This era of big data is defined by the generation of a large amount of scientific data in various fields, including industry, agriculture, transportation, education, medicine, and finance. The evolution of big data stimulates a growing interest in scientific programming. Data analysis technology could be introduced to scientific programming, forming a set of data-driven computational methodologies and techniques, which deepen the understanding and solution of complex problems. Through data-driven scientific programming, computer systems acquire the ability to learn and extract knowledge from data, and to analyze and solve real-world problems in many fields, eliminating the need of explicit programming. The existing models and methods could be improved by integrating scientific programming with big data techniques, making data-centric software products and services more robust, intelligent, reliable, and efficient. This integration will become a key enabler for the next wave of intelligent software systems and engineering.

Therefore, this Special Issue intends to report high-quality innovative applications of data-driven scientific programing that enhances and improves engineering methods, and develops real and sustainable environments for data-intensive science and intelligent engineering. We welcome the original research and review papers on the theories and applications, which effectively apply big data techniques and intelligent analysis into scientific programming. Considering the interdisciplinary nature of data-driven scientific programming, the integration of multiple approaches are highly valued, especially those in the following fields: model utilization and development, programming languages, as well as libraries, simulations, environments, platforms, and software tools of scientific programming. The Special Issue will focus on supporting and improving scientific and engineering computing, in the presence of big data techniques and intelligent analysis methods.

Potential topics include but are not limited to the following:

• Data-driven scientific programming algorithms, methods, and languages
• Data-driven software infrastructure, architectures, and platforms for scientific programming
• Intelligent analysis approaches for scientific programming problems
• Novel uses of data analysis algorithms for scientific programming
• Scientific programming methods and models for data-driven engineering
• Scientific programming methods for data-based intelligent analysis systems