A self-organizing cortical layer 4 model applied to large-scale imaging data analysis

Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽

10.3390/electronics10141670 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1670

Author(s):

Waheeb Abu-Ulbeh ◽

Maryam Altalhi ◽

Laith Abualigah ◽

Abdulwahab Ali Almazroi ◽

Putra Sumari ◽

...

Keyword(s):

Data Analysis ◽

Structural Equation ◽

Large Scale ◽

Review Paper ◽

Essential Element ◽

Routine Activities ◽

Criminological Theory ◽

Equation Modeling ◽

Future Research ◽

Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽

10.1093/gigascience/giaa133 ◽

2020 ◽

Vol 9 (12) ◽

Author(s):

Ariel Rokem ◽

Kendrick Kay

Keyword(s):

Ridge Regression ◽

Large Scale ◽

Imaging Data ◽

Regularization Technique ◽

Large Scale Data ◽

Novel Approach ◽

Manual Exploration ◽

L2 Norm ◽

Software Implementations ◽

Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

Advances in Meteorology ◽

10.1155/2017/8576150 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 9

Author(s):

Adeoluwa Akande ◽

Ana Cristina Costa ◽

Jorge Mateu ◽

Roberto Henriques

Keyword(s):

Extreme Precipitation ◽

Large Scale ◽

Northern Region ◽

Extreme Weather Events ◽

Self Organizing Map ◽

Spatial And Temporal Patterns ◽

Self Organizing Maps ◽

The North ◽

Precipitation Regimes ◽

Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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Microcomputers in Political Science

News for Teachers of Political Science ◽

10.1017/s0197901900005079 ◽

1983 ◽

Vol 38 ◽

pp. 1-9

Author(s):

Herbert F. Weisberg

Keyword(s):

Data Analysis ◽

Political Science ◽

Large Scale ◽

Turnaround Time ◽

General Purpose ◽

Batch Mode ◽

New Era ◽

Large Scale Data ◽

The Social ◽

Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

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When didactics meet data science: process data analysis in large-scale mathematics assessment in France

Large-scale Assessments in Education ◽

10.1186/s40536-020-00085-y ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Franck Salles ◽

Reinaldo Dos Santos ◽

Saskia Keskpaik

Keyword(s):

Data Analysis ◽

Large Scale ◽

Data Science ◽

Mathematics Assessment ◽

Process Data ◽

Meet Data

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The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

mSphere ◽

10.1128/msphere.00327-17 ◽

2017 ◽

Vol 2 (5) ◽

Cited By ~ 65

Author(s):

Gaorui Bian ◽

Gregory B. Gloor ◽

Aihua Gong ◽

Changsheng Jia ◽

Wei Zhang ◽

...

Keyword(s):

Data Analysis ◽

Gut Microbiota ◽

Large Scale ◽

Compositional Data ◽

Healthy Lifestyle ◽

Compositional Data Analysis ◽

Surprising Result ◽

Microbiota Composition ◽

Cross Sectional ◽

Age Cohorts

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

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