About the Journal

Journal Description

What is EDSJ?

The European Data Science Journal (EDSJ)   p-ISSN 3050-9572 en e-ISSN 3050-9580 is an open-access, peer-reviewed international scholarly journal that publishes high-quality and original research across all areas of data science. The journal provides a global platform for researchers, academicians, and industry professionals to share advancements in data-driven methods, theory, and applications.

EDSJ aims to support the data science community by ensuring that innovative research remains freely accessible, fostering collaboration and accelerating scientific progress.

Open Access & Publication Model

• All articles are immediately and permanently free for readers upon publication.

• To sustain open-access publishing, operational costs such as peer review, editorial work, production, and archiving are covered through author publication fees collected only after acceptance.

• The open-access model ensures maximum global visibility and citation potential for published work.

Editorial & Peer-Review Ethics

EDSJ maintains strict ethical standards and rigorous peer-review procedures. All submissions must be:

• Original and unpublished

• Not under review in any other journal

• Prepared in accordance with ethical research and publication standards

Manuscripts undergo an initial editorial screening. Those that meet quality and relevance criteria are evaluated by expert reviewers. Editorial decisions are based on scholarly merit, originality, clarity, and relevance to the journal’s scope.

EDSJ adheres to recognized ethical frameworks for handling concerns such as plagiarism, data fabrication, conflicts of interest, authorship disputes, and research misconduct.

Preservation & Archiving

EDSJ implements a robust preservation strategy to ensure long-term access and permanent availability of all published articles. This includes:

• Secure backup of all published content

• Redundant storage systems to ensure reliability

• Long-term digital preservation measures to maintain access even if the journal platform changes in the future

These steps ensure that the scholarly record remains intact and accessible for future generations.

Retraction & Correction Policy

To maintain academic integrity, EDSJ may issue:

• Corrections (Erratum / Corrigendum)

• Expressions of Concern

• Retractions

• Removals (in rare and serious cases)

Actions are taken in cases of:

• Proven research misconduct

• Ethical violations

• Honest errors that significantly impact results or interpretation

• Legal or copyright issues

The editorial team follows established ethical guidelines to ensure fair and transparent handling of such matters.

Privacy & Data Handling Policy

EDSJ respects and protects authors’ and reviewers’ personal information. The journal may collect data such as names, email addresses, institutional affiliations, and technical metadata necessary for managing submissions and communications.

Personal data are:

• Stored securely

• Accessible only to authorized editorial staff

• Used solely for journal-related purposes

Authors may request access, correction, or removal of their personal data and may opt out of non-essential communications.

Contact Information

Authors and readers may contact the editorial office for:

• Submission inquiries

• Manuscript status updates

• Editorial board communication

• General journal questions

Email: editor@esa-research.com

Scope of EDSJ

EDSJ publishes high-quality research that advances the theory, methodology, and application of data science. The journal welcomes contributions from both academic and applied perspectives.

Areas of Interest include, but are not limited to:

Core Data Science Methods

• Machine Learning

• Deep Learning

• Data Mining

• Statistical Modeling

• Predictive and Prescriptive Analytics

• Natural Language Processing

• Computer Vision

Data Engineering & Infrastructure

• Big Data Systems

• Data Pipelines

• Cloud and Distributed Computing

• Database Systems and Data Warehousing

Data Science Applications

• Healthcare Analytics

• Financial Modeling

• Social Science Data Analysis

• Energy and Environmental Data

• Business Intelligence

• Education and Learning Analytics

• Cybersecurity and Fraud Detection

Ethics & Responsible Data Science

• Algorithmic Fairness

• Bias and Transparency

• Explainable AI

• Data Privacy and Governance

Visualization & Decision Support

• Data Visualization Techniques

• Human-Computer Interaction in Data Science

• Decision-support Systems

• Dashboarding and Reporting Models

Open Science & Reproducibility

• Data Sharing

• Reproducible Research Practices

• Benchmark Datasets and Tools

• Replication Studies

EDSJ welcomes original research articles, reviews, methodological papers, short communications, technical reports, and case studies that contribute to the development and application of data science.