cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6uvetn
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-87.70399999999999 15.950851, -87.777056 15.945276, -87.848302 15.928689, -87.915972 15.901501, -87.978392 15.864386, -88.034019 15.818261, -88.08148300000001 15.76427, -88.11961700000001 15.703747, -88.147487 15.638186, -88.16441500000001 15.569206, -88.169995 15.498509, -88.164102 15.427835, -88.146891 15.358924, -88.118798 15.29347, -88.08052000000001 15.233081, -88.033007 15.179238, -87.977428 15.133263, -87.91515200000001 15.096282, -87.847706 15.0692, -87.776743 15.052682, -87.70399999999999 15.04713, -87.63125700000001 15.052682, -87.560294 15.0692, -87.492848 15.096282, -87.430572 15.133263, -87.374993 15.179238, -87.32747999999999 15.233081, -87.289202 15.29347, -87.261109 15.358924, -87.243898 15.427835, -87.238005 15.498509, -87.243585 15.569206, -87.260513 15.638186, -87.288383 15.703747, -87.326517 15.76427, -87.373981 15.818261, -87.429608 15.864386, -87.492028 15.901501, -87.559698 15.928689, -87.630944 15.945276, -87.70399999999999 15.950851))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 135 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005752-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v9a3kz
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89097 24.812449, 88.81361 24.806872, 88.738175 24.790279, 88.666541 24.763084, 88.600486 24.725963, 88.541645 24.679837, 88.491471 24.625852, 88.451194 24.565346, 88.421798 24.499816, 88.403992 24.430884, 88.398196 24.360249, 88.404532 24.289653, 88.422826 24.220832, 88.452609 24.155477, 88.493133 24.095191, 88.54339299999999 24.041449, 88.602149 23.995566, 88.667956 23.958664, 88.739203 23.931643, 88.81415 23.915163, 88.89097 23.909624, 88.96778999999999 23.915163, 89.042737 23.931643, 89.113984 23.958664, 89.17979099999999 23.995566, 89.238547 24.041449, 89.28880700000001 24.095191, 89.329331 24.155477, 89.35911400000001 24.220832, 89.377408 24.289653, 89.38374399999999 24.360249, 89.377948 24.430884, 89.360142 24.499816, 89.330746 24.565346, 89.290469 24.625852, 89.240295 24.679837, 89.181454 24.725963, 89.115399 24.763084, 89.04376499999999 24.790279, 88.96832999999999 24.806872, 88.89097 24.812449))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005753-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.64d8jg
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.374 6.416132, 117.303298 6.410561, 117.234341 6.393985, 117.168831 6.366814, 117.108385 6.329718, 117.054492 6.283612, 117.008481 6.229634, 116.971483 6.169116, 116.944407 6.103548, 116.927917 6.034548, 116.922414 5.963815, 116.928031 5.89309, 116.944624 5.824116, 116.971782 5.758589, 117.008833 5.698121, 117.054862 5.644199, 117.108737 5.59815, 117.169131 5.561104, 117.234558 5.533973, 117.303412 5.517423, 117.374 5.511861, 117.444588 5.517423, 117.513442 5.533973, 117.578869 5.561104, 117.639263 5.59815, 117.693138 5.644199, 117.739167 5.698121, 117.776218 5.758589, 117.803376 5.824116, 117.819969 5.89309, 117.825586 5.963815, 117.820083 6.034548, 117.803593 6.103548, 117.776517 6.169116, 117.739519 6.229634, 117.693508 6.283612, 117.639615 6.329718, 117.579169 6.366814, 117.513659 6.393985, 117.444702 6.410561, 117.374 6.416132))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 42 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005755-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fe2fhb
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-86.181 15.995849, -86.25407199999999 15.990274, -86.325334 15.973687, -86.393019 15.946499, -86.45545300000001 15.909384, -86.511092 15.863259, -86.558567 15.809268, -86.59670800000001 15.748745, -86.624584 15.683184, -86.641515 15.614205, -86.647096 15.543507, -86.641201 15.472834, -86.623987 15.403923, -86.59588599999999 15.33847, -86.55759999999999 15.278081, -86.510076 15.224239, -86.454486 15.178264, -86.392197 15.141283, -86.324737 15.114202, -86.253758 15.097684, -86.181 15.092132, -86.108242 15.097684, -86.037263 15.114202, -85.969803 15.141283, -85.90751400000001 15.178264, -85.851924 15.224239, -85.8044 15.278081, -85.766114 15.33847, -85.738013 15.403923, -85.720799 15.472834, -85.714904 15.543507, -85.720485 15.614205, -85.737416 15.683184, -85.765292 15.748745, -85.803433 15.809268, -85.850908 15.863259, -85.906547 15.909384, -85.968981 15.946499, -86.036666 15.973687, -86.107928 15.990274, -86.181 15.995849))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 27 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005757-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.adj5cx
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89097 24.812449, 88.81361 24.806872, 88.738175 24.790279, 88.666541 24.763084, 88.600486 24.725963, 88.541645 24.679837, 88.491471 24.625852, 88.451194 24.565346, 88.421798 24.499816, 88.403992 24.430884, 88.398196 24.360249, 88.404532 24.289653, 88.422826 24.220832, 88.452609 24.155477, 88.493133 24.095191, 88.54339299999999 24.041449, 88.602149 23.995566, 88.667956 23.958664, 88.739203 23.931643, 88.81415 23.915163, 88.89097 23.909624, 88.96778999999999 23.915163, 89.042737 23.931643, 89.113984 23.958664, 89.17979099999999 23.995566, 89.238547 24.041449, 89.28880700000001 24.095191, 89.329331 24.155477, 89.35911400000001 24.220832, 89.377408 24.289653, 89.38374399999999 24.360249, 89.377948 24.430884, 89.360142 24.499816, 89.330746 24.565346, 89.290469 24.625852, 89.240295 24.679837, 89.181454 24.725963, 89.115399 24.763084, 89.04376499999999 24.790279, 88.96832999999999 24.806872, 88.89097 24.812449))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005758-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.asutuh
title: Occurrence Download

description: A dataset containing 1626204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Haliaeetus albicilla",
 "Year 1980-2026"
 ]
}

 The dataset includes 1626204 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005720-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9aj3mm
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.398 6.454131, 117.327293 6.44856, 117.258331 6.431984, 117.192816 6.404813, 117.132366 6.367717, 117.078469 6.321611, 117.032455 6.267633, 116.995454 6.207115, 116.968377 6.141547, 116.951886 6.072547, 116.946383 6.001814, 116.952001 5.931089, 116.968595 5.862115, 116.995755 5.796588, 117.032809 5.73612, 117.078841 5.682199, 117.13272 5.63615, 117.193117 5.599104, 117.258549 5.571973, 117.327408 5.555423, 117.398 5.549861, 117.468592 5.555423, 117.537451 5.571973, 117.602883 5.599104, 117.66328 5.63615, 117.717159 5.682199, 117.763191 5.73612, 117.800245 5.796588, 117.827405 5.862115, 117.843999 5.931089, 117.849617 6.001814, 117.844114 6.072547, 117.827623 6.141547, 117.800546 6.207115, 117.763545 6.267633, 117.717531 6.321611, 117.663634 6.367717, 117.603184 6.404813, 117.537669 6.431984, 117.468707 6.44856, 117.398 6.454131))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 39 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005760-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5683/sp3/ndgqku
title: IEA CO2 Emissions from Fuel Combustion Statistics: Greenhouse Gas Emissions from Energy. CO2 emissions by product and flow, 1960-2019

description: This dataset presents detailed statistics on carbon dioxide (CO2) emissions from fuel combustion by product (coal/peat, oil, gas) and flow (manufacturing, main activity producer, construction and more). Data are presented from 1960 onwards. [Data editions 2015-2020]
description: IEA CO2 Emissions from Fuel Combustion Statistics
description: Series Name Change
DOI: 10.15468/dl.2ytyuh
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-87.693 16.025848, -87.76608299999999 16.020273, -87.837355 16.003686, -87.90505 15.976498, -87.967493 15.939382, -88.023141 15.893258, -88.070622 15.839267, -88.108769 15.778743, -88.13664900000001 15.713183, -88.153582 15.644204, -88.159164 15.573506, -88.153268 15.502833, -88.13605 15.433923, -88.107946 15.368469, -88.069654 15.308081, -88.02212299999999 15.254239, -87.966525 15.208265, -87.90422700000001 15.171284, -87.83675700000001 15.144203, -87.76576900000001 15.127685, -87.693 15.122133, -87.620231 15.127685, -87.549243 15.144203, -87.481773 15.171284, -87.41947500000001 15.208265, -87.363877 15.254239, -87.316346 15.308081, -87.278054 15.368469, -87.24995 15.433923, -87.232732 15.502833, -87.22683600000001 15.573506, -87.232418 15.644204, -87.249351 15.713183, -87.277231 15.778743, -87.315378 15.839267, -87.362859 15.893258, -87.41850700000001 15.939382, -87.48095000000001 15.976498, -87.54864499999999 16.003686, -87.619917 16.020273, -87.693 16.025848))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 134 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005761-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ad5ce5
title: Occurrence Download

description: A dataset containing 2804 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vespa affinis"
 ]
}

 The dataset includes 2804 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005762-260120142942310/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.31004272
title: Description of Instruments/Artifacts

description: This dataset supports a Systematic Mapping study investigating instruments and artifacts for evaluating chatbots in educational contexts. The repository provides a detailed description of the instruments/artifacts found.
DOI: 10.6084/m9.figshare.31004272.v1
title: Description of Instruments/Artifacts

description: This dataset supports a Systematic Mapping study investigating instruments and artifacts for evaluating chatbots in educational contexts. The repository provides a detailed description of the instruments/artifacts found.
DOI: 10.15468/dl.usz6fe
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89097 24.812449, 88.81361 24.806872, 88.738175 24.790279, 88.666541 24.763084, 88.600486 24.725963, 88.541645 24.679837, 88.491471 24.625852, 88.451194 24.565346, 88.421798 24.499816, 88.403992 24.430884, 88.398196 24.360249, 88.404532 24.289653, 88.422826 24.220832, 88.452609 24.155477, 88.493133 24.095191, 88.54339299999999 24.041449, 88.602149 23.995566, 88.667956 23.958664, 88.739203 23.931643, 88.81415 23.915163, 88.89097 23.909624, 88.96778999999999 23.915163, 89.042737 23.931643, 89.113984 23.958664, 89.17979099999999 23.995566, 89.238547 24.041449, 89.28880700000001 24.095191, 89.329331 24.155477, 89.35911400000001 24.220832, 89.377408 24.289653, 89.38374399999999 24.360249, 89.377948 24.430884, 89.360142 24.499816, 89.330746 24.565346, 89.290469 24.625852, 89.240295 24.679837, 89.181454 24.725963, 89.115399 24.763084, 89.04376499999999 24.790279, 88.96832999999999 24.806872, 88.89097 24.812449))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005763-260120142942310/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.28080597
title: Recovery of sulphate from industrial wastewater in the form of strontium sulphate and optimisation using hybrid metaheuristic approach: RSM-GA and RSM-PSO

description: Large quantities of inorganic salts from process effluent are inevitable and a threat to the environment. Sulphates were the major anions (initial concentration = 70 g/L) in pigment industry effluent. Strontium ions could recover the sulphate by precipitation as SrSO<sub>4</sub>, a commercially useful precipitate. The stoichiometric ratio of precipitant, mixing, and temperature were optimised by response surface methodology (RSM) and the precipitant ratio had the maximum influence (Sensitivity 99.62%). The model created by RSM showed notable performance (predicted R<sup>2</sup> = 0.9987). Further enhancement of optimisation was done by coupling RSM with a genetic algorithm (GA) and particle swarm optimisation (PSO). Both of them were able to determine the global optima efficiently when their hyper-parameters were optimised. In GA, the crossover ratio was higher than the mutation ratio. PSO converged close to 20 iterations, exhibiting better exploration and exploitation capacity. The optimised values of stoichiometric ratio (1.35), temperature (30.7°C), and mixing speed (230 rpm) were validated experimentally, which gave 99.95% sulphate utilisation. The precipitation reaction followed first-order kinetics (R<sup>2</sup> = 0.9908) and was majorly dependent on precipitant concentration. Preliminary cost estimation with Aspen showed that chemical precipitation could be an employable option for utilising high levels of sulphate from wastewater.
DOI: 10.6084/m9.figshare.28080597.v1
title: Recovery of sulphate from industrial wastewater in the form of strontium sulphate and optimisation using hybrid metaheuristic approach: RSM-GA and RSM-PSO

description: Large quantities of inorganic salts from process effluent are inevitable and a threat to the environment. Sulphates were the major anions (initial concentration = 70 g/L) in pigment industry effluent. Strontium ions could recover the sulphate by precipitation as SrSO<sub>4</sub>, a commercially useful precipitate. The stoichiometric ratio of precipitant, mixing, and temperature were optimised by response surface methodology (RSM) and the precipitant ratio had the maximum influence (Sensitivity 99.62%). The model created by RSM showed notable performance (predicted R<sup>2</sup> = 0.9987). Further enhancement of optimisation was done by coupling RSM with a genetic algorithm (GA) and particle swarm optimisation (PSO). Both of them were able to determine the global optima efficiently when their hyper-parameters were optimised. In GA, the crossover ratio was higher than the mutation ratio. PSO converged close to 20 iterations, exhibiting better exploration and exploitation capacity. The optimised values of stoichiometric ratio (1.35), temperature (30.7°C), and mixing speed (230 rpm) were validated experimentally, which gave 99.95% sulphate utilisation. The precipitation reaction followed first-order kinetics (R<sup>2</sup> = 0.9908) and was majorly dependent on precipitant concentration. Preliminary cost estimation with Aspen showed that chemical precipitation could be an employable option for utilising high levels of sulphate from wastewater.
DOI: 10.6084/m9.figshare.31130142
title: Visualising population distribution with choropleth maps: which classification methods are suitable for South African population data?

description: Choropleth maps are among the oldest and still one of the most frequently used techniques for visualising quantitative data, such as population density. Today, each geographic information system (GIS) has a plethora of options for categorising data into classes for choropleth maps. Each option has its pros and cons, depending on the data and the use case. This paper presents the results of a user study to assess the suitability of different data classification methods for effectively visualising population distribution with choropleth maps in South African metropolitan areas. The research focused on geographic accessibility as a use case: population density is visualised on choropleth maps, allowing decision makers to identify over- or underserved areas for the provisioning of facilities. Results show that respondents were more likely to provide correct answers when presented with maps visualising data classified according to quantiles and natural breaks (Jenks), suggesting that these are easier to interpret for assessing and understanding population distribution in South Africa.
DOI: 10.6084/m9.figshare.31130142.v1
title: Visualising population distribution with choropleth maps: which classification methods are suitable for South African population data?

description: Choropleth maps are among the oldest and still one of the most frequently used techniques for visualising quantitative data, such as population density. Today, each geographic information system (GIS) has a plethora of options for categorising data into classes for choropleth maps. Each option has its pros and cons, depending on the data and the use case. This paper presents the results of a user study to assess the suitability of different data classification methods for effectively visualising population distribution with choropleth maps in South African metropolitan areas. The research focused on geographic accessibility as a use case: population density is visualised on choropleth maps, allowing decision makers to identify over- or underserved areas for the provisioning of facilities. Results show that respondents were more likely to provide correct answers when presented with maps visualising data classified according to quantiles and natural breaks (Jenks), suggesting that these are easier to interpret for assessing and understanding population distribution in South Africa.
DOI: 10.15468/dl.mg2n6v
title: Occurrence Download

description: A dataset containing 1988937 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cerastium"
 ]
}

 The dataset includes 1988937 records from 4499 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005679-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hk9b4u
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Centrosema pubescens Benth."
 ]
}

 The dataset includes 22 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009598-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.