Shifting epistemologies for discipline and rigor in educational research: Challenges and opportunities from digital humanities

2019 ◽  
Vol 18 (5) ◽  
pp. 610-621
Author(s):  
Karin Priem ◽  
Lynn Fendler
Keyword(s):  
19Th Century ◽  
Digital Humanities ◽  
Quantitative Research ◽  
Large Scale ◽  
Data Sets ◽  
Scale Analysis ◽  
The Arts ◽  
Large Scale Analysis ◽  
Challenges And Opportunities ◽  
The 19Th Century

This article historicizes “rigor,” discipline,” and “systematic” as inventions of a certain rational spirit of Enlightenment that was radicalized during the 19th century. These terms acquired temporary value in a transition during the 19th century when a culture of research was established within a modern episteme. Beginning in the 20th century, this development was perceived as problematic, triggering criticism from philosophy and the arts, and even within the sciences. “Discipline,” “rigor,” and “systematic” have changed meanings over time, and recent contributions from digital humanities are promising for a renewed critical debate about rigor in research. Both digital humanities and quantitative research deal with big data sets aimed at providing a large-scale analysis. However, unlike most quantitative research, digital humanities explore uncertainties as their main focus. Attention to the human-machine collaboration has led to more expansive thinking in scientific research. Digital humanities go further by advancing a metaperspective that deals with the material hermeneutics of data accumulation itself.

scholarly journals Comparison of Merging and Meta-Analysis as Alternative Approaches for Integrative Gene Expression Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jonatan Taminau ◽  
Cosmin Lazar ◽  
Stijn Meganck ◽  
Ann Nowé
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Meta Analysis ◽  
Microarray Gene Expression Data ◽  
Data Sets ◽  
Expression Data ◽  
Scale Analysis ◽  
Microarray Gene Expression ◽  
Large Scale Analysis ◽  
Microarray Gene

An increasing amount of microarray gene expression data sets is available through public repositories. Their huge potential in making new findings is yet to be unlocked by making them available for large-scale analysis. In order to do so it is essential that independent studies designed for similar biological problems can be integrated, so that new insights can be obtained. These insights would remain undiscovered when analyzing the individual data sets because it is well known that the small number of biological samples used per experiment is a bottleneck in genomic analysis. By increasing the number of samples the statistical power is increased and more general and reliable conclusions can be drawn. In this work, two different approaches for conducting large-scale analysis of microarray gene expression data—meta-analysis and data merging—are compared in the context of the identification of cancer-related biomarkers, by analyzing six independent lung cancer studies. Within this study, we investigate the hypothesis that analyzing large cohorts of samples resulting in merging independent data sets designed to study the same biological problem results in lower false discovery rates than analyzing the same data sets within a more conservative meta-analysis approach.

scholarly journals Distant viewing and multimodality theory: Prospects and challenges

2021 ◽  
pp. 263497952110070
Author(s):  
Tuomo Hiippala
Keyword(s):  
Empirical Research ◽  
Computational Methods ◽  
Digital Humanities ◽  
Large Scale ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Multimodal Corpora ◽  
Immense Potential

This article discusses the prospects and challenges of combining multimodality theory with distant viewing, a recent framework proposed in the field of digital humanities. This framework advocates the use of computational methods to enable large-scale analysis of visual and multimodal materials, which must be nevertheless supported by theories that explain how these materials are structured. Multimodality theory is well-positioned to support this effort by providing descriptive schemas that impose structure on the materials under analysis. The field of multimodality research can also benefit from adopting computational methods, which help to achieve the long-term goal of building large multimodal corpora for empirical research. However, despite their immense potential for multimodality research, the use of computational methods warrants caution, because they involve a number of potentially cascading risks that arise from biases inherent to the underlying data and different approaches to the phenomenon of multimodality.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

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