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An Interdisciplinary Framework for Advancing Scientific Innovation Through Data-Driven Technologies and Sustainable Solutions

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Pages 01-10
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Authors

  • Dr. Nguyen Minh Anh Department of Multidisciplinary Sciences, Vietnam Institute of Interdisciplinary Research, Hanoi, Vietnam Author

DOI

https://doi.org/10.55640/ijssll-06-11-01 ↗

Abstract

Scientific innovation has become an essential driver of economic growth, technological transformation, environmental protection, and societal development. In an increasingly interconnected world, complex challenges such as climate change, environmental degradation, public health emergencies, food insecurity, resource scarcity, and digital inequality require innovative solutions that extend beyond the boundaries of individual academic disciplines. An interdisciplinary research framework integrating data-driven technologies, scientific knowledge, and sustainable development principles offers significant opportunities to address these challenges. Emerging technologies, including Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, the Internet of Things (IoT), cloud computing, blockchain, robotics, biotechnology, and advanced computational modelling, are transforming the methods through which scientific research is conducted and applied. These technologies facilitate large-scale data analysis, predictive modelling, intelligent decision-making, experimental automation, and collaborative knowledge production. However, their effective utilization requires integration among computer science, engineering, environmental science, healthcare, agriculture, economics, and social sciences. This article examines the importance of interdisciplinary collaboration in advancing scientific innovation through data-driven technologies and sustainable solutions. It explores the conceptual foundations, technological applications, research methodologies, implementation challenges, and future perspectives associated with interdisciplinary innovation. Particular attention is given to data quality, research reproducibility, ethical governance, digital infrastructure, environmental impacts, and equitable access to technological resources. The article proposes an integrated framework based on interdisciplinary collaboration, intelligent data infrastructure, responsible technological development, sustainability assessment, and stakeholder participation. It concludes that scientific innovation can achieve greater societal value when technological advancement is combined with collaborative research, ethical responsibility, and long-term sustainability objectives.

Keywords

Interdisciplinary Research , Scientific Innovation , Data-Driven Technologies , Artificial Intelligence , Big Data Analytics , Sustainable Solutions , Digital Transformation , Research Collaboration , Sustainable Development , Technological Innovation

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References

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