1st Place: $5,000 Cash
2nd Place: $2,000 Cash
3rd Place: $1,000 Cash
Build the tools and systems that make user-controlled personal data portable.
The goal is to solve the plumbing: how does data move between systems securely, with user consent intact? Projects might include secure data vaults, interoperability APIs, consent-driven data pipelines, or tools that unify a person's scattered digital history into something they actually own and control.
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The provided synthetic personas each contain data from 8 distinct sources — AI conversations, email, calendar, financial transactions, social posts, a personal lifelog, file metadata, and a persona profile. This mirrors what a real person could download today from Google, OpenAI, their bank, and their social platforms. Each record includes cross-references (refs) linking related entries across files, making it possible to reconstruct coherent events and timelines.
Recommended personas: p01 (Jordan Lee) and p03 (Darius Webb) — both have rich multi-source data well-suited for infrastructure and pipeline work.
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Quick load (Python):
python
`import json
with open("persona_p01/persona_profile.json") as f: profile = json.load(f)
with open("persona_p01/lifelog.jsonl") as f: lifelog = [json.loads(line) for line in f]
work_entries = [e for e in lifelog if "work" in e["tags"]]`
See QUICKSTART.md in the dataset folder for full loading examples in Python and JavaScript.
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