cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6409844
title: Top Goleadores en los Mundiales

description: El dataset consta de un resumen donde incluye la información sobre los mundiales y los 5 primeros jugadores que en el ranking de haber marcado un mayor número de goles. Quedan recogidos también la estadísticas básicas como el número de partidos y sus promedios correspondientes.
DOI: 10.5281/zenodo.6409843
title: Top Goleadores en los Mundiales

description: El dataset consta de un resumen donde incluye la información sobre los mundiales y los 5 primeros jugadores que en el ranking de haber marcado un mayor número de goles. Quedan recogidos también la estadísticas básicas como el número de partidos y sus promedios correspondientes.
DOI: 10.34740/kaggle/dsv/3402286
title: Rose Flowers
DOI: 10.34740/kaggle/dsv/3402347
title: Rose Flowers
DOI: 10.5517/ccdc.csd.cc1pvrv4
title: CCDC 1574857: Experimental Crystal Structure Determination

description: Related Article: Raoudha Soury, Marwa Chaabene, Ashanul Haque, Mahjoub Jabli, Khalaf M. Alenezi, Salman Latif, Fahad Abdulaziz, Amor Bchetnia, Christian Philouze|2022|J.Solid State Chem.|310|123048|doi:10.1016/j.jssc.2022.123048
DOI: 10.5517/ccdc.csd.cc1pvry7
title: CCDC 1574860: Experimental Crystal Structure Determination

description: Related Article: Raoudha Soury, Marwa Chaabene, Ashanul Haque, Mahjoub Jabli, Khalaf M. Alenezi, Salman Latif, Fahad Abdulaziz, Amor Bchetnia, Christian Philouze|2022|J.Solid State Chem.|310|123048|doi:10.1016/j.jssc.2022.123048
DOI: 10.5517/ccdc.csd.cc268dd9
title: CCDC 2033860: Experimental Crystal Structure Determination

description: Related Article: Na-Na Li, Wei Wen, Qian Zhang, Yong-E Gao, Zhi-Ying Li, Jie Yang, Hong-Liang Wang, Yu-Ting Du, Yan-Dong Liu|2022|Microchemical Journal|178|107371|doi:10.1016/j.microc.2022.107371
DOI: 10.5517/ccdc.csd.cc29j9x2
title: CCDC 2130844: Experimental Crystal Structure Determination

description: Related Article: Jianmei Han, Zhongyan Huang, Jingsheng Miao, Yuntao Qiu, Ziyang Xie, Chuluo Yang|2022|Chemical Science|13|3402|doi:10.1039/D2SC00329E
DOI: 10.5517/ccdc.csd.cc2b951w
title: CCDC 2153757: Experimental Crystal Structure Determination

description: Related Article: Nilaj Bandopadhyay, Krishnendu Paramanik, Prafullya Kumar Mudi, Gayetri Sarkar, Muddukrishnaiah Kotakonda, Madhusudan Shit, Bhaskar Biswas, Hari Sankar Das|2022|Polyhedron|218|115783|doi:10.1016/j.poly.2022.115783
DOI: 10.48420/19487642
title: Index to data contents

description: Experimental details from XPS and NEXAFS measurements for TiO2 anatase (101) and rutile (110) after the adsorption of 4-MBA<br>
DOI: 10.48420/19487642.v1
title: Index to data contents

description: Experimental details from XPS and NEXAFS measurements for TiO2 anatase (101) and rutile (110) after the adsorption of 4-MBA<br>
DOI: 10.48420/19487753
title: NEXAFS data TiO2 rutile (110) + 4-MBA

description: NEXAFS dataset from TiO2 rutile (110) surface + 4-MBA
DOI: 10.48420/19487762
title: NEXAFS data TiO2 anatase (101) + 4-MBA

description: NEXAFS dataset from TiO2 anatase (101) + 4-MBA experiments
DOI: 10.48420/19487762.v1
title: NEXAFS data TiO2 anatase (101) + 4-MBA

description: NEXAFS dataset from TiO2 anatase (101) + 4-MBA experiments
DOI: 10.48420/19487777
title: Photoemission data TiO2 rutile (110) + 4-MBA

description: Photoemission data TiO2 rutile (110) clean surface and + 4-MBA (deposition times of 30 minutes and 2 h)
DOI: 10.48420/19487777.v1
title: Photoemission data TiO2 rutile (110) + 4-MBA

description: Photoemission data TiO2 rutile (110) clean surface and + 4-MBA (deposition times of 30 minutes and 2 h)
DOI: 10.48420/19487789
title: Photoemission data TiO2 anatase (101) + 4-MBA

description: Photoemission data TiO2 anatase (101) clean surface and + 4-MBA (deposition times of 20 min. and 2.5 h)
DOI: 10.48420/19487789.v1
title: Photoemission data TiO2 anatase (101) + 4-MBA

description: Photoemission data TiO2 anatase (101) clean surface and + 4-MBA (deposition times of 20 min. and 2.5 h)
DOI: 10.48420/16940209
title: Data for "Emotion Regulation Strategies Mediate the Relationship between Personality and Mental Health during COVID-19"

description: Anxiety and depression are the most prevalent classes of mental illnesses; rates of anxiety and depression have been exacerbated due to the COVID-19 pandemic. Vulnerability to anxiety and depression are affected by risk and resilience factors, such as personality constructs. Recent research (e.g., Lyon et al, 2020; 2021) suggests that, out of all 30 NEO-PI-R personality constructs, variance in anxiety and depression are explained by a small number of personality constructs. However it is unclear which mechanisms mediate the relationship between these personality constructs and anxiety and depression. The purpose of this study was to investigate the mediating effect of emotion regulation strategies on the relationship between personality constructs and COVID-related anxiety and depression. Data were collected from a sample of 210 students at the University of Manchester. Measures included a select number of narrow Big Five personality facets which explain variance in anxiety and depression (facets depression, assertiveness, gregariousness, positive emotion and competence), select COPE Inventory strategies associated with coping with pandemics, and COVID-related anxiety and depression. Measures of COPE strategies and mental health were adapted to refer to coping and mental health in response to COVID pandemic.
DOI: 10.48420/16940209.v1
title: Data for "Emotion Regulation Strategies Mediate the Relationship between Personality and Mental Health during COVID-19"

description: Anxiety and depression are the most prevalent classes of mental illnesses; rates of anxiety and depression have been exacerbated due to the COVID-19 pandemic. Vulnerability to anxiety and depression are affected by risk and resilience factors, such as personality constructs. Recent research (e.g., Lyon et al, 2020; 2021) suggests that, out of all 30 NEO-PI-R personality constructs, variance in anxiety and depression are explained by a small number of personality constructs. However it is unclear which mechanisms mediate the relationship between these personality constructs and anxiety and depression. The purpose of this study was to investigate the mediating effect of emotion regulation strategies on the relationship between personality constructs and COVID-related anxiety and depression. Data were collected from a sample of 210 students at the University of Manchester. Measures included a select number of narrow Big Five personality facets which explain variance in anxiety and depression (facets depression, assertiveness, gregariousness, positive emotion and competence), select COPE Inventory strategies associated with coping with pandemics, and COVID-related anxiety and depression. Measures of COPE strategies and mental health were adapted to refer to coping and mental health in response to COVID pandemic.
DOI: 10.5281/zenodo.6410019
title: Stop-codon recoding in diverse phage lineages may regulate translation of lytic genes

description: <strong>This has some basic datasets for bacteriophages that use alternative genetic codes, and their close standard code relatives. </strong> I have included the following: - Genomes for all alternatively coded phages and their relatives - Predicted proteins all alternatively coded phages and their relatives - A sheet with some basic information about these phages - Untrimmed lysogenic contigs for prophages (Like those shown in Figure 5) - Genomes for crAss-like phages used in the alternative code bias analysis (Figure 4) - Genomes for Agate phages used in the alternative code bias analysis (Figure 4) as well as the ANI analysis in Figure 3A
DOI: 10.6084/m9.figshare.19501882
title: Tabla 6. Medios comunitarios seleccionados para la revisión y características

description: Data selection from systematic literature review 'De la radio comunitaria al podcast: métodos participativos en comunicación oral' (From community radio to podcasting: participatory methods in oral communication).
DOI: 10.6084/m9.figshare.19501882.v2
title: Tabla 6. Medios comunitarios seleccionados para la revisión y características

description: Data selection from systematic literature review 'De la radio comunitaria al podcast: métodos participativos en comunicación oral' (From community radio to podcasting: participatory methods in oral communication).
DOI: 10.6084/m9.figshare.19501882.v1
title: Tabla 6. Medios comunitarios seleccionados para la revisión y características.pdf

description: Data selection from systematic literature review 'De la radio comunitaria al podcast: métodos participativos en comunicación oral' (From community radio to podcasting: participatory methods in oral communication).
DOI: 10.5281/zenodo.6409927
title: Analytics, Visualisation and Machine Learning of General Practitioner Prescribing using Open Health Data

description: Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing.
DOI: 10.5281/zenodo.6409926
title: Analytics, Visualisation and Machine Learning of General Practitioner Prescribing using Open Health Data

description: Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing.
DOI: 10.5519/qd.olsuakhn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5517/ccdc.csd.cc24htp5
title: CCDC 1981417: Experimental Crystal Structure Determination

description: Related Article: Dingheng Lu, Tianbao Yang, Niu Tang, Chenghan Li, Yarong Song, Liang Wang, Wai-Yeung Wong, Shuang-Feng Yin, Yifei Xing, Nobuaki Kambe, Renhua Qiu|2022|Eur.J.Med.Chem.|236|114293|doi:10.1016/j.ejmech.2022.114293
DOI: 10.6084/m9.figshare.19372802
title: Prediction of prophages and their host ranges in pathogenic and commensal Neisseria species

description: This dataset is associated with the bioRxiv preprint "Prediction of prophages and their host ranges in pathogenic and commensal <i>Neisseria </i>species" (doi: 10.1101/2021.12.16.473053), which has been accepted for publication in mSystems.<br>Prophages were predicted in publicly available genome assemblies of 21 different <i>Neisseria </i>species. Using comparative genomics, we investigated the similarity of the predicted prophages to known <i>Neisseria </i>prophages and examined targeting of predictions by CRISPR-Cas systems.<br>See dataset_description.xlsx for a description of each file in the dataset and the methodology used to generate the file.
DOI: 10.6084/m9.figshare.19372802.v2
title: Prediction of prophages and their host ranges in pathogenic and commensal Neisseria species

description: This dataset is associated with the bioRxiv preprint "Prediction of prophages and their host ranges in pathogenic and commensal <i>Neisseria </i>species" (doi: 10.1101/2021.12.16.473053), which has been accepted for publication in mSystems.<br>Prophages were predicted in publicly available genome assemblies of 21 different <i>Neisseria </i>species. Using comparative genomics, we investigated the similarity of the predicted prophages to known <i>Neisseria </i>prophages and examined targeting of predictions by CRISPR-Cas systems.<br>See dataset_description.xlsx for a description of each file in the dataset and the methodology used to generate the file.
DOI: 10.6084/m9.figshare.19372802.v1
title: Prediction of prophages and their host ranges in pathogenic and commensal Neisseria species

description: This dataset is associated with the bioRxiv preprint "Prediction of prophages and their host ranges in pathogenic and commensal <i>Neisseria </i>species" (doi: 10.1101/2021.12.16.473053).<br>Prophages were predicted in publicly available genome assemblies of 21 different <i>Neisseria </i>species. Using comparative genomics, we investigated the similarity of the predicted prophages to known <i>Neisseria </i>prophages and examined targeting of predictions by CRISPR-Cas systems.<br>See dataset_description.xlsx for a description of each file in the dataset and the methodology used to generate the file.
DOI: 10.5281/zenodo.6410157
title: Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.

description: <strong>Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.</strong> <strong>Based on Coast Train data</strong> Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym
DOI: 10.5281/zenodo.6410156
title: Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.

description: <strong>Doodleverse/Segmentation Zoo UNet models for identifying water in orthomosaic photos of coasts.</strong> <strong>Based on Coast Train data</strong> Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. RGB (3 band): red, green, blue Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym
DOI: 10.6084/m9.figshare.19501915
title: Blogs

description: The list of of journals and monographs that use blogs as references.
DOI: 10.6084/m9.figshare.19501915.v1
title: Blogs

description: The list of of journals and monographs that use blogs as references.
DOI: 10.6084/m9.figshare.19501918
title: Dataset

description: This is a description
DOI: 10.6084/m9.figshare.19501921
title: Dataset

description: This is a description
DOI: 10.6084/m9.figshare.19501924
title: Dataset

description: This is a description
DOI: 10.6084/m9.figshare.19501927
title: Dataset

description: This is a description
DOI: 10.6084/m9.figshare.19501933
title: Model

description: This is a description
DOI: 10.6084/m9.figshare.19501933.v1
title: Model

description: This is a description
DOI: 10.6084/m9.figshare.3761562.v2177
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: 10.25919/nts9-wd25
title: Parkes observations for project P595 semester 2021OCTS_16

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 space missions, 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.6410225
title: Stop-codon recoding in bacteriophages may regulate translation of lytic genes

description: <strong>This has some basic datasets for bacteriophages that use alternative genetic codes, and their close standard code relatives. </strong> I have included the following: - Genomes for all alternatively coded phages and their relatives - Predicted proteins all alternatively coded phages and their relatives - A sheet with some basic information about these phages - Terminase treefile (Figure 2A) - Genomes for crAss-like phages used in the alternative code bias analysis (Figure 4) - Genomes for Agate phages used in the alternative code bias analysis (Figure 4) as well as the ANI analysis (Figure 3A) - Untrimmed lysogenic contigs for prophages (Like those shown in Figure 5)
DOI: 10.5281/zenodo.6408918
title: Stop-codon recoding in bacteriophages may regulate translation of lytic genes

description: <strong>This has some basic datasets for bacteriophages that use alternative genetic codes, and their close standard code relatives. </strong> I have included the following: - Genomes for all alternatively coded phages and their relatives - Predicted proteins all alternatively coded phages and their relatives - A sheet with some basic information about these phages - Terminase treefile (Figure 2A) - Genomes for crAss-like phages used in the alternative code bias analysis (Figure 4) - Genomes for Agate phages used in the alternative code bias analysis (Figure 4) as well as the ANI analysis (Figure 3A) - Untrimmed lysogenic contigs for prophages (Like those shown in Figure 5)
DOI: 10.5281/zenodo.6410224
title: TSH vs SEMEN PARAMETERS

description: A dataset of 130 men who were evaluated for couple subfertility. The dataset includes Age, BMI, TSH, semen volume, semen concentration, total sperm count.
DOI: 10.5281/zenodo.6410223
title: TSH vs SEMEN PARAMETERS

description: A dataset of 130 men who were evaluated for couple subfertility. The dataset includes Age, BMI, TSH, semen volume, semen concentration, total sperm count.
DOI: 10.5281/zenodo.6324341
title: KGlove+Glove embedding for MAKG citation network

description: Entity embedding by using KGlove+Glove on MAKG citation network (citation network from https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1)
description: {"references": ["Michael F\u00e4rber: \"The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data\". Proceedings of the 18th International Semantic Web Conference (ISWC'19). Auckland, New Zealand, 2019, pp. 113-129."]}
DOI: 10.5281/zenodo.6324340
title: KGlove+Glove embedding for MAKG citation network

description: Entity embedding by using KGlove+Glove on MAKG citation network (citation network from https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1)
description: {"references": ["Michael F\u00e4rber: \"The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data\". Proceedings of the 18th International Semantic Web Conference (ISWC'19). Auckland, New Zealand, 2019, pp. 113-129."]}
DOI: 10.5281/zenodo.6386897
title: Scientific Data Item Recommendation Benchmark Corpus

description: Open-source generalized benchmark corpus to evaluate scientific data item (e.g. dataset or paper) recommendation methods.
description: {"references": ["F\u00e4rber, Michael. \"The microsoft academic knowledge graph: a linked data source with 8 billion triples of scholarly data.\" International semantic web conference. Springer, Cham, 2019.", "On Bridging Data Centers and Publishers: The Data-Literature Interlinking Service Burton, Adrian; Koers, Hylke; Manghi, Paolo; La Bruzzo, Sandro; Aryani, Amir; Diepenbroek, Michael; SCHINDLER Uwe (2015)", "Persistent Identifiers: a Prerequisite to Establish the Framework for Scholarly Link Exchange\u2014Scholix Mustapha Mokrane; Adrian Burton; Hylke Koers; Markus Stocker (2016)", "The data-literature interlinking service: towards a common infrastructure for sharing data-article links Burton, Adrian; Koers, Hylke; Manghi, Paolo; La Bruzzo, Sandro; Aryani, Amir; Diepenbroek, Michael; SCHINDLER Uwe (2017)", "The Scholix Framework for Interoperability in Data-Literature Information Exchange Adrian Burton, Hylke Koers, Paolo Manghi, Martin Fenner, Sandro La Bruzzo, Amir Aryani, Michael Diepenbroek, Uwe Schindler, Markus Stocker (2017)"]}
DOI: 10.5281/zenodo.6386896
title: Scientific Data Item Recommendation Benchmark Corpus

description: Open-source generalized benchmark corpus to evaluate scientific data item (e.g. dataset or paper) recommendation methods.
description: {"references": ["F\u00e4rber, Michael. \"The microsoft academic knowledge graph: a linked data source with 8 billion triples of scholarly data.\" International semantic web conference. Springer, Cham, 2019.", "On Bridging Data Centers and Publishers: The Data-Literature Interlinking Service Burton, Adrian; Koers, Hylke; Manghi, Paolo; La Bruzzo, Sandro; Aryani, Amir; Diepenbroek, Michael; SCHINDLER Uwe (2015)", "Persistent Identifiers: a Prerequisite to Establish the Framework for Scholarly Link Exchange\u2014Scholix Mustapha Mokrane; Adrian Burton; Hylke Koers; Markus Stocker (2016)", "The data-literature interlinking service: towards a common infrastructure for sharing data-article links Burton, Adrian; Koers, Hylke; Manghi, Paolo; La Bruzzo, Sandro; Aryani, Amir; Diepenbroek, Michael; SCHINDLER Uwe (2017)", "The Scholix Framework for Interoperability in Data-Literature Information Exchange Adrian Burton, Hylke Koers, Paolo Manghi, Martin Fenner, Sandro La Bruzzo, Amir Aryani, Michael Diepenbroek, Uwe Schindler, Markus Stocker (2017)"]}