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JOURNAL OF DATA SCIENCE (JDS)
Aims
The Journal of Data Sciences (JDS) aims to publish original and impactful research that advances the Science, Engineering, and application of Data-driven technologies. The journal encourages contributions that address theoretical developments, methodological innovations, computational frameworks, and practical implementations across diverse sectors.
Scope
Areas of Interest Include but not limited to:
- Data Science Foundations and Methodologies
- Data Analytics and Business Intelligence
- Statistical Analysis and Predictive Modeling
- Machine Learning and Deep Learning Algorithms
- Artificial Intelligence and Intelligent Decision Systems
- Big Data Processing and Advanced Analytics
- Data Mining and Knowledge Discovery
- Data Engineering, Architecture, and Infrastructure
- Data Integration, Warehousing, and Management
- Data Cleaning, Transformation, and Quality Assessment
- Data Visualization and Interactive Analytics
- Computational Statistics and Scientific Computing
- Cloud-Based and Distributed Data Systems
- Database Technologies and Information Systems
- Explainable Artificial Intelligence (XAI)
- Natural Language Processing and Text Analytics
- Time-Series Analysis and Forecasting
- Internet of Things (IoT) and Sensor Data Analytics
- Geospatial, Environmental, and Climate Data Science
- Healthcare, Biomedical, and Clinical Data Analytics
- Financial and Economic Data Modeling
- Social Media and Human-Centered Data Analytics
- Industrial, Manufacturing, and Smart Systems Analytics
- Data Governance, Privacy, Security, and Compliance
- Ethical, Responsible, and Trustworthy AI
- Open Science, Reproducible Research, and Data Sharing
The journal publishes Research Articles, Review Articles, Systematic Reviews, Methodological Papers, Technical Reports, Case Studies, Short Communications, and Emerging Perspectives that contribute to the advancement of data science and evidence-based decision-making.
Objectives
The Journal of Data Sciences (JDS) seeks to:
- Promote excellence in data science research through the publication of high-quality, peer-reviewed scholarly work.
- Encourage interdisciplinary collaboration among researchers, practitioners, and industry experts working with data-intensive technologies.
- Advance innovative methodologies in analytics, machine learning, artificial intelligence, and computational intelligence.
- Support the development of scalable solutions for managing and analyzing complex and large-scale datasets.
- Facilitate the translation of data-driven research into practical applications that address societal, industrial, healthcare, environmental, and economic challenges.
- Foster responsible, ethical, transparent, and trustworthy use of data and artificial intelligence technologies.
- Enhance knowledge dissemination through open-access publishing and global accessibility of scientific findings.
- Encourage reproducible research, open science practices, and evidence-based decision-making.
- Provide a platform for emerging trends, novel technologies, and future directions in data science and analytics.
- Contribute to the global advancement of data-driven innovation, digital transformation, and sustainable development.
Keywords
Data Science, Data Analytics, Data Engineering, Data Mining, Big Data Analytics, Machine Learning, Deep Learning, Artificial Intelligence, Predictive Analytics, Statistical Modeling, Business Intelligence, Data Visualization, Knowledge Discovery, Data Management, Data Governance, Data Security, Data Privacy, Computational Statistics, Scientific Computing, Cloud Computing, Distributed Systems, Explainable AI, Natural Language Processing, Text Mining, Time Series Analysis, Forecasting, Healthcare