cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/cf8p2-4x478
title: Legionella IcmQ71::TevS 42C_On

description: Legionella IcmQ71::TevS 42C_On (HS004 E)
description: <strong>Tilt Series Date:</strong> 2019-05-31</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-51.0°, 51.0°), step: 3.0°, constant angular increment, dosage: 100.0 eV/Å²,  defocus: -6.0 μm,  magnification: 42000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Howard Shuman (University of Chicago). Data collected by Debnath Ghosal and Yuxi Liu.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 3 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-44/rawdata/</p>HS004E-area2-tomo0008.mrc,  Tilt Series (Pixel Size 0.26 nm), 996.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-44/rawdata/HS004E-area2-tomo0008.mrc"
        > <i class="download icon"></i> Download </a></p>

        HS004E-area2-tomo0008_part35_2.rec,  Reconstruction (Pixel Size 1.04 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-05-31-44/3dimage_103722/HS004E-area2-tomo0008_part35_2.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.he4f7y
title: Occurrence Download

description: A dataset containing 124 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is one of (India Biodiversity Portal publication grade dataset, The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris), WII Herbarium Dataset, Royal Botanic Gardens, Kew - Herbarium Specimens, Naturalis Biodiversity Center (NL) - Botany, Herbarium of the French Institute of Pondicherry, NMNH Extant Specimen Records (USNM, US), Royal Botanic Gardens, Kew - Economic Botany Collection Specimens, iNaturalist Research-grade Observations, SeasonWatch - Tree Phenology Dataset (India))",
 "OccurrenceStatus is Present",
 "TaxonKey is Chloroxylon swietenia (Roxb.) DC."
 ]
}

 The dataset includes 124 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006798-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2aatua
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((9.16964 45.24202,9.16882 45.24202,9.16794 45.24235,9.16386 45.24162,9.16339 45.24257,9.16211 45.24257,9.16245 45.24026,9.1569 45.23956,9.15888 45.23775,9.15934 45.23523,9.15864 45.23358,9.15892 45.2316,9.16336 45.23205,9.16417 45.23023,9.1734 45.23113,9.17427 45.23063,9.17684 45.23022,9.17649 45.22931,9.17911 45.22935,9.17923 45.22893,9.18074 45.22877,9.18074 45.22856,9.1801 45.22658,9.18238 45.22781,9.18272 45.2272,9.18483 45.22781,9.18547 45.22764,9.18752 45.22929,9.18804 45.22913,9.1881 45.2288,9.18898 45.2297,9.20296 45.22386,9.20383 45.22229,9.20319 45.2218,9.20476 45.22064,9.20546 45.22097,9.20709 45.21874,9.20913 45.21956,9.20826 45.22097,9.20891 45.22146,9.20943 45.22088,9.20955 45.22183,9.20862 45.22323,9.20821 45.22356,9.21465 45.22854,9.21367 45.23061,9.21519 45.2325,9.21619 45.23328,9.21684 45.23501,9.21689 45.2351,9.21655 45.23559,9.21573 45.23534,9.21503 45.23576,9.21427 45.2358,9.21369 45.23547,9.2138 45.2351,9.21444 45.23316,9.21239 45.23251,9.20998 45.23678,9.20743 45.24142,9.20303 45.24219,9.20277 45.24325,9.20117 45.24436,9.20067 45.24519,9.20138 45.24564,9.20138 45.24578,9.20234 45.24657,9.20345 45.2452,9.20365 45.24533,9.20519 45.24394,9.20472 45.24337,9.20522 45.24254,9.20648 45.24355,9.20896 45.24416,9.20995 45.24416,9.21097 45.24434,9.21244 45.24467,9.21299 45.24475,9.21325 45.2451,9.21262 45.24704,9.213 45.24826,9.21289 45.24869,9.2133 45.24908,9.21278 45.25075,9.21114 45.25047,9.21086 45.25173,9.20992 45.25171,9.20978 45.25181,9.20797 45.25159,9.20883 45.24928,9.20883 45.24928,9.2067 45.24928,9.2056 45.25015,9.20475 45.25031,9.20408 45.25052,9.2035 45.25058,9.19959 45.25193,9.19769 45.25301,9.19695 45.25596,9.1968 45.2561,9.19344 45.25553,9.19355 45.2546,9.19306 45.25434,9.19308 45.25343,9.19345 45.25099,9.19138 45.24914,9.19204 45.24817,9.18822 45.24816,9.18728 45.24758,9.18682 45.24797,9.1865 45.24812,9.18553 45.24771,9.18501 45.24734,9.18515 45.24699,9.18036 45.247,9.17852 45.2469,9.17619 45.24741,9.17584 45.24759,9.17551 45.24769,9.17527 45.24763,9.17454 45.24754,9.17339 45.24786,9.17329 45.24794,9.17278 45.24798,9.17211 45.24806,9.17044 45.24831,9.17025 45.24815,9.17079 45.24741,9.17129 45.24713,9.17137 45.24713,9.17152 45.24687,9.17153 45.24662,9.17112 45.24645,9.16941 45.24594,9.16868 45.24593,9.16756 45.24586,9.16779 45.24538,9.16817 45.24505,9.16778 45.24509,9.1674 45.24502,9.16675 45.24475,9.16844 45.24372,9.16964 45.24202))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 153 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006800-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25438/wes02.29166605
title: City Diplomacy and U.S. Metro Area Exports

description: This is long-form yearly data from 2005-2019 for each of the 100 largest metro areas in the United States in 2019. For each metro area it includes: population, per capita income, metro area export value, USDX, number of foreign Consulate Generals, number of active Sister City International relationships, and the metro area memberships to ten leading international city networks/organizations.
DOI: 10.25438/wes02.29166605.v1
title: City Diplomacy and U.S. Metro Area Exports

description: This is long-form yearly data from 2005-2019 for each of the 100 largest metro areas in the United States in 2019. For each metro area it includes: population, per capita income, metro area export value, USDX, number of foreign Consulate Generals, number of active Sister City International relationships, and the metro area memberships to ten leading international city networks/organizations.
DOI: 10.15468/dl.c9mpqc
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is ZFMK Sound Laboratory",
 "Country is Croatia"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006802-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uumm4r
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Aves (Luomus)",
 "Country is Croatia"
 ]
}

 The dataset includes 26 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006803-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gunreq
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Parrotia subaequalis (H.T.Chang) R.M.Hao & H.T.Wei"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006801-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3sngkk
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Viet Nam, Cambodia, Indonesia, Nepal, Philippines, Malaysia, Chinese Taipei, Bangladesh, Lao People’s Democratic Republic, Thailand, Bhutan)",
 "Geometry POLYGON((77.36830 22.24421,113.63170 22.24421,113.63170 36.75579,77.36830 36.75579,77.36830 22.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Indofevillea khasiana Chatterjee"
 ]
}

 The dataset includes 21 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009220-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15278650
title: A Comprehensive GPU-Accelerated Lattice Boltzmann Framework for High-Reynolds-Number Flow in Complex Rod Bundles
DOI: 10.5281/zenodo.15254186
title: Global wildfire exposure dataset

description: Based on the FireTracks Scientific Dataset, the global wildfire exposure dataset implements space-time alignment between fire clusters and climate as well as socioeconomic data across the globe from 2002 to 2020. The global wildfire exposure dataset is produced to meet the demand for wildfire exposure and its analysis of global inequity. 

The data structure is shown in the following:



fire_clusters.csv: 



Contains detailed records of global wildfire clusters, including fire characteristics, geographic distribution, and population exposure.  






Column
Description
Units
Valid Range
Data Type


cp
fire component label
-
>=0
int64


duration
fire duration
days
>= 1
uint16


maxFRP_sum
sum of maximum fire radiative powers
MW*10
>= 0
float64


area
total burned area
km^2
>= 0.86 (1 MODIS pixel)
float64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


dtime_min
ignition date (YYYY-MM-DD)
-
>= 2002-01-01
datetime64


lat_mean
mean location latitude
degrees
[-180, 180]
float64


lon_mean
mean location longitude
degrees
[-90, 90]
float64


exposure
primary population exposure to wildfire 
persontime
>=0
int64


5km_band_pop
secondary population exposure to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


month
month of occurrence
-
[1, 12]
int64







exposure_four_covariate.csv: 



Aggregates population exposure and covariates for equity-focused analyses, linking wildfire impacts to socioeconomic and climatic drivers. 






Column
Description
Units
Valid Range
Data Type


exposure
population exposed to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


gdp
National Gross Domestic Product for the corresponding year 
million U.S. dollars
>=0
float64


country_pop
national population of the corresponding year
persontime
>=0
int64


fwi
Fire Weather Index
-
>=0
float32


vpd
Vapor Pressure Deficit
kPa
[0, 10]
float32


ndvi
Normalized Difference Vegetation Index
-
[0, 1]
float64


dnbr
Delta Normalized Burn Ratio
-
[-2, 2]
float64


iso3
standardized country code for cross-dataset integration
-
-
string


cluster
results from K-means clustering
-
[0, 2]
int64
DOI: 10.5281/zenodo.15254187
title: Global wildfire exposure dataset

description: Based on the FireTracks Scientific Dataset, the global wildfire exposure dataset implements space-time alignment between fire clusters and climate as well as socioeconomic data across the globe from 2002 to 2020. The global wildfire exposure dataset is produced to meet the demand for wildfire exposure and its analysis of global inequity. 

The data structure is shown in the following:



fire_clusters.csv: 



Contains detailed records of global wildfire clusters, including fire characteristics, geographic distribution, and population exposure.  






Column
Description
Units
Valid Range
Data Type


cp
fire component label
-
>=0
int64


duration
fire duration
days
>= 1
uint16


maxFRP_sum
sum of maximum fire radiative powers
MW*10
>= 0
float64


area
total burned area
km^2
>= 0.86 (1 MODIS pixel)
float64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


dtime_min
ignition date (YYYY-MM-DD)
-
>= 2002-01-01
datetime64


lat_mean
mean location latitude
degrees
[-180, 180]
float64


lon_mean
mean location longitude
degrees
[-90, 90]
float64


population
primary population exposure to wildfire 
persontime
>=0
int64


5km_pop
secondary population exposure to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


month
month of occurrence
-
[1, 12]
int64







exposure_four_covariate.csv: 



Aggregates population exposure and covariates for equity-focused analyses, linking wildfire impacts to socioeconomic and climatic drivers. 






Column
Description
Units
Valid Range
Data Type


population
population exposed to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


gdp
National Gross Domestic Product for the corresponding year 
million U.S. dollars
>=0
float64


country_pop
national population of the corresponding year
persontime
>=0
int64


fwi
Fire Weather Index
-
>=0
float32


vpd
Vapor Pressure Deficit
kPa
[0, 10]
float32


ndvi
Normalized Difference Vegetation Index
-
[0, 1]
float64


dnbr
Delta Normalized Burn Ratio
-
[-2, 2]
float64


iso3
standardized country code for cross-dataset integration
-
-
string


cluster
results from K-means clustering
-
[0, 2]
int64
DOI: 10.5281/zenodo.15278676
title: Global wildfire exposure dataset

description: Based on the FireTracks Scientific Dataset, the global wildfire exposure dataset implements space-time alignment between fire clusters and climate as well as socioeconomic data across the globe from 2002 to 2020. The global wildfire exposure dataset is produced to meet the demand for wildfire exposure and its analysis of global inequity. 

The data structure is shown in the following:



fire_clusters.csv: 



Contains detailed records of global wildfire clusters, including fire characteristics, geographic distribution, and population exposure.  






Column
Description
Units
Valid Range
Data Type


cp
fire component label
-
>=0
int64


duration
fire duration
days
>= 1
uint16


maxFRP_sum
sum of maximum fire radiative powers
MW*10
>= 0
float64


area
total burned area
km^2
>= 0.86 (1 MODIS pixel)
float64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


dtime_min
ignition date (YYYY-MM-DD)
-
>= 2002-01-01
datetime64


lat_mean
mean location latitude
degrees
[-180, 180]
float64


lon_mean
mean location longitude
degrees
[-90, 90]
float64


exposure
primary population exposure to wildfire 
persontime
>=0
int64


5km_band_pop
secondary population exposure to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


month
month of occurrence
-
[1, 12]
int64







exposure_four_covariate.csv: 



Aggregates population exposure and covariates for equity-focused analyses, linking wildfire impacts to socioeconomic and climatic drivers. 






Column
Description
Units
Valid Range
Data Type


exposure
population exposed to wildfire
persontime
>=0
int64


year
year of occurrence
-
[2002, 2020]
int64


country
country of occurrence
-
-
string


continent
continent of occurrence
-
-
string


gdp
National Gross Domestic Product for the corresponding year 
million U.S. dollars
>=0
float64


country_pop
national population of the corresponding year
persontime
>=0
int64


fwi
Fire Weather Index
-
>=0
float32


vpd
Vapor Pressure Deficit
kPa
[0, 10]
float32


ndvi
Normalized Difference Vegetation Index
-
[0, 1]
float64


dnbr
Delta Normalized Burn Ratio
-
[-2, 2]
float64


iso3
standardized country code for cross-dataset integration
-
-
string


cluster
results from K-means clustering
-
[0, 2]
int64
DOI: 10.15482/usda.adc/28199879
title: Data from: Olfactory threshold of dogs (<i>Canis lupus familiaris</i>) to cold-killed Spotted Lantern Fly eggs

description: The Spotted Lantern fly (<i>Lycorma delicatula) </i>is an invasive foreign species that is affecting US forestry and agriculture for which dogs have been deployed to detect and eradicate. However, dogs’ olfactory threshold or limits of detection for this target is unknow. Thus, the aim of this study was to evaluate dogs’ olfactory threshold to dead SLF eggs. Using an automated air dilution olfactometer and an adaptive staircase procedure, we evaluated dogs’ (N =10) olfactory threshold in two experiments. Experiment 1 evaluated the highest air dilution at which dog were able to accurately discriminate between a vial containing cold-killed SLF eggs and a blank vial. In Experiment 2 we added tree bark as background noise to the vials and evaluated the highest air dilution at which dogs were able to discriminate a vial with dead SLF eggs + bark from a vial containing egg free bark. Overall, dogs were able to accurately discriminate dead SLF eggs from a blank vial at a 0.9% (v/v) air dilution. Adding tree bark as background noise increased dogs’ discrimination threshold to 4.1% (v/v) air dilution. Nonetheless, for most of the dogs, their olfactory threshold in Experiment 2 was almost ten-fold higher than in Experiment 1. Our study further confirms that detection dogs are efficient detecting dead SLF eggs and highlight the negative effects background noise can have on olfactory threshold.
DOI: 10.15482/usda.adc/28199879.v1
title: Data from: Olfactory threshold of dogs (<i>Canis lupus familiaris</i>) to cold-killed Spotted Lantern Fly eggs

description: The Spotted Lantern fly (<i>Lycorma delicatula) </i>is an invasive foreign species that is affecting US forestry and agriculture for which dogs have been deployed to detect and eradicate. However, dogs’ olfactory threshold or limits of detection for this target is unknow. Thus, the aim of this study was to evaluate dogs’ olfactory threshold to dead SLF eggs. Using an automated air dilution olfactometer and an adaptive staircase procedure, we evaluated dogs’ (N =10) olfactory threshold in two experiments. Experiment 1 evaluated the highest air dilution at which dog were able to accurately discriminate between a vial containing cold-killed SLF eggs and a blank vial. In Experiment 2 we added tree bark as background noise to the vials and evaluated the highest air dilution at which dogs were able to discriminate a vial with dead SLF eggs + bark from a vial containing egg free bark. Overall, dogs were able to accurately discriminate dead SLF eggs from a blank vial at a 0.9% (v/v) air dilution. Adding tree bark as background noise increased dogs’ discrimination threshold to 4.1% (v/v) air dilution. Nonetheless, for most of the dogs, their olfactory threshold in Experiment 2 was almost ten-fold higher than in Experiment 1. Our study further confirms that detection dogs are efficient detecting dead SLF eggs and highlight the negative effects background noise can have on olfactory threshold.
DOI: 10.3886/e231428
title: Replication data for: Heat Exposure and Youth Migration in Central America and the Caribbean

description: We employ a triple difference-in-difference approach, using censuses and georeferenced temperature data, to quantify heat effects on internal migration in Central America and the Caribbean. A 1-standard deviation increase in heat would affect the lives of 7,314 and 1,578 unskilled young women and men. The effect is smaller than observed in response to droughts and hurricanes but could increase with climate change. Interestingly, youth facing heat waves are more likely to move to urban centers than when exposed to disasters endemic to the region. Research identifying the implications of these choices and interventions available to minimize distress migration is warranted.
DOI: 10.3886/e231428v1
title: Replication data for: Heat Exposure and Youth Migration in Central America and the Caribbean

description: We employ a triple difference-in-difference approach, using censuses and georeferenced temperature data, to quantify heat effects on internal migration in Central America and the Caribbean. A 1-standard deviation increase in heat would affect the lives of 7,314 and 1,578 unskilled young women and men. The effect is smaller than observed in response to droughts and hurricanes but could increase with climate change. Interestingly, youth facing heat waves are more likely to move to urban centers than when exposed to disasters endemic to the region. Research identifying the implications of these choices and interventions available to minimize distress migration is warranted.
DOI: 10.5281/zenodo.15493222
title: Swallow-AE
DOI: 10.5281/zenodo.15552464
title: Swallow-AE
DOI: 10.15468/dl.mx9sy7
title: Occurrence Download

description: A dataset listing the 144 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Bangladesh",
 "TaxonKey is Orchidaceae"
 ]
}

 The dataset's 144 records were derived from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008906-250515123054153/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/13796226
title: League of Legends Matches – 150k In-Game Stats
DOI: 10.15468/dl.m93we2
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.0688 29.39913, -112.9188 29.39913, -112.9188 29.54913, -113.0688 29.54913, -113.0688 29.39913))"
 ]
}

 The dataset includes 19 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046032-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.34emcb
title: Occurrence Download

description: A dataset containing 403 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lophostoma brasiliense"
 ]
}

 The dataset includes 403 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046033-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mcye5k
title: Occurrence Download

description: A dataset containing 158208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-10-25",
 "EventDate is less than or equal to 2025-11-24",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 158208 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046026-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.