Data Science is an emerging field of science, which requires a multidisciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs rethinking and redesign of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects by design the whole lifecycle of data handling in modern, data driven research and the digital economy. This paper presents the EDISON Data Science Framework (EDSF) that is intended to create a foundation for the Data Science profession definition. The EDSF includes the following core components: Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), and Data Science Professional profiles (DSP profiles). The MC-DS is built based on CF-DS and DS-BoK, where Learning Outcomes are defined based on CF-DS competences and Learning Units are mapped to Knowledge Units in DS-BoK. In its own turn, Learning Units are defined based on the ACM Classification of Computer Science (CCS2012) and reflect typical courses naming used by universities in their current programmes. The paper provides example how the proposed EDSF can be used for designing effective Data Science curricula and reports the experience of implementing EDSF by the Champion Universities that cooperate with the EDISON project.
Important legal and economic motivations exist for the design and engineering industry to address and integrate digital long-term preservation into product life cycle management (PLM). Investigations revealed that it is not sufficient to archive only the product design data which is created in early PLM phases, but preservation is needed for data that is produced during the entire product lifecycle including early and late phases. Data that is relevant for preservation consists of requirements analysis documents, design rationale, data that reflects experiences during product operation and also metadata like social collaboration context. In addition, also the engineering environment itself that contains specific versions of all tools and services is a candidate for preservation. This paper takes a closer look at engineering preservation use case scenarios as well as PLM characteristics and workflows that are relevant for long-term preservation. Resulting requirements for a long-term preservation system lead to an OAIS (Open Archival Information System) based system architecture and a proposed preservation service interface that respects the needs of the engineering industry.
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