cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7402524
title: afids/afids-data: v1.0.1

description: curated anatomical landmarks placements for common neuroimaging templates and datasets
DOI: 10.5281/zenodo.7640692
title: afids-data

description: Curated anatomical landmarks placements for common neuroimaging templates and datasets.
description: This "super" dataset we release here is best viewed and downloaded from the main github release found on: https://github.com/afids/afids-data. Not all data files may appear as they are "linked" to other existing repositories. Please review the LICENSE.md and README.md for more instructions on Data Usage Agreements and aquiring neuroimaging data.
DOI: 10.5281/zenodo.7363873
title: afids-data: Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

description: Curated anatomical landmarks placements for common neuroimaging templates and datasets.
description: This "super" dataset we release here is best viewed and downloaded from the main github release found on: https://github.com/afids/afids-data. Not all data files may appear here as they are "linked" to other existing repositories. Please review the LICENSE.md and README.md for more instructions on Data Usage Agreements and aquiring neuroimaging data.
DOI: 10.5281/zenodo.7640832
title: afids-data

description: Curated anatomical landmarks placements for common neuroimaging templates and datasets.
description: This "super" dataset we release here is best viewed and downloaded from the main github release found on: https://github.com/afids/afids-data. Not all data files may appear here as they are "linked" to other existing repositories. Please review the LICENSE.md and README.md for more instructions on Data Usage Agreements and aquiring neuroimaging data.
DOI: 10.5281/zenodo.7641532
title: afids-data: Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

description: Curated anatomical landmarks placements for common neuroimaging templates and datasets.
description: This "super" dataset we release here is best viewed and downloaded from the main github release found on: https://github.com/afids/afids-data. Not all data files may appear here as they are "linked" to other existing repositories. Please review the LICENSE.md and README.md for more instructions on Data Usage Agreements and aquiring neuroimaging data.
DOI: 10.5281/zenodo.8072124
title: afids-data: Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

description: Curated anatomical landmarks placements for common neuroimaging templates and datasets.
description: This "super" dataset we release here is best viewed and downloaded from the main github release found on: https://github.com/afids/afids-data. Not all data files may appear here as they are "linked" to other existing repositories. Please review the LICENSE.md and README.md for more instructions on Data Usage Agreements and aquiring neuroimaging data.
DOI: 10.6084/m9.figshare.3761562.v2624
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.8066019
title: Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil

description: Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil our understanding of disease outbreaks is hampered by limited viral genomic data. Determining the timing and spatial corridors of YFV spread, as well as the geographic hotspots that link the endemic north of the country with epidemic extra-Amazonian regions, are central to predicting and preventing future outbreak and epidemics. Here, we tracked the recent spread of the virus by integrating genome sequences with both epidemiological and vector data. Through a combination of phylogenetic and epidemiological models we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated <em>Aedes aegypti</em> was able to capture the seasonality of reported human infections. Spatial modelling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centres in the Southeast. Phylodynamic analysis unravelled the circulation of three distinct YFV lineages, and provided proof of the directionality of a known spatial corridor of viral spread that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology in a One Health framework can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.
DOI: 10.5281/zenodo.8066018
title: Genomic epidemiology unveils the dynamics and spatial corridor behind the Yellow Fever virus outbreak in Southern Brazil

description: Despite the considerable morbidity and mortality of yellow fever virus (YFV) infections in Brazil our understanding of disease outbreaks is hampered by limited viral genomic data. Determining the timing and spatial corridors of YFV spread, as well as the geographic hotspots that link the endemic north of the country with epidemic extra-Amazonian regions, are central to predicting and preventing future outbreak and epidemics. Here, we tracked the recent spread of the virus by integrating genome sequences with both epidemiological and vector data. Through a combination of phylogenetic and epidemiological models we reconstructed the recent transmission history of YFV within different epidemic seasons in Brazil. A suitability index based on the highly domesticated <em>Aedes aegypti</em> was able to capture the seasonality of reported human infections. Spatial modelling revealed spatial hotspots with both past reporting and low vaccination coverage, which coincided with many of the largest urban centres in the Southeast. Phylodynamic analysis unravelled the circulation of three distinct YFV lineages, and provided proof of the directionality of a known spatial corridor of viral spread that connects the endemic North with the extra-Amazonian basin. This study illustrates that genomics linked with eco-epidemiology in a One Health framework can provide new insights into the landscape of YFV transmission, augmenting traditional approaches to infectious disease surveillance and control.
DOI: 10.5883/ds-ibieu
title: Birds as potential suppressing agents of eucalypt plantations insect pests.

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/ds-ibinr02
title: DNA Barcoding of Portuguese Lacewings (Neuroptera) and Snakeflies (Raphidioptera) (Insecta, Neuropterida)

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/ds-chrysoru
title: First Discovery of the North American Leaf mining Moth, Chrysaster ostensackenella (Lepidoptera: Gracillariidae) in Russia: the Genetic Diversity of a Novel Pest in Invaded vs. Native Range

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/ds-iaast
title: DNA Barcode library of the endemic rich avifauna of the oceanic islands of the Gulf of Guinea

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/ds-brp1
title: Declining populations of Astacus astacus drag European Branchiobdella parasita (Annelida: Clitellata) ectosymbionts to serious biodiversity loss

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.34740/kaggle/dsv/5998998
title: Indian Unicorn startups 2023 Latest updated
DOI: 10.6084/m9.figshare.23564007.v1
title: Supporting Data: Actinide Arene-Metalates: Ion Pairing Effects on the Electronic Structure of Unsupported Uranium-Arenide Sandwich Complexes

description: Supporting computational data for the following publication: <br> J. Murillo, R. Bhowmick, K. L. M. Harriman, A. Gomez-Torres, J. Wright, R. Meulenberg, P. Miró, A. Metta-Magaña, M. Murugesu, B. Vlaisavljevich, and S. Fortier “Actinide Arene-Metalates: Ion Pairing Effects on the Electronic Structure of Unsupported Uranium-Arenide Sandwich Complexes” <em>Chem. Sci.</em> <strong>2021</strong>, 12, 13360-13372.
DOI: 10.6084/m9.figshare.23564007
title: Supporting Data: Actinide Arene-Metalates: Ion Pairing Effects on the Electronic Structure of Unsupported Uranium-Arenide Sandwich Complexes

description: Supporting computational data for the following publication: <br> J. Murillo, R. Bhowmick, K. L. M. Harriman, A. Gomez-Torres, J. Wright, R. Meulenberg, P. Miró, A. Metta-Magaña, M. Murugesu, B. Vlaisavljevich, and S. Fortier “Actinide Arene-Metalates: Ion Pairing Effects on the Electronic Structure of Unsupported Uranium-Arenide Sandwich Complexes” <em>Chem. Sci.</em> <strong>2021</strong>, 12, 13360-13372.
DOI: 10.5281/zenodo.8049179
title: CALIPR Framework Myelin Water Imaging Supporting Data

description: Data supporting the publication: <em><strong>The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity</strong></em>
DOI: 10.5281/zenodo.8049178
title: CALIPR Framework Myelin Water Imaging Supporting Data

description: Data supporting the publication: <em><strong>The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity</strong></em>
DOI: 10.25345/c56q1ss95
title: MassIVE MSV000092241 - DIA analysis from a large scale patient cohort for Cerebrospinal Fluid Biomarker Discovery in Alzheimer Disease
title: Diagnosis of Alzheimer disease (AD) primarily relies on cognitive assessments combined with imaging and cerebrospinal fluid (CSF) biomarkers of amyloid and tau proteins. These biomarkers correlate well with levels of neuropathology, but may be inadequate predictors of cognitive trajectory or treatment response to novel therapeutics. Analysis of larger sets of biomarkers together could improve the diagnosis and prognosis of AD, examine the interaction with and differentiate between comorbid pathophysiologies, and measure treatment efficacy. Here, we report data from a Data-independent Acquisition (DIA) analysis used to simultaneously quantify hundreds of proteins in a large-scale patient cohort from a neurology clinic, all of whom had clinical indication for diagnostic lumbar puncture. We aim to define markers that can stratify and diagnose AD in this clinically-complex cohort. Samples from 408 patients were collected from the clinic according to standardized collection and processing protocols. Samples were prepared in 21 different batches of 24 (23 samples plus one common pool) and analyzed by LC-MS/MS on an Orbitrap Fusion using a DIA method with non-overlapping 25m/z windows from 400-1000 m/z, with a resolution of 120K for MS1 and 60K for MS2. Each sample was injected in duplicate with 4 column washes between each sample. All samples from each batch were run sequentially, with multiples days separating each batch.
DOI: 10.6084/m9.figshare.23564085
title: Length-weight data for Utaka fish from Mbenji Island and surrounding waters of Lake Malawi

description: This dataset contains length-weight data for Utaka fish from Mbenji Island and surrounding waters of Lake Malawi <br>
DOI: 10.6084/m9.figshare.23564085.v1
title: Length-weight data for Utaka fish from Mbenji Island and surrounding waters of Lake Malawi

description: This dataset contains length-weight data for Utaka fish from Mbenji Island and surrounding waters of Lake Malawi <br>
DOI: 10.17909/b0kt-xa81
title: SPT0418-47 Imaging Data

description: Archival Level 3 Imaging Data of SPT0418-47 used for paper TEMPLATES: Characterization of a Merger in the Dusty Lensing SPT0418-47 System
DOI: 10.6084/m9.figshare.23564070.v1
title: Supporting Data: CASPT2 molecular geometries of Fe(II) spin-crossover complexes

description: Supporting computational data for the following publication: <br> B. A. Finney, S. Roy Chowdhury, C. Kirkvold, and B. Vlaisavljevich “CASPT2 Geometry Optimizations of Fe(II) Spin-Crossover Complexes” <em>Phys. Chem. Chem. Phys.</em> <strong>2022</strong>, 24, 1390-1398.
DOI: 10.6084/m9.figshare.23564070
title: Supporting Data: CASPT2 molecular geometries of Fe(II) spin-crossover complexes

description: Supporting computational data for the following publication: <br> B. A. Finney, S. Roy Chowdhury, C. Kirkvold, and B. Vlaisavljevich “CASPT2 Geometry Optimizations of Fe(II) Spin-Crossover Complexes” <em>Phys. Chem. Chem. Phys.</em> <strong>2022</strong>, 24, 1390-1398.
DOI: 10.6084/m9.figshare.23564088
title: Supporting Data: Sulfur-Containing Analogs of the Reactive [CuOH]<sup>2+</sup> Core

description: Supporting computational data for the following publication: <br> W. Wu, J. Tehranchi De Hont, R. Parveen, B. Vlaisavljevich, and W. B. Tolman “Sulfur-Containing Analogs of the Reactive [CuOH]<sup>2+</sup> Core” <em>Inorg. Chem.</em> <strong>2021</strong>, 60, 7, 5217-5223.
DOI: 10.6084/m9.figshare.23564088.v1
title: Supporting Data: Sulfur-Containing Analogs of the Reactive [CuOH]<sup>2+</sup> Core

description: Supporting computational data for the following publication: <br> W. Wu, J. Tehranchi De Hont, R. Parveen, B. Vlaisavljevich, and W. B. Tolman “Sulfur-Containing Analogs of the Reactive [CuOH]<sup>2+</sup> Core” <em>Inorg. Chem.</em> <strong>2021</strong>, 60, 7, 5217-5223.
DOI: 10.5281/zenodo.8072167
title: Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution

description: This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS.
DOI: 10.5281/zenodo.8072166
title: Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution

description: This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS.
DOI: 10.6084/m9.figshare.23564124.v1
title: Supporting Data: Structural Characterization of the [CuOR]<sup>2+</sup> Core”

description: Supporting computational data from the following publication: <br> V. M. Krishnan, D. Y. Shopov, C. J. Bouchey, W. D. Bailey, R. Parveen, B. Vlaisavljevich, and W. B. Tolman “Structural Characterization of the [CuOR]2+ Core” <em>J. Am. Chem. Soc.</em> <strong>2021</strong>, 143, 9, 3295-3299.
DOI: 10.5281/zenodo.7782708
title: Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes

description: Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes". <strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</strong>
DOI: 10.5281/zenodo.7782709
title: Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes

description: Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes". <strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</strong>
DOI: 10.5281/zenodo.8072245
title: Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes

description: Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes". <strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</strong>
DOI: 10.5517/ccdc.csd.cc2c410l
title: CCDC 2178618: Experimental Crystal Structure Determination

description: Related Article: A.H. Udaya Kumar, K.J. Pampa, Nanishankar V. Harohally, Dipankar Das, Biswa Nath Ghosh, N.K. Lokanath|2023|Mater.Chem.Phys.|306|128031|doi:10.1016/j.matchemphys.2023.128031
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.cc2drsrq
title: CCDC 2227404: Experimental Crystal Structure Determination

description: Related Article: Gayyur, Shivani Choudhary, Ruchir Kant, Nayan Ghosh|2023|Org.Lett.|25|4270|doi:10.1021/acs.orglett.3c01258
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.cc2dzcnf
title: CCDC 2233725: Experimental Crystal Structure Determination

description: Related Article: Shuxuan Liu, Ka Lok Chan, Zhenyang Lin, Jianwei Sun|2023|J.Am.Chem.Soc.|145|12802|doi:10.1021/jacs.3c03557
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: 10.34740/kaggle/dsv/5999439
title: Most Popular movies on TMDB
DOI: 10.34740/kaggle/dsv/5999592
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.23291147
title: LLM-Assisted Content Analysis (LACA): Coded data and model reasons

description: Description This resources consists of calibrations sets (N=100) for 4 publically available datasets using the LLM-Assisted Content Analysis method (LACA). Each dataset contains the following columns: <br> <strong>text_id: </strong> Unique ID for each text document <strong>code_id: </strong> Unique ID for each code category <strong>text: </strong> Document text that's been coded <strong>original_code:</strong> Coded response from the original datasets <strong>replicated_code:</strong> Coded response from independent coding exercise from our study team <strong>model_code:</strong> Coded response generated from the LLM (GPT-3.5-turbo) <strong>reason:</strong> LLM generated reason for coding decision <br> Additional details on methods and definitions of individual code categories are available in the following paper: <br> Chew, R., Bollenbacher, J., Speer, J., Wenger., M, Kim., A. (2023) LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding. <br> Trump Tweets <strong>Citation</strong>: Coe, Kevin, Berger, Julia, Blumling, Allison , Brooks, Katelyn , Giorgi, Elizabeth , Jackson, Jennifer , … Wellman, Mariah . Quantitative Content Analysis of Donald Trump’s Twitter, 2017-2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-04-01. https://doi.org/10.3886/E118603V1 <strong>Source</strong>: https://www.openicpsr.org/openicpsr/project/118603/version/V1/view <br> BBC News <strong>Citation</strong>: Greene, D., & Cunningham, P. (2006). Practical solutions to the problem of diagonal dominance in kernel document clustering. In <em>Proceedings of the 23rd international conference on Machine learning</em> (pp. 377-384). <strong>Source</strong>: https://www.kaggle.com/datasets/shivamkushwaha/bbc-full-text-document-classification <br> Contrarian Claims <strong>Citation</strong>: Coan, T. G., Boussalis, C., Cook, J., & Nanko, M. O. (2021). Computer-assisted classification of contrarian claims about climate change. Scientific reports, 11(1), 22320. <strong>Source</strong>: https://socialanalytics.ex.ac.uk/cards/data.zip <br> Ukraine Water Problems <strong>Citation</strong>: Afanasyev S, N. B, Bodnarchuk T, S. V, M. V, T. V, Yu V, K. G, V. D, Konovalenko O, O. K, E. K, Lietytska O, O. L, V. M, Marushevska O, Mokin V, K. M, Osadcha N, O. I (2013) River Basin Management Plan for Pivdenny Bug: river basin analysis and measures <strong>Source</strong>: https://www.kaggle.com/datasets/vbmokin/nlp-reports-news-classification
DOI: 10.5281/zenodo.8072287
title: Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling

description: This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.
DOI: 10.5281/zenodo.8072286
title: Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling

description: This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.