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What is Taguette?
Taguette is a free, open-source, web-based tool for tagging documents — the core activity in qualitative data analysis. You upload a set of documents, build a hierarchy of tags, and highlight passages, attaching tags and notes you can retrieve and reorganize later. It is aimed at researchers, students and small teams who need to code interviews, field notes, transcripts or open-ended survey responses without buying a commercial qualitative analysis package.
What you actually do with it
- Upload a corpus — gather your interviews, transcripts or written notes into one project.
- Build a tag tree — create parent and child tags so themes can be nested rather than kept as a flat list.
- Code by highlighting — select a portion of text and assign one or more tags, plus a short note explaining your reasoning.
- Recall and reorganize — pull up everything under a tag, rename or merge tags, and export results when you are done.
Who it suits, and where it stops
| Situation | Taguette fits well | Consider something else |
|---|---|---|
| Small to mid-size text project | Yes — quick setup, no licence cost | — |
| You want to inspect or host the code | Yes — open source, BSD-3-Clause licensed | — |
| Audio, video or image sources | No — it works on documents | Tools with native media transcription and coding |
| Mixed-methods statistics | No — it is a tagging tool | Software that combines stats and coding |
| Very large teams with strict audit needs | Limited | Platforms built for multi-coder workflow management |
The practical trade-off is simplicity versus depth. Because it runs in a browser and does one job, you can start coding the same day; the cost is that features common in heavyweight packages — automatic transcription, visual coding of media, detailed inter-coder reliability reporting — are not its focus.
A concrete next step
If you have a folder of interview transcripts, register a free account, create one project, and upload three documents as a trial. Build a starter tag tree from your research questions, code only the first two pages of each document, then export and read the result. That short loop tells you whether the tagging model matches how you think before you commit a whole study to it. The project's own guide and user documentation, linked from Taguette, walk through the interface; the source and issue tracker are also public if you want to check activity or self-host.
How do I upload and organize my qualitative documents in Taguette?
Upload your documents to Taguette as a project, then organize them by tagging passages rather than by moving files around. Taguette is an open-source, web-based tool for qualitative data analysis: you upload a collection of documents, build a hierarchy of tags, and annotate portions of documents with tags and notes you can recall and organize later. Registration is free.
The basic workflow
- Create a project and upload your documents as a collection.
- Read through a document and select the passage you want to mark.
- Apply a tag to that selection and add a note if the meaning needs explaining.
- Repeat across documents, adding new tags as themes emerge and nesting them under broader ones.
- Later, pull up everything under a tag to compare how that theme appears across your whole document set.
The important shift is that the tag hierarchy, not the folder structure, becomes your filing system. A single paragraph can carry several tags, so overlapping themes don't force you to choose one home for a passage.
Organizing tags so the hierarchy stays useful
Start flat and let structure emerge from the data. A common practical approach is a small number of parent tags — for example, Barriers, Motivations, Process — with specific child tags underneath as you notice repeated ideas. Two habits keep this manageable:
- Write a one-line definition for each tag you create, so you apply it consistently a week later.
- Use notes for the reasoning behind a tag ("this is about cost, not distance") rather than creating near-duplicate tags.
If you already have a codebook from a previous study, build the tag hierarchy before you start coding; if you're doing exploratory work, expect to rename and merge tags partway through.
Where it fits and where it doesn't
Taguette suits interview transcripts, open-ended survey responses, field notes, and other text-heavy material where the goal is thematic coding and retrieving quotes by theme. It is a lighter option than full CAQDAS packages: if you need mixed-methods statistics, complex inter-coder reliability reporting, or multimedia coding of audio and video, check whether those features exist before committing.
For a concrete case: a graduate student with 20 interview transcripts can create a project, upload the transcripts, code a first interview to draft tags, then work through the rest and export the tagged passages to support the write-up. The trade-off is that you supply the analytical discipline — the tool stores and retrieves your coding, it doesn't generate themes for you.
Next step: read the official guide at Taguette to confirm the exact upload and export steps for the current version, then run a short pilot on two documents to test your tag hierarchy before coding the full set.
Can I use Taguette for collaborative coding of interview transcripts?
Yes. Taguette is built for exactly this kind of work: it is a web-based tool where you upload a set of documents, build a hierarchy of tags, and highlight passages in those documents, attaching tags and notes you can retrieve later. Interview transcripts are a natural fit, since the core workflow is marking up text segments and organizing them under a shared coding scheme.
For a team, the practical question is how collaboration is set up. Taguette supports projects with multiple members, so a typical arrangement is one project per study, with each coder working through the same transcript set and applying the same tag hierarchy. Because tags are stored as a shared structure rather than free-form comments, you can compare where two coders applied the same tag and where they diverged — the basis for a coding comparison or consensus discussion.
Taguette
H3 Practical setup for a coding team
- Agree on a codebook before coding starts, and treat the tag hierarchy as the codebook. Renaming or merging tags later is possible, but it is easier to settle the top-level structure first.
- Decide whether everyone codes every transcript or whether you split the set and double-code a sample for reliability checks.
- Use notes on tagged excerpts for analytic memos, disagreements or edge cases, so reasoning survives beyond the highlight itself.
- Keep a shared convention for how you handle overlapping codes on the same passage.
H3 Trade-offs to weigh
Taguette is deliberately lightweight: it is about tagging and retrieving text, not about building complex mixed-methods models or running statistical reliability calculations for you. If your project needs heavy quantitative inter-coder metrics, survey integration or advanced visualization, you may end up exporting tagged excerpts into another tool. If your priority is a low-friction, transparent coding environment that a small team can pick up quickly, the simplicity works in your favor.
A useful next step: run a pilot on two transcripts with two coders, compare tagged excerpts side by side, and refine the codebook before committing the full dataset. If the tag hierarchy holds up at that scale, the rest of the coding will go faster.
Is Taguette suitable for academic research or thesis projects?
Yes. Taguette is well suited to academic research and thesis projects, especially qualitative studies where you need to tag text passages and organize them by theme. The site describes it as an open-source, web-based document tagging tool for qualitative data analysis: you upload a collection of documents, build a hierarchy of tags, and annotate passages with tags and notes you can recall and organize later. That workflow maps directly onto the coding and theme-building steps of an interview study, a document analysis, or a literature review.
Where it fits in a thesis workflow
- Coding interview transcripts, field notes or open-ended survey responses
- Building a codebook as a tag hierarchy, then refining it as themes emerge
- Pulling tagged excerpts back together to write up findings
- Working on a small team where a supervisor or co-coder needs to see the same tagged documents
Practical trade-offs
| Strength | Limitation to plan around |
|---|---|
| Free and open source, so no budget line and no lock-in | Fewer built-in analysis features than commercial CAQDAS packages |
| Web-based, so you can code from different machines | You depend on a browser and, in many setups, a network connection |
| Tag hierarchies plus notes keep coding decisions visible | Heavy statistical or mixed-methods analysis is outside its scope |
| Runs under a permissive BSD-3-Clause license | You or your institution handle setup, hosting and backups |
For a master's or PhD project, the deciding factor is usually the type of analysis. If your method is thematic analysis, framework analysis or basic content coding of text, Taguette covers the core loop. If your methodology requires advanced visualization, statistical integration or strict audit trails demanded by a specific journal or funder, check those requirements before committing.
A concrete next step
Take three de-identified transcripts, upload them, and build a starter hierarchy of five to eight tags. If you can code a full transcript and retrieve your excerpts in a form you would be willing to quote in a draft chapter, it will carry you through a thesis. If you find yourself wanting features it does not offer, that is the moment to compare alternatives. For general orientation on tools in this space, see Taguette for the project itself and QDR if you need a home for sharing qualitative data alongside your thesis.
How does Taguette compare to other qualitative data analysis software like NVivo or Dedoose?
Taguette sits at the lightweight, free end of the qualitative data analysis (QDA) spectrum, while NVivo and Dedoose are commercial suites built for larger, more structured projects. Taguette's own description is narrow and clear: it is an open-source, web-based tool for uploading documents, building a hierarchy of tags, and annotating passages with tags and notes you can retrieve and reorganize later. That is essentially the core coding workflow, without the extra analytical, visualization and team-management layers that commercial packages tend to bundle in.
H3 Where each one fits
- Taguette: best when you want to code text documents, keep a transparent tag structure, and avoid licensing costs or a heavy learning curve. Suited to students, solo researchers, small projects, and anyone who wants to read and tag rather than run statistical-style analyses.
- NVivo: aimed at researchers who need deep querying, matrix comparisons, mixed-methods data, and detailed reporting across many source types. The cost is licensing and time spent learning the interface.
- Dedoose: oriented toward team-based projects, particularly mixed-methods work with coding, excerpting and collaborative review across a shared workspace.
H3 Practical trade-offs
| Dimension | Taguette | NVivo / Dedoose |
|---|---|---|
| Cost model | Free and open source | Commercial licensing |
| Setup | Web-based, minimal onboarding | More configuration and training |
| Core coding | Upload documents, tag passages, add notes | Same, plus query and visualization tools |
| Team features | Basic | Stronger collaboration and audit features |
| Best for | Small to medium text-focused studies | Large, multi-researcher or mixed-methods projects |
A concrete scenario: a master's student coding 20 interview transcripts for themes can upload them to Taguette, build a tag tree, and export or review coded excerpts without paying for a seat. A research team coordinating five coders, tracking inter-coder agreement and combining survey data would likely find the commercial tools' collaboration and query features worth the cost.
Next step: list what your project actually requires — number of documents, number of coders, whether you need quantitative or mixed-methods analysis, and whether you must share a workspace. If your needs stop at tagging and retrieving text passages, start with Taguette; if they extend into querying, team workflows or mixed data, evaluate the commercial options against that list.
What are the system requirements and installation options for self-hosting Taguette?
Self-hosting Taguette means running a small web application on your own server so that you and your collaborators can reach it through a browser. The requirements are modest: a machine that can run the Taguette server, a way to serve it over HTTP(S), and enough storage for your documents and the database that holds tags and notes. Because it is a web-based tool, the people doing the analysis only need a modern browser—they do not install anything on their own computers.
Taguette is distributed as free and open-source software under a BSD-3-Clause license, so you are free to inspect, modify and deploy it yourself. The project publishes user documentation and accepts issue reports via GitLab or email, which is where you should check for the current, authoritative installation steps and version-specific notes.
Installation options in practice
- Run it directly on a server or VM. Install the application and its dependencies, point it at a database, and put a reverse proxy in front for TLS. This suits a lab or department with an existing server.
- Container-based deployment. If the project provides an official container image, this is usually the least fiddly route: pull the image, mount persistent storage, set the connection details, and start it. Check the documentation for whether an official image exists rather than assuming one does.
- Local or single-user instance. You can run it on your own laptop or a small always-on machine for a solo project, which avoids exposing anything to the internet.
Choosing between them
| Situation | Better fit |
|---|---|
| One researcher, one project | Local install or a small VM |
| A team sharing tags and documents | Server or container behind a reverse proxy |
| You want reproducible, upgradeable deployments | Container, if officially supported |
| Restricted data that cannot leave your network | Self-hosted instance on internal infrastructure |
Practical next step
Before committing, confirm three things in the official documentation: the supported runtime and database, whether an official container image is published, and the backup procedure for your database and uploaded files. Then do a trial run with a throwaway project—upload a few documents, tag them, restart the server, and verify that your tags and notes survive. That single test will tell you more about whether the setup suits your workflow than any feature list.
For the authoritative install instructions, start at Taguette and follow the link to the user documentation.
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