A Syntax for Self-Tracking

The author demonstrates that effective, context-free self-tracking does not require proprietary software or centralized servers. By using a simple text file format, individuals can record diverse life metrics—from health indicators to environmental factors—without being confined to narrow app categories. This approach prioritizes data privacy and personal control, allowing for the aggregation of varied data points that might reveal unexpected correlations, such as the link between sleep temperature and next-day energy levels. To make this method scalable and analyzable, the text defines a flexible syntax using specific delimiters like colons and question marks to structure observations, measurements, and A/B tests. By integrating version control and text editors, the system supports both freeform logging and structured data entry. This design enables users to retrospectively analyze their lives, cleaning and organizing raw data over time while maintaining the freedom to log events spontaneously as they occur, rather than adhering to rigid scientific study protocols. This article is highly relevant to the open_data movement as it champions data sovereignty and interoperability. It shows how personal data can remain under the user’s control, stored in plain, readable formats that avoid vendor lock-in. By establishing a lightweight, human-readable standard for self-tracking, it encourages the broader adoption of open formats for personal information, facilitating future portability and analysis without reliance on closed ecosystems or opaque cloud services.

Source: gibney.org
Published on 2023-06-28

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