Author
Keywords
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 programmes), citizen-birthed contributions appear as a major opportunity in the harvesting and enrichment of data sets. With more content made available on the net by a variety of local actors, we may have reached a moment when collecting and analysing spatio-historical information appears “easier”, with citizens acting 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. This contribution discusses feedbacks from a research initiative aimed at better circumscribing the difficulties one has to foresee if wanting to harvest and visualise pieces of data on minor heritage collections and then to derive from them spatial, temporal and thematic knowledge. The contribution focuses on four major aspects: a feedback on the information and on the information available, a description grid for factors of imperfection to be anticipated, visual solutions we have experimented in order to support analytical tasks, and lessons learnt in terms of relations between academics and information providers.

Year of Publication
2020
Journal
GL-Conf. Series: Conf. Proc.
Volume
2019-December
Issue
1
Number of Pages
81-99,
Date Published
2020/06//undefined
Publication Language
English
ISBN-ISSN
2364415X (ISSN)
Accession Number
WOS:000590215600005
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088168791&doi=10.1007%2fs41060-019-00194-0&partnerID=40&md5=7d3491f0767dd30318f876cc9848a2f6
DOI
10.1007/s41060-019-00194-0
Alternate Journal
Int. J. Data Sci. Anal.
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