Data science: a natural ecosystem

• Fusion of the complexities of the data universe (five levels of complexity) with the data life cycle (five phases). • Holistic approaches to a natural ecosystem: the Essential Data Science. • Systemic approach to integration of the Essential data science with other disciplines: the Pan Data Science. • Fusion of Mission & Visions with task-based approaches to define data agents and data scientists. This manuscript provides a systemic and data-centric view of what we term e ssential data science, as a n atural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific g oals. The data scientist is an abstract entity that comes from the logical organization of data agents with their actions. Data scientists face challenges that are defined according to the m issions. We define specific discipline-induced data science, which in turn allows for the definition of p an-data science, a natural ecosystem that integrates specific disciplines with the essential data science. We semantically split the essential data science into computational, and foundational. By formalizing this ecosystemic view, we contribute a general-purpose, fusion-oriented architecture for integrating heterogeneous knowledge, agents, and workflows—relevant to a wide range of disciplines and high-impact applications.