The dataLegend ecosystem for historical statistics

Hoekstra, Rinke; Meroño‐Peñuela, Albert; Rijpma, Auke; Zijdeman, Richard; Ashkpour, A.; Dentler, Kathrin; Zandhuis, Ivo; Rietveld, Laurens

doi:10.1016/j.websem.2018.03.001

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…Another strong benefit of a digitised civil registry is that it can be matched with other sources and serve as a basis for further historical reconstructions. Currently, an infrastructure is being developed by the CLARIAH project to reconstruct the socioeconomic context of the 19th century, both on the individual and contextual level (Hoekstra et al, 2018). LINKS serves as the backbone for this project, as it clearly structures the (partial) life courses of everyone who lived in 19th century the Netherlands.…”

Section: Resultsmentioning

confidence: 99%

From Matched Certificates to Related Persons

Mourits¹,

Dijk²,

Mandemakers³

2020

hlcs

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For the Netherlands, a rich new data source has become available which contains indexed civil certificates for multiple generations of individuals: LINKS. The current version of the dataset contains information on 1.7 million demographic events for the province of Zeeland in the 19th and early 20th centuries and will be extended to other provinces in the Netherlands in the near future. To be able to study demographic behaviour, life courses and family relations need to be reconstructed from the civil certificates. This paper describes the steps that are taken to move from the LINKS database, which contains digitised birth, marriage, and death certificates and relational information between individuals on these certificates, to LINKS-gen, which contains over six hundred thousand life courses, family reconstructions for up to seven generations, and fertility, marital, mortality, and occupational status information, ready for analysis. We present procedures for variable construction and data cleaning. Furthermore, we give a short overview of the LINKS database, discuss quality checks, and give advice on selection of relevant cases necessary to move from LINKS to LINKS-gen. The paper is accompanied by R-scripts to convert and construct the datafiles.

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Section: Resultsmentioning

confidence: 99%

From Matched Certificates to Related Persons

Mourits¹,

Dijk²,

Mandemakers³

2020

hlcs

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“…For example, census data on the historical Dutch women labour force have been used in comparative studies with other nations, finding that Dutch women might have had a higher participation in the labour market than previously suggested (Schmidt & van Nederveen Meerkerk, 2012). Although it would require for other sources to use Linked Data as data representation paradigm, the extension of this study to cover databases of other countries would be not only feasible, but only require to use the same queries over different datasets; a practice that we see already happening in other areas of socioeconomic history (Hoekstra et al, 2018).…”

Section: Data Usability and New Possibilitiesmentioning

confidence: 91%

Improving Access to the Dutch Historical Censuses with Linked Open Data

Meroño‐Peñuela

Ashkpour

Gilissen³

et al. 2018

Res. Data J Humanit. Soc. Sci.

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The Dutch Historical Censuses (1795–1971) contain statistics that describe almost two centuries of History in the Netherlands. These censuses were conducted once every 10 years (with some exceptions) from 1795 to 1971. Researchers have used its wealth of demographic, occupational, and housing information to answer fundamental questions in social economic history. However, accessing these data has traditionally been a time consuming and knowledge intensive task. In this paper, we describe the outcomes of the cedar project, which make access to the digitized assets of the Dutch Historical Censuses easier, faster, and more reliable. This is achieved by using the data publishing paradigm of Linked Data from the Semantic Web. We use a digitized sample of 2,288 census tables to produce a linked dataset of more than 6.8 million statistical observations. The dataset is modeled using the rdf Data Cube, Open Annotation, and prov vocabularies. The contributions of representing this dataset as Linked Data are: (1) a uniform database interface for efficient querying of census data; (2) a standardized and reproducible data harmonization workflow; and (3) an augmentation of the dataset through richer connections to related resources on the Web.

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“…Ontology engineering techniques have widely been employed to formally organise domain-specific knowledge and provide interactive, semanticallyenhanced data querying, exploration, and visualisation in a number of crossdisciplinary settings [12,24,25]. A large number of vocabularies to semantically describe research outputs have been proposed, e.g.…”

Section: Methods and Applicationsmentioning

confidence: 99%

Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

2020

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A meta-analysis is a Science of Science method widely used in the medical and social sciences to review, aggregate and quantitatively synthesise a body of studies that address the same research question. With the volume of research growing exponentially every year, conducting meta-analyses can be costly and inefficient, as a significant amount of time and human efforts needs to be spent in finding studies meeting research criteria, annotating them, and properly performing the statistical analyses to summarise the findings. In this work, we show these issues can be tackled with semantic representations and technologies, using a social science scenario as case-study. We show how the domain-specific content of research outputs can be represented and used to facilitate their search, analysis and synthesis. We present the very first representation of the domain of human cooperation, and the application we built on top of this to help experts in performing meta-analyses semi-automatically. Using few application scenarios, we show how our approach supports the various phases meta-analyses, and more in general contributes towards research replication and automated hypotheses generation.

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The dataLegend ecosystem for historical statistics

Cited by 9 publications

References 34 publications

From Matched Certificates to Related Persons

From Matched Certificates to Related Persons

Improving Access to the Dutch Historical Censuses with Linked Open Data

Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

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