Data Science and Applications is an interdisciplinary peer-reviewed journal that addresses the development that data has become a crucial factor for a variety of scientific fields. This journal covers aspects around scientific data over the whole range from data creation, mining, discovery, curation, modeling, processing, and management to analysis, prediction, visualization, user interaction, communication, sharing, and re-use. We are interested in general methods and concepts, as well as specific tools, infrastructures, and applications.

The ultimate goal is to unleash the power of scientific data to deepen our understanding of physical, biological, and digital systems, gain insight into human social and economic behavior, and design new solutions for daily life and the future.

The importance of scientific data, from text, audio, visual content such as sensor and weblog data is rising. New methods to extract, transport, pool, refine, store, analyze, and visualize data are needed to unleash their power while simultaneously making tools and workflows easier to use by the public at large.

The journal invites contributions ranging from theoretical and foundational research, platforms, methods, applications, and tools in all areas. We welcome papers which add a social, geographical, and temporal dimension to Data Science research, as well as application-oriented papers that prepare and use data in discovery research.

This journal focuses on methods, infrastructure, and applications around the following core topics:

  • - scientific data mining, machine learning, and Big Data analytics
  • - scientific data management, network analysis, and knowledge discovery
  • - scholarly communication and (semantic) publishing
  • - research data publication, indexing, quality, and discovery
  • - data wrangling, integration, and provenance of scientific data
  • - trend analysis, prediction, and visualization of research topics
  • - scalable computing, analysis, and learning for Data Science
  • - scientific web services and executable workflows
  • - scientific analytics, intelligence, and real time decision making
  • - socio-technical systems
  • - social impacts of Data Science

An important goal of the journal is to promote an environment to produce and share annotated data to the wider research community. The development and use of data and metadata standards are critical for achieving this goal. Authors should ensure that any data used or produced in the study is represented with community-based data formats and metadata standards. The journal will be published in English and has effective reviewing process: we intend to provide a response in one to three months after receiving the manuscript. The journal follows an open access publishing model.

Focus and Scope

Our journal covers all the studies based on data  analysis from both lifeand social sciences. Your data-based works can also be accepted in areas not mentioned below.

Theoretical Foundations:

  • Probabilistic and Statistical Models and Theories
  • Learning Theory
  • Optimization Methods
  • Data Compression and Sampling
  • Statistical Learning
  • Evolutionary Computation
  • Operating Systems
  • Compiler Design
  • Computer Education
  • Deep Learning
  • Financial Modeling
  • Forecasting
  • Classification and Clustering
  • Learning Classifiers
  • Parallel and Distributed Learning
  • Scientific Data and Big Data Analytics
  • Artificial Intelligence
  • Scalable Analysis and Learning
  • Educational Data Mining
  • Data Pre-Processing, Sampling and Reduction
  • High Dimensional Data, Feature Selection and Feature Transformation
  • High Performance Computing for Data Analytics
  • Architecture, Management and Process for Data Science

Machine Learning and Knowledge Discovery:

  • Knowledge Discovery Theories, Models and Systems
  • Human-Machine İnteraction For Knowledge Discovery and Management
  • Biomedical Knowledge Discovery, Analysis of Micro-Array and Gene Deletion Data
  • Machine Learning for High-Performance Computing
  • Learning for Streaming Data
  • Machine Learning Over The Cloud
  • Knowledge Based Neural Networks
  • Spatial/Temporal Data
  • Knowledge Discovery from Heterogeneous, Unstructured and Multimedia Data
  • Knowledge Discovery in Network And Link Data
  • Knowledge Discovery in Social Networks
  • Data And Knowledge Visualization
  • Cross Media Data Analytics
  • Big Data Visualization, Modeling and Analytics
  • Multimedia/Stream/Text/Visual Analytics
  • Database Technology

Computational Data Science:

  • Databases
  • Big Data
  • Computational Theories for Big Data Analysis
  • Computational Intelligence for Pattern Recognition and Medical Imaging
  • Incremental Learning – Theory, Algorithms And Applications in Big Data
  • Sparse Data, Feature Selection and Feature Transformation
  • Intelligent Information Retrieval
  • Probabilistic And İnformation-Theoretic Methods
  • Support Vector Machines and Kernel Methods
  • Time Series Analysis
  • High Performance/Parallel Computing
  • Search and Mining
  • Data Acquisition, Integration, Cleaning
  • Data Visualizations
  • Semantic Based Data Mining
  • Data Wrangling
  • Decision Making from İnsights, Hidden Patterns
  • Optimization for Data Analytics
  • Computer Architecture for Data Analytics
  • Computer Graphics for Data Analytics
  • Computer Application for Data Analytics


  • Bioinformatics Applications
  • Biometrics Applications
  • Biomedical Informatics Applications
  • Computational Neuroscience Applications
  • Information Retrieval Applications
  • Healthcare Applications
  • Collaborative Filtering Applications
  • Human Activity Recognition Applications
  • Natural Language Processing Applications
  • Web Search Applications
  • Image Analysis Applications
  • Parallel and Distributed Data Applications
  • Data Streams Mining Applications
  • Graph Mining Applications
  • Spatial Data Mining Applications
  • Multimedia Data Mining Applications
  • Pre-Processing Techniques  Applications
  • Data And Information Networks Applications
  • Data And Information Privacy and Security Applications
  • Data And Information Semantics Applications
  • Data Management in Smart Grid Applications
  • Data Mining Algorithms Applications
  • Data Mining Systems Applications
  • Data Structures and Data Management Applications
  • Database and Information System Performance Applications
  • Electronic Commerce and Web Technologies Applications
  • Electronic Government & Eparticipation Applications
  • Sensor Data Management Applications
  • Database Systems & Applications
  • Statistical and Scientific Databases Applications
  • Temporal, Spatial and High Dimensional Databases
  • Security and Information Assurance
  • Soft Computing
  • Software Engineering
  • Web and Internet Computing
  • Theoretical Computer Science
  • Natural Language Processing Applications
  • Information Technology Management
  • Modeling and Simulation

Peer Review Process

 The research journal Data Science and Applications peer review all the material submitted using OJS. Some the submissions are rejected without being sent out for external per review on the grounds of priority, insufficient originality and scientific flaws. A decision on such papers is taken quickly, usually within 4-8 weeks.

The remaining manuscripts should be evaluated by two reviewers and by the anonymous expert member of the Editorial board, usually within 4-8 months. Authors are welcome to suggest suitable independent reviewers.

Once the paper was reviewed and the comments from reviewers were received final decision concerning the publishing is made by the Editorial board usually within 4-8 weeks.

Authors must to revise the article in accordance with remarks or to give reasonable explanation of ignoring some remark. Article must be revised during the 4 weeks counting from the day the article has been returned to the author. After the review, revised articles are to be submitted to the Editorial Board through OJS along with detailed response in written for the reviewers concerning the performed corrections. Corrections must be provided in other text color.

The Editorial board is the competent authority in taking decision(s) on selection or rejection of the manuscripts of all the research/review/similar articles submitted to the Journal. The decision taken by Editorial board is final in all respects and no further comments/communications from authors in this regard is expected.

It may be noted that, the aforesaid policy may change with or without notice to the authors/ contributors of this journal. The decision of Journal in respect of management of a manuscript in every step is taken as final.

Publication Frequency

Data Science and Applications is a semiannualresearch journal.  Beginning from 2018, the journal is published regularly at the end of each following month: No. 1 - June, No. 2 - December.

Open Access Policy

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.