Introduction
Qualitative Data Analysis (QDA) is a research methodology that involves assigning tags or codes to text data — such as interview transcripts, field notes, and historical documents — to identify patterns and themes. Traditionally, commercial software like NVivo and ATLAS.ti has been widely used, but their expensive license fees can be a barrier for researchers and students.
This article introduces Taguette, a free open-source qualitative data analysis tool that has gained attention as an alternative to NVivo and ATLAS.ti.
What is Taguette?
Taguette is an open-source tool for performing qualitative analysis of text data. Published under the BSD License, it is completely free to use. Developed by researchers at New York University, it aims to democratize academic research.
It features a simple interface that runs in a web browser, allowing users to get started intuitively without any programming knowledge.
Key Features
Text Tagging and Coding
Taguette’s core functionality is assigning tags (codes) to selected portions of text. While reading a document, users select important passages and assign predefined or new tags. Tags can be managed hierarchically and organized by category.
Highlight Display
Tagged text is highlighted, providing visual confirmation of which tags are applied to which sections. Even when multiple tags are applied to the same text, they are immediately apparent.
Export Functionality
Analysis results can be exported in various formats:
- HTML — viewable in browsers
- CSV — usable in spreadsheets and data analysis tools
- XLSX — Excel format
- DOCX — Word format
- Coded documents — text extractions organized by tag
Project Management
Multiple documents can be managed together as a project. The same tag set can be applied across multiple documents for cross-document analysis.
Collaboration
The web-based interface allows multiple researchers to collaborate on the same project. Installation on a server facilitates team usage.
Supported File Formats
Taguette can import documents in the following formats:
- DOCX (Word)
- HTML
- TXT (plain text)
- EPUB
- ODT (OpenDocument)
- Markdown
Getting Started
Taguette can be used in several ways.
Online Version
Simply visit app.taguette.org to start using it without any installation.
Local Installation
With a Python environment, installation via pip is straightforward.
pip install taguette
taguette
Docker is also an option.
docker run -p 7465:7465 remram/taguette
Use Cases in DH
Historical Document Analysis
Tags for themes, personal names, place names, and other categories can be applied to historical document texts to analyze patterns across entire documents. For example, tagging multiple diaries and letters to investigate when specific themes are most frequently mentioned.
Interview Research
Taguette can be used to analyze interview transcripts in Digital Humanities projects. Thematic codes can be assigned to speakers’ statements, systematically organizing common themes and differences.
Literary Text Analysis
Tags for motifs, character emotions, rhetorical devices, and other categories can be applied to literary texts such as novels and poetry. Extracting and comparing passages by code enables deeper understanding of a work’s structure and themes.
Educational Use
As an alternative to expensive commercial software, Taguette can be used in courses and student research projects. Being free while covering the basic workflow of qualitative analysis makes it ideal for teaching research methodology.
Conclusion
Taguette is a free open-source tool equipped with the essential features needed for qualitative data analysis. As an alternative to expensive commercial software, it provides an environment where researchers and students can easily begin qualitative analysis. Its accessibility through a web browser and low barrier to entry are additional attractions. Consider trying it as an introductory text analysis tool for DH research.