cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8298704
title: Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres

description: This repository contains the source data for the manuscript <strong>"Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres" </strong> The statistical model of 50 layers, 4-6 layers for TpDPP, TpTta, and TpTab. A code for building the statistical structure for layered materials by stacking layers following the Maxwell-Boltzmann energy distribution of their stacking modes. Full Changelog: https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1
description: {"references": ["https://pubs.acs.org/doi/abs/10.1021/jacs.2c09838"]}
DOI: 10.6084/m9.figshare.23671431
title: <b>Previous immunity shapes immune responses to SARS-CoV-2 booster vaccination and Omicron breakthrough infection risk</b>

description: Minimum dataset used in the manuscript entitled "Previous immunity shapes immune responses to SARS-CoV-2 booster vaccination and Omicron breakthrough infection risk" by Pérez-Alós, L et al. (2023).
DOI: 10.6084/m9.figshare.23671431.v1
title: <b>Previous immunity shapes immune responses to SARS-CoV-2 booster vaccination and Omicron breakthrough infection risk</b>

description: Minimum dataset used in the manuscript entitled "Previous immunity shapes immune responses to SARS-CoV-2 booster vaccination and Omicron breakthrough infection risk" by Pérez-Alós, L et al. (2023).
DOI: 10.5517/ccdc.csd.cc2fhr90
title: CCDC 2249462: Experimental Crystal Structure Determination

description: Related Article: Noimur Rahman, Nisha Kushwah, K. Indira Priyadarsini|2023|New J.Chem.|47|15686|doi:10.1039/D3NJ01423A
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.cc2fhrb1
title: CCDC 2249463: Experimental Crystal Structure Determination

description: Related Article: Noimur Rahman, Nisha Kushwah, K. Indira Priyadarsini|2023|New J.Chem.|47|15686|doi:10.1039/D3NJ01423A
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.cc2fjh3l
title: CCDC 2250169: Experimental Crystal Structure Determination

description: Related Article: Amirbek D. Radzhabov, Natalia S. Soldatova, Daniil M. Ivanov, Mekhman S. Yusubov, Vadim Yu. Kukushkin, Pavel S. Postnikov|2023|Org.Biomol.Chem.|21|6743|doi:10.1039/D3OB00833A
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.cc2fwldb
title: CCDC 2260842: Experimental Crystal Structure Determination

description: Related Article: Mayank Gupta, Varad Daoo, Jayant K. Singh|2023|Dalton Trans.|52|11621|doi:10.1039/D3DT01455J
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.cc287crn
title: CCDC 2092461: Experimental Crystal Structure Determination

description: Related Article: Yuan-Ren Ma, Xue-Jiao Lv, Qing Dong, Yong-Chao Ming, Yan-Kai Liu|2023|Org.Lett.|25|5929|doi:10.1021/acs.orglett.3c01919
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.22762076.v2
title: SpikeSeq Supplemental Material

description: The SARS-CoV-2 spike protein is a highly immunogenic and mutable protein that is the target of vaccine prevention and antibody therapeutics. This makes the encoding S-gene an important sequencing target. The SARS-CoV-2 sequencing community overwhelmingly adopted tiling amplicon-based strategies for sequencing the entire genome. As the virus evolved, primer mismatches inevitably led to amplicon drop-out. Given the exposure of the spike protein to host antibodies, mutation occurred here most rapidly, leading to amplicon failure over the most insightful region of the genome. To mitigate this, we developed SpikeSeq, a targeted method to amplify and sequence the S-gene. We evaluated 20 distinct primer designs through iterative <i>in silico</i> and <i>in vitro</i> testing to select the optimal primer pairs and run conditions. Once selected, periodic <i>in silico</i> analysis monitor primer conservation as SARS-CoV-2 evolves. Despite being designed during the Beta wave, the selected primers remain &gt; 99% conserved through Omicron as of 2023-04-14. To validate the final design, we compared SpikeSeq data and National SARS-CoV-2 Strain Surveillance whole-genome data for 321 matching samples. Consensus sequences for the two methods were highly identical (99.998%) across the S-gene. SpikeSeq can serve as a complement to whole-genome surveillance or be leveraged where only S-gene sequencing is of interest. While SpikeSeq is adaptable to other sequencing platforms, the Nanopore platform validated here is compatible with low to moderate throughputs, and its simplicity better enables users to achieve accurate results, even in low resource settings.
DOI: 10.23728/fmi-b2share.259e11fc78b84b2f83a4d99cc93bf0fe
title: Data for the manuscript "Evaluating the potential of acoustic data for avian migration analyses in comparison with citizen science and weather radar observations" by Weisshaupt et al. submitted to J AppEcol

description: Tables and figures of the supporting information of the manuscript "Evaluating the potential of acoustic data for avian migration analyses in comparison with citizen science and weather radar observations" submitted to J App Ecol by Nadja Weisshaupt
DOI: 10.6084/m9.figshare.22762076.v3
title: SpikeSeq Supplemental Material

description: The SARS-CoV-2 spike protein is a highly immunogenic and mutable protein that is the target of vaccine prevention and antibody therapeutics. This makes the encoding S-gene an important sequencing target. The SARS-CoV-2 sequencing community overwhelmingly adopted tiling amplicon-based strategies for sequencing the entire genome. As the virus evolved, primer mismatches inevitably led to amplicon drop-out. Given the exposure of the spike protein to host antibodies, mutation occurred here most rapidly, leading to amplicon failure over the most insightful region of the genome. To mitigate this, we developed SpikeSeq, a targeted method to amplify and sequence the S-gene. We evaluated 20 distinct primer designs through iterative <i>in silico</i> and <i>in vitro</i> testing to select the optimal primer pairs and run conditions. Once selected, periodic <i>in silico</i> analysis monitor primer conservation as SARS-CoV-2 evolves. Despite being designed during the Beta wave, the selected primers remain &gt; 99% conserved through Omicron as of 2023-04-14. To validate the final design, we compared SpikeSeq data and National SARS-CoV-2 Strain Surveillance whole-genome data for 321 matching samples. Consensus sequences for the two methods were highly identical (99.998%) across the S-gene. SpikeSeq can serve as a complement to whole-genome surveillance or be leveraged where only S-gene sequencing is of interest. While SpikeSeq is adaptable to other sequencing platforms, the Nanopore platform validated here is compatible with low to moderate throughputs, and its simplicity better enables users to achieve accurate results, even in low resource settings.
DOI: 10.25345/c5p26qd74
title: MassIVE MSV000092780 - Multiomics Sample Preparation Workflow for Simultaneous Cleanup of DNA, RNA, and Proteins using ProMTag
title: Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe here a method for preparing high-quality DNA, RNA, and protein or peptides for whole genome sequencing, RNA sequencing, and whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. The proteins are then digested to generate peptides ready for mass spectrometric proteome analysis. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver and lung tissue.
DOI: 10.6084/m9.figshare.22354567
title: TDC BBB-Martins Model Results Summary

description: Summary of collection of models that make predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset
DOI: 10.6084/m9.figshare.22354567.v1
title: TDC BBB-Martins Model Results Summary

description: Summary of collection of models that make predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset
DOI: 10.6084/m9.figshare.22354561
title: Random Forest TDC-BBB predictions

description: Random Forest predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Random Forest model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354561.v2
title: Random Forest TDC-BBB predictions

description: Random Forest predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Random Forest model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354561.v1
title: Random Forest TDC-BBB predictions

description: Random Forest predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Random Forest model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354576
title: Logistic Regression predictions for TDC BBB-Martins Leaderboard

description: Logistic Regression predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Logistic Regression model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354576.v1
title: Logistic Regression predictions for TDC BBB-Martins Leaderboard

description: Logistic Regression predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Logistic Regression model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.24052236
title: Comprehensive Data on the Prevalence of Psychiatric Symptoms in UK University Students: Data Files SPSS and XLSX Format and Variable Codes

description: Dataset accompanying the data descriptor for publication in Scientific Data entitled: Data on the prevalence of psychiatric symptoms in UK university students. More specifically, the current data provides crucial information concerning the prevalence of anxiety, depression, mania, insomnia, stress, suicidal ideation, psychotic experiences and loneliness amongst a sample of N=1408 UK university students. A cross-sectional online questionnaire-based study was implemented. Online recruitment for this dataset began on September 17<sup>th</sup>, 2018, and ended on the 30<sup>th</sup> July 2019. Eight validated measures were used: Generalized Anxiety Disorder Scale; Patient Health Questionnaire; The Mood Disorder Questionnaire; The Sleep Condition Indicator; The Perceived Stress Scale; Suicidal Behaviours Questionnaire-Revised; The Prodromal Questionnaire 16 (PQ-16); and the University of California Loneliness Scale.
DOI: 10.6084/m9.figshare.22354585
title: Deep Neural Network predictions for TDC BBB-Martins Leaderboard

description: Deep Neural Network predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Deep Neural Network model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354585.v2
title: Deep Neural Network predictions for TDC BBB-Martins Leaderboard

description: Deep Neural Network predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Deep Neural Network model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354585.v1
title: Deep Neural Network predictions for TDC BBB-Martins Leaderboard

description: Deep Neural Network predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Deep Neural Network model. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354588
title: Ensemble Predictions for TDC BBB-Martins Leaderboard

description: Ensemble predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Ensemble model that takes validation set predictions from Logistic Regression and Deep Neural Network models as base learners. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.22354588.v1
title: Ensemble Predictions for TDC BBB-Martins Leaderboard

description: Ensemble predictions for a drugs ability to penetrate the blood brain barrier using Therapeutic Data Commons BBB-Martins dataset. <br> Includes .tsv files with the actual and predicted classifications of BBB permeability for each drug in the TDC test set using a Ensemble model that takes validation set predictions from Logistic Regression and Deep Neural Network models as base learners. Note that a vlassification label of 0 means not-permeable and a label of 1 means the drug is BBB permeable.
DOI: 10.6084/m9.figshare.23467163
title: Analysis Of Carbon Nano Particle Variant As The Propellant Fuel To Increase Specific Impulses Of Rockets

description: This study uses coal and coconut shell nanocarbon variant fuels to compare the precise thrust and impulse parameters of various propellant fuels. The key factors affecting rocket performance are specific impulses and impulses. The fuel type, material composition, heat flow, and burning period characteristics all affect the precise thrust and impulses. It has been demonstrated that nanocarbon has valuable properties and a lengthy combustion time.
DOI: 10.6084/m9.figshare.23467163.v2
title: Analysis Of Carbon Nano Particle Variant As The Propellant Fuel To Increase Specific Impulses Of Rockets

description: This study uses coal and coconut shell nanocarbon variant fuels to compare the precise thrust and impulse parameters of various propellant fuels. The key factors affecting rocket performance are specific impulses and impulses. The fuel type, material composition, heat flow, and burning period characteristics all affect the precise thrust and impulses. It has been demonstrated that nanocarbon has valuable properties and a lengthy combustion time.
DOI: 10.5281/zenodo.8298823
title: FloodsNet_UNOSAT

description: **Test upload to create DOI. Version 2 will have full data described below.** This represents the UNOSAT flood training data as part of the FloodsNet data set.
DOI: 10.5281/zenodo.8298822
title: FloodsNet_UNOSAT

description: **Test upload to create DOI. Version 2 will have full data described below.** This represents the UNOSAT flood training data as part of the FloodsNet data set.
DOI: 10.18739/a2n87311n
title: Fire perimeters for eastern Siberia taiga and tundra from 2001-2020

description: We developed a fire perimeter dataset for eastern Siberian taiga and tundra zones from 2001-2020 based on Landsat imagery. Our study area spanned 7.8 million square kilometers across eight ecozones of eastern Siberian taiga and tundra from approximately 61-72.5°North (N) and 100°East (E)-176°West (W). We used the cloud computing power of Google Earth Engine to access the Landsat archive. We generated composite images for the annual growing season (May - September), which allowed us to mitigate missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. Finally, we converted the annual dNBR images to binary burned or unburned imagery that was used to vectorize fire perimeters. We map 22,110 fires burning 150.5 million hectares over 20 years.
DOI: 10.18739/a2ff3m154
title: Fire perimeters for eastern Siberia taiga and tundra from 2001-2020

description: We developed a fire perimeter dataset for eastern Siberian taiga and tundra zones from 2001-2020 based on Landsat imagery. Our study area spanned 7.8 million square kilometers across eight ecozones of eastern Siberian taiga and tundra from approximately 61-72.5°North (N) and 100°East (E)-176°West (W). We used the cloud computing power of Google Earth Engine to access the Landsat archive. We generated composite images for the annual growing season (May - September), which allowed us to mitigate missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. Finally, we converted the annual dNBR images to binary burned or unburned imagery that was used to vectorize fire perimeters. We map 22,110 fires burning 150.5 million hectares over 20 years.
DOI: 10.18739/a2gb1xj4m
title: Fire perimeters for eastern Siberia taiga and tundra from 2001-2020

description: We developed a fire perimeter dataset for eastern Siberian taiga and tundra zones from 2001-2020 based on Landsat imagery. Our study area spanned 7.8 million square kilometers across eight ecozones of eastern Siberian taiga and tundra from approximately 61-72.5°North (N) and 100°East (E)-176°West (W). We used the cloud computing power of Google Earth Engine to access the Landsat archive. We generated composite images for the annual growing season (May - September), which allowed us to mitigate missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. Finally, we converted the annual dNBR images to binary burned or unburned imagery that was used to vectorize fire perimeters. We map 22,110 fires burning 150.5 million hectares over 20 years.
DOI: 10.5517/ccdc.csd.cc2cpnxn
title: CCDC 2195572: Experimental Crystal Structure Determination

description: Related Article: Jing-Li Qi, Jiajing Wu, Yue Guo, Zhong-Ping Xu, Wenlong Liu, Sheng-Ping Guo|2023|Inorg.Chem.Front.|10|3319|doi:10.1039/D3QI00297G
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.cc2cpnyp
title: CCDC 2195573: Experimental Crystal Structure Determination

description: Related Article: Jing-Li Qi, Jiajing Wu, Yue Guo, Zhong-Ping Xu, Wenlong Liu, Sheng-Ping Guo|2023|Inorg.Chem.Front.|10|3319|doi:10.1039/D3QI00297G
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: 10.18739/a2kk94d36
title: Broadband albedo measurements of the sea ice surface during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign in the Central Arctic Ocean, April – September 2020

description: This dataset contains broadband albedo measurements made on the sea ice surface from approximately 1-meter (m) elevation during April – September 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Central Arctic Ocean. Measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters (or 10 meters on leg 3), (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. Broadband albedo data was collected using a Kipp and Zonen albedometer. This dataset is collocated with the spectral albedo dataset (doi.org/10.18739/A2FT8DK8Z) and albedo photo dataset (doi.org/10.18739/A2B27PS3N).