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tubecleanR
tubecleanR
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Description

The tubecleanR tool implemented as R package provides functions for cleaning and preprocessing YouTube comment data collected using the R packages tuber or vosonSML. It addresses potential measurement errors by offering structured routines for handling typical challenges, such as separating text, emoticons, and paradata. This helps researchers prepare high-quality datasets for analysis. A tutorial demonstrates its use on a synthetic dataset generated with Google Gemini, replicating the structure of real YouTube comment data.

Further information: This tool was developed as part of the KODAQS project, a partnership between GESIS, the University of Mannheim, and LMU Munich.

Details

Transparency, Reproducibility, and Ethical Considerations II
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Transparency, Reproducibility, and Ethical Considerations IIThis presentation discusses the tension between openness and privacy/data protection as well as potential solutions to this, the concept of FAIR data, and how to ensure and increase the reuse value/potential of data (and other materials) while also ensuring that legal requirements and ethical standards are met. This presentation is the tenth part of a set of presentations on the topic of "Tools and Workflows".
Learning Resource Type
Course
File Format
MP4, PDF
Languages
English
Media Type
Presentation, Video
Publication Date
2026-09-23
License
CCBYSA
Authors
Keywords
#FAIR data#data protection
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Binder
4
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BinderThis presentation continues the exploration of literate programming with Binder while integrating dependency management to achieve one-click online reproducibility. The mybinder.org service, as well as its documentation (MyBinder Documentation), and relevant sections in The Turing Way handbook (Zero to Binder) will be introduced. This presentation is the ninth part of a set of presentations on the topic of "Tools and Workflows".
Learning Resource Type
Course
File Format
MP4, PDF
Languages
English
Media Type
Presentation, Video
Publication Date
2026-09-23
License
CCBYSA
Authors
Keywords
#literate programming#Binder
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Basics of Transparent and Robust Research Practices
3
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Basics of Transparent and Robust Research PracticesThis presentation discusses what transparency, robustness, openness, and reproducibility mean and why they matter for data quality. In the more practical part, we will cover some relevant computer literacy essentials and, e.g., talk about operating and file systems, as well as character encoding. We will also cover the basics of good coding practice, such as commenting, formatting, functions, etc. This presentation is the first part of a set of presentations on the topic of "Tools and Workflows".
Learning Resource Type
Course
File Format
MP4, PDF
Languages
English
Media Type
Presentation, Video
Publication Date
2026-09-23
License
CCBYSA
Authors
Keywords
#transparency#robustness#openness#reproducibility
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tubecleanR
7
0
0
tubecleanRThe tubecleanR tool implemented as R package provides functions for cleaning and preprocessing YouTube comment data collected using the R packages tuber or vosonSML. It addresses potential measurement errors by offering structured routines for handling typical challenges, such as separating text, emoticons, and paradata. This helps researchers prepare high-quality datasets for analysis. A tutorial demonstrates its use on a synthetic dataset generated with Google Gemini, replicating the structure of real YouTube comment data. Further information: This tool was developed as part of the KODAQS project, a partnership between GESIS, the University of Mannheim, and LMU Munich.
Learning Resource Type
Assessment, Code Notebook, Tutorial
File Format
QMD
Languages
English
Media Type
Code, Text
Publication Date
2025-08-15
License
CCBYNC
Authors
Keywords
#digtal behavioral data
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