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Chronologue Datasets

Chronologue provides a set of example datasets to demonstrate how memory traces can be transformed into structured calendar events. These are useful for onboarding, testing new features, and exploring integrations between natural language prompts and .ics scheduling.

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Directory Structure

All datasets are located in the data/datasets/ directory. Each dataset includes:
  • A .json file with memory traces
  • A corresponding .ics file generated by Chronologue
  • A markdown summary with notes or prompts

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Grocery Scheduling

File: grocery_scheduling.json Use this dataset to test weekly or monthly grocery planning. Each item includes: 
{
  "timestamp": "2025-04-30T09:00:00Z",
  "type": "grocery",
  "list_type": "weekly",
  "item": "eggs",
  "quantity": "12",
  "notes": "for breakfast"
}

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Sports Schedule

File: sports_schedule.json This dataset is built from real or simulated game schedules for a favorite team (e.g. “New York Knicks”). Each entry is timestamped and includes location and opponent. Example format: 
{
  "timestamp": "2025-05-01T19:00:00Z",
  "type": "event",
  "title": "Knicks vs. Celtics",
  "location": "Madison Square Garden",
  "recurrence": "none"
}
→ Load and convert with dataset_loader.py.

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Chat history

File: chat_history.json Traces from conversation with an assistant, tagged with intent or observation type. Example format: 
{
  "timestamp": "2025-04-29T14:00:00Z",
  "type": "observation",
  "task_id": "daily_review",
  "content": "Need to restock ethanol for lab by Thursday."
}

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Course Curriculum

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Course Curriculum

File: course_curriculum.json Useful for planning academic pacing using lecture topics and due dates. 
{
  "timestamp": "2025-05-06T10:00:00Z",
  "type": "curriculum",
  "course": "MIT Linear Algebra",
  "topic": "Week 1 - Vectors and Matrices",
  "material_link": "https://ocw.mit.edu"
}