> For the complete documentation index, see [llms.txt](https://dots.gitbook.io/dots-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dots.gitbook.io/dots-docs/analysis/dashboard.md).

# Dashboard

The **Dashboard** (currently in beta) gives you a bird's-eye view of your platform's activity and the patterns emerging from your research data. It has two tabs: **General** and **Pattern Discovery**.<br>

<figure><img src="/files/Sm0V8U4KpfVWHpDfooZ2" alt=""><figcaption></figcaption></figure>

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### General Tab

Quick-glance metrics for your platform:

* **Total Datasets** — Number of publishing templates you've set up
* **Total Documents** — Published documents across all datasets
* **% Analysed** — A weighted score showing how much of your data has been enriched using AI features (annotations, summaries, categorization, AI chat, reports)
* **Reports Created** — Published custom reports
* **Time Saved** — Estimated hours saved through AI-assisted work (auto-annotations, summaries, categorization, etc.)
* **Recent Activity Feed** — A real-time log of who did what and when — document creation, annotations, comments, AI actions, report publishing, and more

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### Pattern Discovery Tab (Pattern Insights)

This is the core analytical feature. It automatically analyzes your **annotation tags** across all documents and surfaces patterns in your qualitative data. It answers questions like: *What themes keep showing up? Which ideas tend to appear together? Are there surprising or non-obvious connections in the data?*

It has **six sub-tabs**:

1. **Summary** — Top-level stats (total annotations, unique documents, themes, sub-themes, tags) plus AI-generated key takeaways across all analyses
2. **Prevalence** — *"How common is each tag?"* — Ranks every tag by how frequently it appears and across how many documents, so you can see which themes dominate your data
3. **Co-occurrence** — *"What shows up together?"* — Shows which tag pairs appear on the same documents, revealing compound patterns (e.g., "Access to Finance" and "Gender Barriers" frequently tagged on the same interviews)
4. **Association Strength** — *"Is this pairing meaningful or coincidental?"* — Goes beyond simple co-occurrence to measure whether two tags appear together more than you'd expect by chance. High scores flag genuinely significant connections
5. **Similarity** — *"Which tags behave alike?"* — Identifies tags that consistently appear on the same set of documents, useful for spotting redundant tags or tightly linked concepts
6. **Conditional Patterns** — *"If this tag is present, how likely is that tag?"* — Reveals directional relationships (e.g., "In 4 out of 5 documents where 'Policy Gap' appears, 'Stakeholder Frustration' also appears")

#### AI-Powered Insights

Each tab also includes **AI-generated insight bullets** — plain-English summaries that highlight what's noteworthy, surprising, or worth investigating further. These insights:

* Reference your **actual tag names** so they're immediately traceable
* Use natural language (no statistical jargon)
* Are framed as discoveries: *"What stands out is..."*, *"Interestingly..."*
* Can be expanded to view **sample annotation excerpts** from your data for verification

Insights are cached and automatically refresh when your annotation data changes significantly (>10% change).

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> **Note:** This is a beta feature — metrics, UX, and information architecture are actively being refined. We'd love your feedback as we continue to improve it.