cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23728/b2share.426cc15f73dc47b98b6b2af9d740ea98
title: TERENO Eifel Lower Rhine Valley, Annual Phenology, 2019- 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019- 2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalized Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.5281/zenodo.8211179
title: Global 10-m spatial distribution of Water-surface photovoltaics (2019-2021)

description: The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map, with overall accuracy exceeding 96%.
DOI: 10.5281/zenodo.8211178
title: Global 10-m spatial distribution of Water-surface photovoltaics (2019-2021)

description: The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map, with overall accuracy exceeding 96%.
DOI: 10.23728/b2share.a6ef9b030de240f198581261be9de6c1
title: Kalkalpen National Park, Annula Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.192a897c521b4c6babfc1b9db65163b2
title: Gran Paradiso National Park, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.399c0653601a46488082e0f5edafc0df
title: TERENO Harz/Central German Lowland LTER, Annual Phenology, 2019

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the year 2019.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.6084/m9.figshare.23857386
title: Effect of alkali metal chlorides on photocatalysis.opju

description: Origin file containing hydrogen evolution data from glycerol photoreforming over a TiO<sub>2</sub> catalyst in the presence of LiCl, NaCl or KCl
DOI: 10.23728/b2share.5b175810bd504f2f8607a9ed9f078809
title: Gesäuse National Park, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.9752dbeeab904279a422d7fae4b31ae4
title: Lake Kerkini National Park - Greece, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of the years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.5519/qd.ts7nbx0l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5246711 records)
DOI: 10.23728/b2share.68d0d103ee904b3886ac19d455f4089a
title: Murgia Alta Protected, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence generated from Sentinel-2 Level-2A time series of years 2019-2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.ed077d58bb9444f696a275fa6f249a59
title: Gran Paradiso National Park, phenology, 2019

description: Phenology metrics, green-up, maturity, senescence extracted from Sentinel-2 Level-2A time series of year 2019.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format : SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.9a8aa6c218e54a3d8c08e3912e6364de
title: LTSER Dutch Wadden Sea Area - Netherlands, Annual phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of the year 2020.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.23728/b2share.58dde320216e41a5a97b8f4b287efb4b
title: Braila Islands - Romania, Annual Phenology, 2019-2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of years 2019, 2020 and 2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: TechnicalInfo: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.6084/m9.figshare.23256374
title: Quantitative proteomics data

description: Proteomics data set
DOI: 10.6084/m9.figshare.23256374.v1
title: Quantitative proteomics data

description: Proteomics data set
DOI: 10.23728/b2share.cafe3eb1d4ef4e648bd4f8f058ab5dfb
title: LTSER Montado Portugal, Annual Phenology, 2019 - 2021

description: Phenology metrics, green-up, maturity, senescence was generated using Sentinel-2 Level-2A time series of the years 2019 -2021.
description: The phenology product is generated from a time series (1 year). The algorithm uses Normalised Difference Vegetation Index (NDVI).
description: The final output consists of raster layers in GeoTIFF format with the following values per pixel: SOS : start of the season, expressed as Day Of the Year (DOY) SOSV: Vegetation Index value at SOS POS: peak of the season, expressed as Day Of the Year (DOY) PSOV: Vegetation Index value at POS EOS: end of season, expressed as Day Of the Year (DOY) EOSV: Vegetation Index value at EOS
DOI: 10.6084/m9.figshare.23544741
title: IRBP HGT trees

description: Full phylogenetic reanalyses of Dataset S2 and S3 from Kalluraya et al. 2023, PNAS. <br> Kalluraya_S2.fas_JTTC50FIG4.contree: Consensus tree and full ultrafast bootstrap supports inferred under best-fitting JTT+C50+F+I+G4 model for Dataset S2 from Kalluraya et al. <br> Kalluraya_S3.fas_EXEHOG4.contree: Consensus tree and full ultrafast bootstrap supports inferred under best-fitting EX_EHO+G4 model for Dataset S3 from Kalluraya et al..
DOI: 10.5281/zenodo.8214421
title: UVic-MOBI Anthropogenic Biogeochemistry Simulations

description: Model results from preindustrial and anthropogenic scenarios (i.e. prescribed atmospheric CO2 (SRES A2 scenario) and atmospheric nutrient pollutant deposition (Lamarque et al., 2013; Myriokefalitakis et al., 2018) on the UVic-MOBI Earth System Climate model (Somes et al., 2021). The code is available at https://github.com/chrissomes/UVic2.9.
description: {"references": ["S. Myriokefalitakis, A. Ito, M. Kanakidou, A. Nenes, M. C. Krol, N. M. Mahowald, et al. Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study Biogeosciences 2018 Vol. 15 Issue 21 Pages 6659-6684 DOI: 10.5194/bg-15-6659-2018", "J. F. Lamarque, F. Dentener, J. McConnell, C. U. Ro, M. Shaw, R. Vet, et al. Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes Atmospheric Chemistry and Physics 2013 Vol. 13 Issue 16 Pages 7997-8018 DOI: 10.5194/acp-13-7997-2013", "C. J. Somes, A. W. Dale, K. Wallmann, F. Scholz, W. Yao, A. Oschlies, et al. Constraining Global Marine Iron Sources and Ligand\u2010Mediated Scavenging Fluxes With GEOTRACES Dissolved Iron Measurements in an Ocean Biogeochemical Model Global Biogeochemical Cycles 2021 Vol. 35 Issue 8 DOI: 10.1029/2021gb006948"]}
DOI: 10.5281/zenodo.8214420
title: UVic-MOBI Anthropogenic Biogeochemistry Simulations

description: Model results from preindustrial and anthropogenic scenarios (i.e. prescribed atmospheric CO2 (SRES A2 scenario) and atmospheric nutrient pollutant deposition (Lamarque et al., 2013; Myriokefalitakis et al., 2018) on the UVic-MOBI Earth System Climate model (Somes et al., 2021). The code is available at https://github.com/chrissomes/UVic2.9.
description: {"references": ["S. Myriokefalitakis, A. Ito, M. Kanakidou, A. Nenes, M. C. Krol, N. M. Mahowald, et al. Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study Biogeosciences 2018 Vol. 15 Issue 21 Pages 6659-6684 DOI: 10.5194/bg-15-6659-2018", "J. F. Lamarque, F. Dentener, J. McConnell, C. U. Ro, M. Shaw, R. Vet, et al. Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes Atmospheric Chemistry and Physics 2013 Vol. 13 Issue 16 Pages 7997-8018 DOI: 10.5194/acp-13-7997-2013", "C. J. Somes, A. W. Dale, K. Wallmann, F. Scholz, W. Yao, A. Oschlies, et al. Constraining Global Marine Iron Sources and Ligand\u2010Mediated Scavenging Fluxes With GEOTRACES Dissolved Iron Measurements in an Ocean Biogeochemical Model Global Biogeochemical Cycles 2021 Vol. 35 Issue 8 DOI: 10.1029/2021gb006948"]}
DOI: 10.5517/ccdc.csd.cc2dr31b
title: CCDC 2226731: Experimental Crystal Structure Determination

description: Related Article: Pierluigi Mondelli, Pascal Kaienburg, Francesco Silvestri, Rebecca Scatena, Claire Welton, Martine Grandjean, Vincent Lemaur, Eduardo Solano, Mathias Nyman, Peter N. Horton, Simon J. Coles, Esther Barrena, Moritz Riede, Paolo Radaelli, David Beljonne, G. N. Manjunatha Reddy, Graham Morse|2023|J.Mater.Chem.A|11|16263|doi:10.1039/D3TA03284A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dzxht
title: CCDC 2234247: Experimental Crystal Structure Determination

description: Related Article: Li Wang, Yu-Lian Zhu, Teng-Fei Zheng, Zi-Hao Zhu, Yan Peng, Yong-Quan Wu, Jing-Lin Chen, Sui-Jun Liu, He-Rui Wen|2023|Dalton Trans.|52|10567|doi:10.1039/D3DT01057K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1v2f
title: CCDC 2236094: Experimental Crystal Structure Determination

description: Related Article: Pierluigi Mondelli, Pascal Kaienburg, Francesco Silvestri, Rebecca Scatena, Claire Welton, Martine Grandjean, Vincent Lemaur, Eduardo Solano, Mathias Nyman, Peter N. Horton, Simon J. Coles, Esther Barrena, Moritz Riede, Paolo Radaelli, David Beljonne, G. N. Manjunatha Reddy, Graham Morse|2023|J.Mater.Chem.A|11|16263|doi:10.1039/D3TA03284A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fgxtn
title: CCDC 2248672: Experimental Crystal Structure Determination

description: Related Article: Joseph D. Gbubele, Tomasz Misiaszek, Miłosz Siczek, Tomasz K. Olszewski|2023|Org.Biomol.Chem.|21|6180|doi:10.1039/D3OB00924F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fgxvp
title: CCDC 2248673: Experimental Crystal Structure Determination

description: Related Article: Joseph D. Gbubele, Tomasz Misiaszek, Miłosz Siczek, Tomasz K. Olszewski|2023|Org.Biomol.Chem.|21|6180|doi:10.1039/D3OB00924F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fkm6v
title: CCDC 2251257: Experimental Crystal Structure Determination

description: Related Article: Valerii Y. Sirenko, Olesia I. Kucheriv, Igor O. Fritsky, Elzbieta Gumienna-Kontecka, Ioan-Andrei Dascălu, Sergiu Shova, Il'ya A. Gural'skiy|2023|Dalton Trans.|52|10545|doi:10.1039/D3DT02056H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fkm7w
title: CCDC 2251258: Experimental Crystal Structure Determination

description: Related Article: Valerii Y. Sirenko, Olesia I. Kucheriv, Igor O. Fritsky, Elzbieta Gumienna-Kontecka, Ioan-Andrei Dascălu, Sergiu Shova, Il'ya A. Gural'skiy|2023|Dalton Trans.|52|10545|doi:10.1039/D3DT02056H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fq1fn
title: CCDC 2255511: Experimental Crystal Structure Determination

description: Related Article: Mónica Martínez-Junquera, Elena Lalinde, M. Teresa Moreno|2023|Inorg.Chem.|62|11849|doi:10.1021/acs.inorgchem.3c01196
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fq1gp
title: CCDC 2255512: Experimental Crystal Structure Determination

description: Related Article: Mónica Martínez-Junquera, Elena Lalinde, M. Teresa Moreno|2023|Inorg.Chem.|62|11849|doi:10.1021/acs.inorgchem.3c01196
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fzmpr
title: CCDC 2263765: Experimental Crystal Structure Determination

description: Related Article: Shunya Masuda, Sotaro Kusumoto, Masaya Okamura, Shiro Hikichi, Ryuya Tokunaga, Shinya Hayami, Yang Kim, Yoshihiro Koide|2023|Dalton Trans.|52|10531|doi:10.1039/D3DT01822A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.20363/heterobranchia-bangka-prj-1.2
title: First Study on Marine Heterobranchia (Gastropoda, Mollusca) in Bangka Archipelago, North Sulawesi, Indonesia

description: As ephemeral, benthic, secondary consumers usually associated with sessile coral reef organisms, marine heterobranchs are good indicators of the health of marine tropical habitats. Thus, marine Heterobranchia have recently become a major target for monitoring programs. For this work, an extensive survey was made in Bangka Archipelago, the first of its kind in this area. Bangka Archipelago is composed of small islands and the adjacent coastline of North Sulawesi. The substrate is dominated by biogenic reefs and volcanic rocks, thus forming highly diverse habitats. In total, 149 species were collected and/or photo-documented in September 2017 and September 2018, which represents the dry season. They can be assigned to the Cephalaspidea, Aplysiida, Pleurobranchida, Nudibranchia, and Eupulmonata. Thirty-three species are new to science, highlighting the lack of knowledge about the group and especially about this region. Our data provide a baseline for future monitoring surveys, as the anthropogenic pressures on Bangka Archipelago are increasing due to enhanced touristic activities, but also due to large scale environmental changes caused by previous mining operation activities.
DOI: 10.5281/zenodo.8214657
title: Evaluation of local movement of Bemisia tabaci MEAM1 and tomato severe rugose virus transmission to tomato plants using labeled and unlabeled whiteflies

description: The data presents the effects of two types of labels on whiteflies' behavior. Besides, it shows the flight distances at close range and ToSRV transmission.
DOI: 10.5281/zenodo.8214656
title: Evaluation of local movement of Bemisia tabaci MEAM1 and tomato severe rugose virus transmission to tomato plants using labeled and unlabeled whiteflies

description: The data presents the effects of two types of labels on whiteflies' behavior. Besides, it shows the flight distances at close range and ToSRV transmission.
DOI: 10.5281/zenodo.7876323
title: A SQL dataset

description: This version is closed. Please download from another version.
DOI: 10.5281/zenodo.4680455
title: Test Dataset for Whole Slide Image Registration

description: Dataset associated to these documents: https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/wsi_registration_fjii_qupath/ Sampled processed by the EPFL histology core facility More documentation to come...