Citizen Science (CS) initiatives have proliferated in different scientific and social fields, producing vast amounts of data. Existing CS projects usually adopt PPSR Core as a data and metadata standard. However, these projects are still not FAIR (Findable, Accessible, Interoperable and Reusable)-compliant. We propose to use DCAT as a data and metadata standard since it helps to improve the interoperability of CS data catalogs and all the FAIR features. For this purpose, in this paper we present a model-driven approach to make CS data FAIR. Our approach has the following contributions: (i) the definition of a metamodel based on PPSR Core, (ii) the definition of a DCAT profile for CS, (iii) a definition of set of automated transformations from PPSR Core to DCAT. Finally, the implementation of the model-driven process has been validated by evaluating several FAIR metrics. The results show that our proposal has significantly improved the FAIR quality of CS projects.

Discovering the main features of virality patterns in Twitter is the focus of this research. Five trending topics related to the COVID-19 pandemic were selected for the study, with Spanish as the target language. To carry out the discovery of virality patterns, we applied opinion mining techniques that enable us to structure the information based on the polarity of the messages and the emotions they contain. After transforming the information from an unstructured textual representation to a structured one, data mining techniques were applied, specifically association rules mining. Message patterns with the highest virality (high shares and high likes), and at the same time the most relevant characteristics of the patterns with less impact were extracted. After an exhaustive analysis of the most relevant non-redundant rules, it can be concluded that messages with a high-negative polarity and a very high emotional charge, especially emotions that have intensified with the COVID-19 pandemic, such as fear, sadness, anger and surprise are more likely to go viral in social media. By contrast, messages with little news coverage in the media, few authors, and the absence of surprise are relevant features when it comes to seeing messages with very low dissemination in social media.

Open Government concept is experiencing an upswing. Open Government is based on three concepts (transparency, participation and collaboration) that require accessing data. To provide this access, Open Data Portals are being implemented around the world by every kind of organizations, mainly in the public sector. The aim of an Open Data Portal is exposing data in such a way that reusing is facilitated. Therefore, it is necessary to define a quality and maturity model to evaluate the characteristics of an Open Data Portal, considering different factors that can contribute to reusing potential, such like visualization, usability, granularity, data integration, reputation, relevancy, availability and reutilization. Also, effectively promoting data reusing implies setting specific norms to promote standardization among institutions, ministries and central governments' offices into the same country. This paper presents a formal proposal to evaluate - based in expert criteria - the quality and the maturity of an open data portal.

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge from big data could be of greater value if it is available for later consumption and reusing. In this paper, we present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD). The main contribution of this paper is an approach for democratizing big data through reusing the knowledge gained from data mining processes after being semantically annotated as LOD, then obtaining Linked Open Knowledge. Our work is based on a model-driven viewpoint in order to easily deal with the wide diversity of open data formats.

The University of Alicante (UA), in Spain, is aligned with an Open Government strategy. Within this strategy, UA is carrying out the OpenData4U (Open Data for Universities) project which aims to provided mechanisms to open data from universities, finding out how open data contributes to open government in universities and to encourage reusing open data (not only for the sake of transparency, but also as a basis of novel data-intensive business models related to universities). This paper describes one of the output of the project: an approach for opening data from universities keeping in mind data quality criteria, and tailored to no specific technological scenario. This approach allowed UA to launch its open data portal http://datos.ua.es that is also reviewed in this paper. Also, some research challenges related to university open data are enumerated.

Data is everywhere, and non-expert users must be able to exploit it in order to extract knowledge, get insights and make well-informed decisions. The value of the discovered knowledge could be of greater value if it is available for later consumption and reusing. In this paper, we present the i¬rst version of the Knowledge Spring Process, an infrastructure that allows non-expert users apply user-friendly data mining techniques on Open Data sources and share results as Linked Open Data. The main contribution of this paper is the concept of reusing the knowledge gained from data mining processes after been semantically annotated in the RDF i¬le as Linked Open Data (Linked Open Knowledge). A model driven approach is proposed in order to maintain a standard structure having into account the diversity of the data formats.

Data that can be reused and redistributed without any restriction is called Open Data. These two features make its quality can be greatly affected. To date, the most used quality criteria of Open Data are those established in the 5-Stars Model. This article aims to extend this model and corroborate the existence of specific quality criteria for Open Data and its corresponding measurement mechanisms. We propose a new Quality Model for Open Data portals which is exposed from two points of view: qualitative and quantitative. To illustrate the use of this model, we implemented a study case based on real open data about Municipality of Perez Zeledon in Costa Rica, which was evaluated with the qualitative model.

OLAP (On-line Analytical Processing) technologies rely on multidimensional models to provide decision makers with appropriate structures allowing them to intuitively analyze data. However, these multidimensional models may be potentially large, thus becoming too complex to be understood at a glance. Current approaches for OLAP design are focused on providing analysts with a single multidimensional schema derived from their previously stated information requirements, but this is not sufficient to lighten the complexity of the decision making process. To overcome this drawback, the authors propose personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behavior. In this paper, they present a new approach for personalizing OLAP systems at the conceptual level based on the underlying multidimensional model, a user model and a set of personalization rules. Transformations are defined by means of a model-driven strategy to assist in the process of obtaining the corresponding personalized OLAP schemas from these models.