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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03027 |
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| _version_ | 1866913926315245568 |
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| author | Verhagen, Mark D. Stroebl, Benedikt Liu, Tiffany Liu, Lydia T. Salganik, Matthew J. |
| author_facet | Verhagen, Mark D. Stroebl, Benedikt Liu, Tiffany Liu, Lydia T. Salganik, Matthew J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Book of Life approach: Enabling richness and scale for life course research Verhagen, Mark D. Stroebl, Benedikt Liu, Tiffany Liu, Lydia T. Salganik, Matthew J. Computation and Language 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. |
| title | The Book of Life approach: Enabling richness and scale for life course research |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.03027 |