <li>Skow.KofamKOALA.detail.tsv --> KO annotation of the Skow1.1 genome assembly using KofamKOALA (detailed output)</li> <li>Skow.KofamKOALA.detail.tsv --> KO annotation of the Skow1.1 genome assembly using KofamKOALA (mapper output)</li> <li>SkowAnnotations.GO.tsv --> GO annotation of the Skow1.1 genome assembly</li> <li>SkowAnnotations.PF.tsv --> PF annotation of the Skow1.1 genome assembly</li> <li>SkowAnnotations.PP.tsv --> PP annotation of the Skow1.1 genome assembly</li> </ul> ### Enrichment Data ### <ul> <li>DESeqEnrichments.tsv --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by DESeq2.</li> <li>MaSigProEnrichments.tsv --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by MaSigPro.</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo44/100

Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130

The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products. This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from: GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 . the first 10 lines of gbifid.tsv.gz as extracted via <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> are: <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via: https://gbif.org/occurrence/2997162320 and https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771 respectively. This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them.  In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it.  Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .

opencc-zeroMar 2023View details →

zenodo44/100

Dissemination of information in event-based surveillance, a case study of Avian Influenza - dataset

This dataset contains a set of tables corresponding to the manual analysis of outbreak-related reports detected by two event-based surveillance tools, PADI-web and HealthMap, supporting the submitted article "Dissemination of information in event-based surveillance, a case study of Avian Influenza". The reports were published between 1 July 2018 and 30st June 2019 and described one or several avian influenza outbreaks.  We collected 337 reports from PADI-web and 115 from HealthMap. Two epidemiologists identified all the reported events in the news, and classified them as official (notified to the World Organization for Animal Health) or non-official. In order to trace back the source of the event’s information, the epidemiologist manually traced the information pathway of all events mentioned in the PADI-web and HealthMap news. The pathway was deducted from the sources cited in the news. When a source was cited with a hyperlink, we followed the hyperlink to retrace the information pathway as far as possible to the primary source. For each cited source, we created a pair of emitter SE and receptor sources SR. We labelled each new source with its type (e.g. online news source, national veterinary authority, etc.). We also recorded their geographical focus (local, national or international) and their specialization in the animal health news coverage (general or specialized). The script for data analyses is available at https://github.com/SarahVal/EBS-network.

opencc-by-4.0Jul 2022View details →

zenodo44/100

Lepidoptera genomics based on 88 chromosomal reference sequences informs population genetic parameters for conservation

This repository contains (1) germline mutations called by the DeepVariant (v1.1.0) pipeline in VCF format; (2) rejected substitution scores calculated by the Genomic Evolutionary Rate Profiling (GERP++) software on each species and chromosome; and (3) the phylogenetic tree used as guide tree in the Cactus alignment.

opencc-by-4.0Apr 2023View details →

zenodo44/100

Revised database of the Soil Information System of Latin America and the Caribbean, SISLAC version 1.2

The SISLAC_database version 1.2 contains the revised version of the SISLAC database in comma-separated values (csv) format. This database was reviewed and the inconsistencies found in the profiles and in the description of their horizons were corrected.  The key field between both tables is the profile identifier, column profile_id.

opencc-byApr 2023View details →

zenodo44/100

Supporting information for "Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa"

Description In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa 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 P. trichocarpa genome for 1,419 genotypes. Directories: <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> Data: 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. <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 P. trichocarpa 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> Figures: 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. Logs: 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). Models: The <code>models</code> folder includes the CNN implementations in PyTorch as well as the leaf tracing and vein growing algorithms at inference time. <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> Notebooks: The <code>notebooks</code> folder includes Jupyter notebooks used for model training, model inference, and figure generation. <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> Utils: The <code>utils</code> folder includes utility scripts implemented in Python that assist in model training and inference. <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> Weights: 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. Citation: <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>

opengpl-2.0Jan 2023View details →

zenodo44/100

Simulation output for Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study

This repository contains the (compressed) simulation output from Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study. On a Linux-based system, extract the contents with: <pre><code class="language-bash">tar -xvzf raw_output_compressed.tar.gz </code></pre> Please refer to the published article (https://doi.org/10.3389/fpsyg.2023.1253452) and the associated GitHub (<a href="https://github.com/carldelfin/stability-of-bivariate-correlations">github.com/carldelfin/stability-of-bivariate-correlations</a>) for additional information.

opencc-by-4.0May 2023View details →

zenodo44/100

BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding

Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at <a href="https://sulab-sever.u-aizu.ac.jp/ACNS2023/">ACNS-2023</a>. The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board. The file is a numpy array stored using the np.save API. The file can be directly used for running the notebooks provided in the <a href="https://github.com/benoitgerard/sca-bike">publication github</a>.

opencc-by-4.0Jun 2023View details →

zenodo44/100

SeaLiT Ontology - An extension of CIDOC-CRM for the modelling of Maritime History information

The SeaLiT Ontology is a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous information related to maritime history. It aims at providing the semantic definitions needed to transform disparate, localised information sources of maritime history into a coherent global resource. It also serves as a common language for domain experts and IT developers to formulate requirements and to agree on system functionalities with respect to the correct handling of historical information. The ontology uses and extends the <a href="https://www.cidoc-crm.org/">CIDOC Conceptual Reference Model</a> (ISO 21127:2014), in particular version 7.2.1, as a general ontology of human activity, things and events happening in space and time. The ontology has been developed following a bottom-up process from primary data collected in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). SeaLiT is an international research project, funded by the ERC Starting Grant 2016, which explores the transition from sail to steam navigation and its effects on seafaring populations in the Mediterranean and the Black Sea between the 1850s and the 1920s. More information about the construction of the SeaLiT Ontology, the considered data sources, as well as their transformation to a knowledge graph using the SeaLiT Ontology, can be found in the following papers: <blockquote> P. Fafalios, A. Kritsotaki, and M. Doerr, "The SeaLiT Ontology – An Extension of CIDOC-CRM for the Modeling and Integration of Maritime History Information". ACM Journal on Computing and Cultural Heritage, 2023. <a href="https://doi.org/10.1145/3586080">https://doi.org/10.1145/3586080</a> [<a href="https://arxiv.org/pdf/2301.04493.pdf">pdf</a>, <a href="https://users.ics.forth.gr/~fafalios/files/bibs/fafaliosSeaLiTOntology2023.bib">bib</a>] </blockquote> <blockquote> P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, and M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>] </blockquote> The (resolvable) namespace of the ontology is: <a href="http://www.sealitproject.eu/ontology/">http://www.sealitproject.eu/ontology/</a> An OWL implementation of the ontology is available at: <a href="https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl">https://sealitproject.eu/ontology/SeaLiT_Ontology_v1.2.owl</a> Knowledge graphs that make use of the SeaLiT Ontology are available at: <a href="https://zenodo.org/record/6460841">https://zenodo.org/record/6460841</a>. These RDF datasets integrate information of 16 different types of archival sources related to maritime history, including crew lists, payrolls, registers of different types, censuses, and employment records.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Supporting Information for "The First GECAM Observation Results on Terrestrial Gamma-ray Flashes and Terrestrial Electron Beams"

Additional Supporting Information <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>   Data Description We have uploaded 86 data files. These are: <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>

opencc-by-4.0Dec 2022View details →

zenodo44/100

The Upper Bound of Information Diffusion in Code Review

More details on <a href="https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review">https://github.com/michaeldorner/information-diffusion-boundaries-in-code-review</a>

opencc-by-4.0May 2023View details →

zenodo44/100

Supporting Information Files

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.

opencc-by-4.0Jun 2023View details →

zenodo44/100

NewSOC, supplementary information to WT2.5.4 "Cells with honeycomb structured oxygen electrodes": electrochemical and post-mortem data sets.

These are data set, related to participation of the IEN in project NewSOC. It includes results of the SEM-EDS analysis of the cells with hexagonal current collecting net and electrochemical performance data, both EIS and C-V characteristics. Results are grouped in zip archives, named according to cell design, “infill-net”. Compositions: LNF – LaNi0.6Fe0.4O3 (net) LSC – La0.6Sr0.4CoO3-d (infill) LSF – La0.5Sr0.5FeO3-d (infill) LSCCF – La0.6Sr0.4Co0.15Cu0.05Fe0.8O3-d (net) PCM – Pr0.5Ca0.5MnO3 (net) BSCFM – Ba0.5Sr0.5Co0.725Fe0.2Mo0.075O3-d (infill)   Data presentation. C-V characteristics: This is text files, generated by Zahner galvanostat, with self-decriptional titles. “05iv_650c_100h2+100h2o_500air.txt” –  measurement at 650°C, 100 mL/min H2 and 100 mL/min H2O on fuel side, 500 mL/min of air on air side. EIS data: EIS results were extracted from proprietary binary files, generated by Zahner galvanostat, and raw data is generally meaningless except the owners of such hardware. So, extracted EIS can be found in Excel files, used in analysis, in sheet “Experimental”. Other sheets in xlsx include some metadata (“info”), results of the equivalent circuit fit (“fit”) and some plots. Fit results might not be relevant.   SEM Post-mortem results presented as SEM images (tif or jpg files) and pdf files with results of the EDS analysis. LSC-LNF (air flow 1 L/min, current density 0.25 A/cm2) test_1_07:   03_eis20200917142808.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H2; 05_eis20200917123105.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 08_eis20200917123857.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 10_eis20200917131537.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; test_1_08: 03_eis20200917125707.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H2; 09_eis20200917130019.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.09 H2+ 0.21 H2O; 10_eis20200917130600.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.06 H2+ 0.14 H2O; 12_eis20200917130737.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.12 H2+ 0.28 H2O; test_3_14: 01_eis20210517102042.xlsx– EIS, SOFC, 700°C, Flows L/min: F:0.1 H2+0.1 N2; 05_eis20210517102625.xlsx– EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 07_eis20210517102305.xlsx– EIS, SOEC, 700°C, flows L/min: F:0.06 H2+ 0.14 H2O; SEM (post-mortem after test_1_07) ogniwo_310_2020  ****.jpg - surface   BSCMF-PCM (SOFC, air flow 0.5 L/min) test_2_14 01eis_700c_cc4a_100h2+100n2_500air_eqc20220110112836.xlsx –700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02eis_700c_cc4a_100h2+100h2o_500ai_eqc20220110112724.xlsx –700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04eis_650c_cc3a_100h2+100h2o_500ai_eqc20220110112503.xlsx–650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06eis_650c_cc3a_100h2+100n2_500air_eqc20220110112332.xlsx –650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 07eis_625c_cc3a_100h2+100n2_500air_eqc20220110112214.xlsx –625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 08eis_625c_cc3a_100h2+100h2o_500ai_eqc20220110111834.xlsx–625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; test_1_67 01_eqc20220831134628.xlsx–700°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02_eqc20220831134724.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04_eqc20220831135442.xlsx–650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 06_eqc20220831135622.xlsx - 650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08_eqc20220831135801.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 09_eqc20220831135933.xlsx –650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; SEM (post-mortem of the test_2_14) 493-2021-1.pdf, 493-2021-1i.pdf, 493-2021-2.pdf, 493-2021-2-2.pdf, 493-2021-2-3.pdf –cross-sections with EDS 493_2021_*_**.tif  - cross-sections   BSCMF-LSCCF (SOFC, air flow 0.5 L/min)                                test_2_08 01_eis20211104144342.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 03eis_700c__eis20211108102241.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04eis_700c__eis20211108102401.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08eis__eis20211110094642.xlsx - 650°C, 0.1875  A/cm2 Flows L/min: F:0.1 H2+0.1 N2 10eis__eis20211110094945.xlsx - 650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 11eis__eis20211110101358.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 13eis__eis20211110100837.xlsx- 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2;                                test_2_26 04_eqc20220817114103.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06_eqc20220817121529.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 07_eqc20220802084119.xlsx - 650°C, 0.1875  A/cm2 Flows L/min: F:0.1 H2+0.1 08_eqc20220802084616.xlsx - 650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 10_eqc20220802150705.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 11_eqc20220802150108.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; SEM (post-mortem of the test_2_26) 661_BSCMF_LSCCF_p.pdf  – cross-section with EDS 661_BSCMF_LSCCF_PM.pdf - surface with EDS 661_BSCMF_LSCCF_p_01.tif, 661_BSCMF_LSCCF_p_02.tif, 661_BSCMF_LSCCF_p_03.tif, 661_BSCMF_LSCCF_p_04.tif  -  cross-section, infill zone 661_BSCMF_LSCCF_p_05.tif, 661_BSCMF_LSCCF_p_06.tif - cross-section, net zone zone 661_BSCMF_LSCCF_PM_**.tif  - surface   LSF-LSCCF (SOFC, air flow 0.5 L/min) test_1_65 01_eqc20220816105347.xlsx - 700°C, 0. 1875 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 02_eqc20220816105941.xlsx - 700°C, 0. 1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04_eqc20220816112311.xlsx - 650°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06_eqc20220816111640.xlsx - 650°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 07_eqc20220816142436.xlsx- 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 08_eqc20220816142842.xlsx-625°C, 0.625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; SEM (post-mortem) 663_LSF_LSCCF_p.pdf – cross-section with EDS 663_LSF_LSCCF_PM.pdf - surface with EDS 663_LSF_LSCCF_P_GR_**.tif – cross-section of the cell 663_LSF_LSCCF_P_LSCCF_**.tif – surface of the LSCCF grid 663_LSF_LSCCF_PM_**.tif - surface of the LSF infill     LSF-PCM (SOFC, air flow 0.5 L/min) tests_1_66 01_eqc20220831133210.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02_eqc20220831133409.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 03_eqc20220831133928.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 05_eqc20220831134048.xlsx - 650°C, 0.0625  A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 06_eqc20220831134142.xlsx - 650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08_eqc20220831134237.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 10_eqc20220831134412.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; SEM (post-mortem) 658_LSF_PCM_p.pdf – cross-section with EDS 658_LSF_PCM_PM.pdf - surface with EDS 658_LSF_PCM_P_LSF_**.tif  – cross-section, infill zone 658_LSF_PCM_P_pcm_**.tif - cross-section, net zone 658_LSF_PCM_PM_**.tif - surface description.pdf - pdf version of this information.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Single-cell information extracted from IMC example data

If you are working with these files, please cite them as follows: Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a><ul><li>gated_cells.zip: contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li>spe.rds: SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li>images.rds: CytoImageList object containing the spillover-corrected images.</li><li>masks.rds: CytoImageList object containing the segmentation masks.</li></ul>

opencc-by-4.0May 2022View details →

zenodo44/100

Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland

Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.

opencc-by-4.0Jul 2023View details →

zenodo44/100

Chironomid taxa relative abundance information and lake identifiers for: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period

File 1: Relative abundances for chironomid taxa used in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period. Lake_ID corresponds to the lake IDs attributed to each lake sampled as part of the LakePulse Network File 2: Lake_ID, lake name, latitude, longitude, sampling date, province, and ecozone for the 69 lakes examined in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period.

opencc-by-4.0Jul 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record