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Nowadays, both researchers and clinicians alike have to deal with increasingly larger datasets, specifically also in the context of mental health data. Sophisticated tools for dataset visualization of information from various item-based instruments, such as questionnaire data or data from digital applications or clinical documentations, are still lacking, specifically for an integration at multiple levels and for use in both data organization and appropriate construction for its valid use in subsequent analyses.
Methods
Here, we introduce ItemComplex, a Python-based framework for ex-post visualization of large datasets. The method exploits the comprehensive recognition of instrument alignments and the identification of new content networks and graphs based on item similarities and shared versus differential conceptual bases within and across data and studies.
Results
The ItemComplex framework was evaluated using four existing large datasets from four different cohort studies and demonstrated successful data visualization across multi-item instruments within and across studies. ItemComplex enables researchers and clinicians to navigate through big datasets reliably, informatively, and quickly. Moreover, it facilitates the extraction of new insights into construct representations and concept identifications within the data.
Conclusions
The ItemComplex app is an efficient tool in the field of big data management and analysis addressing the growing complexity of modern datasets to harness the potential hidden within these extensive collections of information. It is also easily adjustable for individual datasets and user preferences, both in the research and clinical field.
Dividing the Latin language into neat chronological periods will not work without severe reservations. Usages that may seem to be ‘early’ often turn out not to be confined to a particular period, or alternatively their attestations may be genre-related, that is characteristic of a genre that happens to survive mainly from an early period. As for ancient grammarians and commentators on the language, no single concept of what early Latin is may be extracted from their works. Early Latin, or the Latin of ueteres, was a different thing for different commentators. One could use ‘early Latin’ (arbitrarily) of the Latin of the period before about 100 BC, provided that one excludes from the category ‘early’ usages which, though they were current early, also remained current beyond that time. Latin is attested over many centuries, and it was definitely not static. There was not however an entity ‘early Latin’ in use until a convenient date, which then changed into ‘classical Latin’. Recovery of early phenomena requires careful analysis of the distribution, comparative evidence across periods and genres, and a distinction between usage and fashion.
Language is arguably the most important cultural tool that humans have ever invented. In this book, using English as our specific object of choice, we will look at the cognitive basis of language and discover how all aspects of it, from inventing new words to uttering full sentences, rest on one central cognitive unit: the construction. As we will see in this chapter, a core property of languages is that they are complex sign systems. I will first introduce the classic definition of words as linguistic signs, that is, as arbitrary pairings of form and meaning. Next, we shall see that even morphemes or abstract syntactic patterns are best analysed as form-meaning pairings. All of these different types of signs will be captured by the notion of the construction. Besides, instead of a strict dichotomy of words and rules, we will treat language as a system that ranges from simple word constructions to complex syntactic constructions. Finally, we will explore the basic assumptions shared by all approaches that consider the construction the basic notion of syntactic analysis (so-called Construction Grammars) and outline how these differ from Chomskyan Mainstream Generative Grammar.
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