Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration.

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users-hereby called partisan communities-without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment.

Algunas reflexiones sobre los datos abiertos enlazados en Cuba / Some Reflections on Linked Open Data in Cuba

Cuba está realizando una introducción intensiva de las tecnologías de la información y la comunicación en la sociedad, con el desarrollo y el despliegue de sistemas de información para aumentar la eficiencia de varios sectores económicos y sociales. En este contexto, varias instituciones gubernamentales están publicando datos; sin embargo, estos datos se publican utilizando diversos formatos y estándares, sin relaciones explícitas entre ellos, con alta dispersión y baja capacidad de integración de datos. En este artículo presentamos algunas reflexiones sobre la necesidad del uso de los datos abiertos enlazados en Cuba y algunos desafíos que afectan la adopción masiva de ellos en el país. Señalamos que la apertura de los datos abiertos enlazados en Cuba requiere el uso de mecanismos adecuados relacionados con una infraestructura tecnológica actualizada, un marco legal adecuado y una sinergia adecuada entre los productores y los consumidores de los datos abiertos enlazados en el país(AU)

Cuba is carrying out an intensive introduction of the ICT in the society, developing and deploying information systems to increase the efficiency of several economic and social sectors. In this context, several government institutions are publishing open data; however, these data are published using diverse formats and standards, without explicit relations between them, with high dispersion and low data integration capabilities. In this paper, we state some reflections about the use of the linked open data in Cuba and some challenges affecting the massive adoption of them in the country. We point out that the openness of the linked open data in Cuba requires the use of adequate mechanisms related to an updated technological infrastructure, an adequate legal framework and an adequate synergy between the producers and consumers of the linked open data in the country(AU)

A guide to evaluating linkage quality for the analysis of linked data.

Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.

BRIDG: a domain information model for translational and clinical protocol-driven research.

BACKGROUND: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. OBJECTIVE: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. METHODS: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. RESULTS: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. INTERPRETATION: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .