Global Scientific Hub is an open-access publisher committed to advancing the dissemination of knowledge through high-quality, peer-reviewed journals. We provide an inclusive platform for researchers, academics, and professionals to share original studies, innovative ideas, and practical applications across diverse fields of science, technology, business, and social sciences.
Our mission is to support the growth of academic communities by upholding rigorous publishing standards, encouraging interdisciplinary approaches, and connecting research with real-world solutions. Guided by transparency, quality, and accessibility, Global Scientific Hub contributes to shaping a sustainable and knowledge-driven future through the power of open access publishing.
ISSN online: under process
Journal of Emerging Business Innovation Management (JEBIM) is a peer-reviewed platform dedicated to advancing knowledge on innovative business practices, emerging trends, and modern management strategies. The journal publishes original research, case studies, and conceptual papers that explore how organizations adapt, innovate, and thrive in dynamic business environments. It serves as a resource for scholars, practitioners, and policymakers seeking insights into innovation-driven growth and sustainable business management.
ISSN online: under process
Journal of Data Science and Innovation (JDSI) is a peer-reviewed platform dedicated to advancing knowledge at the intersection of data-driven research and innovative practices. The journal publishes high-quality articles that explore the application of data science methods, tools, and technologies across diverse domains, including business, healthcare, engineering, social sciences, and emerging digital ecosystems. With a focus on both theoretical development and practical implementation, it provides a forum for scholars, practitioners, and industry experts to share original research, case studies, and applied solutions that harness data for impactful decision-making and transformative innovation. The journal also encourages interdisciplinary work that bridges data science with fields such as artificial intelligence, machine learning, big data analytics, and innovation management.