Maintenance and curation of large-sized biological taxonomies are complex and laborious activities. Information visualization systems use interactive visual interfaces to facilitate analytical reasoning on complex information. Several approaches such as treemaps, indented lists, cone trees, radial trees, and many others have been used to visualize and analyze a single taxonomy. In addition, methods such as edge drawing, animation, and matrix representations have been used for comparing trees. Visualizing similarities and differences between two or more large taxonomies is harder than the visualization of a single taxonomy. On one hand, less space is available on the screen to display each tree; on the other hand, differences should be highlighted. The comparison of two alternative taxonomies and the analysis of a taxonomy as it evolves over time provide fundamental information to taxonomists and global initiatives that promote standardization and integration of taxonomic databases to better document biodiversity and support its conservation. In this work we assess how ten user visualization tasks for the curation of biological taxonomies are supported by several visualization tools. Tasks include the identification of conditions such as congruent taxa, splits, merges, and new species added to a taxonomy. We consider tools that have gone beyond the prototype stage, that have been described in peer-reviewed publications, or are in current use. We conclude with the identification of challenges for future development of taxonomy comparison tools.

Este artículo profundiza en la comprensión y aplicación del pensamiento computacional, una habilidad crucial en la era digital. Concebido inicialmente por Wing en 2006, este concepto no solo implica la codificación y programación de computadoras, sino que también se extiende a la resolución de problemas, el diseño de sistemas y la comprensión del comportamiento humano basado en los principios de la informática. Con el paso del tiempo, este enfoque ha evolucionado para incluir habilidades críticas como la abstracción, el análisis de datos, la modelización y la automatización de soluciones. Este estudio explora diversas interpretaciones del pensamiento computacional, desde ser una técnica para idear soluciones hasta una competencia intelectual para el análisis y solución de problemas reales. A través del análisis de literatura y evaluación de distintas herramientas y metodologías, la investigación destaca en particular la importancia de habilidades como la descomposición de problemas, abstracción, pensamiento algorítmico y evaluación en la educación y práctica profesional. A pesar de la ausencia de una definición unificada, el pensamiento computacional se reconoce como una habilidad multidimensional esencial, subrayando la necesidad de estrategias de enseñanza y evaluación efectivas. Además, se enfatiza la necesidad de establecer una definición universal de pensamiento computacional, seguida de la identificación clara de sus dimensiones y elementos constitutivos. El desarrollo de índices precisos y métricas objetivas es crucial para evaluar eficazmente el nivel de pensamiento computacional, lo que sentará las bases para una investigación rigurosa y metódica en este campo.

The use and management of Big Data in the political sphere has posed unprecedented challenges concerning democratic governance, equity, and the rule of law. As Big Data establishes itself as a resource of growing value, it is imperative to address one of the most critical challenges: data sustainability. Data sustainability involves social and ethical considerations relating to the correct use of personal data. Lack of informed consent and transparency in collecting and using personal data raises serious concerns regarding privacy and individual rights. It is necessary to define regulations and public policies that guarantee citizens’ digital rights based on ethical and democratic standards associated with data management. This article aims to review the literature in the context of data sustainability to identify how Big Data is used, particularly emphasizing its application in the policy domain and the challenges it poses for democratic governance, equity, and law. We have used systematic mapping methodology to collect relevant papers, finding 28 papers associated with democratic governance and Big Data in the context of data sustainability. From the review of these papers, there appears to be a lack of proposals focusing on applying or implementing democratic governance and Big Data. Furthermore, there seem to be no measures to assess the application of Big Data in democratic governance. From these, the need to move towards the definition of formal models that integrate Big Data practices in democratic governance is identified.

Computational thinking (CT) skills are central to higher-education programming, yet assessing them at scale is challenging. Automated source-code analysis is a promising path, but its validity at university level is still poorly understood. We examined how far code-based metrics can capture traces of two CT skills: abstraction and algorithmic thinking. In a controlled study, 44 students enrolled in a Data Structures course completed four programming exercises designed to elicit evidence of the two target computational thinking skills. Each submission was assessed using three independent sources: an automated score based on structural code metrics, a rubric-based score by experts, and a CT self-efficacy scale. The three sources were compared using Spearman correlations, quartile analyses, and qualitative inspection of fourteen concordant and divergent cases. At the per-student level, the three sources did not converge for either skill. Per-exercise, one converged with expert evaluation, while another showed that the sources captured distinct, complementary facets of the skill—an exercise-design effect. Qualitative cases reinforced this view, illustrating what each source reads: from structural shape to semantic correctness. Automated code-based assessment of CT can provide informative structural evidence, but works best as one element of a multi-source approach in which structural signals complement, rather than replace, expert evaluation.