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It has previously been shown that infant mortality clusters in a subset of families, a phenomenon which was observed in historical populations as well as contemporary developing countries. A transmission of death clustering across generations has also been shown in Belgium, but it is unknown whether such effects are specific to the studied context or are also found in other areas. The current article introduces a special issue devoted to analysing intergenerational transmissions of infant mortality across the maternal line in Belgium, the Netherlands, northern and southern Sweden, and Norway. Taking advantage of the Intermediate Data Structure (IDS), the five empirical studies created datasets for analysis and ran statistical models using exactly the same programs, which are also published within the special issue. These works are the first set of studies using the IDS on several databases for comparative purposes. Consistent results across the studied contexts were shown: transfers of infant mortality across the maternal line were seen in all five areas. In addition, the works have shown that there are large advantages of adopting the IDS for historical demographic research. The structure has in fact allowed researchers to conduct studies which were fully comparable, transparent and replicable.

Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance, early life exposures, and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations. This paper is one of five studies that analyses intergenerational transmissions in infant mortality by using a common program to create the dataset for analysis and run the statistical models with data stored in the Intermediate Data Structure. The results of this study show that in five rural parishes in Scania, the southernmost province of Sweden, during the years 1740-1968 infant mortality was transmitted across generations. Children whose maternal grandmothers experienced two or more infant deaths had higher risks of dying in infancy. The results remained consistent when restricting the sample only to cases where the grandmother had been observed for her entire reproductive history or when controlling for socioeconomic status. When running sex specific models, significant effects of the number of infant deaths of the grandmother were observed for girls but not for boys.

The Intermediate Data Structure (IDS) was developed as a strategy aimed at standardizing the dissemination of micro-level historical demographic data. The structure provides a common and clear data strategy which facilitates studies that consider several databases, and the development and exchange of software. Based on my own experiences from working with the IDS, in this article I provide reflections on the use of IDS to create datasets for analysis and to conduct comparative demographic research.

The Scanian Economic-Demographic Database (SEDD) is a high-quality longitudinal data resource spanning the period 1646-1967. It covers all individuals born in or migrated to the city of Landskrona and five rural parishes in western Scania in southern Sweden. The entire population present in the area is fully covered after 1813. At the individual level, SEDD combines various demographic and socioeconomic records, including causes of death, place of birth and geographic data on the place of residence within a parish. At the family level, the data contain a combination of demographic records and information on occupation, landholding and income. The data for 1813-1967 was structured in the model of the Intermediate Data Structure (IDS). In addition to storing source data in the SEDD IDS tables, a wide range of individual- and context-level variables were constructed, which means that most types of analyses using SEDD can be conducted without the need of further elaboration of the data. This article discusses the source material, linkage methods, and structure of the database.

Studies conducted in historical populations and developing countries have evidenced the existence of clustering in infant deaths, which could be related to genetic inheritance and/or to social and cultural factors such as education, socioeconomic status or parental care. A transmission of death clustering has also been found across generations. One way of expanding the knowledge on intergenerational transfers in infant mortality is by conducting comparable studies across different populations. The Intermediate Data Structure (IDS) was developed as a strategy aimed at simplifying the collecting, storing and sharing of historical demographic data. The current work presents two programs that were developed in STATA to construct a dataset for analysis and run statistical models to study intergenerational transfers in infant mortality using databases that are stored in the IDS. The programs use information stored in the IDS tables and after elaborating such information produce Excel files with results. They can be used with any longitudinal database constructed from church books, civil registers, or population registers.

The Intermediate Data Structure (IDS) provides a common structure for storing and sharing historical demographic data. The structure also facilitates the construction of different open-access software to extract information from these tables and construct new variables. The article Using the Intermediate Data Structure (IDS) to Construct Files for Analysis (Quaranta 2015) presented a series of concepts and programs that allow the user to construct a rectangular episodes file for longitudinal statistical analysis using data stored in the IDS. The current article discusses, in detail, each of these programs, describing their technicalities, structure and syntax, and also explaining how they can be used.

The use of longitudinal historical micro-level demographic data for research presents many challenges. The Intermediate Data Structure (IDS) was developed to try to solve some of these challenges by facilitating the storing and sharing of such data. This article proposes an extension to the IDS, which allows the standardization and storage of constructed variables. It also describes how to produce a rectangular episodes file for statistical analysis from data stored in the IDS and presents programs developed for such purpose.

Historical Life Course Studies, a journal in population studies, aims to stimulate and facilitate the implementation of IDS (Intermediate Data Structure, a standard data format for large historical databases), and to publish the results from (comparative) research with the help of large historical databases. The journal publishes not only empirical articles, but also descriptions (of the construction) of new and existing large historical databases, as well as articles dealing with database documentation, the transformation of existing databases into the IDS format, the development of algorithms and extraction software and all other issues related to the methodology of large historical databases.