Citizen contributions and minor heritage: feedback on modeling and visualising an information mash-up

Abstract : The citizen science paradigm and the practices related to it have for the last decade called a wide attention, beyond academics, in many application fields with as a result a significant impact on discipline-specific research processes and on information sciences as such. Indeed, in the specific context of minor heritage (tangible and intangible cultural heritage assets that are left aside from large official heritage programs), citizen-birthed contributions appear as a major opportunity in the harvesting and enrichment of data sets (notwithstanding data quality and heterogeneity issues). In parallel, it seems we have today reached a moment when the acquisition and analysis of spatio-historical information appears "easier" since citizens are seen as potential (and legitimate) sensors. But is it really "easier"? And if so, at what cost? Having a closer look on practical challenges behind the curtain can avoid turning the above mentioned opportunity into a lost one. In this contribution we present a research initiative that aims at better circumscribing the difficulties one has to foresee when wanting to harvest and visualize pieces of data on minor heritage collections, and then to derive from them spatial, temporal, and thematic knowledge. The contribution focuses on three aspects: a short analysis of citizen contributions in the context of minor heritage, a description of the case study and of the data modelling bottlenecks we are facing, and an exemplification of the visual analysis solutions we experiment in order to portray and question our understanding of collections. The case study acts as a test bench helping to investigate data harvesting and modelling challenges, as well as potential added-value of the visualization step.
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Submitted on : Thursday, October 4, 2018 - 1:13:57 PM
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Jean-Yves Blaise, Iwona Dudek, Gamze Saygi. Citizen contributions and minor heritage: feedback on modeling and visualising an information mash-up. The IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2018, Oct 2018, Torino, Italy. ⟨halshs-01887679⟩

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