SIAM Journal on Mathematics of Data Science is an esteemed publication within the fields of applied mathematics and data science, published by SIAM PUBLICATIONS. This journal serves as a vital platform for researchers and practitioners, dedicated to disseminating high-quality research that addresses complex mathematical problems arising in the context of data science. The journal aims to bridge the gap between rigorous mathematical theory and practical applications, fostering interdisciplinary collaboration among mathematicians, data scientists, and statisticians. With its commitment to excellence, the SIAM Journal on Mathematics of Data Science contributes significantly to advancing the understanding and development of mathematical methodologies that analyze and interpret large datasets effectively. Researchers and professionals will find it an invaluable resource with its comprehensive articles, insightful reviews, and original research papers, which represent the forefront of innovative mathematical approaches in the evolving landscape of data science. For those interested in contributing to this dynamic field, the journal provides an array of access options tailored to diverse audiences.

Vietnam Journal of Computer Science, published by World Scientific Publishing Co Pte Ltd, serves as a prominent platform for researchers and professionals in the rapidly evolving field of computer science. Launched as an Open Access journal in 2013, it aims to disseminate high-quality research across various subfields, including Artificial Intelligence, Computational Theory and Mathematics, Computer Vision, and Information Systems. With its ISSN 2196-8888 and E-ISSN 2196-8896, the journal provides valuable insights and contributes to the growing body of knowledge in computer science, particularly in Southeast Asia. Despite its relatively recent establishment, the journal has achieved significant rankings, including Q3 status in multiple categories and notable visibility in Scopus metrics, evidencing its commitment to fostering innovative research. This journal is essential for those looking to stay at the forefront of computational advancements and applications, particularly in Vietnam and beyond, facilitating an engaging dialogue among scholars and industry professionals.

Communications for Statistical Applications and Methods is a vital academic journal dedicated to advancing the field of statistics, with a particular focus on practical applications and methodologies. Published by the Korean Statistical Society, this journal has become a significant resource for researchers, practitioners, and students engaged in statistical sciences and its diverse applications in various fields including finance and modeling. Operating without an Open Access format, the journal is accessible through institutional subscriptions, allowing a broad audience to benefit from its insights. The journal covers works from its inception in 2017 to 2024, and although it currently ranks in the Q4 and Q3 quartiles across various mathematical and statistical categories, its commitment to quality research makes it a noteworthy platform for emerging trends and innovations. The journal not only serves to disseminate knowledge but also fosters collaboration among statisticians, ensuring that crucial advancements in statistical applications are communicated effectively.

Wiley Interdisciplinary Reviews: Computational Statistics is a leading journal published by WILEY, renowned for its influential contributions to the field of statistics and its application in computational studies. With an impressive impact factor reflected in its 2023 categorization as Q1 in Statistics and Probability, this journal ranks among the top in its category, positioned at 20 out of 278 in Scopus, placing it in the 92nd percentile for its discipline. The journal spans from 2009 to 2024 and offers a rich repository of interdisciplinary insights that encompass both theoretical advancements and practical applications of computational statistics, making it an invaluable resource for researchers, professionals, and students alike. While it does not currently offer open access, the journal's commitment to high-quality, peer-reviewed content ensures that it remains a trusted source for cutting-edge developments and methodologies in the rapidly evolving realm of computational statistics.

Big Data and Cognitive Computing is a premier open-access journal published by MDPI, dedicated to advancing research in the dynamic fields of artificial intelligence, computer science, information systems, and management information systems. Since its inception in 2017, the journal has established a significant presence, reflected in its impressive categorization within the Q2 quartiles for multiple disciplines in the 2023 rankings. Situated in Switzerland, the journal provides a vital platform for researchers, professionals, and students to publish groundbreaking work and access high-quality articles, enhancing the exploration of big data applications powered by cognitive computing. With an increasing global emphasis on data-driven decision-making, Big Data and Cognitive Computing offers unrestricted access to innovative research findings, addressing both theoretical and practical aspects. The journal's contributions are integral for those looking to stay at the forefront of technological advancements and their implications across various sectors.

Japanese Journal of Statistics and Data Science, published by SPRINGERNATURE, is a leading academic journal dedicated to the advancement of statistical methodologies and data science applications, with a focus on fostering innovative research and discourse within the field. Since its inception in 2018, the journal has sought to bridge theory and practice, embracing emerging trends and interdisciplinary approaches that contribute to the ever-evolving landscape of statistics, probability, and computational theory. Hailing from Germany, the journal holds an impressive Q3 ranking in both Computational Theory and Mathematics and Statistics and Probability, reflecting its commitment to high-quality, impactful research. With an accessible ISSN of 2520-8756 and E-ISSN 2520-8764, the journal invites a global audience of researchers, professionals, and students to explore its rich array of articles and findings, all aimed at furthering knowledge and application in the realm of data science.

Advances in Data Analysis and Classification is a premier journal published by SPRINGER HEIDELBERG, focusing on the dynamic intersections of applied mathematics, computer science applications, and statistics. Established in 2007, this journal has rapidly gained recognition in the academic community, evidenced by its placement in the Q2 quartile across multiple categories in 2023, including Applied Mathematics and Statistics and Probability. With a strong Scopus ranking, where it stands 68th among 278 in Statistics and Probability, and 190th among 635 in Applied Mathematics, the journal serves as a platform for interdisciplinary research and innovation in data analysis techniques. This journal not only offers a rich repository of scholarly articles but also fosters the dissemination of cutting-edge methodologies and their practical applications. Researchers, professionals, and students alike will find invaluable insights relevant to their work and studies, reinforcing the journal's critical role in advancing knowledge and practices in data science and analysis.

INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS

Published by WORLD SCIENTIFIC PUBL CO PTE LTD, the INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS plays a pivotal role within the fields of Computer Science and Information Systems. With a focus on the development and application of cooperative information systems, this journal aims to foster innovation and collaborative research among scholars and professionals. Established in 1996, the journal has consistently published high-quality research that contributes to a better understanding of cooperative methodologies, enhancing system efficiencies across various applications. While its impact factor remains undisclosed, the journal is positioned in the Q4 category of the Scopus rankings, indicating a growing presence in the academic community. Although it does not offer open access, its rich repository of articles remains accessible to subscribers and relevant institutions. Located in Singapore, this journal will continue to serve as a critical resource for researchers, practitioners, and students keen on exploring the evolving dynamics of information systems in today's interconnected world.

Data Science and Engineering is a premier open access journal published by SPRINGERNATURE, dedicated to advancing the fields of data science, artificial intelligence, computational mechanics, and information systems. Since its inception in 2016, this journal has rapidly established itself as a leader in the academic community, boasting an impressive Q1 ranking in multiple computer science categories, including Artificial Intelligence, Software, and Information Systems. With a commitment to disseminating high-quality research, it caters to a diverse audience of researchers, professionals, and students eager to explore the intersection of data and technology. The journal's robust global reach, combined with its respected reputation, empowers authors to share their findings widely, facilitating breakthroughs and innovations across the digital landscape. Join the vibrant community of scholars contributing to this integral field of study, and stay informed with the latest research by accessing the journal freely online.

Big Data Research, published by Elsevier, is a leading academic journal dedicated to the exploration and advancement of Big Data methodologies and technologies. With an ISSN of 2214-5796, and a commendable impact reflected in its Scopus rankings—ranking Q2 in Computer Science Applications and Q1 in Information Systems—this journal offers a prominent platform for researchers and practitioners to share innovative findings in the realm of data science, analytics, and management. Since its inception in 2014, Big Data Research has fostered a multidisciplinary approach, addressing cutting-edge topics crucial for both academic inquiry and real-world applications. The journal's objectives include advancing the understanding of data-intensive systems, promoting essential methodologies for big data analytics, and enhancing data-driven decision-making processes across various industries. As an open access journal, Big Data Research is committed to disseminating knowledge widely, allowing researchers, professionals, and students to stay at the forefront of developments in this fast-evolving field. Its influence in the academic community is further underscored by a strong commitment to quality and relevance, making it an essential resource for anyone interested in the transformative power of big data.