Editorial Policies
- Advertisement Policies
- Peer Review Process
- Creative Commons License
- COPE Recommendation for Conflict of Interest (specific to editors)
- Publisher Policies
- Conflict of Interest and Financial Disclosures (specific to editors)
- Publication Ethics and Malpractice Statement
- Editorial Process
- Editorial Policies
- COPE Recommendation for Plagiarism
1. Advertisement Policies
The Data Science Journal accepts advertisements that are relevant to the field of data science, artificial intelligence, machine learning, statistics, and computational modeling. Advertisements must align with the academic and scientific focus of the journal and cannot conflict with its editorial values. The journal maintains a strict separation between editorial content and advertising to avoid any potential bias or confusion among readers.
Advertisements may be placed for products, services, conferences, workshops, or software related to the field of data science, as long as they are not misleading or false. The journal reserves the right to reject any advertisements that do not meet these standards or that may damage the reputation of the journal.
All advertisements will be clearly distinguished from research articles, reviews, and other editorial content. The publication's editorial team ensures that advertisements do not influence the peer-review process, editorial decision-making, or any aspects of content published in the journal. By maintaining this strict policy, the *Data Science Journal* ensures transparency and protects its editorial independence, fostering trust within the academic and research community.
2. Peer Review Process
The Data Science Journal uses a **double-blind peer review process**, where both the authors and reviewers remain anonymous to each other. Upon submission, the manuscript is first evaluated by the editorial team to ensure it fits within the scope of the journal. If deemed appropriate, it is sent out for review to two or more independent experts in the field of data science.
Reviewers assess the manuscript’s originality, scientific rigor, relevance, methodology, clarity, and overall contribution to the field. They are expected to provide detailed, constructive feedback to authors, helping to improve the quality of the work. Based on the reviewers' evaluations, the editor-in-chief makes the final decision on whether to accept, reject, or request revisions to the manuscript.
The peer review process is designed to maintain the journal’s high standards and ensure that only high-quality, scientifically valid research is published. Authors are required to make necessary revisions based on the feedback provided by reviewers, ensuring that the published research is accurate and robust. Transparency, fairness, and impartiality are essential to this process, which is central to maintaining the integrity and credibility of the journal.
3. Creative Commons License
The Data Science Journal follows an open-access model and all articles are published under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. This means that readers and researchers are free to share, distribute, and adapt the content of published articles for any lawful purpose, including commercial use, as long as proper credit is given to the original author(s) and the journal.
This open-access policy is designed to promote the widespread dissemination of research and encourages collaboration and innovation within the global data science community. Authors retain the copyright to their work but allow others to use, modify, and build upon their research, provided that the original work is appropriately cited.
The use of a Creative Commons license ensures that the journal's content is accessible to a wide audience, including researchers, practitioners, and educators around the world. This model helps reduce barriers to accessing high-quality academic content, particularly for researchers in developing regions or those without institutional access to subscription-based journals.
By adopting this license, the journal supports open science and aims to make high-impact research in data science available to all.
4. COPE Recommendation for Conflict of Interest (specific to editors)
The *Data Science Journal* adheres to the guidelines set forth by the *Committee on Publication Ethics* (COPE) regarding conflicts of interest for editors. Editors are required to disclose any financial, professional, or personal relationships that may influence their editorial decisions. A conflict of interest can arise from financial support, academic partnerships, personal relationships, or even intellectual disagreements with the authors of a manuscript.
If an editor has any potential conflict of interest regarding a particular submission, they must recuse themselves from the decision-making process for that manuscript. In such cases, another editor will be assigned to handle the manuscript to ensure impartiality and fairness. Editors are also expected to inform the editorial board of any conflicts of interest that may arise, whether disclosed by the authors or detected during the review process.
By following COPE’s recommendations, the *Data Science Journal* ensures that its editorial process remains transparent, unbiased, and free from external influence. This fosters trust in the journal’s academic integrity and ensures that all manuscripts are judged solely on their scientific merit and relevance to the field of data science.
5. Publisher Policies
The Data Science Journal operates under strict publisher policies designed to promote transparency, academic rigor, and ethical standards. The journal follows a robust editorial and peer review process to ensure the integrity and quality of the research it publishes. The journal is committed to maintaining the highest standards of publication ethics and follows guidelines from organizations such as the *Committee on Publication Ethics* (COPE).
The publisher ensures that all submitted manuscripts undergo thorough plagiarism checks and are reviewed by experts in the field of data science. In addition, the journal adheres to policies regarding the disclosure of conflicts of interest, financial support, and ethical research practices, especially concerning studies involving human subjects or sensitive data.
The publisher also maintains an open-access model, making research freely available to the global scientific community. By doing so, the journal aims to facilitate the broad dissemination of knowledge, allowing researchers, educators, and industry professionals to access the latest developments in data science.
These policies are central to maintaining the journal’s credibility, ensuring that its published content is both scientifically rigorous and ethically sound.
6. Conflict of Interest and Financial Disclosures (specific to editors)
The Data Science Journal expects its editorial board members to disclose any financial, personal, or professional relationships that may present a conflict of interest during the editorial process. A conflict of interest could include financial investments, consultancy arrangements, or personal relationships that might affect an editor's objectivity in making editorial decisions.
In order to ensure transparency, editors must disclose any conflicts at the time of their appointment and update these disclosures regularly. If an editor is involved in the review of a manuscript in which they have a conflict of interest, they are expected to recuse themselves from handling that submission and a substitute editor will take over the responsibility.
By adhering to these policies, the *Data Science Journal* ensures that editorial decisions are based solely on the academic merit of the manuscript, free from external influence. This approach maintains the integrity and impartiality of the journal’s editorial process and ensures that the publication remains a reliable source of high-quality, unbiased research.
7. Publication Ethics and Malpractice Statement
The Data Science Journal is committed to upholding the highest standards of publication ethics and maintaining the integrity of its scholarly content. The journal follows the guidelines outlined by the *Committee on Publication Ethics* (COPE) to address any issues related to academic misconduct, including plagiarism, data falsification, and unethical research practices.
All authors are required to submit original research and must ensure proper citation of all sources used in their work. Plagiarism, whether in the form of copying text or using data without permission, will not be tolerated. The journal uses plagiarism detection software to check submissions and will reject any manuscript that is found to contain plagiarized content.
If issues of misconduct arise after publication, the journal follows COPE’s guidelines for errata, corrections, and retractions, and will take appropriate action to correct the scholarly record. This may include issuing a correction, retracting the paper, or notifying the academic community.
By adhering to these ethical standards, the journal ensures that its content is credible, trustworthy, and contributes to the advancement of knowledge in the field of data science.
8. Editorial Process
The editorial process of the Data Science Journal is designed to ensure that only high-quality, scientifically rigorous research is published. Upon submission, all manuscripts undergo an initial screening by the editorial team to determine their relevance to the journal's scope and ensure they meet the minimum quality standards.
If the manuscript is deemed suitable, it is then sent for **double-blind peer review**, where experts in the field of data science assess the manuscript’s methodology, originality, and contribution to the field. The reviewers provide feedback, and authors are asked to revise their manuscripts accordingly. The editor-in-chief makes the final decision regarding acceptance, revision, or rejection, based on the reviewers’ recommendations.
The editorial team maintains transparency and ensures that no conflicts of interest influence the decision-making process. The goal of the editorial process is to maintain high academic standards while promoting the publication of research that pushes the boundaries of knowledge in data science.
9. Editorial Policies
The Data Science Journal adheres to a set of editorial policies that govern the submission, review, and publication of research articles. These policies ensure the journal’s content remains relevant, scientifically rigorous, and ethically sound.
Manuscripts submitted to the journal must be original and not under review elsewhere. Authors are required to disclose any conflicts of interest, including financial relationships, personal biases, or affiliations with organizations that could affect their objectivity. All research involving human subjects or sensitive data must adhere to ethical guidelines, and appropriate approval must be obtained from relevant ethics committees.
The editorial board is responsible for ensuring that all content published in the journal meets the highest standards of academic integrity, rigor, and clarity. The journal follows a **double-blind peer review** process to ensure impartial evaluation and fair treatment of all manuscripts.
10. COPE Recommendation for Plagiarism
The Data Science Journal follows the *Committee on Publication Ethics* (COPE) recommendations for addressing plagiarism. Plagiarism is considered a serious ethical violation and will not be tolerated under any circumstances. Authors are expected to submit only original work, and all sources must be appropriately cited.
