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administrations, taking into account the specific requirements of the data to be
maintained. This may apply to the selection criteria determining what to preserve
and how, against different criteria for data acquisition and publications, as well as
limitations concerning the available resources for data storage and maintenance.
13.3.1.3 Information Integration from Multiple Sources
The last use case we would like to discuss concerns the integration of data from
different sources and, in particular, the integration of crowd-sourced data, such
as data mined from social media platforms, with research and government data.
The ability to make available different views of the same phenomena not only is
beneficial to their better understanding but also may help the effective use of data
for practical purposes. For instance, many interesting examples are available of
Smart Cities applications able to integrate official data with real-time feedback on
traffic, accidents, emergencies, and others (Craglia & Granell, 2014). However, a
number of issues prevent the adoption of data integration as a common practice for
data publishers. One of the main obstacles is related to the risk perceived by data
publishers of being considered accountable for the information coming from other
data sources, using different criteria for data acquisition, processing, and validation.
For instance, public administrations are, in general, reluctant to integrate other
data sources because they can be considered as “official” data. A similar concern is
shared by the scientific community.
The ability to express the context of data—in particular, their provenance and
attribution—may contribute to lower the barriers to data integration. This would
provoke a more effective use of crowd-sourced data, which are nowadays recognized
by both the public sector and the scientific community as an increasingly impor-
tant source of information, not only for statistical analyses but also for knowledge
acquisition (see, e.g., the relationship between folksonomies and ontologies; Mika,
2007), data validation, generation, and quality control, that is, as an input for the
data curation process.
The RDA recently highlighted the particular relevance of location information
to shape contextual information.* The capacity of geospatial data to offer a com-
mon reference frame and, thus, to enable the integration of previously decoupled
PSI or scientific data will be further investigated in this context.
13.3.2 Exemplary Solutions
Synergies such as those described previously in this chapter stimulated the develop-
ment of new solutions across both domains. We are now going to highlight three
specific examples that illustrate central innovations in this emerging landscape.
*
https://www.rd-alliance.org/bof-geospatial-information-meeting-rda3.html.
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