cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5w669k9m
title: MassIVE MSV000094600 - Untargeted metabolomic (LC-MS-MS) analysis of the spermosphere of eight common bean genotypes (Phaseolus vulgaris L)
title: Seeds are crucial for plant reproduction, dispersal, and agriculture. Seed quality and vigour greatly impact crop production, referring to their ability to germinate rapidly and uniformly under varying environmental conditions, producing healthy seedlings that can withstand biotic and abiotic stress accentuated by global climate change. During germination, seeds release exudates, complex mixtures of organic and inorganic molecules, into the micro-environment surrounding them, known as the spermosphere. These exudates play a pivotal role in seedling development and overall plant fitness by influencing microbial selection, growth, and interactions in the spermosphere, ultimately shaping the plant's microbiome. To understand the composition and functional properties of germinating seed exudates, we conducted our study using different common bean (Phaseolus vulgaris L.) genotypes. Seeds were harvested in the year 2020 from eight different common bean genotypes (French commercial cultivars of Phaseolus vulgaris) cultivated in two different locations from the FNAMS field experimental stations: Gers in South-west of France (43.956991, 0.392127) and Maine et Loire (47.470532, -0.394526) in the Loire Valley. The common bean genotypes namely, VEZ, CON, CAP, FAC, FLA, LIN, DEE and VAN were chosen to represent the highest diversity among breeding programmes and qualitative seed traits in France by the seed company Vilmorin-Mikado SAS (Limagrain group). One seed lot corresponds to seeds from one genotype produced in one location. Independent biological replicates were conducted for each seed lot. Thus, 48 samples were analyzed, corresponding to three replicates of eight genotypes produced in two locations. Spermosphere collection of germinating seeds involved the imbibition of 30 seeds for each sample at four distinct time intervals: 2, 8, 16, and 24 hours. The collected spermosphere, consisting of the water surrounding germinating seeds, was gathered for each time interval and subsequently pooled. Subsequently we investigated the diversity of metabolites of each pooled spermosphere using untargetted metabolomic (LC-MS/MS) analysis.
DOI: 10.25549/pcra-c14-36226
title: Edward Titterington; PMU correspondence, 1920-1925

description: [description english/roman)] Correspondence.
DOI: 10.15468/dl.8damqb
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asperula oetaea (Boiss.) Heldr. ex Halácsy"
 ]
}

 The dataset includes 42 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066083-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tu952h
title: Occurrence Download

description: A dataset containing 863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stenagostus rufus (De Geer, 1774), Acmaeoderella flavofasciata (Piller & Mitterpacher, 1783))",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 863 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0156692-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrxheu
title: Occurrence Download

description: A dataset containing 569 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Niwaella delicata (Niwa, 1937)"
 ]
}

 The dataset includes 569 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0337540-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-36254
title: Edward Titterington; PMU Men's training home, 1919

description: [description english/roman)] Correspondence.
DOI: 10.15468/dl.r8aw7j
title: Occurrence Download

description: A dataset containing 770586 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poecile palustris (Linnaeus, 1758)"
 ]
}

 The dataset includes 770586 records from 823 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0142027-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2572qh
title: Occurrence Download

description: A dataset containing 41348 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-113.651889 53.369508, -113.773785 53.441536, -113.874998 53.441536, -113.752993 53.369508, -113.651889 53.369508))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 41348 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047655-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26008/1912/bco-dmo.986825.1
title: Compound-specific carbon stable isotopes of amino acids in planktic foraminifera from Santa Barbara Basin sediment traps from 2018 to 2021

description: We measured carbon and nitrogen compound-specific stable isotopes of amino acids (CSI-AA) in the shells the three most abundant species of planktic foraminifera from Santa Barbara Basin sediment traps from 2018 to 2021: Globigerina bulloides (d’Orbigny, 1826), Neogloboquadrina incompta (Cifelli, 1961), and Turborotalita quinqueloba (Natland, 1938). Multiple sediment trap collections were combined to constitute representative samples. The N. incompta sample included individuals from sediment trap collections from May 2019 to October 2021 (n=4082). The T. quinqueloba sample included individuals from December 2018 to October 2021 (n=4964). G. bulloides was sufficiently abundant to split into seasonal samples: spring/summer from February 2019 to July 2019 (n=5081) and fall/winter from October to January in years 2018-2019, 2019-2020, 2020-2021 (n=3132). The collection of these data were supported by NSF OCE. This dataset includes only the carbon CSI-AA data. Nitrogen data are available in a related dataset.
DOI: 10.15468/dl.kf9bbe
title: Occurrence Download

description: A dataset containing 53001 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is VIS - Fishes in estuarine waters in Flanders, Belgium"
 ]
}

 The dataset includes 53001 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047626-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3fa97s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((113.501 -0.896817, 113.430728 -0.902383, 113.362186 -0.918945, 113.297061 -0.946094, 113.236955 -0.983163, 113.183348 -1.029239, 113.137561 -1.083188, 113.100719 -1.143682, 113.073732 -1.209231, 113.057263 -1.278223, 113.051719 -1.348958, 113.057237 -1.419695, 113.073683 -1.488693, 113.100652 -1.554251, 113.137482 -1.614757, 113.183265 -1.668718, 113.236876 -1.714807, 113.296994 -1.751887, 113.362137 -1.779046, 113.430703 -1.795613, 113.501 -1.801181, 113.571297 -1.795613, 113.639863 -1.779046, 113.705006 -1.751887, 113.765124 -1.714807, 113.818735 -1.668718, 113.864518 -1.614757, 113.901348 -1.554251, 113.928317 -1.488693, 113.944763 -1.419695, 113.950281 -1.348958, 113.944737 -1.278223, 113.928268 -1.209231, 113.901281 -1.143682, 113.864439 -1.083188, 113.818652 -1.029239, 113.765045 -0.983163, 113.704939 -0.946094, 113.639814 -0.918945, 113.571272 -0.902383, 113.501 -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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047656-251120083545085/datasets/export for details.
DOI: 10.15468/dl.vagxbv
title: Occurrence Download

description: A dataset containing 799 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 rostratum"
 ]
}

 The dataset includes 799 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047658-251120083545085/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.24041892.v3
title: Dataset: <b>Late Middle Pleistocene Tectonic Inversion in the Mazarr</b><b>ó</b><b>n Graben (Betics , SE Iberia)</b>

description: This repository contains datasets used for the article <i>"</i><b>Late Middle Pleistocene Tectonic Inversion in the Mazarr</b><b>ó</b><b>n Graben (Betics , SE Iberia)</b><i>"</i><b> </b>to be<b> </b>published in TECTONICS<i>.</i>The dataset includes three zip files:<b>HillShadedMaps.zip: </b>This folder contains four TIFF files with the hill-shaded relief from the LiDAR-based 2m DEM by the IGN agency, MDT02; https://centrodedescargas.cnig.es/CentroDescargas/index.jsp#<b>UninterpretedTrenches.zip: </b>This folder contains three PNG files with uninterpreted photologs of the road cut and the two trenches carried out in the paleoseismic survey of the study. Each file is referred to the figure and to the site as named in the paper.<b>ERT_profile.zip: </b>This folder contains three files associated with the electrical resistivity profile (ERT) at the Las Lomas site. Two text files containing (1) raw data of the profile and (2) electrode positions acquired with differential GPS. A .dat file corresponding to the ERT raw data, including topography, stored in the Res2dinv format.
DOI: 10.15468/dl.4ye8w5
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gymnopogon doellii"
 ]
}

 The dataset includes 55 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047659-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15003130
title: Canopy height map in China - 2021 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.15003131
title: Canopy height map in China - 2021 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.w4qbqb
title: Occurrence Download

description: A dataset containing 2836959 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Specimen)",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia",
 "Year 2000-2025"
 ]
}

 The dataset includes 2836959 records from 344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047537-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t2ea7t
title: Occurrence Download

description: A dataset containing 646 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 rude-pannum"
 ]
}

 The dataset includes 646 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047661-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34847/nkl.6eb98ur3
title: Living with Risk – Risk Tolerance and Prosocial Behaviour in the Shadow of Mount Semeru

description: The dataset and the analysis scripts of the research paper Living with Risk – Risk Tolerance and Prosocial Behaviour in the Shadow of Mount Semeru.
DOI: 10.5281/zenodo.15004388
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...