When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance (...) estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed. (shrink)

One of the characteristics of Big Data is that it often involves “imperfect” information. This paper examines the work of John Graunt in the tabulation of diseases in London and the development of a life table using the “imperfect data” contained in London’s Bills of Mortality in the 1600s. London’s Bills of Mortality were Big Data for the 1600s, as they included information collected over time, the depth and accuracy of which improved gradually. The main shortcoming of (...) the data available at the time was its nonuniform upkeep and the lack of depth of variables included at its outset. Due to these characteristics, it provides a perfect model for the examination of imperfect Big Data, as it has been analyzed, criticized, and interpreted repeatedly since the 1600s. (shrink)

This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition and spatiotemporal Koopman decomposition. These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used to (...) either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models. (shrink)

In reliability engineering and lifetime analysis, many units of the product fail with different causes of failure, and some tests require stress higher than normal stress. Also, we need to design the life experiments which present methodology for formulating scientific and engineering problems using statistical models. So, in this paper, we adopted a partially constant stress accelerated life test model to present times to failure in a small period of time for Gompertz life products. Also, considering that, units are failing (...) with the only two independent causes of failure and tested under type-I generalized hybrid censoring scheme the data built. Obtained data are analyzed with two methods of estimations, maximum likelihood and Bayes methods. These two methods are used to construct the point and interval estimators with the help of the MCMC method. The developed results are measured and compared under Monte Carlo studying. Also, a data set is analyzed for illustration purposes. Finally, some comments are presented to describe the numerical results. (shrink)

Positioning technologies, such as GPS are widespread in society but are used only sparingly in behavioural science research, e.g., because processing positioning technology data can be cumbersome. The current work attempts to unlock positioning technology potential for behavioural science studies by developing and testing a research tool to analyse GPS tracks. This tool—psyosphere—is published as open-source software, and aims to extract behaviours from GPSs data that are more germane to behavioural research. Two field experiments were conducted to test (...) application of the research tool. During these experiments, participants played a smuggling game, thereby either smuggling tokens representing illicit material past border guards or not. Results suggested that participants varied widely in variables, such as course and speed variability and distance from team members in response to the presence of border guards. Subsequent analyses showed that some of these GPS-derived behavioural variables could be linked to self-reported mental states, such as fear. Although more work needs to be done, the current study demonstrates that psyosphere may enable researchers to conduct behavioural experiments with positioning technology, outside of a laboratory setting. (shrink)

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government-owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, (...) the quality of open government data needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision-making and management. In fact, we are dealing with decision-making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high-degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision-making technique including the DEMATEL technique have been used to express the solutions and identify the cause-and-effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up-to-datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision-making and proper management in the complex system of citizens and government organizations. (shrink)

In “Analyzing COVID-19 Sex Difference Claims: The Harvard GenderSci Lab,” Marion Boulicault and Sarah Richardson summarize some of the groundbreaking work that they’re doing at the Harvard GenderSci Lab. Since March 2020, their lab has been analyzing, interrogating, and critiquing sex essentialist explanations of COVID-19 outcome disparities that are fairly ubiquitous in news media. Using interdisciplinary tools from feminist philosophy, science studies, and critical public health, they work collaboratively with two goals: (i) to critically examine COVID-19 sex difference research and (...) (ii) to explore and elevate the role of social variables in driving biological disparities. They argue that in public health research, media, and messaging, data on sex disparities must be contextualized both to avoid reinforcing harmful sex essentialist assumptions and also to help the public understand the complex ways in which social factors influence these patterns. They argue that within the context of COVID-19, doing so can both clarify risks and save lives. Their contribution to this issue describes their methods and shares some of their findings. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes (...) Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use. (shrink)

This book focuses on analyzing data on current initiatives and best practices in citizen engagement and education programs across various disciplines by highlighting emergent research and application techniques within citizen science initiatives.

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine (...) learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

Researchers in the fields of sign language and gesture studies frequently present their participants with video stimuli showing actors performing linguistic signs or co-speech gestures. Up to now, such video stimuli have been mostly controlled only for some of the technical aspects of the video material, leaving open the possibility that systematic differences in video stimulus materials may be concealed in the actual motion properties of the actor’s movements. Computer vision methods such as OpenPose enable the fitting of body-pose models (...) to the consecutive frames of a video clip and thereby make it possible to recover the movements performed by the actor in a particular video clip without the use of a point-based or markerless motion-tracking system during recording. The OpenPoseR package provides a straightforward and reproducible way of working with these body-pose model data extracted from video clips using OpenPose, allowing researchers in the fields of sign language and gesture studies to quantify the amount of motion pertaining only to the movements performed by the actor in a video clip. These quantitative measures can be used for controlling differences in the movements of an actor in stimulus video clips or, for example, between different conditions of an experiment. In addition, the package also provides a set of functions for generating plots for data visualization, as well as an easy-to-use way of automatically extracting metadata from large sets of video files. (shrink)

Longitudinal qualitative research in nursing is rare but becoming more common. Data collection and analysis over time provide some intriguing possibilities to better understand processes, development, and change in illness experience, healthcare organizations, and self‐management. This paper aims to present a process for analyzing qualitative longitudinal data material, namely the Pattern‐Oriented Longitudinal Analysis approach (POLA). We developed this approach after synthesizing experiences from two longitudinal qualitative projects and comparing our procedures and reflections with the relevant literature. Using the (...) POLA approach, researchers can describe complexity and variation in changes over time. During the analysis process, emphasis is put on visualizing and identifying change at both an individual and a group level. Ontological and epistemological assumptions for the approach are also described. The benefit of this approach is the possibility to describe complexity and diversity in processes over time, which is important for the development of nursing knowledge. The analysis approach can be further used and developed by researchers seeking to understand variance or contextual features in processes and changes over time. (shrink)

The aim of the study is to reveal the underlying structure of issues of university students taking online classes during the COVID-19 pandemic period. The overall design of the study includes a review of contemporary literature and field survey for data collection and analysis. Discourse of literature coupled with expert opinion has been employed for identification of issues. Interpretive Structural Modeling is used for the determination of intra-issue relationships and analyzing the underlying structure. Cross impact matrix multiplication applied to (...) classification is used as a technique for classifying issues on the basis of driving–dependence power. Results of the literature show that there are 21 major issues faced by the students taking online classes. ISM shows that lack of institutional guidelines, lack of regulators’ guidelines, stress of pandemic situation, and abrupt start of online classes are the most critical issues. MICMAC analysis reveals that there is no autonomous issue, 4 are independent, 6 other issues are dependent, and the remaining 11 are linking. This is a valuable study having practical implications for regulators, students, parents, and society to understand the current problem. It is an original attempt that contributes toward literature in the form of a structural model and a diagram of classification of issues. (shrink)

Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory (...) is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed. (shrink)

This article argues that the fact that an action will compound a prior injustice counts as a reason against doing the action. I call this reason The Anti-Compounding Injustice principle or aci. Compounding injustice and the aci principle are likely to be relevant when analyzing the moral issues raised by “big data” and its combination with the computational power of machine learning and artificial intelligence. Past injustice can infect the data used in algorithmic decisions in two distinct ways. (...) Sometimes prior injustice undermines the accuracy of the data itself. In these contexts, improving accuracy will also help to avoid compounding injustice. Other times, past injustice produces real-world differences among people with regard to skills, health, wealth, and other traits that employers, lenders, and others seek to measure. When decisions are based on accurate data that itself results from prior injustice, these decisions can also compound injustice. This second dynamic has received less attention than the first but is especially important because improving the accuracy of data will not mitigate this unfairness. (shrink)

This article examines the potential of art and archaeological remains for the study of women's social history in early Christianity. Part I considers important sources for art and archaeological data; the received method and classification criteria for the discipline of early Christian art and archaeology; and the types of problems both earlier and contemporary approaches to the material remains present for scholars. Part II proposes an approach to understanding early Christian art and material culture as part of a larger (...) ongoing social discourse in the ancient Mediterranean world where cultural representations take part in the lively social and aural culture of life lived in close proximity to others. This approach places art and archaeological evidence in the foreground and documentary sources in the background as corroborative evidence. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is (...) the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

Hughes and Nicholson suggest that recognizing oneself is easier from face vs. voice stimuli, that a combined presentation of face and voice actually inhibits self-recognition relative to presentation of face or voice alone, that the left hemisphere is superior in self-recognition to the right hemisphere, and that recognizing self requires more effort than recognizing others. A re-examination of their method, data, and analyses unfortunately shows important ceiling effects that cast doubts on these conclusions.

We will argue that clarifying the “moral core” of consent offers a common metric by which we can evaluate how well different legal frameworks are able to protect the central moral rights and interests at stake. We begin by revisiting how legal frameworks for digital consent developed in order to see where there may be common moral ground and where these different cultures diverge on the issue of protection of personal information. We then turn to ethics to clarify the central (...) interests and rights at stake in morally transformative consent, in order to provide a common basis for evaluating the different legal frameworks. Ultimately, we seek the moral core of digital consent in order to reimagine its role in international conflicts. (shrink)

Debate over the closure of DeVasco High School shows that data-driven accountability was a methodological and administrative processes that produced both transparency and opacity. Data, when applied to a system of accountability, produced new capabilities and powers, and as such were political. It created second-hand representations of important objects of analysis. Using these representations administrators spoke on behalf of the school, the student and the classroom, without having to rely on the first-person accounts of students, teachers or principals. (...) They empowered one group—central city administrators—over another—teachers and principals. After analyzing the form these policies took, this article concludes that it is necessary to rethink the processes that create visibility and invisibility. Public data obscured the voices, experiences and collective traumas of students and faculty within the school. A narrow focus on activities within the schools rendered invisible the structural decisions made by the Department of Education in New York City—to favor small schools over large, comprehensive ones. In order to create understanding, and a sense of common purpose, those who are spoken for in simplified data must also be given the opportunity to debate the representations of their performance and quality. (shrink)

Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and detail the steps (...) that researchers must consider before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data. (shrink)

This investigation analyzed Islamic spirituality as measured by a Muslim Experiential Religiousness Scale. Iranian university and seminary students responded to this instrument along with the Psychological Measure of Islamic Religiousness and Perceived Stress and Self-Esteem scales. Muslim Experiential Religiousness correlated predictably with all PMIR sub-scales, Perceived Stress, and Self-Esteem, and mediated almost all relationships of the PMIR Islamic Beliefs subscale with religious functioning. When evaluated by participants, Muslim Experiential Religiousness items proved to be “rational” relative to their Muslim religious ideals. (...) Women in an Islamic seminary scored higher on Muslim Experiential Religiousness than Islamic women in a more general university, and Muslim Experiential Religiousness also mediated the many other religious differences observed between these two student groups. These data most importantly identified the Muslim Experiential Religiousness Scale as a useful instrument for testing hypotheses about the dynamics of Muslim spirituality. (shrink)

PurposeThis study aimed to examine the long-term association between social activity, physical function decline and cognitive function, as well as verify the long-term mediating effect of physical function decline on the relationship between social activity and cognitive function.MethodsData from the Korean Longitudinal Study of Aging that was collected over 10 years was analyzed. The sample included 10,240 adults aged 45–93 years. Multivariate latent growth modeling was applied to verify the long-term effect of social activity and physical function on cognitive function.ResultsThe (...) results revealed that social activity had a positive impact on cognitive function and negative impact on physical function decline after controlling for age and education level. Additionally, physical function decline negatively influenced cognitive function. Finally, social activity indirectly affected cognitive function through physical function decline.ConclusionThe contribution of this study was to test the long-term effect social activity on physical and cognitive function. (shrink)

The paper presents the theoretical and methodological aspects of research on the development of argument- ation in elementary school children. It presents a theoretical framework detailing psychological mechanisms responsible for the acquisition and transfer of argumentative discourse and demonstrates several applications of the framework, described in sufficient detail to guide future empirical investigations of oral, written, individual, or group argumentation performance. Software programs capable of facilitating data analysis are identified and their uses illustrated. The analytic schemes can be used (...) to analyze large amounts of verbal data with reasonable precision and efficiency. The conclusion addresses more generally the challenges for and possibilities of empirical study of the development of argumentation. (shrink)

In this commentary, I propose a framework for thinking about data quality in the context of scientific research. I start by analyzing conceptualizations of quality as a property of information, evidence and data and reviewing research in the philosophy of information, the philosophy of science and the philosophy of biomedicine. I identify a push for purpose dependency as one of the main results of this review. On this basis, I present a contextual approach to data quality in (...) scientific research, whereby the quality of a dataset is dependent on the context of use of the dataset as much as the dataset itself. I exemplify the approach by discussing current critiques and debates of scientific quality, thus showcasing how data quality can be approached contextually. (shrink)

Enhancing urban development vitality and optimizing the allocation of regional industrial factors require a comprehensive analysis of listed companies, such as the overall distribution network, agglomeration evolution trend, industrialization layout, and driving mechanism. Using 1,624 A-share listed companies in China's Yangtze River Economic Belt as research area, this study integrated trend surface analysis, exploratory spatial data analysis, standard deviation ellipse, and spatial regression model methods. The main results are as follows: The overall quantity scale of the listed companies in (...) the Yangtze River Economic Belt has achieved significant growth, but the spatial difference of location selection persists. The spatial configuration formed a hierarchical urban distribution pattern with the Yangtze River Delta region as the agglomeration core and the provincial capitals as the fulcrum. Listed companies accelerate the expansion of the Yangtze River Delta region. Chengdu, Wuhan, Changsha, and other central-western provincial capitals gradually increased the region’s attractiveness. High-high and low-low agglomerations remain the main forms of agglomeration. There are significant differences in the location selection of listed companies with varying specialization levels, forming a relatively different alienated high-value distribution structure among various industry types. The levels of knowledge spillover, city scale, and policy support level are major factors affecting the location selection process of listed companies in the Yangtze River Economic Belt. For low-level city network with few listed companies, city scale and knowledge spillover levels are significant determinants for the development of headquarters’ economy. For high-level city network, along with the level of knowledge spillovers and policy support, globalization level has an important contribution to the shaping of the location advantages of attracting the layout of listed companies. (shrink)

Analysing time-dependent independent variables requires the use of process-oriented statistical models. Yet social scientists have often had to use data collected at a single point in time, making their task difficult. Making several assumptions about the covariates, the present study uses survival analysis and other statistical techniques to analyse the 1996 South African population census data and examine the effects of selected independent variables on the timing of parenthood in the country. It was found that the onset (...) of parenthood occurs late in South Africa compared with the pattern in most other African societies. While education plays a role in the postponement of parenthood within racial groups, it fails to explain the differences between African and Coloured women on the one hand, and White and Asian women on the other hand, a finding that suggests the existence of two regimes of family formation in South African society. (shrink)

Interactions among membrane proteins regulate numerous cellular processes, including cell growth, cell differentiation and apoptosis. We need to understand which proteins interact, where they interact and to which extent they interact. This article describes a set of novel approaches to measure, on the surface of living cells, the number of clusters of proteins, the number of proteins per cluster, the number of clusters or membrane domains that contain pairs of interacting proteins and the fraction of one protein species that interacts (...) with another protein within these domains. These data can then be interpreted in terms of the function of the protein‐protein interactions. BioEssays 26:196–203, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to (...) analyze interdisciplinary research activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human-Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. (shrink)

The role of figurative languages such as metaphor and metonymy in creativity has been studied in cognitive linguistics. These methods can also be applied to analyze non-linguistic data such as pictures and gestures. In this paper we analyze fashion design by focusing on visual metaphor and metonymy. The nature of creativity in fashion design is not fully studied from a cognitive perspective compared to other related fields such as art. We especially focus on the aspect of fashion design as (...) a communication tool between the designer and the audience to convey designer’s image of human. Photos from two fashion shows are analyzed. We carried out an experiment to compare how human images in two shows are interpreted by those who are familiar with fashion and those who are not. We got three results: (1) As far as figurative (metaphorical and metonymic) interpretations of human image are concerned, two groups with different familiarity to fashion have significantly different patterns of responses to two shows. (2) For non-figurative interpretations (such as physical or personal attributes), no significant difference in the pattern of response to the show was observed between two groups. The participants as a whole, however, responded to the two shows differently. (3) In addition for non-figurative interpretation, fashion experts found significantly more attributes in human images than the others. The results show that the analysis of figurative interpreations is effective in seeing how the familiarity to fashion affects the mode of seeing fashion shows. (shrink)

Social changes in Turkey, especially with the 1980s, brought along an irreversible differentiation in the lives of individuals. One of the most striking of these differentiations has emerged in the lives of individuals who generally take care to live according to the religious rules. In this sense, the changing social structure has caused the religious segment, which is described as conservative in Turkish society, to develop a new lifestyle that can be interpreted within the framework of the coexistence of religiosity (...) and modernity, in other words, which largely coincides with the eclectic paradigm, which is one of the secularization theories. This new way of life becomes visible especially in the leisure time activities of individuals in daily life practices. Today, leisure time appears as the time period in which individuals generally spend money, such as resting, having fun, spending time for themselves, and in this sense, activities based on consumption. Therefore, it is impossible to think of leisure time separately from consumption. As a matter of fact, in this study, a field study was conducted on a sample of headscarved women in order to understand the changings in the lifestyle of the conservative individuals. The Leisure Time Tendency Scale for Heascarved Women, which was createdto carry out the research, was applied to 706 headscarved female individuals living in Istanbul, at least 15 years old, who defined themselves as religious. The data obtained from the field has been analyzed by Smart PLS Version 3.3.2. and IBM SPSS Statistics 26 package programs. With the analysis of the data, it has tried to be understood whether conservative women with headscarf participating in this research experience a secularization in their leisure time activities. (shrink)