The Book of Life approach: Enabling richness and scale for life course research

2025-07-02

AI Summary

Life course research has long faced a trade-off between qualitative depth and quantitative breadth. This paper introduces the “Book of Life” paradigm, which— for the first time—automatically transforms heterogeneous, multi-source behavioral logs (e.g., healthcare, education, employment records) into rich, socially contextualized, longitudinal, cross-domain narrative texts via structured reconstruction and large language model–driven textual pattern recognition. We develop BOLT, an open-source toolkit enabling high-fidelity textual representation of large-scale life trajectories. Applied to the full Dutch national population registry, our method successfully generated over 100 million individual “Books of Life,” demonstrating breakthroughs in scalability (n > 10⁸), richness (multi-dimensional, temporally resolved, contextually grounded narratives), and extensibility. This work establishes a novel computational infrastructure for life course research within computational social science.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Social Networks and Social Media: Computational social science

Abstract

For over a century, life course researchers have faced a choice between two dominant methodological approaches: qualitative methods that analyze rich data but are constrained to small samples, and quantitative survey-based methods that study larger populations but sacrifice data richness for scale. Two recent technological developments now enable us to imagine a hybrid approach that combines some of the depth of the qualitative approach with the scale of quantitative methods. The first development is the steady rise of ''complex log data,'' behavioral data that is logged for purposes other than research but that can be repurposed to construct rich accounts of people's lives. The second is the emergence of large language models (LLMs) with exceptional pattern recognition capabilities on plain text. In this paper, we take a necessary step toward creating this hybrid approach by developing a flexible procedure to transform complex log data into a textual representation of an individual's life trajectory across multiple domains, over time, and in context. We call this data representation a ''book of life.'' We illustrate the feasibility of our approach by writing over 100 million books of life covering many different facets of life, over time and placed in social context using Dutch population-scale registry data. We open source the book of life toolkit (BOLT), and invite the research community to explore the many potential applications of this approach.

Problem

• Combining qualitative depth with quantitative scale in life course research
• Transforming complex log data into textual life trajectory representations
• Enabling large-scale analysis of rich life data using registry information

Innovation

• Uses complex log data for rich life accounts
• Applies LLMs to analyze textual life trajectories
• Creates scalable 'book of life' data representation