cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15003055
title: Canopy height map in China - 2021 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: 10.5281/zenodo.15003056
title: Canopy height map in China - 2021 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: 10.5281/zenodo.17901277
title: Coppice forest certified methodology SS06020121-V3 - data

description: The data set presents the results of a certified methodological procedure called Cultivation practices in low and medium forests for the production of assortments usable in laminated wood products for structural and non-structural applications (SS06020121-V3), which presents the results of research and development providing the user with a methodological approach for specifying and adapting the cultivation procedures of low to medium forests with the potential to obtain assortments usable for the production of laminated wood products for structural and non-structural applications, i.e. products with significant added value. It is primarily about defining the parameters of second-generation sapling assortments usable for the production of laminated wood for structural and non-structural applications and formulating basic cultivation principles that could lead to the acquisition of such assortments in the management of low and medium forests with regard to the qualitative requirements of wood for its application for the glued laminated timber program.
DOI: 10.5281/zenodo.17901278
title: Coppice forest certified methodology SS06020121-V3 - data

description: The data set presents the results of a certified methodological procedure called Cultivation practices in low and medium forests for the production of assortments usable in laminated wood products for structural and non-structural applications (SS06020121-V3), which presents the results of research and development providing the user with a methodological approach for specifying and adapting the cultivation procedures of low to medium forests with the potential to obtain assortments usable for the production of laminated wood products for structural and non-structural applications, i.e. products with significant added value. It is primarily about defining the parameters of second-generation sapling assortments usable for the production of laminated wood for structural and non-structural applications and formulating basic cultivation principles that could lead to the acquisition of such assortments in the management of low and medium forests with regard to the qualitative requirements of wood for its application for the glued laminated timber program.
DOI: 10.11588/duerer.online.693af0e45f0aa
title: Bärtiger Greis, https://d-nb.info/gnd/119457985X
DOI: 10.5281/zenodo.15003076
title: Canopy height map in China - 2021 part 2

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.15003075
title: Canopy height map in China - 2021 part 2

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: 10.17026/ar/2okwh3
title: PAN-00161188 - terra sigillata plate type Drag. 31

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00161188&lt;/p&gt;
description: Modified: 2025-11-10T11:35:09+01:00
DOI: 10.15468/dl.q5dy98
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.541 -0.896817, 103.470728 -0.902383, 103.402186 -0.918945, 103.337061 -0.946094, 103.276955 -0.983163, 103.223348 -1.029239, 103.177561 -1.083188, 103.140719 -1.143682, 103.113732 -1.209231, 103.097263 -1.278223, 103.091719 -1.348958, 103.097237 -1.419695, 103.113683 -1.488693, 103.140652 -1.554251, 103.177482 -1.614757, 103.223265 -1.668718, 103.276876 -1.714807, 103.336994 -1.751887, 103.402137 -1.779046, 103.470703 -1.795613, 103.541 -1.801181, 103.611297 -1.795613, 103.679863 -1.779046, 103.745006 -1.751887, 103.805124 -1.714807, 103.858735 -1.668718, 103.904518 -1.614757, 103.941348 -1.554251, 103.968317 -1.488693, 103.984763 -1.419695, 103.990281 -1.348958, 103.984737 -1.278223, 103.968268 -1.209231, 103.941281 -1.143682, 103.904439 -1.083188, 103.858652 -1.029239, 103.805045 -0.983163, 103.744939 -0.946094, 103.679814 -0.918945, 103.611272 -0.902383, 103.541 -0.896817))"},{"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/0047654-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48420/30069739
title: A mixed methods study to examine social workers' implementation of national guidelines with children in care who self-harm

description: This research involves a mixed methods study investigating children’s social workers' implementation of NICE guidelines on self-harm with children in care.The National Institute for Health and Care Excellence (NICE) guidelines on self-harm (2022) aim to improve the quality of care for individuals who self-harm in all services and settings. A rapid review of the literature revealed an absence of research on social workers' implementation of the guidelines and their implementation with children. This study aims to address this gap in the literature. Children in care are at increased risk of self-harm, with risk rates between two and four-fold higher than children without care experience. However, very little is known about how this risk is assessed and managed. Although social work professionals are expected to take the NICE guidelines on self-harm fully into account, alongside the individual needs, preferences, and values of the child, it is not known if children's social workers are aware of and implement the NICE guidelines on self-harm with children in care.Data from social workers who have worked with children in care who have actively self-harmed will be collated to gain a deep, multifaceted understanding of views and experiences and highlight potential barriers to NICE guideline implementation in this group.A mixed-methods approach will be used, consisting of two research phases. The first phase involves collecting data from a large-scale online quantitative survey, exploring children’s social workers’ knowledge of the NICE guidelines on self-harm and their opportunities, capabilities, and motivations to implement them with children in care. In the second phase, semi-structured interviews with social workers will be conducted to identify specific barriers and facilitators to NICE guideline implementation with children in care who self-harm.The study will provide important insights into the factors influencing how children’s social workers assess and manage self-harm and contribute to the literature on NICE guideline implementation. The results of this study will inform the development of interventions aimed at improving the quality of care and support of children in care who self-harm.
DOI: 10.48420/30069739.v1
title: A mixed methods study to examine social workers' implementation of national guidelines with children in care who self-harm

description: This research involves a mixed methods study investigating children’s social workers' implementation of NICE guidelines on self-harm with children in care.The National Institute for Health and Care Excellence (NICE) guidelines on self-harm (2022) aim to improve the quality of care for individuals who self-harm in all services and settings. A rapid review of the literature revealed an absence of research on social workers' implementation of the guidelines and their implementation with children. This study aims to address this gap in the literature. Children in care are at increased risk of self-harm, with risk rates between two and four-fold higher than children without care experience. However, very little is known about how this risk is assessed and managed. Although social work professionals are expected to take the NICE guidelines on self-harm fully into account, alongside the individual needs, preferences, and values of the child, it is not known if children's social workers are aware of and implement the NICE guidelines on self-harm with children in care.Data from social workers who have worked with children in care who have actively self-harmed will be collated to gain a deep, multifaceted understanding of views and experiences and highlight potential barriers to NICE guideline implementation in this group.A mixed-methods approach will be used, consisting of two research phases. The first phase involves collecting data from a large-scale online quantitative survey, exploring children’s social workers’ knowledge of the NICE guidelines on self-harm and their opportunities, capabilities, and motivations to implement them with children in care. In the second phase, semi-structured interviews with social workers will be conducted to identify specific barriers and facilitators to NICE guideline implementation with children in care who self-harm.The study will provide important insights into the factors influencing how children’s social workers assess and manage self-harm and contribute to the literature on NICE guideline implementation. The results of this study will inform the development of interventions aimed at improving the quality of care and support of children in care who self-harm.
DOI: 10.48420/30069739.v2
title: A mixed methods study to examine social workers' implementation of national guidelines with children in care who self-harm

description: This research involves a mixed methods study investigating children’s social workers' implementation of NICE guidelines on self-harm with children in care.The National Institute for Health and Care Excellence (NICE) guidelines on self-harm (2022) aim to improve the quality of care for individuals who self-harm in all services and settings. A rapid review of the literature revealed an absence of research on social workers' implementation of the guidelines and their implementation with children. This study aims to address this gap in the literature. Children in care are at increased risk of self-harm, with risk rates between two and four-fold higher than children without care experience. However, very little is known about how this risk is assessed and managed. Although social work professionals are expected to take the NICE guidelines on self-harm fully into account, alongside the individual needs, preferences, and values of the child, it is not known if children's social workers are aware of and implement the NICE guidelines on self-harm with children in care.Data from social workers who have worked with children in care who have actively self-harmed will be collated to gain a deep, multifaceted understanding of views and experiences and highlight potential barriers to NICE guideline implementation in this group.A mixed-methods approach will be used, consisting of two research phases. The first phase involves collecting data from a large-scale online quantitative survey, exploring children’s social workers’ knowledge of the NICE guidelines on self-harm and their opportunities, capabilities, and motivations to implement them with children in care. In the second phase, semi-structured interviews with social workers will be conducted to identify specific barriers and facilitators to NICE guideline implementation with children in care who self-harm.The study will provide important insights into the factors influencing how children’s social workers assess and manage self-harm and contribute to the literature on NICE guideline implementation. The results of this study will inform the development of interventions aimed at improving the quality of care and support of children in care who self-harm.
DOI: 10.15468/dl.frarak
title: Occurrence Download

description: A dataset containing 167876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Butterfly distribution records for the UK from Butterfly Conservation, up to 2019",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 1979",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 167876 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047625-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.