Using RDF to Describe and Link Social Science Data to Related Resources on the Web

Krämer, Stefan; Leahey, Amber; Southall, Humphrey; Vampras, J.; Wackerow, Joachim

doi:10.3886/ddisemanticweb01

Cited by 6 publications

(3 citation statements)

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“…Secondly, there needs to be some way of extracting a wider range of content in an analyzable format. We believe that Linked Data provides a way forward, well suited to the semantic structures already built, and we have started to explore concepts and build interfaces (Southall et al 2011;Kramer et al 2012).…”

Section: Resultsmentioning

confidence: 99%

Rebuilding the Great Britain Historical GIS, Part 3:Integrating Qualitative Content for a Sense of Place

Southall

2014

Historical Methods: A Journal of Quantitative and Interdiscipli

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We describe the integration of old maps, descriptive gazetteers and a large library of travel writing into the Great Britain Historical GIS, presenting a range of approaches to geo-referencing diverse historical sources. While previous parts focused on legally defined administrative areas and statistical reporting units, these qualitative sources concern a less formal geography of "places". We link these to administrative units in two ways: places are contained within units, but units are named after places and are consequently subsidiary to them. While rejecting existing gazetteer data standards, the approach aligns well with that of historical place-name researchers. The final section describes how the structure interacts with search engines to support a very popular web site for lifelong learners.

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Section: Resultsmentioning

confidence: 99%

Rebuilding the Great Britain Historical GIS, Part 3:Integrating Qualitative Content for a Sense of Place

Southall

2014

Historical Methods: A Journal of Quantitative and Interdiscipli

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“…There are a number of use cases [9,10] and linking opportunities [11] which depend on the availability of complementary ontologies and vocabularies for the publication of geospatial and statistical linked data, for example, coverages that are simple timeseries datasets -where a time-varying property is measured at a fixed location.…”

Section: Linking In To Acorn-sat -A Weathermentioning

confidence: 99%

The ACORN-SAT linked climate dataset

Lefort

Haller

Taylor

et al. 2017

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In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube [13] based on the W3C Semantic Sensor Network ontology [5] and the W3C RDF Data Cube vocabulary [6]. We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.

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“…Work on implementing the DDI metadata model using Semantic Web standards had begun at a workshop in September 2011 (http://www.dagstuhl.de/11372), with an early focus on how best to relate Resource Description Framework (RDF)-described datasets to other related resources and objects (publications, geographies, organizations, people, etc.) in the Semantic Web (see Kramer et al, 2012). In October 2012, the DDI Alliance established the Moving Forward project to create a model-based specification for DDI.…”

Section: Looking Forward: Ddi Versionmentioning

confidence: 99%