Synthetic 3D Pap smear nucleus generation

Gómez Aguilar, S. (2010). Synthetic 3D Pap smear nucleus generation. http://hdl.handle.net/10251/10215.Archivo delegad

Ciclo de Mejora Docente para la asignatura de Diseño y aplicaciones multimedia del Grado en Comunicación Audiovisual

Experiencia de diseño, realización y autoevaluación de un ciclo de mejora de 12 horas para la asignatura de Diseño y Aplicaciones Multimedia del Grado en Comunicación Audiovisual en la Universidad de Sevilla durante el curso 2018/2019

A software service supporting software quality forecasting

Software repositories such as source control, defect tracking systems and project management tools, are used to support the progress of software projects. The exploitation of such data with techniques like forecasting is becoming an increasing need in several domains to support decision-making processes. However, although there exist several statistical tools and languages supporting forecasting, there is a lack of friendly approaches that enable practitioners to exploit the advantages of creating and using such models in their dashboard tools. Therefore, we have developed a modular and flexible forecasting service allowing the interconnection with different kinds of databases/data repositories for creating and exploiting forecasting models based on methods like ARIMA or ETS. The service is open source software, has been developed in Java and R and exposes its functionalities through a REST API. Architecture details are provided, along with functionalities’ description and an example of its use for software quality forecasting.Peer ReviewedPostprint (author's final draft

FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture

The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository