cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15170402
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project.

(1) Tn5_NN_model.h5

Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.

(2) Tn5ModelTutorial.ipynb

Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.

(3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz

h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.

(4) dispModel.tar.gz

Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.

(5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds

Motif PWMs used in our study.

(6) TFBS_model.h5

Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. 

(7) clusterLabels.txt, clusterLabelsAllTFs.txt

Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.

(8) BMMCTutorial.tar.gz

Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.

(9) PBMC_bulk_ATAC_tutorial fragments files.

Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.

(10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).

Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15224770
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project.

(1) Tn5_NN_model.h5

Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.

(2) Tn5ModelTutorial.ipynb

Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.

(3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz

h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.

(4) dispModel.tar.gz

Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.

(5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds

Motif PWMs used in our study.

(6) TFBS_model.h5

Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. 

(7) clusterLabels.txt, clusterLabelsAllTFs.txt

Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.

(8) BMMCTutorial.tar.gz

Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.

(9) PBMC_bulk_ATAC_tutorial fragments files.

Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.

(10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).

Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.

(11) CTCF_degron.tar.gz.

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details.
DOI: 10.5281/zenodo.15399859
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project.

(1) Tn5_NN_model.h5

Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.

(2) Tn5ModelTutorial.ipynb

Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.

(3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, mm39_bias_v2.h5, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz

h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.

(4) dispModel.tar.gz

Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.

(5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds

Motif PWMs used in our study.

(6) TFBS_model.h5

Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. 

(7) clusterLabels.txt, clusterLabelsAllTFs.txt

Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.

(8) BMMCTutorial.tar.gz

Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.

(9) PBMC_bulk_ATAC_tutorial fragments files

Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.

(10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).

Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.

(11) CTCF_degron.tar.gz

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details. 

(12) obsBias.tsv

Input data used for training the Tn5 bias model. For more details see https://github.com/buenrostrolab/PRINT/blob/main/code/predictBias.py (line 84)
DOI: 10.5281/zenodo.7121026
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project.

(1) Tn5_NN_model.h5

Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.

(2) Tn5ModelTutorial.ipynb

Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.

(3) hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, mm39_bias_v2.h5, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz

h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.

(4) dispModel.tar.gz

Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.

(5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds

Motif PWMs used in our study.

(6) TFBS_model.h5

Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. 

(7) clusterLabels.txt, clusterLabelsAllTFs.txt

Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.

(8) BMMCTutorial.tar.gz

Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.

(9) PBMC_bulk_ATAC_tutorial fragments files

Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.

(10) PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).

Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.

(11) CTCF_degron.tar.gz

Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details. 

(12) obsBias.tsv

Input data used for training the Tn5 bias model. For more details see https://github.com/buenrostrolab/PRINT/blob/main/code/predictBias.py (line 84)
DOI: 10.5281/zenodo.7121027
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project.


(1) Tn5_NN_model.h5


Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.


(2) Tn5ModelTutorial.ipynb, Tn5ModelTutorial.html


Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.


(3) hg38Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz


h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.


(4) dispModel.tar.gz


Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.


(5) cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds


Motif PWMs used in our study.


(6) TFBS_model.h5, TFBS_model_cluster_I.h5


Pre-trained TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present.


TFBS_model.h5 is the "TF habitation model" used in our study. It was trained using data of TFs from all TF clusters.


TFBS_model_cluster_I.h5 was instead only trained on cluster 1 TFs (the TFs that leave the strongest footprints) and is in general not applicable to other TFs.


(7) clusterLabels.txt, clusterLabelsAllTFs.txt


Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.
DOI: 10.5281/zenodo.7790194
title: Multi-scale footprinting

description: Data associated with the multi-scale footprinting project. (1) <strong>Tn5_NN_model.h5</strong> Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model. (2) <strong>Tn5ModelTutorial.ipynb, Tn5ModelTutorial.html</strong> Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences. (3) <strong>hg38Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz</strong> h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model. (4) <strong>dispModel.tar.gz</strong> Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting. (5) <strong>cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds</strong> Motif PWMs used in our study. (6) <strong>TFBS_model.h5, TFBS_model_cluster_I.h5</strong> Pre-trained TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. TFBS_model.h5 is the "TF habitation model" used in our study. It was trained using data of TFs from all TF clusters. TFBS_model_cluster_I.h5 was instead only trained on cluster 1 TFs (the TFs that leave the strongest footprints) and is in general not applicable to other TFs. (7) <strong>clusterLabels.txt, clusterLabelsAllTFs.txt</strong> Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs. (8) <strong>BMMCTutorial.tar.gz</strong> Data needed for our tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.
DOI: 10.15468/dl.tz2mhc
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Registros de especies priorizadas de mamíferos de Colombia, recopilados a partir de la información de fototrampeo",
 "TaxonKey is Puma yagouaroundi (É.Geoffroy Saint-Hilaire, 1803)"
 ]
}

 The dataset includes 89 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029058-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15400030
title: Cardiac Tamponade as a Complication of Type A Acute Aortic Dissection: A Comprehensive Analysis from 25 Years of Registry Data

description: Cardiac tamponade is a life-threatening complication of Type A acute aortic dissection (AAD), significantly increasing morbidity and mortality. This article synthesizes 25 years of registry data to elucidate the pathophysiology, clinical presentation, diagnostic challenges, and management strategies for cardiac tamponade in the context of Type A AAD. We analyze demographic trends, risk factors, imaging modalities, and surgical outcomes, drawing from global registries such as the International Registry of Acute Aortic Dissection (IRAD) and other multicenter studies. The integration of advanced imaging, timely surgical intervention, and multidisciplinary approaches has improved survival rates, though challenges persist in delayed diagnoses and resourcelimited settings. This review provides a comprehensive framework for clinicians and researchers, supported by illustrative figures and an extensive reference list adhering to Vancouver citation standards.
DOI: 10.5281/zenodo.15400031
title: Cardiac Tamponade as a Complication of Type A Acute Aortic Dissection: A Comprehensive Analysis from 25 Years of Registry Data

description: Cardiac tamponade is a life-threatening complication of Type A acute aortic dissection (AAD), significantly increasing morbidity and mortality. This article synthesizes 25 years of registry data to elucidate the pathophysiology, clinical presentation, diagnostic challenges, and management strategies for cardiac tamponade in the context of Type A AAD. We analyze demographic trends, risk factors, imaging modalities, and surgical outcomes, drawing from global registries such as the International Registry of Acute Aortic Dissection (IRAD) and other multicenter studies. The integration of advanced imaging, timely surgical intervention, and multidisciplinary approaches has improved survival rates, though challenges persist in delayed diagnoses and resourcelimited settings. This review provides a comprehensive framework for clinicians and researchers, supported by illustrative figures and an extensive reference list adhering to Vancouver citation standards.
DOI: 10.15468/dl.8fd2pv
title: Occurrence Download

description: A dataset listing the 15769 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "StateProvince is one of (magdalena)",
 "GadmGid is one of (COL.20_2)",
 "BasisOfRecord is one of (Occurrence evidence, Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Fossil, Living Specimen)"
 ]
 }
 ]
}

 The dataset's 15769 records were derived from 482 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029053-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9e437j
title: Occurrence Download

description: A dataset containing 86465 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Stercorarius parasiticus (Linnaeus, 1758)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 86465 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029051-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14754771
title: Dataset for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidence Learning"

description: The foundational datasets for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidence Learning"  include data_train.pkl and data_test.pkl. The former serves as the training dataset for Bayesian Evidence Learning (BEL), while the latter contains laboratory data. All computations and figures presented in the paper are derived from these two datasets.
DOI: 10.5281/zenodo.14754770
title: Dataset for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidential Learning"

description: The foundational datasets for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidential Learning"  include data_train.pkl and data_test.pkl. The former serves as the training dataset for Bayesian Evidence Learning (BEL), while the latter contains laboratory data. All computations and figures presented in the paper are derived from these two datasets.
DOI: 10.5281/zenodo.15399961
title: Dataset for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidential Learning"

description: The foundational datasets for "Uncertainty Analysis of Contaminant Sources in Fracture Networks: A Framework Integrating Falsification and Bayesian Evidential Learning"  include data_train.pkl and data_test.pkl. The former serves as the training dataset for Bayesian Evidence Learning (BEL), while the latter contains laboratory data. All computations and figures presented in the paper are derived from these two datasets.
DOI: 10.15468/dl.p3qn4v
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Monitoreo pasivo de fauna silvestre con cámaras trampa en el Magdalena Medio, Santander y Cesar, Colombia - Proyecto FIBRAS",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Puma yagouaroundi (É.Geoffroy Saint-Hilaire, 1803)"
 ]
}

 The dataset includes 77 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029061-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ezy3z
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX.21.210_2",
 "TaxonKey is Begonia heracleifolia Cham. & Schltdl."
 ]
}

 The dataset includes 14 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029062-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x9f7mu
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Inventario de fauna presente en la jurisdicción de Corantioquia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Puma yagouaroundi (É.Geoffroy Saint-Hilaire, 1803)"
 ]
}

 The dataset includes 30 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029063-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.