cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15006898
title: Above-ground carbon map in China - 2019

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.15006899
title: Above-ground carbon map in China - 2019

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.zze9h5
title: Occurrence Download

description: A dataset containing 12729 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Casiornis rufus (Vieillot, 1816)"
 ]
}

 The dataset includes 12729 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017019-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ebvk73
title: Occurrence Download

description: A dataset containing 1356 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Casiornis fuscus P.L.Sclater & Salvin, 1873"
 ]
}

 The dataset includes 1356 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017020-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s3p769
title: Occurrence Download

description: A dataset containing 12155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ochotona princeps (Richardson, 1828)"
 ]
}

 The dataset includes 12155 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017021-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15051956
title: BRDF corrected hyperspectral remote sensing reflectance

description: This dataset represents the hyperspectral remote sensing reflectance calculated from the above water radiometry data deployed on the ship of ferry that traverse across the Strait of Georgia. This data was collected during the spring and summer of 2018 and 2019.
DOI: 10.5281/zenodo.15051957
title: BRDF corrected hyperspectral remote sensing reflectance

description: This dataset represents the hyperspectral remote sensing reflectance calculated from the above water radiometry data deployed on the ship of ferry that traverse across the Strait of Georgia. This data was collected during the spring and summer of 2018 and 2019.
DOI: 10.6084/m9.figshare.28620227
title: Code used in the study "Substantial contribution of trees outside forests to above-ground carbon across China"

description: This is the initial version of the code used in the paper, "<b>Substantial contribution of trees outside forests to above-ground carbon across China</b>".<br>AuthorsYang 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<br>Affiliationsa 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, Francec UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, Franced College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, Chinae Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, Chinaf Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, Chinag Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmarkh Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Chinai Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, Chinaj State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Chinak ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, Chinal 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, Chinan Electronic Information School, Wuhan University, Wuhan, China o Wuhan Institute of Quantum Technology, Wuhan, Chinap State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, Chinaq Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China<br>Corresponding AuthorYang Su yang.su@ens.fr +33 1 89 10 07 67 École Normale Supérieure – PSL<br>To use the code, please cite this paper or contact the corresponding author for more details.FundingArtificial Intelligence for forest monitoring from space – AI4ForestsAgence Nationale de la RechercheFind out more...
DOI: 10.6084/m9.figshare.28620227.v1
title: Code used in the study "Substantial contribution of trees outside forests to above-ground carbon across China"

description: This is the initial version of the code used in the paper, "<b>Substantial contribution of trees outside forests to above-ground carbon across China</b>".<br>AuthorsYang 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<br>Affiliationsa 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, Francec UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, Franced College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, Chinae Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, Chinaf Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, Chinag Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmarkh Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Chinai Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, Chinaj State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Chinak ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, Chinal 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, Chinan Electronic Information School, Wuhan University, Wuhan, China o Wuhan Institute of Quantum Technology, Wuhan, Chinap State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, Chinaq Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China<br>Corresponding AuthorYang Su yang.su@ens.fr +33 1 89 10 07 67 École Normale Supérieure – PSL<br>To use the code, please cite this paper or contact the corresponding author for more details.FundingArtificial Intelligence for forest monitoring from space – AI4ForestsAgence Nationale de la RechercheFind out more...
DOI: 10.15468/dl.sp7ref
title: Occurrence Download

description: A dataset containing 7676 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Papio ursinus (Kerr, 1792)"
 ]
}

 The dataset includes 7676 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017022-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pdhpfe
title: Occurrence Download

description: A dataset containing 982 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhopornis ardesiacus (Wied-Neuwied, 1831)"
 ]
}

 The dataset includes 982 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017023-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.28624661
title: Supplementary information files for "Microfibre ingestion by the Asian Clam (Corbicula fluminea) is dependent on fibre type and biofilm development"

description: Supplementary files for article "Microfibre ingestion by the Asian Clam (Corbicula fluminea) is dependent on fibre type and biofilm development"<br><br>Fibrous microplastics represent an anthropogenic pollutant affecting aquatic systems globally. However, fibres formed from natural materials (e.g., cotton or wool) have only recently been recognised as potentially posing similar ecological threats as their synthetic counterparts. In this study we employed a laboratory-based aquarium experiment to examine the ingestion of preselected anthropogenic (polyester – microplastic) and ‘natural’ (cotton) microfibres by the Asian Clam (Corbicula fluminea). We considered how the ingestion, retention, and rejection of preselected microfibres (specific, distinctive colours), differed associated with fibre type (cotton vs polyester), biofilm development (control – no biofilm / uncultured, 1-week culturing and 4-week culturing) and time (1-48 hours). We found that the ingestion of microfibres was complex, dependent on the interaction of culturing and fibre type. Greater retention of synthetic microfibres was recorded compared to ‘natural’ microfibres as the duration of culturing increased. We also observed that ingestion of microfibres was immediate but that microfibres were rejected and visually observed in pseudofaeces. Our results suggest that the time microfibres spend within the environment, allowing biofilm to develop on their surface, influences the ingestion of microfibres and we call for further studies to consider this in the future.<br>© The Author(s) CC-BY-NC-ND 4.0
DOI: 10.17028/rd.lboro.28624661.v1
title: Supplementary information files for "Microfibre ingestion by the Asian Clam (Corbicula fluminea) is dependent on fibre type and biofilm development"

description: Supplementary files for article "Microfibre ingestion by the Asian Clam (Corbicula fluminea) is dependent on fibre type and biofilm development"<br><br>Fibrous microplastics represent an anthropogenic pollutant affecting aquatic systems globally. However, fibres formed from natural materials (e.g., cotton or wool) have only recently been recognised as potentially posing similar ecological threats as their synthetic counterparts. In this study we employed a laboratory-based aquarium experiment to examine the ingestion of preselected anthropogenic (polyester – microplastic) and ‘natural’ (cotton) microfibres by the Asian Clam (Corbicula fluminea). We considered how the ingestion, retention, and rejection of preselected microfibres (specific, distinctive colours), differed associated with fibre type (cotton vs polyester), biofilm development (control – no biofilm / uncultured, 1-week culturing and 4-week culturing) and time (1-48 hours). We found that the ingestion of microfibres was complex, dependent on the interaction of culturing and fibre type. Greater retention of synthetic microfibres was recorded compared to ‘natural’ microfibres as the duration of culturing increased. We also observed that ingestion of microfibres was immediate but that microfibres were rejected and visually observed in pseudofaeces. Our results suggest that the time microfibres spend within the environment, allowing biofilm to develop on their surface, influences the ingestion of microfibres and we call for further studies to consider this in the future.<br>© The Author(s) CC-BY-NC-ND 4.0
DOI: 10.6084/m9.figshare.26317678
title: Dataset and scripts to perform phylogenetic reconstruction and climate modelling on the evolution and spatial distribution of melanism in vipers

description: In this work we investigate the evolutionary significance of melanism in Viperidae, under an ecological perspective by accounting for the thermal advantages provided by melanism to thermoregulation efficiency, thus testing for the effect of climate on the spatial distribution of this phenotypic trait.
DOI: 10.15468/zpx76g
title: The spontaneous flora of Botanical Gardens in Poland 1992-1999

description: This dataset contains data on plants that occur spontaneously in 8 polish botanical gardens. Species that are not cultivated were the focus of this study - a taxon is only marked as present if it was found outside its cultivation area. The data was collected by Halina Galera, during 8 years of botanical surveys that took place between 1992 and 1999.
DOI: 10.5281/zenodo.14619101
title: Canopy height map in China - 2019 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...