cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7306770
title: HG005_Son.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. <em>et al.</em> High-coverage, long-read sequencing of Han Chinese trio reference samples. <em>Sci Data</em> <strong>6</strong>, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
description: {"references": ["Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2", "Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3:160025 doi: 10.1038/sdata.2016.25 (2016)."]}
DOI: 10.5281/zenodo.7307266
title: HG006_Father.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307265
title: HG006_Father.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307277
title: HG007_Mother.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG007(mother)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307276
title: HG007_Mother.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG007(mother)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307245
title: HG005_Son.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307244
title: HG005_Son.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG005(son)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307258
title: HG006_Father.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG006(father)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307257
title: HG006_Father.R1.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG006(father)_R1 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307283
title: HG007_Mother.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG007(mother)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5281/zenodo.7307282
title: HG007_Mother.R2.fastq.gz (GIAB_ChineseTrio_30X)

description: This is a sub-sampled version (30X) of ChineseTrio HG007(mother)_R2 fastq files published by NIST's Genome in a Bottle project.<br> We created them for practicing variant-call processes.<br> As the total size of these data amounts to more than 200GB, we also prepared 10X fastq files. The original data is available under:https://github.com/genome-in-a-bottle/giab_data_indexes<br> The original article is below;<br> Wang, YC., Olson, N.D., Deikus, G. et al. High-coverage, long-read sequencing of Han Chinese trio reference samples. Sci Data 6, 91 (2019). https://doi.org/10.1038/s41597-019-0098-2<br> We are permitted to published sub-sampled versions by Genome in a Bottle project.
DOI: 10.5517/ccdc.csd.cc2ckn6t
title: CCDC 2191706: Experimental Crystal Structure Determination

description: Related Article: Hasrat Ali, Johan E. van Lier|2022|Steroids|187|109092|doi:10.1016/j.steroids.2022.109092
description: Related Article: Johannes VAN LIER|2022|CSD Communication|||
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.cc2cks4w
title: CCDC 2191828: Experimental Crystal Structure Determination

description: Related Article: Víctor García Lopez|2022|CSD Communication|||
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.cc2cks5x
title: CCDC 2191829: Experimental Crystal Structure Determination

description: Related Article: Víctor García Lopez|2022|CSD Communication|||
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.cc2cksb2
title: CCDC 2191834: Experimental Crystal Structure Determination

description: Related Article: Saraswathi V|2022|CSD Communication|||
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.cc2d77nk
title: CCDC 2211498: Experimental Crystal Structure Determination

description: Related Article: Y.-J. Kong, J.-T. Wang, L. Song|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|685|doi:10.1107/S2053229622009809
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.cc2c7vyf
title: CCDC 2182305: Experimental Crystal Structure Determination

description: Related Article: Ying Cheng, Zeyuan Zhang, Xianglong Duan, Mingming Zhang|2022|Dalton Trans.|51|16517|doi:10.1039/D2DT02197H
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.cc2c7vzg
title: CCDC 2182306: Experimental Crystal Structure Determination

description: Related Article: Ying Cheng, Zeyuan Zhang, Xianglong Duan, Mingming Zhang|2022|Dalton Trans.|51|16517|doi:10.1039/D2DT02197H
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.cc2bvjvl
title: CCDC 2170460: Experimental Crystal Structure Determination

description: Related Article: Kuntal Pal|2022|CSD Communication|||
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.21531672
title: data1.zip

description: biodiversity ecosystem service human footprint
DOI: 10.6084/m9.figshare.21277187.v2
title: Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net

description: Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, for example, groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that this marginal likelihood approximates that of elastic net models. The ridge group penalties are then transformed to elastic net group penalties by matching the ridge prior variance to the elastic net prior variance as function of the group penalties. The method allows for overlapping groups and unpenalized variables, and is easily extended to other penalties. For a model-based simulation study and two cancer genomics applications we demonstrate a substantially decreased computation time and improved or matching performance compared to other methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21277187
title: Fast Marginal Likelihood Estimation of Penalties for Group-Adaptive Elastic Net

description: Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, for example, groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that this marginal likelihood approximates that of elastic net models. The ridge group penalties are then transformed to elastic net group penalties by matching the ridge prior variance to the elastic net prior variance as function of the group penalties. The method allows for overlapping groups and unpenalized variables, and is easily extended to other penalties. For a model-based simulation study and two cancer genomics applications we demonstrate a substantially decreased computation time and improved or matching performance compared to other methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21277187.v1
title: Fast marginal likelihood estimation of penalties for group-adaptive elastic net

description: Elastic net penalization is widely used in high-dimensional prediction and variable selection settings. Auxiliary information on the variables, e.g. groups of variables, is often available. Group-adaptive elastic net penalization exploits this information to potentially improve performance by estimating group penalties, thereby penalizing important groups of variables less than other groups. Estimating these group penalties is, however, hard due to the high dimension of the data. Existing methods are computationally expensive or not generic in the type of response. Here we present a fast method for estimation of group-adaptive elastic net penalties for generalized linear models. We first derive a low-dimensional representation of the Taylor approximation of the marginal likelihood for group-adaptive ridge penalties, to efficiently estimate these penalties. Then we show by using asymptotic normality of the linear predictors that this marginal likelihood approximates that of elastic net models. The ridge group penalties are then transformed to elastic net group penalties by matching the ridge prior variance to the elastic net prior variance as function of the group penalties. The method allows for overlapping groups and unpenalized variables, and is easily extended to other penalties. For a model-based simulation study and two cancer genomics applications we demonstrate a substantially decreased computation time and improved or matching performance compared to other methods.
DOI: 10.6084/m9.figshare.21526548
title: Egyptian Song Lyrics Translated into English via Human Translators and Google Neural Translation Model

description: This dataset is the first parallel corpus of Egyptian Arabic song lyrics with their human and machine translations. Human translations are generated by professional human translators with extensive experience with subtitling Egyptian movies and songs into English. Machine translations are generated by the neural model of Google translate in September 2022. <br> The dataset is the basis of the research findings that can be found at ... <br> <br>
DOI: 10.6084/m9.figshare.21526548.v1
title: Egyptian Song Lyrics Translated into English via Human Translators and Google Neural Translation Model

description: This dataset is the first parallel corpus of Egyptian Arabic song lyrics with their human and machine translations. Human translations are generated by professional human translators with extensive experience with subtitling Egyptian movies and songs into English. Machine translations are generated by the neural model of Google translate in September 2022. <br> The dataset is the basis of the research findings that can be found at ... <br> <br>
DOI: 10.48505/nims.3735
title: Supercon 2 Dataset

description: The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records from 37700 papers. The material (or sample) information is represented by name, chemical formula, and material class, and is characterised by shape, doping, substitution variables for components, and substrate as adjoined information. The properties include the Tc superconducting critical temperature and, when available, applied pressure with the Tc measurement method.
DOI: 10.57745/fnp82r
title: weevil hyperspectral_analyzer.ipynb

description: :unav
DOI: 10.57745/pnwews/uvzw9v
title: Database_sheet.pdf

description: :unav
DOI: 10.57745/d2d1vm
title: reflectance_discriminating_wavelength.ipynb

description: :unav
DOI: 10.57745/zjdo2h
title: weevil_spectrum_avg_norm_no_outliers.csv

description: :unav
DOI: 10.6084/m9.figshare.21532341
title: Ambush site selection by a green bamboo pit viper: Relation to prey abundance and comparison between juveniles and adults

description: Prey density and prey characteristics should consider simultaneously when examining ambush site of <em>Trimeresurus stejnegeri</em>. Stomach content of the snakes suggested that prey with high density did not necessarily forage by the snake. Active season, predator avoidance tactic, and density could overall affect the snake in foraging. In addition, juvenile and adult <em>T. stejnegeri </em>selected different ambushing site without ontogenetic diet shift suggested intraspecific competition may occur in sit-and-wait snakes.
DOI: 10.6084/m9.figshare.21128035.v1
title: Data for "Spatiotemporal sizes of specific types of lightning flashes"

description: This dataset is for a manuscript titled "Spatiotemporal sizes of specific types of lightning flashes". The data of our results are included in the 'Data for Figure xx.fig'. These files can be opened by MATLAB R2021a(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citations.
DOI: 10.6084/m9.figshare.16884601
title: A Graph-based approach for representing and storing address in geodatabase

description: Please refer to the README file.
DOI: 10.5517/ccdc.csd.cc2d8bz0
title: CCDC 2212562: Experimental Crystal Structure Determination

description: Related Article: C.J. Allen, A.M. Olden, J.L. Yates, K.A. Wheeler, E.R. Abbey|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|681|doi:10.1107/S2053229622010002
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.cc2cwssv
title: CCDC 2201458: Experimental Crystal Structure Determination

description: Related Article: Dhananjay Dey, Saikat Seth, T.P. Mohan, Deepak Chopra|2022|J.Mol.Struct.|1273|134380|doi:10.1016/j.molstruc.2022.134380
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.