cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15004387
title: Canopy height map in China - 2022 part 1

description: This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

 

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

 

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

 

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

 

To use the data, please cite this paper or contact the corresponding author for more details.

 

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...
DOI: 10.15468/dl.ye97h3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.456 -0.894817, 102.385728 -0.900383, 102.317186 -0.916945, 102.252061 -0.944094, 102.191955 -0.981163, 102.138349 -1.027239, 102.092561 -1.081188, 102.05572 -1.141682, 102.028732 -1.207232, 102.012263 -1.276223, 102.006719 -1.346958, 102.012237 -1.417695, 102.028683 -1.486693, 102.055653 -1.552251, 102.092482 -1.612757, 102.138266 -1.666718, 102.191876 -1.712807, 102.251994 -1.749887, 102.317138 -1.777046, 102.385703 -1.793613, 102.456 -1.799181, 102.526297 -1.793613, 102.594862 -1.777046, 102.660006 -1.749887, 102.720124 -1.712807, 102.773734 -1.666718, 102.819518 -1.612757, 102.856347 -1.552251, 102.883317 -1.486693, 102.899763 -1.417695, 102.905281 -1.346958, 102.899737 -1.276223, 102.883268 -1.207232, 102.85628 -1.141682, 102.819439 -1.081188, 102.773651 -1.027239, 102.720045 -0.981163, 102.659939 -0.944094, 102.594814 -0.916945, 102.526272 -0.900383, 102.456 -0.894817))"},{"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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047662-251120083545085/datasets/export for details.
DOI: 10.15468/dl.c8rarq
title: Occurrence Download

description: A dataset containing 41592287 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "EventDate 2008-01-01-2025-01-01",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 41592287 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047627-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.csku9k
title: Occurrence Download

description: A dataset containing 1087 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum torvum"
 ]
}

 The dataset includes 1087 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047663-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15004813
title: Canopy height map in China - 2022 part 3

description: This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

 

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

 

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

 

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

 

To use the data, please cite this paper or contact the corresponding author for more details.

 

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...
DOI: 10.5281/zenodo.15004812
title: Canopy height map in China - 2022 part 3

description: This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

 

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

 

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

 

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

 

To use the data, please cite this paper or contact the corresponding author for more details.

 

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...
DOI: 10.7910/dvn/jy4obk
title: Beyond Universal AI Models: A Comparative Analysis of Vision-Driven (GCC) vs. Regulation-Driven (EU) Adoption Frameworks

description: This dataset contains replication materials for the study "Beyond Universal AI Models: A Comparative Analysis of Vision-Driven (GCC) vs. Regulation-Driven (EU) Adoption Frameworks". The data supports a mixed-methods approach combining a survey of public sector professionals in the Gulf Cooperation Council (GCC) with a document analysis of National AI Strategies (NASs).

The repository includes the following files:

1. AI Adoption Survey (Responses).xlsx: Contains raw, anonymized survey responses (n=203) collected from public sector professionals across six GCC countries: Saudi Arabia, UAE, Qatar, Bahrain, Kuwait, and Oman. The survey measures four key constructs: Technical Infrastructure (TI), Organizational Readiness (OR), Governance Environment (GE), and AI Outcomes (AIO).

2. SmartPLS Report.xlsx: Provides the cleaned dataset and the complete output from the Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis used to validate the study's theoretical model.

3. The Codebook (v2): A detailed protocol for the document analysis of GCC National AI Strategies4. This document outlines the inclusion/exclusion rules and definitions for coding capacity-building references (TI, OR, GE) 5. It also details the methodology for Term-Salience Scoring (TF-IDF) used to quantify the emphasis on specific AI capabilities within the strategies.

4. Supplementary Materials: A compilation of five appendices providing extended statistical diagnostics:
- Appendix A: Detailed survey respondent demographics by country.
- Appendix B: Item-level descriptive statistics (Mean, Standard Deviation) for all survey constructs.
- Appendix C: Clustering diagnostics, including Elbow plots and within-cluster sum of squares, supporting the identification of distinct adoption clusters.
- Appendix D: Principal Component Analysis (PCA) details, including KMO values (0.937), Bartlett's Test results (p&lt;.001), and Scree plots confirming the factor solution.
- Appendix E: Extended structural model diagnostics, reporting Coefficient of Determination (R^2) and Effect Sizes (f^2) for the predictor paths.
DOI: 10.15468/dl.d3x6fs
title: Occurrence Download

description: A dataset containing 197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rotaria",
 "Year 2023-2023"
 ]
}

 The dataset includes 197 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047664-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nkr7mx
title: Occurrence Download

description: A dataset containing 1155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum tridynamum"
 ]
}

 The dataset includes 1155 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047665-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7yuup5
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.455 -0.887817, 103.384728 -0.893383, 103.316187 -0.909945, 103.251061 -0.937094, 103.190956 -0.974163, 103.137349 -1.020239, 103.091562 -1.074188, 103.05472 -1.134682, 103.027733 -1.200232, 103.011264 -1.269223, 103.00572 -1.339959, 103.011239 -1.410696, 103.027685 -1.479693, 103.054654 -1.545252, 103.091483 -1.605757, 103.137267 -1.659718, 103.190877 -1.705807, 103.250995 -1.742887, 103.316138 -1.770046, 103.384703 -1.786613, 103.455 -1.792181, 103.525297 -1.786613, 103.593862 -1.770046, 103.659005 -1.742887, 103.719123 -1.705807, 103.772733 -1.659718, 103.818517 -1.605757, 103.855346 -1.545252, 103.882315 -1.479693, 103.898761 -1.410696, 103.90428 -1.339959, 103.898736 -1.269223, 103.882267 -1.200232, 103.85528 -1.134682, 103.818438 -1.074188, 103.772651 -1.020239, 103.719044 -0.974163, 103.658939 -0.937094, 103.593813 -0.909945, 103.525272 -0.893383, 103.455 -0.887817))"},{"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 55 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047666-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15004943
title: Canopy height map in China - 2022 part 4

description: This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

 

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

 

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

 

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

 

To use the data, please cite this paper or contact the corresponding author for more details.

 

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...
DOI: 10.5281/zenodo.15004944
title: Canopy height map in China - 2022 part 4

description: This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

 

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

 

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

 

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

 

To use the data, please cite this paper or contact the corresponding author for more details.

 

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...
DOI: 10.15468/dl.udtvg4
title: Occurrence Download

description: A dataset containing 974686 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -45.914722",
 "DecimalLatitude is less than or equal to -40.914722",
 "DecimalLongitude is greater than or equal to 145.0",
 "DecimalLongitude is less than or equal to 150.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 974686 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047624-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zzppg
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lithachne horizontalis"
 ]
}

 The dataset includes 87 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047667-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.