OPEN DATA REUSE IV
randomly to six of the portal managers so that they could rate the portals taking into account that they had not been included in their previous questionnaire. This survey asked about their knowledge of other open data portals and their perceived prestige. Both were classified into three categories. The final reputation was obtained by the voters’ most frequent rating of the reputation of other open data portals, based on the respondent’s own apparent knowledge of the voted portal. A manual analysis of 330 open data portals was also carried out to identify their characteristics and degree of maturity. It was found that 148 were PODP, 33 were non-operational and 163 were valid. To analyse the degree of reuse of open data, the datasets published on the open data portals were reviewed. Specifically, 300 datasets were sampled, of which 272 (90.67 %) were valid and the rest were found to have no content (28). The MELODA 5 metric was applied to analyse the degree of reuse of open data (datasets) published in the sampled portals. Of the portals analysed, 38 of them have a section for data-driven applications and services. As for the developed services, they were detected in the sample of 1083 applications/ services listed in the application sections of the portals. 21 of them were not operational, which represents a percentage of 34.90 %. To complete the information, a direct analysis of the applications and services that the data portals themselves provide as accredited reusers of data was also carried out, identifying the authors and extracting data from the corporate portals of each one of them. We sampled 63 applications and services from the portals that had inventoried services based on open data, chosen at random. This is a statistically significant sample for a 12-point
interval with a confidence level of 95 %, following the same approach and tool as above.
In line with Abella, Ortiz-de-Urbina-Criado and De- Pablos-Heredero (2017), the data extracted for each of these services is: • The themes of the service according to the NTI-RISP classification, from which its equivalent can be converted into DCAT-AP (European Union, 2017). • Geolocation characteristics. • The real-time characteristic of the service. • The type of author of the application. • The sustainability mechanism if there is one. Sustainability describes the economic viability of the service in the medium term, either because there is an entity that supports the costs, or because the service has its own revenue generation mechanisms. For example, in the case of a corporate service, the entity that publishes the service supports its costs even though it has no directly related revenues. Whether it has any business model that includes a source of revenue other than that of the entity publishing the data and, if so, what type. The categories used are:
• Ads (ads would support the cost of the application/service).
• Institutional support (for example, the support of a public entity).
• Freemium (part of the service is free and another part is paid).
• Entity promotion (SME marketing; used to advertise the author).
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