An Ecosystem for Linked Humanities Data

Abstract

Abstract. The main promise of the digital humanities is the ability to perform scholar 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. This paper presents QBer and the underlying structured data hub, which address the long tail of research data by catering for the needs of individual scholars. QBer 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 QBer, and evaluate our first results by showing how our system facilitates two use cases in socio-economic history.

The dataLegend ecosystem for historical statistics

“…Similarly, data integration in Linked Data is also based on a large scale, one-off effort (e.g. in RISIS SMS [14], 6 DIVE 7 and WarSampo [15]), or is amongst pre-existing Linked Data sources (OpenPHACTS [16]).…”

“…While we acknowledge that, in isolation, these are Semantic Web problems in their own right, we consider this particular combination of challenges to best fit the digital humanities domain; and our proposed combination of solutionsthe dataLegend ecosystem -the core novelty of our contribution. This paper presents the dataLegend platform and its key components, QBer 1 [7] and grlc [8]. dataLegend integrates a selection of large datasets from social history.…”

grlc Makes GitHub Taste Like Linked Data APIs

“…This particular use case focuses on validating the hypothesis that prenatal and early-life conditions have a strong impact on socioeconomic and health outcomes later in life, by using 1891 census records of Canada and Sweden. These were converted to Linked Data with QBer [6], and analyzed in the statistical environment R. Before grlc, loading the data to be analyzed implied the manual download of a SPARQL query resultset in a file, and then loading this file in R. This was mitigated with the R SPARQL package [5]. However, this resulted in hard-coded, hardly reusable, and difficult to maintain queries.…”

“…In recent Linked Data projects such as CEDAR [9] and CLARIAH-SDH [6] we followed a practice of storing, curating, and publishing illustrative SPARQL queries of their use cases using GitHub repositories. These queries are then used by various client applications to access Linked Data.…”

The dataLegend Ecosystem for Historical Statistics

“…While we acknowledge that, in isolation, these are Semantic Web problems in their own right, we consider this particular combination of challenges to best fit the digital humanities domain; and our proposed combination of solutionsthe dataLegend ecosystem -the core novelty of our contribution. This paper presents the dataLegend platform and its key components, QBer [7] and grlc [8]. dataLegend integrates a selection of large datasets from social history.…”