scholarly journals Extending the Intermediate Data Structure (IDS) for longitudinal historical databases to include geographic data

2014 ◽  
Vol 1 ◽  
pp. 27-46
Author(s):  
Finn Hedefalk ◽  
Lars Harrie ◽  
Patrick Svensson
Keyword(s):  
Data Structure ◽  
Demographic Data ◽  
Extended Version ◽  
Intermediate Data ◽  
Common Structure ◽  
Geographic Data ◽  
Extensible Markup ◽  
Geographic Datasets ◽  
Historical Databases

The Intermediate Data Structure (IDS) is a standardised database structure for longitudinal historical databases. Such a common structure facilitates data sharing and comparative research. In this study, we propose an extended version of IDS, named IDS-Geo, that also includes geographic data. The geographic data that will be stored in IDS-Geo are primarily buildings and/or property units, and the purpose of these geographic data is mainly to link individuals to places in space. When we want to assign such detailed spatial locations to individuals (in times before there were any detailed house addresses available), we often have to create tailored geographic datasets. In those cases, there are benefits of storing geographic data in the same structure as the demographic data. Moreover, we propose the export of data from IDS-Geo using an eXtensible Markup Language (XML) Schema. IDS-Geo is implemented in a case study using historical property units, for the period 1804 to 1913, stored in a geographically extended version of the Scanian Economic Demographic Database (SEDD). To fit into the IDS-Geo data structure, we included an object lifeline representation of all of the property units (based on the snapshot time representation of single historical maps and poll-tax registers). The case study verifies that the IDS-Geo model is capable of handling geographic data that can be linked to demographic data.

scholarly journals STATA Programs for Using the Intermediate Data Structure (IDS) to Construct Files for Statistical Analysis

2016 ◽  
Vol 3 ◽  
pp. 1-19
Author(s):  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Statistical Analysis ◽  
Open Access ◽  
Demographic Data ◽  
Intermediate Data ◽  
Common Structure ◽  
Current Article ◽  
Using Data ◽  
New Variables ◽  
Extract Information

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.

scholarly journals Historical Databases Now and in the Future

2021 ◽  
Vol 10 ◽  
pp. 9-12
Author(s):  
Kris Inwood ◽  
Hamish Maxwell-Stewart
Keyword(s):  
Data Structure ◽  
The Netherlands ◽  
Longitudinal Data ◽  
Practical Implementation ◽  
High Quality ◽  
Rule Based ◽  
Intermediate Data ◽  
The Future ◽  
Historical Databases ◽  
Historical Sample

Kees Mandemakers has enriched historical databases in the Netherlands and internationally through the development of the Historical Sample of the Netherlands, the Intermediate Data Structure, a practical implementation of rule-based record linking (LINKS) and personal encouragement of high quality longitudinal data in a number of countries.

scholarly journals Program for Studying Intergenerational Transmissions in Infant Mortality Using the Intermediate Data Structure (IDS)

2018 ◽  
Vol 7 ◽  
pp. 11-27
Author(s):  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Infant Mortality ◽  
Demographic Data ◽  
Intergenerational Transfers ◽  
Infant Deaths ◽  
Intermediate Data ◽  
Different Populations ◽  
Social And Cultural Factors ◽  
Death Clustering ◽  
Intergenerational Transmissions

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.

scholarly journals Editorial

2018 ◽  
Vol 5 ◽  
pp. 1-2
Author(s):  
Paul Puschmann ◽  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Life Course ◽  
Comparative Research ◽  
Population Studies ◽  
Data Format ◽  
Standard Data ◽  
Intermediate Data ◽  
Historical Databases ◽  
Life Course Studies ◽  
Historical Life

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.

scholarly journals Reflections on the Use of the Intermediate Data Structure (IDS) in Historical Demographic Research

2021 ◽  
Vol 10 ◽  
pp. 76-80
Author(s):  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Demographic Data ◽  
Demographic Research ◽  
Intermediate Data ◽  
Micro Level

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.

scholarly journals Editorial

2015 ◽  
Vol 2 ◽  
pp. 37-37
Author(s):  
Koen Matthijs ◽  
Paul Puschmann
Keyword(s):  
Data Structure ◽  
Life Course ◽  
Comparative Research ◽  
Population Studies ◽  
Data Format ◽  
Standard Data ◽  
Intermediate Data ◽  
Historical Databases ◽  
Life Course Studies ◽  
Historical Life

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.

scholarly journals Using the Intermediate Data Structure (IDS) to Construct Files for Statistical Analysis

2015 ◽  
Vol 2 ◽  
pp. 86-107
Author(s):  
Luciana Quaranta
Keyword(s):  
Data Structure ◽  
Statistical Analysis ◽  
Demographic Data ◽  
Intermediate Data ◽  
Micro Level ◽  
And Storage

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.

Empirical Strategies for Intersectional Research

2021 ◽  
pp. 29-46
Author(s):  
Christian Dyogi Phillips
Keyword(s):  
Los Angeles ◽  
Data Collection ◽  
Asian American ◽  
Demographic Data ◽  
Asian American Women ◽  
Candidate Emergence ◽  
Political Processes ◽  
Intersectionality Theory ◽  
Data Collection And Analysis

Chapter 2 specifies how the book’s research design operationalizes intersectionality theory through its multi-method and multilevel data collection and analysis. This includes an expanded discussion of how using this framework to analyze Asian American women and men, and Latina and Latino candidates, facilitates new understandings of the relationship between race-gendered political processes and electoral opportunity within those communities, and more generally across other groups. The chapter then details the data collection processes for the book’s original datasets. The first is the Gender Race and Communities in Elections dataset, encompassing candidate and district demographic data for every state legislative general election from 1996 to 2015 in 49 states. Next, the American Leadership Survey of state legislators fielded in 2015 is described. And finally, the design for a multi-method case study of Asian American and Latina/o candidate emergence in Los Angeles County is presented.

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