cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25x3y5
title: CCDC 2023027: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25x3z6
title: CCDC 2023028: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25x408
title: CCDC 2023029: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25x419
title: CCDC 2023030: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25x42b
title: CCDC 2023031: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25x43c
title: CCDC 2023032: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25xbr6
title: CCDC 2023238: Experimental Crystal Structure Determination

description: Related Article: Teng Xue, David A. Vicic|2020|Organometallics|39|3715|doi:10.1021/acs.organomet.0c00544
DOI: 10.5517/ccdc.csd.cc25nyng
title: CCDC 2016136: Experimental Crystal Structure Determination

description: Related Article: Tarun Kumar Pal, Subrata Paul, Jewel Hossen, Ashraful Alam, Chanmiya Sheikh, Arkajyoti Paul, Ryuta Miyatake|2021|J.Mol.Struct.|1226|129397|doi:10.1016/j.molstruc.2020.129397
DOI: 10.5517/ccdc.csd.cc1z4qc6
title: CCDC 1821788: Experimental Crystal Structure Determination

description: Related Article: Satish S. Bhat, S. Naveen, Vidyanand K. Revankar, N. K. Lokanath, Rahul V. Pinjari, Vijay Kumbar, Kishore Bhat|2020|New J.Chem.|44|17442|doi:10.1039/D0NJ03421E
DOI: 10.5517/ccdc.csd.cc1z4qkd
title: CCDC 1821794: Experimental Crystal Structure Determination

description: Related Article: Satish S. Bhat, S. Naveen, Vidyanand K. Revankar, N. K. Lokanath, Rahul V. Pinjari, Vijay Kumbar, Kishore Bhat|2020|New J.Chem.|44|17442|doi:10.1039/D0NJ03421E
DOI: 10.6084/m9.figshare.13207529
title: DebtProvidersMatchingEstimators.dta

description: 85 non-financial Spanish companies traded on the Mercado Continuo as of 2015.
DOI: 10.6084/m9.figshare.13207529.v1
title: DebtProvidersMatchingEstimators.dta

description: 85 non-financial Spanish companies traded on the Mercado Continuo as of 2015.
DOI: 10.5281/zenodo.4263851
title: Secondary succession over semi-natural grassland communities in the Pyrenees mountain range

description: Raster images to monitor the spatial evolution of secondary succession over semi-natural grassland communities in the Pyrenees mountain range in Spain, during the last 36 years (1984-2019).
description: Visualize data in GEE app
DOI: 10.5281/zenodo.4066554
title: Secondary succession over semi-natural grassland communities in the Pyrenees mountain range

description: Raster images to monitor the spatial evolution of secondary succession over semi-natural grassland communities in the Pyrenees mountain range in Spain, during the last 36 years (1984-2019).
description: Visualize data in GEE app
DOI: 10.5281/zenodo.4264271
title: Spatial explicit modeling of the probability of land abandonment in the Spanish Pyrenees

description: Raster outputs of land abandonment probability and uncertainty over semi-natural grassland communities in the Pyrenees mountain range in Spain.
description: Visualize data in GEE app
DOI: 10.5281/zenodo.4264270
title: Spatial explicit modeling of the probability of land abandonment in the Spanish Pyrenees

description: Raster outputs of land abandonment probability and uncertainty over semi-natural grassland communities in the Pyrenees mountain range in Spain.
description: Visualize data in GEE app
DOI: 10.21227/s8ja-c535
title: Model6

description: Data Recovered with RandomForest
DOI: 10.18112/openneuro.ds003354.v1.0.0
title: Effective Connectivity of Empathy and Theory of Mind in Individuals with High Psychopathic Traits
DOI: 10.1285/i20705948v13n2p284
title: Discrimination and Classification model from Multivariate Exponential Power Distribution

description: It is common to assume a normal distribution when discriminating and classifying a multivariate data based on some attributes. But when such data is lighter or heavier in both tails than the normal distribution, then the probability of misclassification becomes higher giving unreliable result. This study proposed multivariate exponential power distribution a family of elliptically contoured model as underlining model for discrimination and classification. The distribution has a shape parameter which regulate the tail of the symmetric distribution to mitigate the problem of both lighter and heavier tails data, this generalizes the normal distribution and thus will definitely gives a lower misclassification error in discrimination and classification. The resulting discriminant model was compared with fisher linear discriminant function when applying to real data.
DOI: 10.1285/i20705948v13n2p293
title: A New Generalized Log-Logistic Erlang Truncated Exponential Distribution with Applications

description: We introduce a new distribution via the Marshall-Olkin generator called the Marshall-Olkin Log-logistic Erlang-Truncated Exponential (MOLLoGETE) distribution. Some structural properties of the distribution including series expansion of the density function, sub-models, hazard function, moments, conditional moments, mean deviations, distribution of order statistics, R´enyi entropy and maximum likelihood estimates are presented. The new density function is an infinite linear combinations of Burr XII-Erlang-Truncated Exponential distributions. The new generalization is applied to real data sets to evaluate the model performance.
DOI: 10.1285/i20705948v13n2p358
title: The interpoint depth for directional data

description: The notion of the interpoint depth is applied to spherical spaces by us-ing an appropriate angular distance function for data lying on the surfaceof the unit hypersphere. The traditional multivariate methods, indeed, arenot suitable for the analysis of directional data and this holds true also fordistance measures and related depth based methods. The interpoint depthfor directional data possesses some nice properties and can be used for highdimensional data analysis. This notion of depth is particularly useful toinvestigate local features of distribution such as multimodality and can beexploited to deal with many statistical problems. The behavior of the pro-posed depth is investigated by means of simulated data. In addition threeinteresting applications are presented.
DOI: 10.1285/i20705948v13n2p375
title: Empowering Detection of Malicious Social Bots and Content Spammers on Twitter by Sentiment Analysis

description: The role of Twitter as a platform to share opinions has been growing in the recent years especially since it has been widely used by public personae such as politicians, personalities of the show business, and other influencers to communicate with the public. For these reasons, the use of social bots to manipulate information and influence people's opinions is also growing. In this paper, we use a supervised classification model to distinguish bots from legitimate users on Twitter. More specifically, we show the importance of sentiment features in bot-human account detection. Moreover, we evaluate our detection model by testing on Russian bot accounts who are the most recent set of social bots that appeared on Twitter to show that these techniques may be easily adapted to work on new, unseen types of social bots.
DOI: 10.1285/i20705948v13n2p390
title: Computationally efficient univariate filtering for massive data

description: The vast availability of large scale, massive and big data has increased the computational cost of data analysis. One such case is the computational cost of the univariate filtering which typically involves fitting many univariate regression models and is essential for numerous variable selection algorithms to reduce the number of predictor variables. The paper manifests how to dramatically reduce that computational cost by employing the score test or the simple Pearson correlation (or the t-test for binary responses). Extensive Monte Carlo simulation studies will demonstrate their advantages and disadvantages compared to the likelihood ratio test and examples with real data will illustrate the performance of the score test and the log-likelihood ratio test under realistic scenarios. Depending on the regression model used, the score test is 30 - 6,000 times faster than the log-likelihood ratio test and produces nearly the same results. Hence this paper strongly recommends to substitute the log-likelihood ratio test with the score test when coping with large scale data, massive data, big data, or even with data whose sample size is in the order of a few tens of thousands or higher.
DOI: 10.1285/i20705948v13n2p436
title: CLUSTATIS: cluster analysis of blocks of variables

description: The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies.
DOI: 10.1285/i20705948v13n2p454
title: Detecting drivers of basketball successful games: an exploratory study with machine learning algorithms

description: This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.
DOI: 10.1285/i20705948v13n2p474
title: University student achievements and international mobility: The case of University of Cagliari

description: This paper aims to detect which are the drivers leading to victory for basketball matches in NBA, the American National Basketball Association. First games for regular seasons from 2004-2005 to 2017-2018 have been summarized in terms of box scores and Dean's four factors. Then box scores and four factors have been used as classication independent variables to identify victory drivers, focusing on Golden StateWarriors matches. Both CART and Random Forests machine learning techniques have been applied, and results are compared to assess the more suitable approach.
DOI: 10.1285/i20705948v13n2p498
title: Economic growth and mental well-being in Italian regions

description: Measuring economic growth is not only an arithmetic problem, it also involvesan economic vision and a philosophy for the choice of indicators (i.e.Gross Domestic Product). Today it seems more relevant to measure not onlythe economic well-being as it is, using Gross Domestic Product, but also thepeople's well-being, considering other dimensions such as health and happiness.Over the years, national accounts and Gross Domestic Product havenever changed their nature, instead combining social and economic indicatorscan oer important indications for well-being research. As pointed outby the World Health Organization, health is well-being, but it is not alwayscoincident with a high level of Gross Domestic Product. In particular, asregards the mental health condition it has become an important aspect inmeasuring people's well-being. Mental health has been considered an importantsignal of the society's discomfort due to economic growth that, generally,is argued to be caused by the disamenities of the "industrial lifestyle". Thisstudy involves an empirical investigation using a panel econometric model inItalian regions, considering antidepressant expenditures per capita (an expressionof society's unhappiness) as dependent variable and, as covariates,the old age index, the poverty rate, the employment rate, the Neet rate, andthe percentage of public expenditure in services.
DOI: 10.1285/i20705948v13n2p519
title: How perceived variety impacts on choice satisfaction: a two-step approach using the CUB class of models and best-subset variable selection

description: In consumer research, marketing, public policy and other fields, individ- uals' choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim of the present study is to apply a novel approach to model perceived variety, in or- der to better understand the perceptions of individuals about the variety of the possible choice options and to model the impact of perceived variety and individuals' characteristics on the choice outcome satisfaction. We resort to the class of cub (Combination of Uniform and Binomial random variables) models for rating data that model the respondents' decision process as a combination of two latent components, called feeling and uncertainty, that express, respectively, the level of agreement with the item being evaluated and the human indecision surrounding any discrete choice. The model ap- plied in this paper is an alternative to the most common models used in the studies of human judgments and decisions, whenever attitudes, perceptions and opinions are measured by means of questionnaires having questions with ordered response categories. The chosen approach is composed of two steps: (1) we construct measures of feeling and uncertainty of perceived variety by means of cub and (2) we investigate their impact (eventually together with personal characteristics) on choice satisfaction. The R FastCUB package is exploited to select the best set of covariates to include in the final model.
DOI: 10.1285/i20705948v13n2p536
title: Type II Exponentiated Half Logistic Generated Family of Distributions with Applications

description: A new family of distributions called type II exponentiated half logistic is introduced and studied. Four new special models are presented. Some mathematical properties of the new family are studied. Explicit expressions for the moments, probability weighted moments, quantile function, mean deviation, order statistics and Rényi entropy are investigated. Parameter estimation of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family.
DOI: 10.1285/i20705948v13n2p562
title: A non-parametric density estimate adaptation for population abundance when the shoulder condition is violated

description: The non-parametric kernel density estimation is used in practice to estimate population abundance using the line transect sampling. In general, the classical kernel estimator of f(0) tends to be underestimated. In this article, a shifted logarithmic transformation of perpendicular distance is proposed for the kernel estimator when the shoulder condition is violated. Mathematically, the proposed estimator is more efficient than the classical kernel estimator. A simulation study is also carried out to compare the performance of the proposed estimators and the classical kernel estimators.
DOI: 10.1285/i20705948v13n2p580
title: Estimation of Population Mean under logarithmic for the Poisson Distributed Study and Auxiliary Variates

description: Use of suitable auxiliary information is always suggested in literature at the planning and estimation stage to make the estimators more powerful in terms of efficiency. Estimation using auxiliary information is common in sampling literature but using distribution of study and auxiliary information at the estimation stage is uncommon and useful specially when dealing with rare variable. This study utilizes the auxiliary information and Poisson distributed variates for proposing the log-type estimator and another generalized estimator for finite population mean under simple random sampling without replacement. The Mean Square Error expressions of the proposed estimators are obtained and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from empirical (point estimation and interval estimation) and theoretical study that use of log type estimators along with suitable auxiliary information for Poisson distributed variates excels the performance of estimators in terms of efficiency.
DOI: 10.6084/m9.figshare.13207541
title: Análise dos fatores de Risco relacionados à ideação e comportamento suicida em militares: Uma revisão integrativa

description: O contexto militar é único em muitos aspectos em relação ao suicídio e comportamento suicida. A última década enfatizada maior ênfase em militares, principalmente devido a relatos de um aumento dramático no número de suicídios e seus fatores de risco . Entre os soldados, assim como na população em geral, transtornos mentais são preditores consistentes de tentativa de suicídio e morte . De acordo com a literatura, entre os principais transtornos mentais identificando: transtornos de personalidade (anti-social, limítrofe, histriônico e narcisista personalidade esquiva, dependente e obsessivo-compulsiva); transtornos alimentares, transtornos do humor, depressão e transtornos de adaptação . <br> <br>
DOI: 10.6084/m9.figshare.13207541.v1
title: Untitled Item

description: O contexto militar é único em muitos aspectos em relação ao suicídio e comportamento suicida. A última década enfatizada maior ênfase em militares, principalmente devido a relatos de um aumento dramático no número de suicídios e seus fatores de risco . Entre os soldados, assim como na população em geral, transtornos mentais são preditores consistentes de tentativa de suicídio e morte . De acordo com a literatura, entre os principais transtornos mentais identificando: transtornos de personalidade (anti-social, limítrofe, histriônico e narcisista personalidade esquiva, dependente e obsessivo-compulsiva); transtornos alimentares, transtornos do humor, depressão e transtornos de adaptação . <br> <br>
DOI: 10.6084/m9.figshare.12680855
title: Dataset of article: "Prevalence and correlates of adherence to the combined movement guidelines among Czech children and adolescents"

description: The manuscript has been published in the BMC Public Health journal.<br>
DOI: 10.6084/m9.figshare.13204940
title: R0220-DLC_output

description: deeplabcut output and processed 3D reconstructions of data for rat 0220."_direct" zip files contain .csv's from the direct view and .mat files with cropping regions"_right/left" zip files contain .csv's from the mirror view and .mat files with cropping regions"_processed" files contain 3D reconstructions and reach analyses
DOI: 10.6084/m9.figshare.13204940.v1
title: R0220-DLC_output

description: deeplabcut output and processed 3D reconstructions of data for rat 0220."_direct" zip files contain .csv's from the direct view and .mat files with cropping regions"_right/left" zip files contain .csv's from the mirror view and .mat files with cropping regions"_processed" files contain 3D reconstructions and reach analyses
DOI: 10.6084/m9.figshare.12300797
title: A predictive model for Chinese children with developmental dyslexia——Based on genetic algorithm optimized back-propagation neural network

description: Dataset, program, and model
DOI: 10.5281/zenodo.4264473
title: I-BiDaaS - CRF - MES Dataset

description: The MES dataset contains sensors data associated with the type of vehicle being produced. This dataset has to be used with SCADA dataset. Comparing the date and time between the data of the MES and the SCADA it is possible to correlate the information contained in the SCADA dataset with the vehicle processed.
DOI: 10.5281/zenodo.4264472
title: I-BiDaaS - CRF - MES Dataset

description: The MES dataset contains sensors data associated with the type of vehicle being produced. This dataset has to be used with SCADA dataset. Comparing the date and time between the data of the MES and the SCADA it is possible to correlate the information contained in the SCADA dataset with the vehicle processed.
DOI: 10.21227/sbnj-5h65
title: test

description: hui submit
DOI: 10.5281/zenodo.4252636
title: spanish_used_car_market: Coches de segunda mano a la venta en España

description: En el siguiente <em>dataset</em> se presentan los datos sobre <strong>coches en venta en el territorio español </strong>obtenidos fruto del desarrollo de la Práctica 1 de la asignatura 'Tipología y Ciclo de Vida de los Datos' del Máster en Ciencia de Datos de la Universitat Oberta de Catalunya, con fin meramente académico. Los datos han sido recolectados utilizando técnicas de <em>web scraping </em>sobre el sitio web www.milanuncios.com. Los atributos del fichero .CSV se definen a continuación: <strong>ad_id</strong>: Identificador del anuncio del coche. <strong>ad_type</strong>: Tipo de anuncio. En nuestro caso, siempre va a ser Oferta. <strong>ad_time</strong>: Tiempo que llevaba publicado el anuncio cuando se recogió la información, en formato X horas o X días. En el caso en que fuese un anuncio destacado, no tenemos esa información. <strong>ad_title</strong>: Título del anuncio de venta, con formato {Marca} – {Modelo}. <strong>car_desc</strong>: Preview de la descripción del anuncio de venta del vehículo. <strong>car_km</strong>: Kilómetros que tiene recorridos el coche. <strong>car_year</strong>: Año de matriculación del vehículo. <strong>car_engine_type</strong>: Tipo de transmisión. Posibles valores: Manual | Automático. <strong>car_door_num</strong>: Número de puertas de las que dispone el coche. <strong>car_power</strong>: Potencia del vehículo, en formato XXX CV. <strong>car_price</strong>: Precio en euros por el que se vende el coche. <strong>advertizer_type</strong>: Indica cuál es el tipo de vendedor del vehículo. Valores posibles: Profesional | Particular. <strong>image_url</strong>: Foto principal del anuncio de venta del coche. <strong>ts</strong>: Hora en la que se recogió la información, con formato YYYY-MM-DD hh:mm:ss.ms. <strong>region</strong>: Provincia en la que se está vendiendo el vehículo.