Mediating Open Data Consumption-Identifying Story Patterns for Linked Open Statistical Data

 2019 Association for Computing Machinery. Statistical data account for a very large proportion of data published on open data platforms. is category of data are which are oen of high quality, value and public interest; are gradually being published as 5-star linked open statistical data or data cubes (LOSD) for easy integration and cross-border comparability. However, publishing open data as linked data (i.e. graph oriented) significantly increases the technical skill requirements for end-user consumption. We address this problem by mediating the exploration and analysis of LOSD published on open data platforms through the use of data stories. Aer providing the requisite background information on LOSD, we identified data story paerns from extant literature and show how these paerns can be employed in analysing LOSD. Subsequently, we provide a case study to illustrate the use of these data story paerns as an end-user domain-specific language to explore and analyse LOSD. We argue that using data stories for exploring and analysing on open data platforms has the potential to significantly increase the adoption and use of (linked) open data.