English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

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.

Improving Access to the Dutch Historical Censuses with Linked Open Data