cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cct9nrh
title: CCDC 754067: Experimental Crystal Structure Determination

description: Related Article: Pei-Hsuan Chiang, Shih-Chen Hsu, Chia-Her Lin|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1302|doi:10.1107/S1600536809039786
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.2.2.30211.84007
title: Distribution of Amphibia in habitats available in Pakistan
DOI: 10.5517/cct9s2y
title: CCDC 754170: Experimental Crystal Structure Determination

description: Related Article: Li Song|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1450|doi:10.1107/S160053680904361X
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: 10.6084/m9.figshare.25680390
title: GMM vs CNN paper by Bonhomme et al. (sub) all scripts and data

description: Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This “pre-distilled” geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.
DOI: 10.6084/m9.figshare.25680390.v2
title: GMM vs CNN paper by Bonhomme et al. (sub) all scripts and data

description: Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This “pre-distilled” geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.
DOI: 10.15468/dl.jdu8pr
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Fungi)",
 "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((45 -75,52.5 -75,52.5 -67.5,45 -67.5,45 -75))"
 ]
}

 The dataset includes 99 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042566-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cct9s3z
title: CCDC 754171: Experimental Crystal Structure Determination

description: Related Article: Wenkuan Li, Handong Yin, Lei Dong, Jing Li, Qijun Zhang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1451|doi:10.1107/S1600536809043554
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/cct9s40
title: CCDC 754172: Experimental Crystal Structure Determination

description: Related Article: Ling Zhu, Zhe An|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1452|doi:10.1107/S1600536809043566
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: 10.6084/m9.figshare.25680390.v1
title: GMM vs CNN paper by Bonhomme et al. (sub) all scripts and data

description: Taxonomical identification of archaeological fruit and seed is of prime importance for any archaeobotanical studies. We compared the relative performance of deep learning and geometric morphometrics at identifying pairs of plant taxa. We used their seeds and fruit stones that are the most abundant recovered organs in archaeobotanical assemblages, and whose morphological identification, chiefly between wild and domesticated types, allow to document their domestication and biogeographical history. We used existing modern datasets of four plant taxa (date palm, barley, olive and grapevine) corresponding to photographs of two orthogonal views of their seeds that were analysed separately to offer a larger spectrum of shape diversity. On these eight datasets, we compared the performance of a deep learning approach, here convolutional neural networks (CNN), to that of a geometric morphometric approach, here outline analyses using elliptical Fourier transforms (EFT). Sample sizes were at minimum eight hundred seeds in each class, which is quite small when training deep learning models but of typical magnitude for archaeobotanical studies. Our objectives were twofold: i) to test whether deep learning can beat geometric morphometrics in taxonomic identification and if so, ii) to test which minimal sample size is required. We ran simulations on the full datasets and also on subsets, starting from 50 images in each binary class. For CNN networks, we deliberately used a candid approach relying on pre-parameterised VGG16 network. For EFT, we used a state-of-the art morphometrical pipeline. The main difference rests in the data used by each model: CNN used bare photographs where EFT used (x, y) outline coordinates. This “pre-distilled” geometrical description of seed outlines is often the most time-consuming part of morphometric studies. Results show that CNN beats EFT in most cases, even for very small datasets. We finally discuss the potential of CNN for archaeobotany, why outline analyses and morphometrics have not yet said their last word by providing quantitative descriptions, and how bioarchaeological studies could embrace both approaches, used in a complementary way, to better assess and understand the past history of species.
DOI: 10.13140/rg.2.2.33462.42561
title: MJMB Editor
DOI: 10.5517/cct9s51
title: CCDC 754173: Experimental Crystal Structure Determination

description: Related Article: Chun-Sen Liu, Xian-Guo Xiao, Min Hu|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1453|doi:10.1107/S1600536809043669
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.2.2.28888.01281
title: Habitats in transect 20210211_017_TZ
DOI: 10.5517/cct9s62
title: CCDC 754174: Experimental Crystal Structure Determination

description: Related Article: Jin-Ping Gao, Wen Zhang, Xue-Feng Shi, Da-Cheng Li|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1455|doi:10.1107/S160053680904389X
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.2.2.26895.71846
title: Dear Israa, Thank you for attending the IEEE Authorship and Open Access Symposium: Best Practices to Get Published to Increase the Exposure and Impact of Your Research webinar. We hope you found the presentation informative, and we have provided a recording of the event for your future reference.
DOI: 10.5517/cct9s84
title: CCDC 754176: Experimental Crystal Structure Determination

description: Related Article: K.Ravichandran, P.Ramesh, C.M.Mahalakshmi, K.Anbalagan, M.N.Ponnuswamy|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1458|doi:10.1107/S1600536809043323
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/cct9s95
title: CCDC 754177: Experimental Crystal Structure Determination

description: Related Article: Wei-Ping Zhang, Wen-Xiu Zhao, Gui-Hua Cui, Shun-Fu Dong|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1460|doi:10.1107/S1600536809043360
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/cct9sb6
title: CCDC 754178: Experimental Crystal Structure Determination

description: Related Article: T.Yonemura|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1463|doi:10.1107/S1600536809043712
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/cct9sc7
title: CCDC 754179: Experimental Crystal Structure Determination

description: Related Article: Xue-Ren Huang, Miao Ou-Yang, Ge-Ge Yang, Xiu-Jin Meng, Yi-Min Jiang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1465|doi:10.1107/S1600536809043918
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/cct9sd8
title: CCDC 754180: Experimental Crystal Structure Determination

description: Related Article: S.Y.Ho, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1466|doi:10.1107/S1600536809043864
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/cct9sf9
title: CCDC 754181: Experimental Crystal Structure Determination

description: Related Article: S.Y.Ho, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1468|doi:10.1107/S1600536809043876
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/cct9sgb
title: CCDC 754182: Experimental Crystal Structure Determination

description: Related Article: Li-Min Li, Fang-Fang Jian, Yu-Feng Li|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1469|doi:10.1107/S1600536809043736
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.2.2.23068.41606
title: Screen Printing on Low Temperature Cofired Ceramics_.pdf
DOI: 10.5517/cct9shc
title: CCDC 754183: Experimental Crystal Structure Determination

description: Related Article: Shang-Sheng Gong, Wen-Kui Dong, Jun-Feng Tong, Li Li, Jian-Chao Wu|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1471|doi:10.1107/S160053680904433X
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/cct9sjd
title: CCDC 754184: Experimental Crystal Structure Determination

description: Related Article: H.D.Arman, P.Poplaukhin, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1472|doi:10.1107/S1600536809044237
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/cct9skf
title: CCDC 754185: Experimental Crystal Structure Determination

description: Related Article: P.Poplaukhin, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1474|doi:10.1107/S1600536809044249
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/cct9slg
title: CCDC 754186: Experimental Crystal Structure Determination

description: Related Article: H.D.Arman, P.Poplaukhin, E.R.T.Tiekink|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1475|doi:10.1107/S1600536809044250
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/cct9smh
title: CCDC 754187: Experimental Crystal Structure Determination

description: Related Article: G.O.Hunter, M.Zeller, B.D.Leskiw|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1476|doi:10.1107/S1600536809044389
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.2.2.14574.95047
title: The list of Part of speech tags (POS) for Uzbek language
DOI: 10.13140/rg.2.2.32820.17283
title: faisal al-doori's c.v
DOI: 10.5517/cct9spk
title: CCDC 754189: Experimental Crystal Structure Determination

description: Related Article: Ai-Zhi Wu, S.W.Ng|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1479|doi:10.1107/S1600536809044419
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/cct9sql
title: CCDC 754190: Experimental Crystal Structure Determination

description: Related Article: Zhe An, Ling Zhu|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1480|doi:10.1107/S1600536809044298
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/cct9srm
title: CCDC 754191: Experimental Crystal Structure Determination

description: Related Article: M.Lamac, A.Spannenberg, P.Arndt, U.Rosenthal|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1481|doi:10.1107/S1600536809044845
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/cct9ssn
title: CCDC 754192: Experimental Crystal Structure Determination

description: Related Article: Yi-Fang Deng, Man-Sheng Chen, Dai-Zhi Kuang, Chun-Hua Zhang|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|m1482|doi:10.1107/S160053680904464X
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/cctg0py
title: CCDC 758250: Experimental Crystal Structure Determination

description: Related Article: Xiao-Hua Zeng, Shou-Heng Deng, Ping Chen, Hong-Mei Wang, Zuan Ma|2009|Acta Crystallogr.,Sect.E:Struct.Rep.Online|65|o3032|doi:10.1107/S1600536809046017
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: 10.6084/m9.figshare.28399442
title: UCSC expression profile of TCGA

description: expression profile of pancancer in UCSC data website
DOI: 10.5281/zenodo.14810282
title: Dataset for Robust Isolated Attosecond Pulse Generation with Self-Compressed Subcycle Drivers from Hollow Capillary Fibers

description: Data set for

Marina Fernández Galán, Javier Serrano, Enrique Conejero Jarque, Rocío Borrego-Varillas, Matteo Lucchini, Maurizio Reduzzi, Mauro Nisoli, Christian Brahms, John C. Travers, Carlos Hernández-García, Julio San Roman, ACS Photonics 2024, 11, 4, 1673–1683

https://doi.org/10.1021/acsphotonics.3c01897
DOI: 10.5281/zenodo.14810283
title: Dataset for Robust Isolated Attosecond Pulse Generation with Self-Compressed Subcycle Drivers from Hollow Capillary Fibers

description: Data set for

Marina Fernández Galán, Javier Serrano, Enrique Conejero Jarque, Rocío Borrego-Varillas, Matteo Lucchini, Maurizio Reduzzi, Mauro Nisoli, Christian Brahms, John C. Travers, Carlos Hernández-García, Julio San Roman, ACS Photonics 2024, 11, 4, 1673–1683

https://doi.org/10.1021/acsphotonics.3c01897
DOI: 10.5281/zenodo.14858340
title: Knowledge Graph of the US Legislation

description: Knowledge Graph of the US Legislation. It adopts the property graph model and can be loaded as a Neo4J database, the most popular property graph management system