Historical Life Course Studies https://hlcs.nl/ <p><em>Historical Life Course Studies</em> is the electronic journal of the European Historical Population Samples Network (EHPS-Net) and is published by the International Institute of Social History (IISH). The journal is the primary publishing outlet for research involved in the conversion of existing European and non-European large historical demographic databases into a common format, the Intermediate Data Structure, and for studies based on these databases. The journal publishes both methodological and substantive research articles.</p> European Historical Population Samples Network en-US Historical Life Course Studies 2352-6343 The Utah Population Database. A Model for Linking Medical and Genealogical Records for Population Health Research https://hlcs.nl/article/view/11681 <p>Improving our understanding of the socio-environmental and genetic bases of disease and health outcomes among individuals, families, and populations over time requires extensive longitudinal data on multiple attributes for entire communities, states or nations. This requirement can be difficult to achieve. In this paper we describe a successful example of a database that meets these needs. The Utah Population Database (UPDB) is a unique and powerful database rarely found in the world that has been addressing these data requirements for over 40 years. The UPDB at the University of Utah is one of the world’s richest sources of in-depth information that supports research on genetics, epidemiology, demography, history, and public health. Genetic researchers have used UPDB to identify and study individuals and families that have higher than normal incidence of diseases or other traits, to analyze patterns of genetic inheritance, and to identify specific genetic mutations. Demographers and other social scientists are increasingly using the UPDB to study issues such as trends in fertility transitions and shifts in mortality patterns for both infants and adults. A central component of the UPDB is an extensive set of Utah family histories, in which family members are linked to demographic and medical information. The UPDB includes medical information about cancer, causes of death, and medical details associated with births. It also includes diagnostic records from statewide insurance claims data and healthcare facilities (hospital discharge, ambulatory surgery, emergency department encounters). UPDB is also linked to Medicare claims data, a federal health insurance program generally for persons age 65 or older. The UPDB provides access to information on more than 11 million individuals and supports nearly 400 research projects. We describe in detail the data components of the UPDB, how it can be accessed, issues related to its development, record linkage, governance and privacy protections, as well as plans for future developments.</p> Ken R. Smith Alison Fraser Diana Lane Reed Jahn Barlow Heidi A. Hanson Jennifer West Stacey Knight Navina Forsythe Geraldine P. Mineau Copyright (c) 2022 Ken R. Smith, Alison Fraser, Diana Lane Reed, Jahn Barlow, Heidi A. Hanson, Jennifer West, Stacey Knight, Navina Forsythe, Geraldine P. Mineau https://creativecommons.org/licenses/by/4.0 2022-05-03 2022-05-03 12 58 77 10.51964/hlcs11681 Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes https://hlcs.nl/article/view/11331 <p>Machine learning approaches achieve high accuracy for text recognition and are therefore increasingly used for the transcription of handwritten historical sources. However, using machine learning in production requires a streamlined end-to-end pipeline that scales to the dataset size and a model that achieves high accuracy with few manual transcriptions. The correctness of the model results must also be verified. This paper describes our lessons learned developing, tuning and using the <em>Occode</em> end-to-end machine learning pipeline for transcribing 2.3 million handwritten occupation codes from the Norwegian 1950 population census. We achieve an accuracy of 97% for the automatically transcribed codes, and we send 3% of the codes for manual verification . We verify that the occupation code distribution found in our results matches the distribution found in our training data, which should be representative for the census as a whole. We believe our approach and lessons learned may be useful for other transcription projects that plan to use machine learning in production. The source code is available at <a href="https://github.com/uit-hdl/rhd-codes">https://github.com/uit-hdl/rhd-codes</a>.</p> Bjørn-Richard Pedersen Einar Holsbø Trygve Andersen Nikita Shvetsov Johan Ravn Hilde Leikny Sommerseth Lars Ailo Bongo Copyright (c) 2022 Bjørn-Richard Pedersen, Einar Holsbø, Trygve Andersen, Nikita Shvetsov, Johan Ravn, Hilde Leikny Sommerseth, Lars Ailo Bongo https://creativecommons.org/licenses/by/4.0 2022-01-06 2022-01-06 12 1 17 10.51964/hlcs11331 Building an Archival Database for Visualizing Historical Networks. A Case for Pre-Modern Korea https://hlcs.nl/article/view/11718 <p>In this paper, we share the experience of collecting and organizing pre-modern Korean historical materials into a searchable digital archive. The Ajou Interdisciplinary Research Group (AIRG) has continuously collected historical data of pre-modern Korea for the past 10 years to assist the study of family history, historical demographics, and social mobility. This paper describes the rich data sources for historical studies of Korea, such as household registers, genealogies, and state examination registers, and we summarize contributions to the study of historical demography and related fields.</p> Seungmin Paek Jong Hee Park Sangkuk Lee Copyright (c) 2022 Seungmin Paek, Jong Hee Park, Sangkuk Lee https://creativecommons.org/licenses/by/4.0 2022-04-21 2022-04-21 12 42 57 10.51964/hlcs11718 The Impact of Microdata in Norwegian Historiography 1970 to 2020 https://hlcs.nl/article/view/11675 <p>The establishment of the Norwegian Historical Data Centre, the 1801 project at the University of Bergen and the data transcriptions and scanned versions of the sources in the National Archives made Norwegian microdata much more available. A more detailed description of the digital techniques applied to the wealth of censuses, church records and other types of nominative data from the 18th century onwards, will be presented in a separate article. Our main focus here is to summarize the impact of the research that has been produced based on the Norwegian historical microdata. These studies span a wide range of fields within social history and historical demography: Emigration, immigration, internal migration, fertility, nuptiality, family history and last but not least mortality studies with a priority given to infant mortality. A recent development is the building of a national historical population register covering the 19th and 20th centuries.</p> Hilde Leikny Sommerseth Gunnar Thorvaldsen Copyright (c) 2022 Hilde L. Sommerseth, Gunnar Thorvaldsen https://creativecommons.org/licenses/by/4.0 2022-03-01 2022-03-01 12 18 41 10.51964/hlcs11675