cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12047241.v3
title: Trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought

description: Raw datasets are available from public sources, which are used in the paper "Trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought".<br>The datasets include:raw data of tree-ring widths from https://www1.ncdc.noaa.gov/pub/data/paleo/treering/; the Climatic Research Unit (CRU), version TS 4.01 from https://crudata.uea.ac.uk/cru/data/hrg/; the gridded SPEI data (SPEIbase v2.5) from http://digital.csic.es/handle/10261/153475; the Regridded Harmonized World Soil Database v1.2 from https://daac.ornl.gov/SOILS/guides/HWSD.html; canopy height from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1; maximum rooting depth from https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html; wood density from https://datadryad.org/handle/10255/dryad.235; SLA, Nm, and Pm from https://github.com/abhirupdatta/global_maps_of_plant_traits; HSM and P50 from https://www.nature.com/articles/nature11688; isohydricity data from https://github.com/agkonings/isohydricity; tree density from https://elischolar.library.yale.edu/yale_fes_data/1/ ;above ground biomass of tropical forest plots from http://www.forestplots.net/data-packages/brienen-et-al-2015.<br>Please cite the corresponding papers correctly and follow the data usage license when using these datasets.
DOI: 10.6084/m9.figshare.12046881
title: images-1L-processed.tar.gz

description: Labelled 1 Lakh data. https://www.kaggle.com/digdeepbro/100000-labeled-captchas<br>
DOI: 10.6084/m9.figshare.12047241.v4
title: Trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought

description: Raw datasets are available from public sources, which are used in the paper "Trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought".<br>The datasets include:raw data of tree-ring widths from https://www1.ncdc.noaa.gov/pub/data/paleo/treering/; the Climatic Research Unit (CRU), version TS 4.01 from https://crudata.uea.ac.uk/cru/data/hrg/; the gridded SPEI data (SPEIbase v2.5) from http://digital.csic.es/handle/10261/153475; the Regridded Harmonized World Soil Database v1.2 from https://daac.ornl.gov/SOILS/guides/HWSD.html; canopy height from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1; maximum rooting depth from https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html; wood density from https://datadryad.org/handle/10255/dryad.235; SLA, Nm, and Pm from https://github.com/abhirupdatta/global_maps_of_plant_traits; HSM and P50 from https://www.nature.com/articles/nature11688; isohydricity data from https://github.com/agkonings/isohydricity; tree density from https://elischolar.library.yale.edu/yale_fes_data/1/ ;above ground biomass of tropical forest plots from http://www.forestplots.net/data-packages/brienen-et-al-2015.<br>Please cite the corresponding papers correctly and follow the data usage license when using these datasets.
DOI: 10.6084/m9.figshare.12046881.v1
title: images-1L-processed.tar.gz

description: Labelled 1 Lakh data. https://www.kaggle.com/digdeepbro/100000-labeled-captchas<br>
DOI: 10.6084/m9.figshare.12047241.v5
title: Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought

description: Raw datasets are available from public sources, which are used in the paper "Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought". doi:10.1038/s41559-020-1217-3https://www.nature.com/articles/s41559-020-1217-3<br>The datasets include:<br>raw data of tree-ring widths from https://www1.ncdc.noaa.gov/pub/data/paleo/treering/; the Climatic Research Unit (CRU), version TS 4.01 from https://crudata.uea.ac.uk/cru/data/hrg/; the gridded SPEI data (SPEIbase v2.5) from http://digital.csic.es/handle/10261/153475; the Regridded Harmonized World Soil Database v1.2 from https://daac.ornl.gov/SOILS/guides/HWSD.html; canopy height from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1; maximum rooting depth from https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html; wood density from https://datadryad.org/handle/10255/dryad.235; SLA, Nm, and Pm from https://github.com/abhirupdatta/global_maps_of_plant_traits; HSM and P50 from https://www.nature.com/articles/nature11688; isohydricity data from https://github.com/agkonings/isohydricity; tree density from https://elischolar.library.yale.edu/yale_fes_data/1/ ;above ground biomass of tropical forest plots from http://www.forestplots.net/data-packages/brienen-et-al-2015.<br>Please cite the corresponding papers correctly and follow the data usage license when using these datasets.
DOI: 10.6084/m9.figshare.12046812
title: Wedeux_2020_GCB.zip

description: The dataset held in the zip file are as follows: <br>* field_data_2011_2014_2015_2016_AGB_TCH_X_Y.csvThis file contains the field and lidar data for the 65 field plots. The field data are the AGB values and the lidar data the TCH.There is also an indication of the year in which the survey was done. For the 2011 data there is also the fiedl survey and the lidar of 2015/2016.<br>* forest_treatmentsIn this shapefile there are the type of forest of the analyzed area: old growth, illegal logging forest, concessinary logging, and concessionary plus illegal logging.<br>* 11298_100x100mGRID_AGB_2011_2014_diffYear_treatment_X_YThis file contains a table with 11298 points extrated over the area on a 100x100 m grid. It contains the points coordinates, the AGB predicted in 2011 and 2014, the yearly increase/decrease, and the forest tretment.<br>* logging_routes_lidar_cooncessionsShapefile of the concessionary logging routes detected with lidar.<br>* logging_routes_lidar_illegalShapefile of the illegal logging routes detected with lidar.<br>* rivers_and_canalsShapefile of the rivers and canals in the study area.<br>
DOI: 10.6084/m9.figshare.12046764
title: Increases in [IP3]i aggravates diastolic [Ca2+] and contractile dysfunction in Chagas’ human cardiomyocytes

description: Chagas cardiomyopathy is the most severe manifestation of human Chagas disease and represents the major cause of morbidity and mortality in Latin America. We previously demonstrated diastolic Ca<sup>2+</sup> alterations in cardiomyocytes isolated from Chagas’ patients with different degrees of cardiac dysfunction. Exposure of cardiomyocytes to agents that enhance inositol 1,4,5 triphosphate (IP3) generation or concentration like endothelin (ET-1) or bradykinin (BK), or membrane-permeant myoinositol 1,4,5-trisphosphate hexakis(butyryloxy-methyl) esters (IP3BM) caused an elevation in diastolic [Ca<sup>2+</sup>] ([Ca<sup>2+</sup>]<sub>d</sub>) that was always greater in cardiomyocytes from Chagas’ than non- Chagas’ subjects, and the magnitude of the [Ca<sup>2+</sup>]<sub>d </sub>elevation was related with the degree of cardiac dysfunction. Incubation with xestospongin-C (Xest-C) a membrane-permeable selective blocker of the IP3 receptors (IP3Rs) significantly reduced [Ca<sup>2+</sup>]<sub>d</sub> in Chagas’ cardiomyocytes but did not have a significant effect in non- Chagas’ cells. Furthermore, cardiomyocytes from Chagas’ patients had a higher intracellular IP3 concentration ([IP3]<sub>i</sub>) and markedly depressed contractile properties compared to control subjects. These results provide additional and convincing support about implications of IP3 in the pathogenesis of Chagas cardiomyopathy in patients at different stages of chronic infection. Additionally, these findings open the door for novel therapeutic strategies oriented to improve cardiac function and quality of life of individuals suffering from Chagas cardiomyopathy.
DOI: 10.6084/m9.figshare.12046764.v1
title: Increases in [IP3]i aggravates diastolic [Ca2+] and contractile dysfunction in Chagas’ human cardiomyocytes

description: Chagas cardiomyopathy is the most severe manifestation of human Chagas disease and represents the major cause of morbidity and mortality in Latin America. We previously demonstrated diastolic Ca<sup>2+</sup> alterations in cardiomyocytes isolated from Chagas’ patients with different degrees of cardiac dysfunction. Exposure of cardiomyocytes to agents that enhance inositol 1,4,5 triphosphate (IP3) generation or concentration like endothelin (ET-1) or bradykinin (BK), or membrane-permeant myoinositol 1,4,5-trisphosphate hexakis(butyryloxy-methyl) esters (IP3BM) caused an elevation in diastolic [Ca<sup>2+</sup>] ([Ca<sup>2+</sup>]<sub>d</sub>) that was always greater in cardiomyocytes from Chagas’ than non- Chagas’ subjects, and the magnitude of the [Ca<sup>2+</sup>]<sub>d </sub>elevation was related with the degree of cardiac dysfunction. Incubation with xestospongin-C (Xest-C) a membrane-permeable selective blocker of the IP3 receptors (IP3Rs) significantly reduced [Ca<sup>2+</sup>]<sub>d</sub> in Chagas’ cardiomyocytes but did not have a significant effect in non- Chagas’ cells. Furthermore, cardiomyocytes from Chagas’ patients had a higher intracellular IP3 concentration ([IP3]<sub>i</sub>) and markedly depressed contractile properties compared to control subjects. These results provide additional and convincing support about implications of IP3 in the pathogenesis of Chagas cardiomyopathy in patients at different stages of chronic infection. Additionally, these findings open the door for novel therapeutic strategies oriented to improve cardiac function and quality of life of individuals suffering from Chagas cardiomyopathy.
DOI: 10.6084/m9.figshare.12046716
title: energyGrids_goodGeneSubset_P14.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046719
title: energyGrids_goodGeneSubset_P28.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046716.v1
title: energyGrids_goodGeneSubset_P14.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046719.v1
title: energyGrids_goodGeneSubset_P28.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046710
title: energyGrids_goodGeneSubset_E18pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046710.v1
title: energyGrids_goodGeneSubset_E18pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046713
title: energyGrids_goodGeneSubset_P4.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046701
title: energyGrids_goodGeneSubset_E13pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046707
title: energyGrids_goodGeneSubset_E15pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046701.v1
title: energyGrids_goodGeneSubset_E13pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046707.v1
title: energyGrids_goodGeneSubset_E15pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046713.v1
title: energyGrids_goodGeneSubset_P4.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046680
title: voxelGeneCoexpression_E11pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.<br>
DOI: 10.6084/m9.figshare.12046692
title: energyGrids_goodGeneSubset_E11pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046692.v1
title: energyGrids_goodGeneSubset_E11pt5.mat

description: The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.The dataset is the basis of our analysis of correlated gene expression (CGE) in the developing mouse brain.
DOI: 10.6084/m9.figshare.12046581
title: Harvest and phylogenetic network analysis of SARS virus genomes (CoV-1 and CoV-2)

description: Data and analyses relating to:<br>Grimm GW, Morrion D (2020). Trees and viruses: the SARS group. <i>Genealogical World of Phylogenetic Networks, </i>posted 30/3/2020<i>.</i>http://phylonetworks.blogspot.com/2020/03/trees-and-viruses-sars-group.html<i><br></i><i><br></i>Grimm GW (2020a). Using Median Networks to study SARS-CoV-2. <i>Genealogical World of Phylogenetic Networks, </i>posted 20/4/2020.https://phylonetworks.blogspot.com/2020/04/using-median-networks-to-study-sars-cov.html<br><i></i>Comment to Forster et al. (2020), <i>PNAS</i>, doi: 10.1073/pnas.2004999117 <br><i></i>The main 7z-archive [7z.org/Wikipedia] includes all used data (non-curated gene bank harvest, curated alignment) and analysis files in standard phylogenetic data formats (FASTA, NEXUS, NEWICK, Splits-NEXUS). For labelling conventions and archive content see ReadMe.txt.<br>New in Version 2: Fully annotated genotype spread sheet, <i>CoV2Genotyping</i> (XLSX) of the CoV-2 subsample included in our original harvest tabulating particular and general mutations patterns. The main archive has been updated.<br>New in Version 3: Archive <i>Hack-and-Fish.7z </i>including analysis files for the experiment described in this post:Grimm GW (2020b). Hack and fish ... for recombination in the SARS group. <i>Genealogical World of Phylogenetic Networks</i>, posted 8/6/2020. https://phylonetworks.blogspot.com/2020/06/hack-and-fish-for-recombination-in-sars.html <br><br><b>Figures</b><i>MLTreeStrictGrCons—</i>Maximum likelihood tree based on strict group consensus sequences and branch support established via non-parametric bootstrapping<i>NNetCPlusRecomb—</i>Uncorrected <i>p-</i>distance (Hamming) based planar phylogenetic network based on the (strict) group consensus<br>data. Coloured lines refer to shared sequence patterns as visible from the alignment (likely recombination events)<i>NNetPlusSupport—</i>Uncorrected <i>p</i>-distance (Hamming) based planar phylogenetic networks based on the non-consensed (original) data (in total, 291 near-complete virus genomes) used to define major groups for consensing approach. Bottom-right, bootstrap consensus network for the same data.<i>MutationPatterns1, ...2—</i>Visualisation of mutation patterns that are either the consequence of homoplasy, i.e. convergent mutation from C to U in independent CoV-2 sublineages, or recombination. See 2<sup>nd</sup> post (Grimm, 2020a) for further details. <i>HnS.All.sumCNet, HnS.All.sumBSNet</i>—Consensus networks of nine bit-wise ML trees (...CNet, strict) and according, pooled ML bootstrap pseudoreplicate trees (...BSNet, only splits with a frequency ≥ 20%). See 5<sup>th</sup> post (Grimm, 2020b) for explanations.<br><br>Referenced and further related posts are linked below. <br>Comments are welcomed; please use the comment option at our blog Genealogical World of Phylogenetic Networks.<br>
DOI: 10.6084/m9.figshare.12046581.v1
title: Harvest and phylogenetic network analysis of SARS virus genomes (CoV-1 and CoV-2)

description: Data and analyses relating to:<br>Grimm GW, Morrion D (2020). Trees and viruses: the SARS group. <i>Genealogical World of Phylogenetic Networks, </i>posted 30/3/2020<i>.</i>http://phylonetworks.blogspot.com/2020/03/trees-and-viruses-sars-group.html<i><br></i><i><br></i>The 7z-archive [7z.org/Wikipedia] includes all used data (non-curated gene bank harvest, curated alignment) and analysis files in standard phylogenetic data formats (FASTA, NEXUS, NEWICK, Splits-NEXUS). For labelling conventions and archive content see ReadMe.txt.<br><b>Figures</b><i>MLTreeStrictGrCons.png—</i>Maximum likelihood tree based on strict group consensus sequences and branch support established via non-parametric bootstrapping<i>NNetCPlusRecomb.png—</i>Uncorrected <i>p-</i>distance (Hamming) based planar phylogenetic network based on the (strict) group consensus<br>data. Coloured lines refer to shared sequence patterns as visible from the alignment (likely recombination events)<i>NNetPlusSupport.png—</i>Uncorrected <i>p</i>-distance (Hamming) based planar phylogenetic networks based on the non-consensed (original) data (in total, 291 near-complete virus genomes) used to define major groups for consensing approach. Bottom-right, bootstrap consensus network for the same data.<br><br>Comments are welcomed; please use the comment option at our blog Genealogical World of Phylogenetic Networks.<br>
DOI: 10.6084/m9.figshare.12046581.v3
title: Harvest and phylogenetic network analysis of SARS virus genomes (CoV-1 and CoV-2)

description: Data and analyses relating to:<br>Grimm GW, Morrion D (2020). Trees and viruses: the SARS group. <i>Genealogical World of Phylogenetic Networks, </i>posted 30/3/2020<i>.</i>http://phylonetworks.blogspot.com/2020/03/trees-and-viruses-sars-group.html<i><br></i><i><br></i>Grimm GW (2020a). Using Median Networks to study SARS-CoV-2. <i>Genealogical World of Phylogenetic Networks, </i>posted 20/4/2020.https://phylonetworks.blogspot.com/2020/04/using-median-networks-to-study-sars-cov.html<br><i></i>Comment to Forster et al. (2020), <i>PNAS</i>, doi: 10.1073/pnas.2004999117 <br><i></i>The main 7z-archive [7z.org/Wikipedia] includes all used data (non-curated gene bank harvest, curated alignment) and analysis files in standard phylogenetic data formats (FASTA, NEXUS, NEWICK, Splits-NEXUS). For labelling conventions and archive content see ReadMe.txt.<br>New in Version 2: Fully annotated genotype spread sheet, <i>CoV2Genotyping</i> (XLSX) of the CoV-2 subsample included in our original harvest tabulating particular and general mutations patterns. The main archive has been updated.<br>New in Version 3: Archive <i>Hack-and-Fish.7z </i>including analysis files for the experiment described in this post:Grimm GW (2020b). Hack and fish ... for recombination in the SARS group. <i>Genealogical World of Phylogenetic Networks</i>, posted 8/6/2020. https://phylonetworks.blogspot.com/2020/06/hack-and-fish-for-recombination-in-sars.html <br><br><b>Figures</b><i>MLTreeStrictGrCons—</i>Maximum likelihood tree based on strict group consensus sequences and branch support established via non-parametric bootstrapping<i>NNetCPlusRecomb—</i>Uncorrected <i>p-</i>distance (Hamming) based planar phylogenetic network based on the (strict) group consensus<br>data. Coloured lines refer to shared sequence patterns as visible from the alignment (likely recombination events)<i>NNetPlusSupport—</i>Uncorrected <i>p</i>-distance (Hamming) based planar phylogenetic networks based on the non-consensed (original) data (in total, 291 near-complete virus genomes) used to define major groups for consensing approach. Bottom-right, bootstrap consensus network for the same data.<i>MutationPatterns1, ...2—</i>Visualisation of mutation patterns that are either the consequence of homoplasy, i.e. convergent mutation from C to U in independent CoV-2 sublineages, or recombination. See 2<sup>nd</sup> post (Grimm, 2020a) for further details. <i>HnS.All.sumCNet, HnS.All.sumBSNet</i>—Consensus networks of nine bit-wise ML trees (...CNet, strict) and according, pooled ML bootstrap pseudoreplicate trees (...BSNet, only splits with a frequency ≥ 20%). See 5<sup>th</sup> post (Grimm, 2020b) for explanations.<br><br>Referenced and further related posts are linked below. <br>Comments are welcomed; please use the comment option at our blog Genealogical World of Phylogenetic Networks.<br>
DOI: 10.6084/m9.figshare.12046581.v2
title: Harvest and phylogenetic network analysis of SARS virus genomes (CoV-1 and CoV-2)

description: Data and analyses relating to:<br>Grimm GW, Morrion D (2020). Trees and viruses: the SARS group. <i>Genealogical World of Phylogenetic Networks, </i>posted 30/3/2020<i>.</i>http://phylonetworks.blogspot.com/2020/03/trees-and-viruses-sars-group.html<i><br></i><i><br></i>Grimm GW (2020). Using Median Networks to study SARS-CoV-2. <i>Genealogical World of Phylogenetic Networks, </i>posted 20/4/2020.https://phylonetworks.blogspot.com/2020/04/using-median-networks-to-study-sars-cov.html<br><i></i>Comment to Forster et al. (2020), <i>PNAS</i>, doi: 10.1073/pnas.2004999117 <br><i></i>The 7z-archive [7z.org/Wikipedia] includes all used data (non-curated gene bank harvest, curated alignment) and analysis files in standard phylogenetic data formats (FASTA, NEXUS, NEWICK, Splits-NEXUS). For labelling conventions and archive content see ReadMe.txt.<br>New in Version 2: Fully annotated genotype spread sheet, <i>CoV2Genotyping</i> (XLSX) of the CoV-2 subsample included in our original harvest tabulating particular and general mutations patterns. The archive has been updated.<br><br><b>Figures</b><i>MLTreeStrictGrCons.png—</i>Maximum likelihood tree based on strict group consensus sequences and branch support established via non-parametric bootstrapping<i>NNetCPlusRecomb.png—</i>Uncorrected <i>p-</i>distance (Hamming) based planar phylogenetic network based on the (strict) group consensus<br>data. Coloured lines refer to shared sequence patterns as visible from the alignment (likely recombination events)<i>NNetPlusSupport.png—</i>Uncorrected <i>p</i>-distance (Hamming) based planar phylogenetic networks based on the non-consensed (original) data (in total, 291 near-complete virus genomes) used to define major groups for consensing approach. Bottom-right, bootstrap consensus network for the same data.<i>MutationPatterns1, ...2—</i>Visualisation of mutation patterns that are either the consequence of homoplasy, i.e. convergent mutation from C to U in independent CoV-2 sublineages, or recombination. See 2nd post (Grimm, 2020; linked below) for further details. <br><br>Comments are welcomed; please use the comment option at our blog Genealogical World of Phylogenetic Networks.<br>
DOI: 10.6084/m9.figshare.12046509
title: Supporting files_Table S1_Taekwondo

description: Training schedule and average session intensity in tested taekwondo athletes in two supplementation periods.<br>