cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15454129
title: Processed Output for TIW Analysis

description: Post-Processed Output for TIW Analysis

These dataset include the processed diagnostics used in the study:

“Consistent projected changes of Tropical Instability Waves under greenhouse warming"

🔹 Archived Content

###CESM-UHR Simulations (Present Day, 2xCO2, 4×CO2)

CESM_TIW_intensity.zip:TIW intensity metrics under three simulations, including TIW-EKE, RMS of SST, SSH, and VVEL.

CESM_energy.zip:Barotropic (BTR) and baroclinic (BCR) energy conversion rates in CESM-UHR.

CESM_feedback.zip:Monthly diagnostics of TIW-induced heat and momentum flux convergence.

🔗 Data Source: https://climatedata.ibs.re.kr

###E3SMv1/v2 Simulations (piControl, abrupt-4×CO2)

E3SMv1_TIW_intensity.zip & E3SMv2_TIW_intensity.zip:Processed TIW intensity metrics based on EKE and SST/SSH.Due to limited variable availability, VVEL-based metric, energy budget and flux diagnostics are not included.

🔗 Data Source: https://aims2.llnl.gov/search/E3SM

###CMIP6 HighResMIP (piControl, abrupt-4×CO2)

CMIP_highresMIP_TIW_intensity.zip:SST-based TIW metric computed from daily outputs of multiple HighResMIP models.

🔗 Data Source: https://aims2.llnl.gov/search/cmip6/

###GLORYS12 Ocean Reanalysis

GLORYS12_TIW_intensity.zip:TIW-EKE and SST/SSH/VVEL-based TIW metrics derived from daily outputs of the global GLORYS12 ocean reanalysis (1/12° horizontal resolution) from CMEMS.

🔗 Data Source: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

🔹 Dataset Summary

1. CESM-UHR Processed Diagnostics

    Four TIW metrics computed from daily bandpass-filtered (10–50 days) outputs for all scenarios (30 years in total):





Eddy Kinetic Energy (EKE)




Standard deviation of sea surface temperature (SST)




Standard deviation of sea surface height (SSH)




Standard deviation of meridional velocity (VVEL)



2. CESM-UHR EKE Budget Dominant Terms





Barotropic conversion (BTR): −⟨u′v′⟩∂⟨u⟩/∂y




Baroclinic conversion (BCR): −g⟨ρ′w′⟩



3. TIW-induced Flux Convergence





Heat flux convergence (HFC): ∇·(u'T'), ∇·(v'T')




Momentum flux convergence (MFC): ∇·(u'v')



4. TIW Metrics from E3SMv1 and E3SMv2

     These files include TIW-related diagnostics based on EKE, SST, and SSH from the piControl and abrupt-4xCO2 simulations:





Eddy kinetic energy derived from kinetic energy decomposition




Bandpass-filtered (10–50 day) SST standard deviation




Bandpass-filtered SSH standard deviation



5. CMIP6 HighResMIP TIW Metrics

     SST-based TIW intensity under piControl and abrupt-4xCO2 simulations for multiple HighResMIP models.

6. GLORYS12 TIW Diagnostics

     TIW metrics calculated from daily GLORYS12 (CMEMS) ocean reanalysis (1/12° resolution) for 1993–2023:





Eddy Kinetic Energy (EKE)




Standard deviation of SST




Standard deviation of SSH




Standard deviation of VVEL



🔖 Citation

If you use this dataset, please cite:Xue, A., & Stevenson, S., et al. (2025). Consistent projected changes of Tropical Instability Wave activity under greenhouse warming. 

Zenodo DOI: https://doi.org/10.5281/zenodo.15454130
DOI: 10.5281/zenodo.15454130
title: Processed Output for TIW Analysis

description: Post-Processed Output for TIW Analysis

These dataset include the processed diagnostics used in the study:

“Consistent projected changes of Tropical Instability Waves under greenhouse warming"

🔹 Archived Content

###CESM-UHR Simulations (Present Day, 2xCO2, 4×CO2)

CESM_TIW_intensity.zip:TIW intensity metrics under three simulations, including TIW-EKE, RMS of SST, SSH, and VVEL.

CESM_energy.zip:Barotropic (BTR) and baroclinic (BCR) energy conversion rates in CESM-UHR.

CESM_feedback.zip:Monthly diagnostics of TIW-induced heat and momentum flux convergence.

🔗 Data Source: https://climatedata.ibs.re.kr

###E3SMv1/v2 Simulations (piControl, abrupt-4×CO2)

E3SMv1_TIW_intensity.zip & E3SMv2_TIW_intensity.zip:Processed TIW intensity metrics based on EKE and SST/SSH.Due to limited variable availability, VVEL-based metric, energy budget and flux diagnostics are not included.

🔗 Data Source: https://aims2.llnl.gov/search/E3SM

###CMIP6 HighResMIP (piControl, abrupt-4×CO2)

CMIP_highresMIP_TIW_intensity.zip:SST-based TIW metric computed from daily outputs of multiple HighResMIP models.

🔗 Data Source: https://aims2.llnl.gov/search/cmip6/

###GLORYS12 Ocean Reanalysis

GLORYS12_TIW_intensity.zip:TIW-EKE and SST/SSH/VVEL-based TIW metrics derived from daily outputs of the global GLORYS12 ocean reanalysis (1/12° horizontal resolution) from CMEMS.

🔗 Data Source: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

🔹 Dataset Summary

1. CESM-UHR Processed Diagnostics

    Four TIW metrics computed from daily bandpass-filtered (10–50 days) outputs for all scenarios (30 years in total):





Eddy Kinetic Energy (EKE)




Standard deviation of sea surface temperature (SST)




Standard deviation of sea surface height (SSH)




Standard deviation of meridional velocity (VVEL)



2. CESM-UHR EKE Budget Dominant Terms





Barotropic conversion (BTR): −⟨u′v′⟩∂⟨u⟩/∂y




Baroclinic conversion (BCR): −g⟨ρ′w′⟩



3. TIW-induced Flux Convergence





Heat flux convergence (HFC): ∇·(u'T'), ∇·(v'T')




Momentum flux convergence (MFC): ∇·(u'v')



4. TIW Metrics from E3SMv1 and E3SMv2

     These files include TIW-related diagnostics based on EKE, SST, and SSH from the piControl and abrupt-4xCO2 simulations:





Eddy kinetic energy derived from kinetic energy decomposition




Bandpass-filtered (10–50 day) SST standard deviation




Bandpass-filtered SSH standard deviation



5. CMIP6 HighResMIP TIW Metrics

     SST-based TIW intensity under piControl and abrupt-4xCO2 simulations for multiple HighResMIP models.

6. GLORYS12 TIW Diagnostics

     TIW metrics calculated from daily GLORYS12 (CMEMS) ocean reanalysis (1/12° resolution) for 1993–2023:





Eddy Kinetic Energy (EKE)




Standard deviation of SST




Standard deviation of SSH




Standard deviation of VVEL



🔖 Citation

If you use this dataset, please cite:Xue, A., & Stevenson, S., et al. (2025). Consistent projected changes of Tropical Instability Wave activity under greenhouse warming. 

Zenodo DOI: https://doi.org/10.5281/zenodo.15454130
DOI: 10.5281/zenodo.17044224
title: Datasets of Determinants of Mobile Investment App Adoption Among Indonesian Investors
DOI: 10.5281/zenodo.17044225
title: Datasets of Determinants of Mobile Investment App Adoption Among Indonesian Investors
DOI: 10.15468/dl.atxced
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Charybdis hoplites"
 ]
}

 The dataset includes 23 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021932-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2jy57m
title: Occurrence Download

description: A dataset containing 576 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Commelina clavata"
 ]
}

 The dataset includes 576 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021931-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ptz6bn
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((31.70908 30.02215,15.85114 -46.32246,148.03833 -55.61068,144.30039 -11.20847,131.72732 -1.3539,99.10529 8.04759,102.50342 23.2259,62.4055 32.51412,31.70908 30.02215))",
 "OccurrenceStatus is Present",
 "TaxonKey is Steno bredanensis"
 ]
}

 The dataset includes 82 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021933-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30041182
title: <b>Data from: </b><b>Functional trait dissimilarity drives arboreal ant community assembly while competitive trait hierarchies shape colony performance in experimental mesocosms</b>

description: Included here are data as well as R code and functions required to produce all analyses reported in the paper "<i>Functional trait dissimilarity drives arboreal ant community assembly while competitive trait hierarchies shape colony performance in experimental mesocosms</i>". Detailed explanations and instructions are provided within each R markdown file.
DOI: 10.6084/m9.figshare.30041182.v1
title: <b>Data from: </b><b>Functional trait dissimilarity drives arboreal ant community assembly while competitive trait hierarchies shape colony performance in experimental mesocosms</b>

description: Included here are data as well as R code and functions required to produce all analyses reported in the paper "<i>Functional trait dissimilarity drives arboreal ant community assembly while competitive trait hierarchies shape colony performance in experimental mesocosms</i>". Detailed explanations and instructions are provided within each R markdown file.
DOI: 10.15468/dl.gwcwj7
title: Occurrence Download

description: A dataset listing the 84 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "Geometry POLYGON((-90.48766 14.59222,-90.47355 14.5892,-90.47727 14.59859,-90.49563 14.60634,-90.48766 14.59222))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 84 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021919-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a5p6q6
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zootoca vivipara",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.6 29.26705, -6.6 50.94, 40.81373 50.94, 40.81373 29.26705, -6.6 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 151 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021846-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hj8t9w
title: Occurrence Download

description: A dataset containing 1467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Juncus atratus"
 ]
}

 The dataset includes 1467 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021934-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17042918
title: pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets

description: De novo peptide sequencing enables the identification of a broad repertoire of major histocompatibility complex class I-associated peptides, providing a valuable source of potential immunotherapeutic targets in cancer. Nevertheless, its practical utility remains limited by the absence of robust methods for confidently determining peptide-spectrum matches and interpreting their proteogenomic information. This chapter presents the pXg workflow, designed to comprehensively and reliably identify peptides derived from de novo sequencing in mass spectrometry-based immunopeptidomics, integrated with matched RNA-seq data. We guide the reader through the use of pXg, covering the rescoring of peptide-spectrum matches and the estimation of a false discovery rate from the popular de novo sequencing tool Casanovo. This ultimately yields rich proteogenomic insights into the identified peptides.
DOI: 10.5281/zenodo.17042919
title: pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets

description: De novo peptide sequencing enables the identification of a broad repertoire of major histocompatibility complex class I-associated peptides, providing a valuable source of potential immunotherapeutic targets in cancer. Nevertheless, its practical utility remains limited by the absence of robust methods for confidently determining peptide-spectrum matches and interpreting their proteogenomic information. This chapter presents the pXg workflow, designed to comprehensively and reliably identify peptides derived from de novo sequencing in mass spectrometry-based immunopeptidomics, integrated with matched RNA-seq data. We guide the reader through the use of pXg, covering the rescoring of peptide-spectrum matches and the estimation of a false discovery rate from the popular de novo sequencing tool Casanovo. This ultimately yields rich proteogenomic insights into the identified peptides.
DOI: 10.15468/dl.d5jfu2
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hydrovatus villiersi, Hydrovatus vulneratus, Hyphydrus signatus, Hyphydrus caffer, Hyphydrus circumflexus, Hyphydrus cycloides, Hyphydrus grandis, Hyphydrus impressus, Hyphydrus komghaensis, Hyphydrus prozeskyi, Hyphydrus residuus, Hyphydrus soni, Laccophilus canthydroides, Laccophilus concisus, Laccophilus contiro, Laccophilus pallescens, Laccophilus immundus, Laccophilus necopinus, Laccophilus remex, Laccophilus shephardi, Laccophilus simplicistriatus, Laccophilus torquatus, Laccophilus vermiculosus, Laccophilus, Leiodytes australis, Leiodytes, Leiodytes evanescens, Leiodytes hieroglyphicus, Nebrioporus capensis, Nebrioporus, Nebrioporus solivagus, Nebrioporus vagrans, Peschetius ultimus, Philaccolus lineatoguttatus, Philaccolus, Philodytes, Platydytes coarctaticollis, Platydytes, Platydytes cooperae, Primospes suturalis, Primospes, Rhantus capensis, Rhantus, Rhantus cicurius, Rhantus concolorans, Sharphydrus capensis, Tikoloshanes eretiformis, Tikoloshanes, Tyndallhydrus caraboides, Tyndallhydrus, Uvarus gschwendtneri, Yola babaulti, Yola dilatata, Yola dohrni, Yola peringueyi, Yola simulantis, Yola swierstrai, Yola tuberculata, Ahaggaria, Ctenelmis discrepans, Ctenelmis, Ctenelmis elegans, Ctenelmis harrisoni, Ctenelmis incerta, Ctenelmis lata, Ctenelmis rufipes, Ctenelmis tibialis, Haplelmis mixta, Helminthocharis cristula, Helminthocharis, Helminthopsis allansoni, Helminthopsis bifida, Helminthopsis ciliata, Helminthopsis elongata, Leielmis georyssoides, Leielmis, Leptelmis fragilis, Microdinodes simoni, Microdinodes transvaalicus, Microdinodes vaalensis, Omotonus notabilis, Pachyelmis amaena, Peloriolus costulatipennis, Peloriolus difficilis, Peloriolus interstitialis, Peloriolus parvulus, Peloriolus patruelis, Peloriolus pilosellus, Potamodytes africanus, Potamodytes brincki, Potamodytes major, Leptelmis major, Protelmis chutteri, Pseudancyronyx humeralis natalensis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021935-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12948356
title: Health Workforce Shortage Areas
DOI: 10.15468/dl.4kxyf6
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lepocinclis acus, Lepocinclis, Eunotia pectinalis, Bacillaria ulna, Haematococcus, Mesotaenium, Microspora, Neidium, Netrium, , Penium, Pleurococcus, Pseudendoclonium, , Stauroneis gracilior, , Achnanthes linearioides, Achnanthidium catenatum, Achnanthes eutrophila, Achnanthes straubiana, Frustulia copulata, Aulacoseira granulata, Vibrio paxillifer, Chroodactylon ornatum, Navicula buderi, Cyclotella pseudostelligera, Encyonema mesianum, Pseudostaurosira parasitica, , Gomphonema parvulum lagenula, Gyrosigma scalproides, Achnanthidium hungaricum, Luticola kotschyi, Navicula cryptonella, Cymbella pusilla, Nitzschia paleacea, Nitzschia sigma, Navicula umbonata, Pseudostaurosira brevistriata, Pyxidicula operculata, Nitzschia delognei, Surirella delicatissima, Fragilaria rumpens, Achnanthidium minutissimum, Pseudostaurosira subconstricta, Myriophyllum, Gomphonema gracile, Sellaphora nigri, Achnanthes catenata, Achnanthes minutissima, Achnanthidium linearioides, Achnanthidium straubianum, Amphora copulata, Aphanizomenon, Aphanizomenon flosaquae, Melosira granulata, Bacillaria paradoxa, Vibrio, Chaetomorpha, Chlorogonium, Cladophora fracta, Compsopogon, Compsopogon coeruleus, Craticula buderi, Cylindrocystis brebissonii, Cymbella minuta, Cymbella silesiaca, Desmidium, Discostella pseudostelligera, Discostella stelligera, Dolichospermum, Dolichospermum flosaquae, Dorofeyukea kotschyi, Enteromorpha, Euglena acus, Eunotia sudetica, Exilaria, Exilaria vaucheriae, Fragilaria capucina rumpens, Fragilaria construens, Fragilaria parasitica, Fragilaria tenera, Frustulia adnata, Frustulia crassinervia, Frustulia tugelae, Gonatozygon, Halamphora, Halamphora coffeaeformis, Himantidium, Himantidium exiguum, Karayevia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021937-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9v353x
title: Occurrence Download

description: A dataset containing 2025 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podocarpus latifolius",
 "Geometry POLYGON ((-17.62504 -34.81917, 51.13387 -34.81917, 51.13387 37.34999, -17.62504 37.34999, -17.62504 -34.81917))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2025 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021936-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30041167
title: Biomass regression models of individual tree.xlsx

description: ### Data 1: Description of the file "Biomass regression models of individual tree.xlsx"<br>This file mainly focuses on the biomass regression models of individual trees. It contains data related to the measured values of leaf, branch, and trunk biomass of different individual trees, as well as the regression models constructed based on this data. The data also involves the measured and estimated values of tree height, diameter at breast height (DBH), and crown width. Regression models are built based on these data to predict biomass using easily measurable tree characteristics.<br><br>### Data 2: Description of the file "Nanfeng - Biomass regression models of first order branch.xlsx"<br>This file mainly presents the biomass regression models for the first - order branches of Nanfeng tangerines. Taking the first - order branches as the research object, it includes the length and diameter of different levels of sub - branches under the first - order branches, as well as the corresponding biomass data. Regression models are constructed based on these data.<br><br>### Data 3: Description of the file "Ponkan - Biomass regression models of first order branch.xlsx"<br>This file mainly provides the biomass regression models for the first - order branches of Ponkan mandarins. Focusing on the first - order branches, it contains the length and diameter of different levels of sub - branches under the first - order branches and the corresponding biomass data. Regression models are established based on these data.
DOI: 10.6084/m9.figshare.30041167.v1
title: Biomass regression models of individual tree.xlsx

description: ### Data 1: Description of the file "Biomass regression models of individual tree.xlsx"<br>This file mainly focuses on the biomass regression models of individual trees. It contains data related to the measured values of leaf, branch, and trunk biomass of different individual trees, as well as the regression models constructed based on this data. The data also involves the measured and estimated values of tree height, diameter at breast height (DBH), and crown width. Regression models are built based on these data to predict biomass using easily measurable tree characteristics.<br><br>### Data 2: Description of the file "Nanfeng - Biomass regression models of first order branch.xlsx"<br>This file mainly presents the biomass regression models for the first - order branches of Nanfeng tangerines. Taking the first - order branches as the research object, it includes the length and diameter of different levels of sub - branches under the first - order branches, as well as the corresponding biomass data. Regression models are constructed based on these data.<br><br>### Data 3: Description of the file "Ponkan - Biomass regression models of first order branch.xlsx"<br>This file mainly provides the biomass regression models for the first - order branches of Ponkan mandarins. Focusing on the first - order branches, it contains the length and diameter of different levels of sub - branches under the first - order branches and the corresponding biomass data. Regression models are established based on these data.
DOI: 10.5281/zenodo.17044072
title: Force and position dataset for "Geometry and Elasticity of a Knitted Fabric"

description: Experimental data of a 51 x 51 stockinette stitch fabric under displacement-imposed cyclic uniaxial tension. Information on the content is indicated in the "Read me" file. These data are featured in the following article:

Poincloux, S., Adda-Bedia, M., & Lechenault, F. (2018). Geometry and elasticity of a knitted fabric. Physical Review X, 8(2), 021075. https://doi.org/10.1103/PhysRevX.8.021075
DOI: 10.15468/dl.csegtx
title: Occurrence Download

description: A dataset containing 169208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thalasseus sandvicensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.6 29.26705, -6.6 50.94, 40.81373 50.94, 40.81373 29.26705, -6.6 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 169208 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021845-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.