cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8167587
title: MOON: Assisting Students in Completing Educational Notebook Scenarios (Artifacts)

description: This repository contains the artifacts supporting the paper "<em>MOON: Assisting Students in Completing Educational Notebook Scenarios</em>" published in the <em>IEEE Symposium on Visual Languages and Human-Centric Computing</em> (VL/HCC) 2023. <strong>moon.zip</strong> contains the source code of MOON at the time of publication <strong>notebooks.zip</strong> contains the example Jupyter notebook used as illustration in the paper as well as the notebook manipulated by students in the evaluation <strong>analysis.zip</strong> contains the Jupyter notebooks used to analyze the raw data extracted in the evaluation as well as the results of the user study conducted with students
DOI: 10.6084/m9.figshare.23716080
title: data.zip

description: Meteorological data and Historical PM2.5 data with Meteorological data from 2016-2018 in Jiangxi,China. The data includes hourly and daily data.
DOI: 10.6084/m9.figshare.23716080.v1
title: data.zip

description: Meteorological data and Historical PM2.5 data with Meteorological data from 2016-2018 in Jiangxi,China. The data includes hourly and daily data.
DOI: 10.6084/m9.figshare.23716149
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.6084/m9.figshare.23716149.v1
title: Supplementary Material for: Predicting Alzheimer’s Disease with Interpretable Machine Learning

description: Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.
DOI: 10.5281/zenodo.7674805
title: Data from "Behavior of reef fishes during a submarine magnitude 5 earthquake"

description: <strong>Behavior of reef fishes during a submarine magnitude 5 earthquake</strong> Appendix S1 gathers video excerpts of 2 minutes recorded by 12 remote underwater cameras on the seafloor of N’Gouja reef (Mayotte) before, during and after the magnitude 5.3 earthquake on 10 November 2020 (12:19:39 local time). Table S1 & Tables S2 gathers data of fish behavior collected from the 12 remote underwater videos (rows) during 10 seconds slots before [be.], during [Earthq.] and after [af.] the earthquake (columns). Table S1 contains the proportion of fish individuals seen fleeing. Table S2 contains the feeding activity of <em>Ctenochaetus striatus</em> (number of bites per individual during 10 s).
DOI: 10.5281/zenodo.7674804
title: Data from "Behavior of reef fishes during a submarine magnitude 5 earthquake"

description: <strong>Behavior of reef fishes during a submarine magnitude 5 earthquake</strong> Appendix S1 gathers video excerpts of 2 minutes recorded by 12 remote underwater cameras on the seafloor of N’Gouja reef (Mayotte) before, during and after the magnitude 5.3 earthquake on 10 November 2020 (12:19:39 local time). Table S1 & Tables S2 gathers data of fish behavior collected from the 12 remote underwater videos (rows) during 10 seconds slots before [be.], during [Earthq.] and after [af.] the earthquake (columns). Table S1 contains the proportion of fish individuals seen fleeing. Table S2 contains the feeding activity of <em>Ctenochaetus striatus</em> (number of bites per individual during 10 s).
DOI: 10.6084/m9.figshare.23716158
title: <strong>Quality of Life and Psychological Problems in Children with and without Celiac Disease</strong>

description: <strong>Objective:</strong> This study aimed to assess the impact of a gluten-free diet on the mental health and quality of life of children with Celiac Disease, a condition triggered by gluten consumption. Comparisons were made between children with and without Celiac Disease and between children's self-reports and parental reports of their quality of life. <strong>Methods</strong>: In this case-control study, the quality of life of children aged 6 to 16 with Celiac Disease was compared to children without Celiac Disease. Additionally, the study aimed to compare the reports of parents and children regarding quality of life. To assess quality of life, questionnaires including the Quality-of-Life Questionnaire (Kid-KINDL) were administered to both children and parents, while the Checklist of Pediatric Symptoms (PSC) was specifically completed by parents. <strong>Results</strong>: The results indicated significant differences (p&lt;0.05) between children with and without Celiac Disease in terms of quality of life, PSC scores, attention, internalizing and externalizing symptoms (as reported by parents), as well as school performance, self-esteem, and emotional well-being (as reported by children). Furthermore, significant differences (p&lt;0.05) were found between children and parents in their reporting of quality of life and self-esteem among children with Celiac Disease. <strong>Conclusion</strong>: This study offers valuable insights into the quality of life, psychological impairments, and related factors in both children with Celiac Disease and those without Celiac Disease. Additionally, it highlights the tendency of children with Celiac Disease parents to underestimate their children quality of life.
DOI: 10.21227/00x2-yn46
title: Wide detection range and sensitivity enhanced dual eccentric-core D-shaped photonic crystal fiber surface plasmon resonance sensor

description: In this paper, we propose and numerically characterize the optical characteristics of a dual eccentric-core D-shaped photonic crystal fiber surface plasmon resonance refractive index sensor. The dual eccentric-core structure can leak more power to plasma film to excite SPR phenomenon than conventional structures. We investigate the influences induce by diameter of air holes, distance between air holes and thickness of gold film. The results show that our sensor has the wavelength sensitivities of 44,000 nm/RIU for odd mode and 39,000 nm/RIU for even mode. Moreover, the sensor has the detection range of RI from 1.00 to 1.37, the maximum figure-of-merit of 325 RIU-1, maximum resolution of 2.27×10-6 RIU for analyte RI change of 0.1 nm and the minimum full-width at half-maximum of 56 nm. The sensor has wide application prospects in methane, blood plasma and methanol detecting.
DOI: 10.6084/m9.figshare.23716170
title: Values and Perfectionism

description: These are the data used in the manuscript.
DOI: 10.34740/kaggle/ds/3537608
title: User Behavior on Instagram📱🤳🙎‍♂️
DOI: 10.23728/b2share.f1e07485392443cfa455c9745f4d95ec
title: Underlying data_Lab-scale and on-field industrial composting of biodegradable plastic blends for packaging_Chong et al. 2023

description: Underlying data_Lab-scale and on-field industrial composting of biodegradable plastic blends for packaging_Chong et al. 2022 (first submission), updated 2023
DOI: 10.34740/kaggle/dsv/6165948
title: Spotify dataset
DOI: 10.5281/zenodo.8163997
title: Supporting Data for Francis et al 2023

description: This is the supporting data for the manuscript of Francis et al 2023 submitted to GRL. This repository is temporary and is only expected to be used for the review process. A final repository and an updated link will be included in the final revised manuscript.
DOI: 10.25403/upresearchdata.23641977.v1
title: ANSYS mechanical tyre model for the TM700 trelleborg tyre

description: This file contains all data as well as Matlab code which contains the extracted data from ANSYS simulations and the processing thereof. The Matlab code processes the data and removes any prestress that the tyre may have experienced due to being in contact with the test surface. The code plots the force-dispalcement curve which is used to determine/measure the stiffness of the tyre. As the study investigates the effect of wear, these force-displacement curves are plotted for 50% and 100% wear of the tyre tread. In addition, this file contains the experiemental data collected using a static tyre test rig for a TM700 Trelleborg tyre. Matlab code (which is in the file format ending with “.m”. Matlab software is needed, through which you open that file.
DOI: 10.25403/upresearchdata.23641977
title: ANSYS mechanical tyre model for the TM700 trelleborg tyre

description: This file contains all data as well as Matlab code which contains the extracted data from ANSYS simulations and the processing thereof. The Matlab code processes the data and removes any prestress that the tyre may have experienced due to being in contact with the test surface. The code plots the force-dispalcement curve which is used to determine/measure the stiffness of the tyre. As the study investigates the effect of wear, these force-displacement curves are plotted for 50% and 100% wear of the tyre tread. In addition, this file contains the experiemental data collected using a static tyre test rig for a TM700 Trelleborg tyre. Matlab code (which is in the file format ending with “.m”. Matlab software is needed, through which you open that file.
DOI: 10.5281/zenodo.8167739
title: Supporting Data for Francis et al 2023

description: This is the supporting data for the manuscript of Francis et al 2023 submitted to GRL. This repository is temporary and is only expected to be used for the review process. A final repository and an updated link will be included in the final revised manuscript.
DOI: 10.5281/zenodo.8163996
title: Supporting Data for Francis et al 2023

description: This is the supporting data for the manuscript of Francis et al 2023 submitted to GRL. This repository is temporary and is only expected to be used for the review process. A final repository and an updated link will be included in the final revised manuscript.
DOI: 10.34740/kaggle/ds/3537616
title: Spotify dataset
DOI: 10.6084/m9.figshare.20445870
title: Didymella tanaceti 2004-2014 population SSR, SDHI and MAT data

description: <em>Didymella tanaceti</em> population collected in 2004-2014 from Tasmania, Australia. <br> <br> Each isolate is an individual. <br> Includes year collected for each isolate <br> Number of Microsatellite repeats are listed for 9 SSR markers. <br> <br> SDHI Substitution is the Succinate dehydrogenase Subunit substitution identified from HRM analysis <br> Mating-type indicates if isolates are of MAT1-1 or MAT1-2 mating type.
DOI: 10.6084/m9.figshare.20445870.v1
title: Didymella tanaceti 2004-2014 population SSR, SDHI and MAT data

description: <em>Didymella tanaceti</em> population collected in 2004-2014 from Tasmania, Australia. <br> <br> Each isolate is an individual. <br> Includes year collected for each isolate <br> Number of Microsatellite repeats are listed for 9 SSR markers. <br> <br> SDHI Substitution is the Succinate dehydrogenase Subunit substitution identified from HRM analysis <br> Mating-type indicates if isolates are of MAT1-1 or MAT1-2 mating type.
DOI: 10.6084/m9.figshare.23715714
title: OOD-GrainSet-maize

description: OOD-GrainSet-mazie for ECAI2023 (Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection) including 40K single-kernel images - 33K healthy kernel images - 7K images containing six types of damaged kernels and impurities, each type has 1k
DOI: 10.2908/tgs00053
title: Long-term unemployment rate (12 months and more) by NUTS 2 regions
title: Taux de chômage de longue durée (12 mois et plus), par région NUTS 2
title: Langzeitarbeitslosenquote (12 Monate und länger), nach NUTS-2-Regionen

description: Der Anteil der Langzeiterwerbslosen entspricht dem Anteil der seit mindestens 12 Monaten erwerbslosen Personen an der Erwerbsbevölkerung, ausgedrückt als Prozentsatz. Die gesamte Erwerbsbevölkerung ergibt sich aus der Gesamtzahl der beschäftigten und unbeschäftigten Bevölkerung. Die Arbeitslosigkeitsdauer ist definiert als der Zeitraum, in welchem Arbeit gesucht wird, oder als die abgelaufene Periode seit der letzten Tätigkeit (wenn diese Periode kürzer ist als der Zeitraum, in welchem Arbeit gesucht wird).
description: The share of long-term unemployment is the share of unemployed persons since 12 months or more in the total active population, expressed as a percentage. The total active population (labour force) is the total number of the employed and unemployed population. The duration of unemployment is defined as the duration of a search for a job or as the period of time since the last job was held (if this period is shorter than the duration of the search for a job).
description: La part du chômage de longue durée correspond au nombre de chômeurs de longue durée (12 mois ou plus) en pourcentage de la population active. La population active totale (main-d'oeuvre) est la somme des personnes ayant un emploi et des chômeurs. La durée du chômage est définie comme la durée de recherche d'un emploi ou le temps écoulé depuis la perte du dernier emploi (si cette période est plus courte que la durée de recherche d'un emploi).
DOI: 10.2908/tgs00054
title: Employment rate of the age group 55-64 by NUTS 2 regions
title: Taux d'emploi des personnes âgées de 55 à 64 ans, par région NUTS 2
title: Erwerbstätigenquote der Altersgruppe 55-64, nach NUTS-2-Regionen

description: Die regionale Beschäftigungsquote der NUTS-2-Regionen der Altersgruppe 55-64 misst den prozentualen Anteil der Erwerbstätigen im Alter zwischen 55 und 64 Jahren an der Bevölkerung dieser Altersgruppe. Erwerbstätige sind alle Personen zwischen 15 und 64 Jahren, die in der Bezugswoche gegen Entgelt, zur Gewinnerzielung oder zur Mehrung des Familieneinkommens mindestens eine Stunde gearbeitet haben oder nicht gearbeitet haben, jedoch einen Arbeitsplatz hatten, von dem sie vorübergehend abwesend waren.
description: Regional (NUTS level 2) employment rate of the age group 55-64 represents employed persons aged 55-64 as a percentage of the population of the same age group. Employed persons are those who, during the reference week, did any work for pay, profit or family gain for at least one hour, or were not at work but had a job or business from which they were temporarily absent.
description: Le taux d'emploi régional (NUTS 2) des personnes âgées de 55 à 64 ans correspond aux personnes occupées de 55 à 64 ans en pourcentage de la population de la même tranche d'âge. Les personnes occupées sont les personnes qui, au cours de la semaine de référence, ont travaillé au moins une heure contre rémunération ou en vue d'un bénéfice ou d'un gain familial, ou qui n'étaient pas sur leur lieu de travail mais avaient un emploi ou une entreprise dont elles étaient temporairement absentes.
DOI: 10.2908/tgs00102
title: Employment rate of the age group 20-64 by NUTS 2 regions
title: Taux d'emploi des personnes âgées de 20 à 64 ans, par région NUTS 2
title: Regionale Erwerbstätigenquote der Altersgruppe 20-64

description: Die regionale (NUTS-Ebene 2) Erwerbstätigenquote für die Altersklasse 20-64 zeigt die Erwerbstätige im Alter von 20-64 Jahren als ein Prozentsatz der Bevölkerung der gleichen Altersklasse. Der Indikator beruht auf der EU-Arbeitskräfteerhebung. Die Erhebung erstreckt sich auf die gesamte in privaten Haushalten lebende Bevölkerung, nicht jedoch auf die in Anstaltshaushalten wie Heimen, Internaten und Krankenhäusern lebenden Personen. Erwerbstätige sind alle Personen von 20/64, die in der Bezugswoche gegen Entgelt, zur Gewinnerzielung oder zur Mehrung des Familieneinkommens mindestens eine Stunde gearbeitet haben oder nicht gearbeitet haben, jedoch einen Arbeitsplatz hatten, von dem sie vorübergehend abwesend waren.
description: Regional (NUTS level 2) employment rate of the age group 20-64 represents employed persons aged 20-64 as a percentage of the population of the same age group. The indicator is based on the EU Labour Force Survey. The survey covers the entire population living in private households and excludes those in collective households such as boarding houses, halls of residence and hospitals. The employed persons are those aged 20-64, who during the reference week did any work for pay, profit or family gain for at least one hour, or were not at work but had a job or business from which they were temporarily absent.
description: Le taux d'emploi régional (niveau NUTS 2) de la population d'âge 20-64 représente les personnes ayant un emploi d'âge 20-64 en pourcentage de la population du même âge. L'indicateur est basé sur l'enquête UE sur les forces de travail. L'enquête couvre l'ensemble de la population vivant dans des ménages privés et exclut les personnes vivant dans des ménages collectifs tels que pensions, hospices et hôpitaux. Les personnes en emploi sont celles de 20-64 ans qui, au cours de la semaine de référence, ont effectué un travail en vue d'une rémunération, d'un bénéfice ou d'un gain familial, ne serait-ce que durant une heure, ou qui n'étaient pas au travail mais qui avaient un emploi ou une activité dont ils étaient momentanément absents.
DOI: 10.48366/r601697
title: Exploring Interpretability of Knowledge Graph-enhanced Large Language Models

description: The large language model (LLM) interpretability indicates to the explanation and understanding of the internal workings and decision-making tasks of a large language model. Knowledge Graphs are utilizing to improve the interpretability of Large Language Models. This comparisons explores 10 models to check different parameters.
DOI: 10.34740/kaggle/dsv/6166367
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.21681107.v2
title: Slip rate data compilation and depth conversion data

description: Data supporting article titled A snapshot of the earliest stages of normal fault growth<br> <br>
DOI: 10.5281/zenodo.7565155
title: Plantago major effects on the innate immunity of Galleria mellonella

description: These data have been published for the purpose of better evaluation of the article titled "Immunosuppressive effect of <em>Plantago major</em> on the innate immunity of <em>Galleria mellonella</em>" by the referees. These data have been obtained as a result of experiments and all rights belong to the publishing author and the supporting organization. This study was supported by Çanakkale Onsekiz Mart University The Scientific Research Coordination Unit (Grant number: FBA-2020-325).
DOI: 10.5281/zenodo.8167820
title: Input data for running a GWAS on penicillin resistance in Streptococcus pneumoniae

description: Results from running the pyseer tutorial at https://pyseer.readthedocs.io/en/master/tutorial.html
DOI: 10.5281/zenodo.8167819
title: Input data for running a GWAS on penicillin resistance in Streptococcus pneumoniae

description: Results from running the pyseer tutorial at https://pyseer.readthedocs.io/en/master/tutorial.html
DOI: 10.5517/ccdc.csd.cc286f0y
title: CCDC 2091539: Experimental Crystal Structure Determination

description: Related Article: Masoud Kazem-Rostami, Angel Orte, Ana M. Ortuño, Arthur H. G. David, Indranil Roy, Delia Miguel, Amine Garci, Carlos M. Cruz, Charlotte L. Stern, Juan M. Cuerva, J. Fraser Stoddart|2022|J.Am.Chem.Soc.|144|9380|doi:10.1021/jacs.2c01554
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.cc2bz02f
title: CCDC 2173784: Experimental Crystal Structure Determination

description: Related Article: Yuchen Hu, Xin Zhang, Rebecca Shu Hui Khoo, Christian Fiankor, Xu Zhang, Jian Zhang|2023|J.Am.Chem.Soc.|145|13929|doi:10.1021/jacs.3c03421
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.cc2c9g05
title: CCDC 2183826: Experimental Crystal Structure Determination

description: Related Article: Jing-Ru Chen, Li-Na Zheng, Xin-Wei Meng, Zhan-Yao Gao, Jing Zhao, Bo Liu, Tao Ding|2022|Dalton Trans.|51|15273|doi:10.1039/D2DT02518C
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.