We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources.

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Open data movement advocates support to public authorities by making available to society the public information they manage. The data released are identified as open government data and the creation of open data portals supports their commitment through open government policies. The worldwide increase of the open data publication is making more necessary the modelling of its impact on society. This research analyses the process of open data publication starting in the internal systems of the organization and reaching the actual reuse of data in reuser's ecosystem surrounding the open data portals. Different reuser's profiles are identified and described within the reuser's ecosystem. Some key elements of the publication process are presented in order to guarantee sustainability of open data initiatives and to further analyse the social and economic impact.

Smart cities are service providers as well as sources of public data. The reuse of cities' data through the application of technology enables the creation of innovative services for citizens. Apps, developed by reusing information, are considered a key indicator for the creation of services. This paper explores the main characteristics of these apps and their relationship to services. The analysis performed in the main smart cities in Europe shows the importance of timely information release and the geo-location of published data. Transport and tourism seem to be the most popular areas of application. This article builds on the area of research in previous studies and includes additional information of apps' characteristics to meet service needs. KeywordsInformation reuse; Smart cities; Datasets; Applications; Apps; Open government. ResumenLas ciudades inteligentes son fuentes de datos públicos, los cuales, debidamente tratados mediante tecnologías pueden reutilizarse creando servicios innovadores para los ciudadanos. Las aplicaciones desarrolladas a este fin son un indicador para evaluar el nivel de creación de tales servicios, por lo que en este trabajo se analizan sus características. El análisis realizado en las principales ciudades inteligentes en Europa revela la importancia de la actualización de la información y de la geolocalización de los datos publicados. También se observa que los temas más populares son el transporte y el turismo. Este trabajo mejora la explicación de los resultados obtenidos en esos otros estudios previos e incluye información sobre otras importantes características de las aplicaciones.

Alberto Abella es ingeniero superior de telecomunicaciones y doctor en Organización de Empresas. Ha publicado artículos en revistas de impacto como El profesional de la información y Cities. Es experto en los campos de open data y open government aplicados al sector público y privado. Trabaja en el desarrollo de políticas y herramientas de open data en desideDatum Data Company.

Smart cities are urban spaces where massive amounts of data are generated and shared creating an ecosystem of service providers. Translating these opportunities into appropriate citizen services requires diagnosis of citizen’s expectations and a projection of the value that these services can generate for them. This article offers a methodology that provides a systematic approach to understand the interaction between citizens and services aimed to improve the design of smart city services and presents a pilot test. The four-phased methodology results in a description of the service, a model to evaluate it and offers quantitative indicators to operate and to improve the design of the service.

An updated metric developed to assess the degree of open data reusability, called MEtric for the evaLuation of Open DAta: Meloda 5 is presented. Previous version of the metric, Meloda 4, had six dimensions: the legal licensing of data, the mechanisms to access the data, the technical standards of the datasets, the data model, the geographic content of the data and the updating frequency. With all these dimensions, the metric provides a quantitative evaluation about how reusable the datasets released are. During the last five years, this metric has been cited and used by some other authors but the extensive use of the metric has brought to light some of its limitations. In order to get deeper insights into the topic, a panel of international experts has been surveyed about two aspects of the metric. First aspect was what other factors should be considered in order to qualify the reusability of a released dataset. And the second aspect was the internal structure, the levels for every dimension of the metric; if they should be increased, merged, removed or divided. Considering the results of the survey, first, we identify the factors / dimensions that should be kept: legal licensing, access to information, technical standard, standardization, geolocation content and updating frequency of data. Second, we consider the inclusion of two new dimensions: dissemination and reputation. Then, we present the new internal structure, the levels for each dimension, and the measures to evaluate the degree of reuse of each dataset. Finally, a standardization of the metric for other steps of the data impact process, data reuse analytics and data-driven services generation are presented together with future research lines.

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