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The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></p>
Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins
<p>We have simulated water budget and energy budget over Indian subcontinental basins, using three scenarios from the Variable Infiltration Capacity (VIC) model by including irrigation and reservoir practices in it:</p> <p>1). No irrigation and reservoir (VIC-NATURAL)<br> 2). Free irrigation and no reservoir (VIC-FREE)<br> 3). Reservoir and restricted irrigation (VIC-MANAGED)</p> <p>Here, we have shared results in below folders.</p> <p>Fig1: Annual precipitation (P) and reservoir locations used in study.<br> Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET.<br> Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED.<br> Fig4: Mean monthly observed and simulated reservoir storage.<br> Fig5: P, ET, total runoff (TR) and LST from one grid.<br> Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run.<br> Fig7: Same as Fig6 but for TR.<br> Fig8: Same as Fig6 but for LST.<br> Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run.<br> Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run.</p> <p>More detail is available in "Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins" paper in JGR-Atmosphere. Or contact at harsh.lovekumar.shah@iitgn.ac.in</p> <p>Harsh Shah</p>
D-PLACE dataset derived from Jenkins et al. 2013 'Global patterns of terrestrial vertebrate diversity and conservation'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Jenkins CN, Pimm SL, Joppa LN. Global patterns of terrestrial vertebrate diversity and conservation. Proc Natl Acad Sci. 2013;110: E2602–E2610.</p> </blockquote>
Terrestrial laser scan data of a experimental plot in Forstamt Billenhagen, 340 a31 (Mecklenburg-Vorpommern, Germany 2023) v2
<p>The area was surveyed using terrestrial laser scanning, and the subsequent derivation of individual tree yield data (BHD, tree height, volume, etc.) was carried out as part of a study to assess the ecosystem services of different forest stands (recorded in March 2023). In this version of the data, transmission errors and unit errors were corrected.</p>
Areas of global importance for conserving terrestrial biodiversity, carbon, and water
<p><strong>Content:</strong><br> This data repository contains the results of the NatureMap ( naturemap.earth/) conservation prioritization effort. The maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water.</p> <p><strong>Usage notes:</strong><br> Maps are supplied at both 10km and 50km resolution unless specified differently in the manuscript.<br> All maps that aim to find priority areas for all species considered in the analysis, utilize a series of representative sets.<br> The ranks for each layer are area-specific and can be used to extract summary statistics by simple subsetting.<br> For example:<br> To obtain the top 30% of land area for biodiversity and carbon, one needs to create a mask of all areas lower than a value of 30 from the respective ranked layers.</p> <p>For convenience two files are supplied that contain the fraction of land area per grid cell times 1000. Multiplying those with the cell area (100km2, respectively 2500km2) gives the exact amount of land area in a given grid cell.<br> These are labelled " globalgrid_mollweide_**km.tif " can be used to create masks for the priority maps.</p> <p><strong>Spatial resolution:</strong></p> <p>10 and 50 km</p> <p><strong>Geographic projection:</strong><br> World Mollweide Equal Area projection<br> PROJ4 ( +proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs )</p> <p><strong>Filename suffix description:</strong></p> <p><em>'minshort_speciestargets'</em><br> =- Problem formulation where targets were achieved by minimzing a shortfall</p> <p><em>'repruns10'</em><br> =- The number of representative that were used to create the ranked layer</p> <p><em>'biome.id'</em><br> =- Species distribution were split by biome, thus creating separate targets for subpopulation</p> <p><em>'withPA'</em><br> =- Fractions of current protected areas (Date: WDPA 2019) were locked in as baseline and starting budget. Approximately 15% of the globe. Note that not entire grid cells, but fractions were locked in and build opon!</p> <p><em>'carbon'</em><br> =- Carbon was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><em>'water'</em><br> =- Water was included in the prioritization and jointly optimized together with the other assets by giving it equal weighting (see manuscript)</p> <p><strong>License:</strong><br> CC-BY-SA 4.0</p> <p><strong>Citation:</strong><br> Jung, Martin, Andy Arnell, Xavier De Lamo, Shaenandhoa Garcia-Rangel, Matthew Lewis, Jennifer Mark, Cory Merow et al. (2021) "Areas of global importance for terrestrial biodiversity, carbon, and water." Nature Ecology & Evolution</p>
Foodscapes - A global clustering of terrestrial food production systems
<p>This repository contains the final outputs from the global <strong>Foodscape</strong> mapping exercise. In this project we aimed to identify broad homologues of foodscape classes, comparable in a minimum set of biophysical and management characteristics, would help to design possible interventions and leverage points for more sustainable agriculture.</p> <p>Provided are maps in spatial geotiff format (.tif) which can be opened in any typical GIS software such as ArcGIC or QGIS. The Resolution is 0.5' degree (WGS 84 or <em>longitude-latitude</em> projection). Both the foodscape map (<em>Foodscapes_combinedGEOTIFF_final.tif</em>) and a reclassified intensity map (<em>Foodscapes_combinedGEOTIFF_intensity.tif</em>) are provided. Accompanied with the tif files are .clr and .qml files, both of which provide layout information on colours used for each code combination in the global maps. The .qml file will be automatically loaded when opening the layer in QGIS.<br> In addition, in the Microsoft Excel sheet "<em>Foodscapes_combinedLEGEND_final.xlsx</em>" a legend with qualitative description and label of each class is provided.</p> <p>If you use this layer in any way, please cite this repository and the describing <a href="https://osf.io/puyzw/">preprint</a>.</p>
Spatiotemporal dynamics in freshwater amphipod assemblages are associated with surrounding terrestrial land use type - Dataset
<p>Biological assemblages are the result of dynamic processes that have explicit temporal and spatial dimensions. While biodiversity patterns can be directly inferred from the structure of these assemblages, an assessment of changes through time and space is needed to understand how organisms initially assembled and how they are responding to local environmental and biotic factors. Small freshwater streams are particularly affected by contemporary anthropogenic activities and biological invasions, yet are commonly less studied, as studies often focus on lakes and large streams. Here, we conducted a spatially explicit analysis of keystone shredder assemblages across eight years in twelve replicated small tributary streams. In each stream, we monitored multiple sites per km stream length. By assessing temporal beta diversity dynamics, defined by the gain or loss of species or abundance-per-species at individual sites, we show that changes in amphipod assemblages occur within the context of the surrounding terrestrial matrix and reflect recent amphipod colonization history. While amphipod composition was mostly constant in streams located in forested catchments, streams embedded in catchments with more extensive agricultural land use displayed more pronounced temporal changes, either driven by colonization of unoccupied upstream locations, or by more pronounced but undirected fluctuations in gains and losses of species or abundance-per-species. Our study thus suggests that agricultural landscapes might destabilize aquatic amphipod assemblages, causing higher temporal changes in community structures, and highlighting the vulnerability of aquatic ecosystems to terrestrial land use drivers.</p>
Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes
<p>I)SUMMARY</p> <p>This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]:</p> <p>- SM: soil moisture from SMOS-IC v2<br> - VOD: vegetation optical depth from SMOS-IC v2<br> - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6<br> - PREC: Rainfall from PERSIANN-CDR v2.2.</p> <p>Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster.</p> <p>II) CONTACT</p> <p>For questions, please e-mail Diego Bueso at diego.bueso@uv.es</p> <p>III) DATABASE</p> <p>We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file</p> <p>IV) CITE</p> <p>To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication: </p> <p>D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, <a href="https://doi.org/10.1093/nsr/nwad026">https://doi.org/10.1093/nsr/nwad026</a> </p>
Data for "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion"
<p>Data used in generating results for the paper <a href="https://doi.org/10.1029/2023JG007457">"Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion."</a></p> <ol> <li>VIIRS_MOD16_MOD17_tower_site_drivers_v9.h5</li> <li>MOD17_5km_global_simulation.zip</li> <li>VNP17_5km_global_simulation.zip</li> </ol> <p>[1] is an HDF5 file containing surface meteorological drivers, MODIS/ VIIRS vegetation fPAR and LAI, and other data necessary for calibrating and validating the MOD17 and VNP17 GPP models at FLUXNET towers. It also contains driver data and field-based NPP data for calibrating and validating MOD17/ VNP17 NPP models.</p> <p>[2] and [3] are the global, 5-km GPP and NPP simulations using the updated MOD17 parameters and new VNP17 model parameters. Other than their 5-km resolution, these global, annual GeoTIFF files are formatted the same as MOD17A3H data; <a href="https://lpdaac.usgs.gov/products/mod17a3hgfv061/">see the User Guide</a> for more information. The same scale factors apply to recover geophysical units: multiply the values by 0.0001 to obtain [kg C m-2 year-1].</p> <p>Please cite the peer-reviewed paper:</p> <blockquote> <p>Endsley, K.A., M. Zhao, J.S. Kimball, S. Devadiga. 2023. "Continuity of global MODIS terrestrial primary productivity estimates in the VIIRS era using model-data fusion." <em>Journal of Geophysical Research: Biogeosciences</em> 128(9).</p> </blockquote>
Phylogenetic analyses of hub genes accompanying the study "Environmental gradients reveal stress hubs predating plant terrestrialization"
<p>135 ML phylogenies of hub genes identified in the study "Environmental gradients reveal stress hubs predating plant terrestrialization"</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>
Clouds and Seasonality on Terrestrial Planets with Varying Rotation Rates
<p>Base Isca namelists used to construct experiment grid and figure plotting notebooks. Includes .zip files that contain post-processed data used to produce the figures—note that .ipynb notebooks have not had file structure changed to match files in this directory, which will need to be changed if the script is re-run (plots are viewable in notebooks).</p>
Terrestrial water data synthesis for hydrological catchments around the world in the Anthropocene
<p>We here synthesise hydro-climatic data reported by previous studies for 65 hydrological catchments around the world [1-7] for further meta-analysis of how water fluxes of precipitation (P), runoff (R), and actual evapotranspiration (ET) on land change in the Anthropocene epoch, from before to after its start in the 1950's [8]. These water flux changes alter how much water ends up sustaining crops and other plants (evapotranspiration) and how much remains for the lateral water flows (runoff) through the landscape and the societal water uses and ecosystems they support. How this partitioning changes as integral part of global change is key for water and food security, and life on land and below water around the world’s land areas and coasts. </p> <p>To distinguish the impacts of direct human drivers on evapotranspiration and runoff changes based on instrumental data for wide-ranging water, human-activity and climate conditions around the world, the 65 study catchments are divided in two comparative sets. One set includes 52 large catchments, selected from two previous data compilations and studies [1,2] and is in the data files referred to as the world set of catchments. The study periods are 1901–2008 for 21 [2] and 1930–1979 for the other 31 [1] of these catchments. The second set includes 13 catchments selected from previous studies [3-7] of both the water flux changes and associated dominant human drivers of these for time periods that are largely consistent with those for the world set of catchments (within 1901-2016); this is in the data files referred to as the known human-shifted set of catchments. The dominant direct human drivers of water-flux changes in these catchments include expanded/intensified rainfed agriculture (RA), irrigated agriculture (IA), or dams and reservoirs for engineered flow regulation (FR), as listed and cited for each known human-shifted catchment in the data files. </p> <p>For each study catchment, we have quantified and report in the data files long-term average P, R and ET values over the total study period and changes in period-average values between two subperiods within it. The subperiods are 1901–1954 and 1955–2008 for 21 [2], and 1930–1954 and 1955–1979 for the other 31 [1] world catchments, and largely consistent for the known human-shifted catchments [3-7] (as listed in the data files). Each study catchment is also classified as water or energy limited by quantifying the associated Budyko-based aridity index PET/P, where PET is potential evapotranspiration. Average PET is estimated as PET≈325+21T+0.9T<sup>2</sup> where T is long-term annual and catchment average surface temperature in °C, calculated from monthly temperature data in the previous catchment studies [1-7]. The resulting PET/P index classifies actual ET/P in each catchment as energy limited for average PET/P < 1 or water limited for average PET/P > 1, as listed in the data files.</p> <p>Other catchment information included in the data files, uploaded in both pdf and xlsx formats, include source references, catchment name, area and station ID and continent and latitude location. Overall, this dataset provides a wide-ranging sample of catchment-wise related, water balance-constrained local-regional changes in average P, R and ET under major RA, IA, FR developments (known for the second human-shifted set of catchments [3-7]) and various other human-activity and climate developments around the world from before to after the Anthropocene start in the 1950’s [8]. </p>
"Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets" Data Repository
<p>This repository contains the data files, analysis Jupyter notebooks and figures from Thompson et al. 2023 "Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets"</p>
Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels
<p>The code and data for the paper entitled "Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels" published in <em>Biology Letters</em>.</p>
Compiled long-term community composition datasets of primary producers and consumers in both freshwater and terrestrial communities
This data package consists of two files to study long-term changes in communities across a range of systems (freshwater and terrestrial) and organism types (from short-lived (sub annual) to long-lived species) that are both primary producers and consumers. We compiled many datasets from publicly available archives (41 datasets are from 14 LTER sites). All datasets must have had a measure of species level abundance to calculate more derived community ecological metrics beyond species richness. These data can be used to study community dynamics over space and time.
University of Michigan Biological Station cumulative food web data for terrestrial habitats, 1909-2023.
Here, we present species and interaction lists for a food web of the aboveground terrestrial habitats at the University of Michigan Biological Station (UMBS). The site is composed predominantly of dry-mesic, northern hardwood forests with patches of wooded wetlands (hardwood conifer swamp). Taxa were sourced from lists provided by UMBS, from resident biologists’ personal observations, museum specimens, online databases, historical censuses, and BioBlitz events. Only those that could be resolved to species-level or were genera with < 20 species in the Nearctic were included. We also excluded species that do not have a significant lifestage or feeding behavior in aboveground terrestrial habitats. Our focal taxonomic groups include vascular plants, arthropods, birds, mammals, reptiles, amphibians. The majority of arthropods are insects; non-insect arthropods were highly underrepresented in our lists. Interactions were sourced from online databases, naturalist observations, and field guides and accumulated into a “metaweb” of all potential interactions between local species. Interactions were checked by experts to plausibly occur in the aboveground terrestrial environments at UMBS, given species’ phenology, traits, and habitat usage. Interactions at any taxonomic level were included, so long as they were approved to potentially occur between all species by our experts. To study the effect of taxonomic resolution on food web structure, in this dataset, we retained records at coarser taxonomic groupings even if more highly resolved records were also approved. We included all direct interactions among species in our system with a bioenergetic flow (i.e., one species consuming another), differentiated by their focal resource. We broadly categorized the resources as animal tissues, either (1) live tissues and as prey, or (2) scavenged as carrion, carcasses, or other decaying animal remains, or as plant tissues, grouped as (3) leaves and stems, including grasses, exudates, et
Cross-site decomposition of leaf litter in terrestrial and aquatic habitats, CWT and LUQ, 2000 (species Buchenavia capitata, Dacryodes excelsa, Guarea guidonia, Quercus prinus).
Comparison of decomposition and nutrient losses from three species of leaf litter in terrestrial and aquatic habitats at CWT and LUQ LTER sites. Overall hypothesis is that macro-consumers have different patterns and impacts on decomposition rates than microbial decomposers, and that these patterns are magnified in litters of low vs. high qualities over the two years of the experiment.
Dendrometer Band Measurements from the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrologic Laboratory, Otto, North Carolina.
Trees for this project were banded with aluminum bands and growth increment markers to accurately measure tree growth at multiple times during the year. Trees were located on each of the five terrestrial gradient plots. Tree species, initial diameter, and subsequent calculated diameters are included for each tree.
Measurements of coarse woody debris at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC from 2003 to 2014
The five Terrestrial Gradient sites were established in the early 1990s as part of the 1990 Coweeta LTER Renewal. The original terrestrial gradient sites were 20 x 40-m. In the late 1990s the plots were expanded to 80 x 80-m and later (around 1998) they were slope-corrected by Clark's lab using survey equipment. In this study, coarse woody debris (CWD) was measured in each of the five terrestrial gradient plots at the Coweeta Hydrologic Lab from 2003 through 2014. The length, diameters, and decay class of coarse wood were measured within each of the plots.
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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.