cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccr7tf6
title: CCDC 692708: Experimental Crystal Structure Determination

description: Related Article: Yu-Fen Liu, Da-Fu Rong, Hai-Tao Xia, Da-Qi Wang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1492|doi:10.1107/S1600536809044997
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/ccryc45
title: CCDC 713407: Experimental Crystal Structure Determination

description: Related Article: Liyuan Wen, Handong Yin, Chuanhua Wang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1442|doi:10.1107/S160053680904358X
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/ccs8xnl
title: CCDC 723560: Experimental Crystal Structure Determination

description: Related Article: P.Kitiphaisalnont, S.Siripaisarnpipat, N.Chaichit|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1284|doi:10.1107/S1600536809039580
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/ccs9t51
title: CCDC 724413: Experimental Crystal Structure Determination

description: Related Article: W.Starosta, J.Leciejewicz|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1291|doi:10.1107/S1600536809039658
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/ccsksrv
title: CCDC 732088: Experimental Crystal Structure Determination

description: Related Article: Chuan-gang Fan, Xin-Ting Wei, Linwei Li|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1412|doi:10.1107/S1600536809041440
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: 10.13140/rg.2.2.30323.45600
title: Bank stability - Top 924 cited publications on Google scholar
DOI: 10.5517/ccsnlxx
title: CCDC 734790: Experimental Crystal Structure Determination

description: Related Article: Kun Qian, Zhi-Hong Yan, Zhong-Wen Chen|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1351|doi:10.1107/S160053680904046X
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/ccsskx0
title: CCDC 738603: Experimental Crystal Structure Determination

description: Related Article: Wen Zhang, Jin-Ping Gao, Xue-Feng Shi, Da-Cheng Li|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1363|doi:10.1107/S1600536809040951
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/cct0wqd
title: CCDC 745634: Experimental Crystal Structure Determination

description: Related Article: Hong-Jun Yang, Yan-Qiu Dang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1304|doi:10.1107/S1600536809039245
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: 10.13140/rg.2.2.35146.90567
title: Guia practica de Python, C y C++
DOI: 10.5517/cct9xff
title: CCDC 754305: Experimental Crystal Structure Determination

description: Related Article: F.N.Khan, P.Manivel, K.Prabakaran, V.R.Hathwar, S.W.Ng|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2732|doi:10.1107/S1600536809041282
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/cct9xgg
title: CCDC 754306: Experimental Crystal Structure Determination

description: Related Article: B.Saravanan, V.Dhayalan, A.K.Mohanakrishnan, G.Chakkaravarthi, V.Manivannan|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2733|doi:10.1107/S1600536809041191
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/cct9xhh
title: CCDC 754307: Experimental Crystal Structure Determination

description: Related Article: P.-C.Cheng, C.-J.Wu, J.-D.Chen|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2734|doi:10.1107/S1600536809040902
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: 10.15468/dl.bmksns
title: Occurrence Download

description: A dataset containing 124681 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Insecta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-7.5 7.5,0 7.5,0 15,-7.5 15,-7.5 7.5))"
 ]
}

 The dataset includes 124681 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042445-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cct9xjj
title: CCDC 754308: Experimental Crystal Structure Determination

description: Related Article: H.C.Devarajegowda, V.Madhura, B.S.Palakshamurthy, S.Jeyaseelan, M.V.Kulkarni|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2735|doi:10.1107/S1600536809040963
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/cct9xkk
title: CCDC 754309: Experimental Crystal Structure Determination

description: Related Article: I.Caracelli, J.Zukerman-Schpector, A.S.Vieira, H.A.Stefani, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2736|doi:10.1107/S1600536809041002
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/cct9xll
title: CCDC 754310: Experimental Crystal Structure Determination

description: Related Article: Yan-Fei Wang, Huang Tang, Yan-Cheng Liu, Zhen-Feng Chen, Hong Liang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2737|doi:10.1107/S1600536809040021
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: 10.13140/rg.2.2.28999.62885
title: Industry Relevant Skills for the Changing World of Work: Insights from our Graduates
DOI: 10.5517/cct9xmm
title: CCDC 754311: Experimental Crystal Structure Determination

description: Related Article: J.Miao, M.Jia, X.Liu, W.Xiong, Z.Chen|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2738|doi:10.1107/S1600536809041269
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/cct9xnn
title: CCDC 754312: Experimental Crystal Structure Determination

description: Related Article: Hai-Xing Liu|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2739|doi:10.1107/S160053680904121X
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/cct9xpp
title: CCDC 754313: Experimental Crystal Structure Determination

description: Related Article: Hai-Zhen Xu, Jian-Ping Xu, Jin Zhang, Yan-Wei Yuan, You-Quan Zhu|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o2740|doi:10.1107/S1600536809040458
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: 10.1594/pangaea.972179
title: Satellite Color Images, Vegetation Indices, and Metabolism Indices from Albstadt, Germany from 1984 – 2023

description: The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document.The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications.In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description).Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
DOI: 10.1594/pangaea.972180
title: Satellite Color Images, Vegetation Indices, and Metabolism Indices from Amberg, Germany from 1985 – 2023

description: The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document.The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications.In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description).Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
DOI: 10.1594/pangaea.972181
title: Satellite Color Images, Vegetation Indices, and Metabolism Indices from Ansbach, Germany from 1985 – 2023

description: The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document.The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications.In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description).Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
DOI: 10.1594/pangaea.972182
title: Satellite Color Images, Vegetation Indices, and Metabolism Indices from Arnsberg, Germany from 1985 – 2023

description: The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document.The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications.In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description).Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.