Harnessing the Fifth Element of Distributional Statistics for Psychological Science: A Practical Primer and Shiny App for Measures of Statistical Inequality and Concentration

Tran, Ulrich S.; Lallai, Taric; Gyimesi, Marton L.; Baliko, Josef; Ramazanova, Dariga; Voracek, Martin

doi:10.3389/fpsyg.2021.716164

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…Finally, there was also a high diversity in the controlled properties, Diversity-index = .882. Diversity was calculated using the Gini-Simpson's index of diversity usually denoted using D or G but here for clarity was denoted using the notation Diversity-index (Keylock, 2005;Lande, 1996;Simpson, 1949;Tran et al, 2021). Here, diversity-index measures the probability that two GMs will control for different properties.…”

Section: Note the Figure Depicts The Distribution Of Different Contro...mentioning

confidence: 99%

A Methodological Framework for Stimuli Control: Insights from Numerical Cognition (published)

Shilat¹,

Henik²,

Galili³

et al. 2023

Preprint

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Non-symbolic numerical stimuli play a crucial role in numerical cognition. Physical properties such as surface area, density, and item circumference are inherently correlated with quantity. The correlations between physical properties and quantity mask the mechanism underlying numerical perception. Non-symbolic stimuli are generated using different generation methods (GMs) aimed at controlling physical properties. The way a GM controls physical properties affects numerical judgments. Here, using a novel data-driven approach, we provide a methodological review of non-symbolic stimuli GMs developed since 2000. Annotators tagged the GMs’ control over physical properties. Next, GMs were qualitatively analyzed for different property controls, terminology, and definitions. The tagging and qualitative analysis provided data suitable for quantitative analysis of GMs. We found that the field thrives with a wide variety of GMs aimed at tackling new methodological and theoretical ideas. However, the field lacks a common language and a method to incorporate new ideas in the existing literature. Furthermore, these shortcomings impair the comparison, replication, and reanalysis of previous studies in light of new ideas. We present guidelines for GMs that will hopefully contribute to the field. First, researchers should define controlled properties explicitly and consistently. Second, researchers should provide the code package used to generate stimuli. Third, researchers should also provide the actual stimuli, coupled with the behavioral and neuronal responses. This last guideline would enable researchers to reanalyze previously obtained data, enabling incorporating new ideas in the context of prior research.

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Section: Note the Figure Depicts The Distribution Of Different Contro...mentioning

confidence: 99%

A Methodological Framework for Stimuli Control: Insights from Numerical Cognition (published)

Shilat¹,

Henik²,

Galili³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age

et al. 2023

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bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data

Schlegel,

Morikone,

et al. 2024

NAR Genomics and Bioinformatics

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B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.

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Harnessing the Fifth Element of Distributional Statistics for Psychological Science: A Practical Primer and Shiny App for Measures of Statistical Inequality and Concentration

Cited by 4 publications

References 56 publications

A Methodological Framework for Stimuli Control: Insights from Numerical Cognition (published)

A Methodological Framework for Stimuli Control: Insights from Numerical Cognition (published)

Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age

bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data

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