cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4022440
title: ID linking table for all samples that have been sequenced in the CRUK-ICGC Prostate Group projects

description: Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the International Cancer Genome Consortium Prostate Cancer UK consortium (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry. Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data. The original aims of the project were: 1. To understand the significance of multifocal prostate cancer. 2. To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting. 3. To understand the molecular basis of development and spread of castration-resistant and metastatic disease 4. To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. This table contains links between all the IDs, in particular between those that have been published in research articles and the The European Genome-phenome Archive (EGA) where the raw sequencing data is held (https://ega-archive.org/studies/EGAS00001000262).
DOI: 10.5281/zenodo.4022439
title: ID linking table for all samples that have been sequenced in the CRUK-ICGC Prostate Group projects

description: Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the International Cancer Genome Consortium Prostate Cancer UK consortium (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry. Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data. The original aims of the project were: 1. To understand the significance of multifocal prostate cancer. 2. To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting. 3. To understand the molecular basis of development and spread of castration-resistant and metastatic disease 4. To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. This table contains links between all the IDs, in particular between those that have been published in research articles and the The European Genome-phenome Archive (EGA) where the raw sequencing data is held (https://ega-archive.org/studies/EGAS00001000262).
DOI: 10.6084/m9.figshare.12937092
title: ProteomeTools(non tryptic) - Prosit fragmentation - Data

description: Non-tryptic ProteomeTools (HCD+CID) data as training, test and holdout dataset for machine learning applications.<br>
DOI: 10.6084/m9.figshare.12936947
title: Prosit(Non tryptic) - Model - Fragmentation

description: Non-tryptic Prosit fragmentation prediction models for HCD and CID<br>
DOI: 10.5281/zenodo.4022436
title: Energy Balance and Time Dependence of a Magnetotail Electron Diffusion Region

description: The data sets and python notebook included are for a submitted manuscript to the Journal of Geophysical Review: Space Physics. An IDL save file has also been included which contains variables from a reconstruction of the event in the manuscript. The time variable in tt2000. Other variables are in units of nW/m^3 by default. <br> <br> The purpose of the work is to determine how electromagnetic energy is converted into plasma energy in the electron diffusion region of magnetic reconnection. Using both spacecraft data and simulations, we investigate how the terms in Poynting's theorem change as reconnection begins and evolves in time.
DOI: 10.5281/zenodo.3932336
title: Energy Balance and Time Dependence of a Magnetotail Electron Diffusion Region

description: The data sets and python notebook included are for a submitted manuscript to the Journal of Geophysical Review: Space Physics. An IDL save file has also been included which contains variables from a reconstruction of the event in the manuscript. The time variable in tt2000. Other variables are in units of nW/m^3 by default. <br> <br> The purpose of the work is to determine how electromagnetic energy is converted into plasma energy in the electron diffusion region of magnetic reconnection. Using both spacecraft data and simulations, we investigate how the terms in Poynting's theorem change as reconnection begins and evolves in time.
DOI: 10.6084/m9.figshare.12931372.v5
title: raw_reads_separate

description: Raw QIIME2 reads files for each study that is included in the meta-analysis presented in "Food or just a free ride? A meta-analysis reveals the global diversity of the Plastisphere"Note that some of these are still uploading. Please contact Robyn Wright with any issues.
DOI: 10.5517/ccdc.csd.cc1nf5cm
title: CCDC 1532000: Experimental Crystal Structure Determination

description: Related Article: Mateusz K. Cybulski, Nicholas A. Beattie, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2020|Chem.-Eur.J.|26|11141|doi:10.1002/chem.202001685
DOI: 10.5517/ccdc.csd.cc1nf5dn
title: CCDC 1532001: Experimental Crystal Structure Determination

description: Related Article: Mateusz K. Cybulski, Nicholas A. Beattie, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2020|Chem.-Eur.J.|26|11141|doi:10.1002/chem.202001685
DOI: 10.5517/ccdc.csd.cc1nf5fp
title: CCDC 1532002: Experimental Crystal Structure Determination

description: Related Article: Mateusz K. Cybulski, Nicholas A. Beattie, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2020|Chem.-Eur.J.|26|11141|doi:10.1002/chem.202001685
DOI: 10.5517/ccdc.csd.cc1nf5gq
title: CCDC 1532003: Experimental Crystal Structure Determination

description: Related Article: Mateusz K. Cybulski, Nicholas A. Beattie, Stuart A. Macgregor, Mary F. Mahon, Michael K. Whittlesey|2020|Chem.-Eur.J.|26|11141|doi:10.1002/chem.202001685
DOI: 10.5517/ccdc.csd.cc21nxc0
title: CCDC 1896932: Experimental Crystal Structure Determination

description: Related Article: Huamin Liang, Ran Liu, Min Zhou, Yue Fu, Chuanfa Ni, Jinbo Hu|2020|Org.Lett.|22|7047|doi:10.1021/acs.orglett.0c02688
DOI: 10.5517/ccdc.csd.cc23xvxt
title: CCDC 1964157: Experimental Crystal Structure Determination

description: Related Article: Lorenzo Lombardi, Daniele Bellini, Andrea Bottoni, Matteo Calvaresi, Magda Monari, Alessandro Kovtun, Vincenzo Palermo, Manuela Melucci, Marco Bandini|2020|Chem.-Eur.J.|26|10427|doi:10.1002/chem.202001373
DOI: 10.5517/ccdc.csd.cc245tx2
title: CCDC 1971814: Experimental Crystal Structure Determination

description: Related Article: Yue Zhao, Qing Lin Guan, Associate Prof Feng Ying Bai, Prof Yong Heng Xing, Li Xian Sun|2020|J.Mol.Struct.|1225|129115|doi:10.1016/j.molstruc.2020.129115
DOI: 10.5517/ccdc.csd.cc248b2v
title: CCDC 1974206: Experimental Crystal Structure Determination

description: Related Article: Yu Zhang, Hao Pan, Wen Liu, Weidi Cao, Xiaoming Feng|2020|Org.Lett.|22|6744|doi:10.1021/acs.orglett.0c02108
DOI: 10.5517/ccdc.csd.cc24bk13
title: CCDC 1976344: Experimental Crystal Structure Determination

description: Related Article: Zonghao Dai, Jin Zhu, Wenbo Su, Wuxian Zeng, Ziqi Liu, Ming Chen, Qingfa Zhou|2020|Org.Lett.|22|7008|doi:10.1021/acs.orglett.0c02558
DOI: 10.5517/ccdc.csd.cc24f2m9
title: CCDC 1978780: Experimental Crystal Structure Determination

description: Related Article: Cohan Huxley, Mario Wibowo, Kah Yean Lum, Shelly Gordon, Sebastian D’Hyon, Hanyu Guan, Xueyi Wang, Yuxi Chen, Jonathan M. White, Kanu Wani|2020|Phytochemistry|179|112478|doi:10.1016/j.phytochem.2020.112478
DOI: 10.5517/ccdc.csd.cc24p7wz
title: CCDC 1986631: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.5517/ccdc.csd.cc24p7x0
title: CCDC 1986632: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.5517/ccdc.csd.cc24p7y1
title: CCDC 1986633: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.5517/ccdc.csd.cc24p7z2
title: CCDC 1986634: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.5517/ccdc.csd.cc24p804
title: CCDC 1986635: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.5517/ccdc.csd.cc24p815
title: CCDC 1986636: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Radmir M. Khisamov, Denis A. Bashirov, Vladislav Yu. Komarov, Maxim S. Molokeev, Alexey A. Ryadun, Enrico Benassi, Sergey N. Konchenko|2020|Cryst.Growth Des.|20|5796|doi:10.1021/acs.cgd.0c00406
DOI: 10.6084/m9.figshare.12925589
title: Data_UserStudy.sav

description: This dataset presents the raw data of a user study on "Evaluating different Cartographic Design Variants for visually communicating Route Efficiency". The data is compatible for use in SPSS. Among other data, this dataset includes information on each participant's route decision, as well as the time needed for decision making.
DOI: 10.6084/m9.figshare.12925589.v3
title: Data_UserStudy.sav

description: This dataset presents the raw data of a user study on "Evaluating different Cartographic Design Variants for visually communicating Route Efficiency". The data is compatible for use in SPSS. Among other data, this dataset includes information on each participant's route decision, as well as the time needed for decision making.
DOI: 10.6084/m9.figshare.12925589.v2
title: Data_UserStudy.sav

description: This dataset presents the raw data of a user study on "Evaluating different Cartographic Design Variants for visually communicating Route Efficiency". The data is compatible for use in SPSS. Among other data, this dataset includes information on each participant's route decision, as well as the time needed for decision making.
DOI: 10.21227/g79g-yc90
title: Tweets of 2014 Brazilian Elections

description: This dataset was extracted from the keywords: Dilma and Aécio Neves, both were candidates for the presidency of Brazil in 2014. This is a dataset where it contains texts in Portuguese and together with the classification of sentiments from the article published in the IEEE ICDM Workshops. Our dataset has over than 100,000 tweets which were collected from the Twitter database considering the period between October 12, 2014, and October 28, 2014, and the elections' results were available at October 26, 2014
DOI: 10.21227/c344-ta68
title: Tweets of 2014 Brazilian Elections

description: This dataset was extracted from the keywords: Dilma and Aécio Neves, both were candidates for the presidency of Brazil in 2014. This is a dataset where it contains texts in Portuguese and together with the classification of sentiments from the article published in the IEEE ICDM Workshops. Our dataset has over than 100,000 tweets which were collected from the Twitter database considering the period between October 12, 2014, and October 28, 2014, and the elections' results were available at October 26, 2014
DOI: 10.25387/g3.12869570
title: Supplemental Material for Mok et al., 2020

description: Supplemental figures, tables, and data accompanying Mok <i>et al.</i> manuscript "PhenoMIP: High-Throughput Phenotyping of Diverse <i>C. elegans </i>Populations via Molecular Inversion Probes"<br>Figure S1 contains abundance and read depth data for MIP library sequencing data from false positive datasets. <br>Figure S2 contains an analysis of read depth data for probe representation and read depth distribution from false positive datasets. <br>Figure S3 contains principal component analysis of pool M11 replicates after subdividing into sets by food source. <br>Figure S4 contains principal component analysis of pool M11 replicates after subdividing into sets by generation. <br>Figure S5 contains violin plots of mean FCR across all strains, separated by fitness class. <br>Figure S6 contains fold-change analysis by food source from MMP strain data. <br>Figure S7 contains an analysis of mean FCR for strains across all datasets and of control strain (VC20019) mean FCR across multiple experiments. <br>Figure S8 contains MIP-MAP data for class 0 and class 1 strains. <br>File SD1 contains sequence information for all MIPs designed for 2007 MMP strains and 40 wild isolates of <i>C. elegans</i>. <br>File SD2 contains MIP abundance and read data used in the analysis of false positive sequencing data. <br>File SD3 contains the mean fold-change rate data for all MMP strains across pooling experiments presented in this manuscript. <br>File SD4 contains mean fold-change rate data for all replicates of the wild isolate strains across the RNAi experiments as well as variant data for ED3052 mapped interval associated with <i>emb-27</i> RNAi suppression. <br>File SD5 contains all sequencing read counts and strain mean abundance data derived from MIP analysis for the pooling experiments presented within the manuscript.
DOI: 10.25387/g3.12869570.v2
title: Supplemental Material for Mok et al., 2020

description: Supplemental figures, tables, and data accompanying Mok <i>et al.</i> manuscript "PhenoMIP: High-Throughput Phenotyping of Diverse <i>C. elegans </i>Populations via Molecular Inversion Probes"<br>Figure S1 contains abundance and read depth data for MIP library sequencing data from false positive datasets. <br>Figure S2 contains an analysis of read depth data for probe representation and read depth distribution from false positive datasets. <br>Figure S3 contains principal component analysis of pool M11 replicates after subdividing into sets by food source. <br>Figure S4 contains principal component analysis of pool M11 replicates after subdividing into sets by generation. <br>Figure S5 contains violin plots of mean FCR across all strains, separated by fitness class. <br>Figure S6 contains fold-change analysis by food source from MMP strain data. <br>Figure S7 contains an analysis of mean FCR for strains across all datasets and of control strain (VC20019) mean FCR across multiple experiments. <br>Figure S8 contains MIP-MAP data for class 0 and class 1 strains. <br>File SD1 contains sequence information for all MIPs designed for 2007 MMP strains and 40 wild isolates of <i>C. elegans</i>. <br>File SD2 contains MIP abundance and read data used in the analysis of false positive sequencing data. <br>File SD3 contains the mean fold-change rate data for all MMP strains across pooling experiments presented in this manuscript. <br>File SD4 contains mean fold-change rate data for all replicates of the wild isolate strains across the RNAi experiments as well as variant data for ED3052 mapped interval associated with <i>emb-27</i> RNAi suppression. <br>File SD5 contains all sequencing read counts and strain mean abundance data derived from MIP analysis for the pooling experiments presented within the manuscript.
DOI: 10.25387/g3.12869570.v1
title: Supplemental Material for Mok et al., 2020

description: Supplemental figures, tables, and data accompanying Mok <i>et al.</i> manuscript "PhenoMIP: High-Throughput Phenotyping of Diverse <i>C. elegans </i>Populations via Molecular Inversion Probes"<br>Figure S1 contains abundance and read depth data for MIP library sequencing data from false positive datasets. <br>Figure S2 contains an analysis of read depth data for probe representation and read depth distribution from false positive datasets. <br>Figure S3 contains principal component analysis of pool M11 replicates after subdividing into sets by food source. <br>Figure S4 contains principal component analysis of pool M11 replicates after subdividing into sets by generation. <br>Figure S5 contains violin plots of mean FCR across all strains, separated by fitness class. <br>Figure S6 contains fold-change analysis by food source from MMP strain data. <br>Figure S7 contains an analysis of mean FCR for strains across all datasets and of control strain (VC20019) mean FCR across multiple experiments. <br>Figure S8 contains MIP-MAP data for class 0 and class 1 strains. <br>File SD1 contains sequence information for all MIPs designed for 2007 MMP strains and 40 wild isolates of <i>C. elegans</i>. <br>File SD2 contains MIP abundance and read data used in the analysis of false positive sequencing data. <br>File SD3 contains the mean fold-change rate data for all MMP strains across pooling experiments presented in this manuscript. <br>File SD4 contains mean fold-change rate data for all replicates of the wild isolate strains across the RNAi experiments as well as variant data for ED3052 mapped interval associated with <i>emb-27</i> RNAi suppression. <br>File SD5 contains all sequencing read counts and strain mean abundance data derived from MIP analysis for the pooling experiments presented within the manuscript.
DOI: 10.5285/cbd2cf78-462a-4968-be20-05f9c125ad10
title: Arctic sea ice and physical oceanography derived from CryoSat-2 Baseline-C Level 1b waveform observations, Oct-Apr 2010-2018

description: This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency's satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 and 2018. CryoSat-2 Level 1b Baseline C observed waveforms have been retracked using a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a sophisticated physically-based treatment of the effect of ice surface roughness on retracked ice and ocean elevations. Methods for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, deriving sea ice freeboard, and converting to thickness are detailed in Landy et al. (In Review). This dataset contains derived sea ice thicknesses from two processing chains, the first using the conventional snow depth and density climatology from Warren et al. (1999) and the second using reanalysis and model-based snow data from SnowModel (Stroeve et al., In Review). Sea surface height and ocean topography grids were derived from only those CryoSat-2 samples classified as leads. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter. Funding was provided by ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS and NERC Project PRE-MELT grant NE/T000546/1.
description: The data is derived from CryoSat-2 Baseline-C Level 1b waveform observations over the Arctic, north of 50 degrees altitude, for 2010 to 2018.
description: European Space Agency's CryoSat-2.
description: NetCDF files were produced from the final gridded satellite observations and contain all associated metadata, or details about auxiliary datasets used in the processor. No smoothing or averaging was performed on the 25-km gridded sea ice and sea surface height parameters. A 300-km Gaussian convolution filter was used to smooth artefacts in the dynamic ocean topography grids, before deriving geostrophic currents. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter.
DOI: 10.15468/u63hqv
title: New sponge species from Seno Magdalena, Puyuhuapi Fjord and Jacaf Canal (Chile)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Bertolino, Marco, Costa, Gabriele, Bavestrello, Giorgio, Pansini, Maurizio, Daneri, Giovanni (2020): New sponge species from Seno Magdalena, Puyuhuapi Fjord and Jacaf Canal (Chile). European Journal of Taxonomy 715: 1-49, DOI: https://doi.org/10.5852/ejt.2020.715
DOI: 10.15468/ex8cvd
title: New species of Euryglossula Michener (Apoidea: Colletidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Batley, Michael (2016): New species of Euryglossula Michener (Apoidea: Colletidae). Records of the Australian Museum 68 (6): 245-261, DOI: 10.3853/j.2201-4349.68.2016.1665, URL: http://dx.doi.org/10.3853/j.2201-4349.68.2016.1665
DOI: 10.6084/m9.figshare.12906320.v1
title: Data Set Comparison of Two Lipid Emulsions on Interleukin-1β, Interleukin-8 and Fatty Acid Composition in Infants Post Gastrointestinal Surgery: A Randomized Trial

description: Data set to research: Comparison of Two Lipid Emulsions on IL-1beta, IL-8, and Fatty Acid Composition in Infants post Gastrointestinal Surgery: A Randomized Trial
DOI: 10.25345/c57n1m
title: MassIVE MSV000086114 - Proteomic study in Pompe disease
title: The raw data of SWATH proteomic analysis of plasma in Pompe disease patients and controls
DOI: 10.25919/5f5a304179d2b
title: Parkes observations for project P595 semester 2020APRS_14

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the FERMI Gamma ray mission, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.5281/zenodo.3580461
title: Dataset for paper "Flow Resistance for a Varying Density of Obstacles on Smooth and Rough Beds"

description: This dataset contains the experimental data that supports the paper: Guillén-Ludeña, S., Lopez, D., Mignot, E., & Riviere, N. (2019). Flow Resistance for a Varying Density of Obstacles on Smooth and Rough Beds. <em>Journal of Hydraulic Engineering</em>, <em>146</em>(2), 04019059. The file Readme.txt contains al explanations regarding the data organization.