State of the field: digital history

Computing and the use of digital sources and resources is an everyday and essential practice in current academic scholarship. The present article gives a concise overview of approaches and methods within digital historical scholarship, focussing on the question: How have the Digital Humanities evolved and what has that evolution brought to historical scholarship? We begin by discussing techniques in which data are generated and machine searchable, such as OCR/HTR, born-digital archives, computer vision, scholarly editions, and Linked Data. In the second section, we provide examples of how data is made more accessible through quantitative text and network analysis. We close with a section on the need for hermeneutics and data-awareness in digital historical scholarship. The technologies described in this article have had varying degrees of effect on historical scholarship, usually in indirect ways. For example, technologies such as OCR and search engines may not be directly visible in a historical argument; however, these technologies do shape how historians interact with sources and whether sources can be accessed at all. It is with this article that we aim to start to take stock of the digital approaches and methods used in historical scholarship which may serve as starting points for scholars to understand the digital turn in the field and how and when to implement such approaches in their work

From Matched Certificates to Related Persons

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

The dataLegend ecosystem for historical statistics

The main promise of the digital humanities is the ability to perform scholarly studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections, produce scarce metadata, require expertise for effective integration, provide poor user support while producing mappings, and present issues at data access. This paper presents the datalegend platform, which addresses the long tail of research data by catering for the needs of individual scholars. datalegend allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present the architecture of datalegend; its core vocabularies and data; and QBer, an interactive, user supportive mapping generator and RDF converter. We evaluate our results by showing how our system facilitates use cases in socio-economic history

The dataLegend ecosystem for historical statistics

The main promise of the digital humanities is the ability to perform scholarly studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections, produce scarce metadata, require expertise for effective integration, provide poor user support while producing mappings, and present issues at data access. This paper presents the datalegend platform, which addresses the long tail of research data by catering for the needs of individual scholars. datalegend allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present the architecture of datalegend; its core vocabularies and data; and QBer, an interactive, user supportive mapping generator and RDF converter. We evaluate our results by showing how our system facilitates use cases in socio-economic history