cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/ewwrol
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/kqyzbg
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/jmaztw
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/bhzm0f
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/xgsf5x
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/8fldvw
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/6by6gx
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/ffq7hh
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/edjo0g
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/dmkgzm
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/krxqxx
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/svdbgc
title: 002_IFNL_Recovery_scRNAseq__05b_GlobalHeterogeneity_Enterocytes.html
DOI: 10.57745/c5xpkt
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/rne4zy
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/my6qcb
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/zitpn9
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/xioncp
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/iebckl
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/seasjj
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/51czs1
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/jtnafa
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/ari1oc
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/bx59th
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/efqz4h
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/bneqdd
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/wefiix
title: 002_IFNL_Recovery_scRNAseq__05b_GlobalHeterogeneity_Enterocytes.html
DOI: 10.57745/zgumfi
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/ceuudi
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/in12mq
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/balcrl
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/b1z6ul
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/8jbzvn
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/7e8nys
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/ba0wwq
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/mvwmuo
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/xoewso
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/uthaz1
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/tuyne5
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/qabpvd
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/jgqyby
title: 002_IFNL_Recovery_scRNAseq__05b_GlobalHeterogeneity_Enterocytes.html
DOI: 10.57745/tvyuaw
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/ftryin
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/wx3hx4
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/ecpofm
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/v344li
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/huutuf
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.6084/m9.figshare.28106395.v1
title: Applying Digital Twin Methods for Process-Structure-Property Correlation Assessment in Metal Additive Manufacturing with Limited Experimental Data

description: Additive manufacturing (AM) is expanding for fabricating complex, high-value, and large-size and low-volume parts. While AM holds significant potential for producing a variety of components, it is essential to conduct quality assessment of AM components to ensure their safe and reliable operation. Low-volume fabrication of parts imposes limitations on the number of inspection samples for quality verification. Limited availability of inspection samples can be addressed by nondestructive testing (NDT) of fabricated samples, and the development of digital twin models. In this study, the Process-Structure-Property (P-S-P) of stainless-steel AM parts fabricated via laser powder bed fusion technique are evaluated using a limited number of inspection samples by various material characterization and NDT. The correlation between process parameters and part properties has been assessed. Results indicate the P-S-P relationship where apart from the ultimate strength values, all experimental results display bimodal distributions, featuring two distinct modes. The correlation analysis reveals that higher laser power is associated with increased RF Z-score and Modulus. Utilizing the experimental findings, a FEM serving as a digital twin of the parts is constructed. This model enables the extension of quality assessment for the parts across a broader spectrum and facilitates the examination of P-S-P relations.
DOI: 10.57745/zd1sjo
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.6084/m9.figshare.27931827
title: Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis

description: This paper presents a finite element model for the dynamic vibration of continuous-welded rails (CWR) under different longitudinal stresses and base conditions. The model supports the physical principles of a vibration-based nondestructive testing (NDT) technique aimed at inferring the lateral and vertical strength, and the neutral temperature of CWR. The model consists of a ~ 5 meters-long rail periodically restrained by distributed translational springs that simulates the lateral and vertical resistance provided by baseplates, fasteners and spikes. Vibration is triggered by a force function equivalent to the mechanical impact of a hammer. The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. Finally, the model is used to quantify any differences in excitation and detection points to optimize the setup of the proposed NDT technique.
DOI: 10.6084/m9.figshare.27931827.v1
title: Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis

description: This paper presents a finite element model for the dynamic vibration of continuous-welded rails (CWR) under different longitudinal stresses and base conditions. The model supports the physical principles of a vibration-based nondestructive testing (NDT) technique aimed at inferring the lateral and vertical strength, and the neutral temperature of CWR. The model consists of a ~ 5 meters-long rail periodically restrained by distributed translational springs that simulates the lateral and vertical resistance provided by baseplates, fasteners and spikes. Vibration is triggered by a force function equivalent to the mechanical impact of a hammer. The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. Finally, the model is used to quantify any differences in excitation and detection points to optimize the setup of the proposed NDT technique.
DOI: 10.6084/m9.figshare.28106395
title: Applying Digital Twin Methods for Process-Structure-Property Correlation Assessment in Metal Additive Manufacturing with Limited Experimental Data

description: Additive manufacturing (AM) is expanding for fabricating complex, high-value, and large-size and low-volume parts. While AM holds significant potential for producing a variety of components, it is essential to conduct quality assessment of AM components to ensure their safe and reliable operation. Low-volume fabrication of parts imposes limitations on the number of inspection samples for quality verification. Limited availability of inspection samples can be addressed by nondestructive testing (NDT) of fabricated samples, and the development of digital twin models. In this study, the Process-Structure-Property (P-S-P) of stainless-steel AM parts fabricated via laser powder bed fusion technique are evaluated using a limited number of inspection samples by various material characterization and NDT. The correlation between process parameters and part properties has been assessed. Results indicate the P-S-P relationship where apart from the ultimate strength values, all experimental results display bimodal distributions, featuring two distinct modes. The correlation analysis reveals that higher laser power is associated with increased RF Z-score and Modulus. Utilizing the experimental findings, a FEM serving as a digital twin of the parts is constructed. This model enables the extension of quality assessment for the parts across a broader spectrum and facilitates the examination of P-S-P relations.
DOI: 10.57745/o8po7w
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/77cdcq
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.6084/m9.figshare.28013708.v2
title: Autophagy-dependent hepatocyte secretion of DBI/ACBP induced by glucocorticoids determines the pathogenesis of Cushing syndrome

description: DBI/ACBP is a phylogenetically ancient hormone that stimulates appetite and lipo-anabolism. In response to starvation, DBI/ACBP is secreted through a noncanonical, macroautophagy/autophagy-dependent pathway. The physiological hunger reflex involves starvation-induced secretion of DBI/ACBP from multiple cell types. DBI/ACBP concentrations subsequently increase in extracellular fluids to stimulate food intake. Recently, we observed that glucocorticoids, which are endogenous stress hormones as well as anti-inflammatory drugs, upregulate DBI/ACBP expression at the transcriptional level and stimulate autophagy in hepatocytes, thereby causing a surge in circulating DBI/ACBP levels. Prolonged increase in glucocorticoid concentrations causes an extreme form of metabolic syndrome, dubbed “Cushing syndrome”, which is characterized by clinical features including hyperphagia, hyperdipsia, dyslipidemia, hyperinsulinemia, insulin resistance, lipodystrophy, visceral adiposity, steatosis, sarcopenia and osteoporosis. Mice and patients with Cushing syndrome exhibit supraphysiological DBI/ACBP plasma levels. Of note, neutralization of extracellular DBI/ACBP protein with antibodies or mutation of the DBI/ACBP receptor (i.e. the GABRG2 subunit of GABR [gamma-aminobutyric acid type A receptor]) renders mice resistant to the induction of Cushing syndrome. Similarly, knockout of <i>Dbi/Acbp</i> in hepatocytes suppresses the corticotherapy-induced surge in plasma DBI/ACBP concentrations and prevents the manifestation of most of the characteristics of Cushing syndrome. We conclude that autophagy-mediated secretion of DBI/ACBP by hepatocytes constitutes a critical step of the pathomechanism of Cushing syndrome. It is tempting to speculate that stress-induced chronic elevations of endogenous glucocorticoids also compromise human health due to the protracted augmentation of circulating DBI/ACBP concentrations. <b>Abbreviations</b>: DBI/ACBP: diazepam binding inhibitor, acyl-CoA binding protein; GABA: gamma-aminobutyric acid; GABAR: gamma-aminobutyric acid type A receptor; GABRG2: gamma-aminobutyric acid type A receptor subunit gamma2.
DOI: 10.57745/5znkjo
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.6084/m9.figshare.28013708
title: Autophagy-dependent hepatocyte secretion of DBI/ACBP induced by glucocorticoids determines the pathogenesis of Cushing syndrome

description: DBI/ACBP is a phylogenetically ancient hormone that stimulates appetite and lipo-anabolism. In response to starvation, DBI/ACBP is secreted through a noncanonical, macroautophagy/autophagy-dependent pathway. The physiological hunger reflex involves starvation-induced secretion of DBI/ACBP from multiple cell types. DBI/ACBP concentrations subsequently increase in extracellular fluids to stimulate food intake. Recently, we observed that glucocorticoids, which are endogenous stress hormones as well as anti-inflammatory drugs, upregulate DBI/ACBP expression at the transcriptional level and stimulate autophagy in hepatocytes, thereby causing a surge in circulating DBI/ACBP levels. Prolonged increase in glucocorticoid concentrations causes an extreme form of metabolic syndrome, dubbed “Cushing syndrome”, which is characterized by clinical features including hyperphagia, hyperdipsia, dyslipidemia, hyperinsulinemia, insulin resistance, lipodystrophy, visceral adiposity, steatosis, sarcopenia and osteoporosis. Mice and patients with Cushing syndrome exhibit supraphysiological DBI/ACBP plasma levels. Of note, neutralization of extracellular DBI/ACBP protein with antibodies or mutation of the DBI/ACBP receptor (i.e. the GABRG2 subunit of GABR [gamma-aminobutyric acid type A receptor]) renders mice resistant to the induction of Cushing syndrome. Similarly, knockout of <i>Dbi/Acbp</i> in hepatocytes suppresses the corticotherapy-induced surge in plasma DBI/ACBP concentrations and prevents the manifestation of most of the characteristics of Cushing syndrome. We conclude that autophagy-mediated secretion of DBI/ACBP by hepatocytes constitutes a critical step of the pathomechanism of Cushing syndrome. It is tempting to speculate that stress-induced chronic elevations of endogenous glucocorticoids also compromise human health due to the protracted augmentation of circulating DBI/ACBP concentrations. <b>Abbreviations</b>: DBI/ACBP: diazepam binding inhibitor, acyl-CoA binding protein; GABA: gamma-aminobutyric acid; GABAR: gamma-aminobutyric acid type A receptor; GABRG2: gamma-aminobutyric acid type A receptor subunit gamma2.
DOI: 10.6084/m9.figshare.28013708.v1
title: Autophagy-dependent hepatocyte secretion of DBI/ACBP induced by glucocorticoids determines the pathogenesis of cushing syndrome

description: DBI/ACBP is a phylogenetically ancient hormone that stimulates appetite and lipo-anabolism. In response to starvation, DBI/ACBP is secreted through a noncanonical, macroautophagy/autophagy-dependent pathway. The physiological hunger reflex involves starvation-induced secretion of DBI/ACBP from multiple cell types. DBI/ACBP concentrations subsequently increase in extracellular fluids to stimulate food intake. Recently, we observed that glucocorticoids, which are endogenous stress hormones as well as anti-inflammatory drugs, upregulate DBI/ACBP expression at the transcriptional level and stimulate autophagy in hepatocytes, thereby causing a surge in circulating DBI/ACBP levels. Prolonged increase in glucocorticoid concentrations causes an extreme form of metabolic syndrome, dubbed “Cushing syndrome”, which is characterized by clinical features including hyperphagia, hyperdipsia, dyslipidemia, hyperinsulinemia, insulin resistance, lipodystrophy, visceral adiposity, steatosis, sarcopenia and osteoporosis. Mice and patients with Cushing syndrome exhibit supraphysiological DBI/ACBP plasma levels. Of note, neutralization of extracellular DBI/ACBP protein with antibodies or mutation of the DBI/ACBP receptor (i.e. the GABRG2 subunit of GABR [gamma-aminobutyric acid type A receptor]) renders mice resistant to the induction of Cushing syndrome. Similarly, knockout of <i>Dbi/Acbp</i> in hepatocytes suppresses the corticotherapy-induced surge in plasma DBI/ACBP concentrations and prevents the manifestation of most of the characteristics of Cushing syndrome. We conclude that autophagy-mediated secretion of DBI/ACBP by hepatocytes constitutes a critical step of the pathomechanism of Cushing syndrome. It is tempting to speculate that stress-induced chronic elevations of endogenous glucocorticoids also compromise human health due to the protracted augmentation of circulating DBI/ACBP concentrations.
DOI: 10.57745/n9ic7x
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/wb19db
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/5vlmup
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/kdratz
title: 002_IFNL_Recovery_scRNAseq__05b_GlobalHeterogeneity_Enterocytes.html
DOI: 10.57745/t0bknu
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/1cg2ia
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/y0gfg9
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/9cebhk
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/wnmcfc
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/vangpb
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/uyqeiq
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/4o4tmk
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/zr3sn2
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/fnlzxn
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/rvkre9
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/llns9g
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/oighau
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/q3ptg1
title: 002_IFNL_Recovery_scRNAseq__02_GlobalHeterogeneity.html
DOI: 10.57745/i1nmr9
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/ar98n5
title: .DS_Store
DOI: 10.57745/cay2dn
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/3ihz4f
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/72jcjm
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/sw3yco
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/th5nc8
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/z3kxep
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/ohnppd
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/a092gs
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/doxvzj
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/qvs5km
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/yf0gxb
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/lbvifp
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/fthgwz
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/eun7yx
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.15468/dl.62a4vr
title: Occurrence Download

description: A dataset containing 3404 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phenacolimax major (A.Férussac, 1807)"
 ]
}

 The dataset includes 3404 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000262-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vzz6x9
title: Occurrence Download

description: A dataset containing 49943 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada)",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus rubra L."
 ]
}

 The dataset includes 49943 records from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106984-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tq5cgr
title: Occurrence Download

description: A dataset containing 96 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus sabiniana Douglas ex D.Don",
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is unknown or invalid"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Continent" : [
 "is North America"
 ]
 }
 }
 ]
}

 The dataset includes 96 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046124-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6qh7e7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Populus qiongdaoensis T.Hong & P.Luo"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075425-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sj3apn
title: Occurrence Download

description: A dataset containing 599 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus taeda L.",
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is unknown or invalid"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is United States of America"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Country" : [
 "is Canada"
 ]
 }
 },
 {
 "not" : {
 "Continent" : [
 "is North America"
 ]
 }
 }
 ]
}

 The dataset includes 599 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046128-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.