Reviewer Policies
1. Reviewers' Guidelines
The *Data Science Journal* relies on the expertise of our reviewers to maintain the highest standards of academic integrity and scientific rigor. As a reviewer, you play a critical role in evaluating the quality, relevance, and contribution of research to the field of data science. To ensure a transparent, unbiased, and fair review process, we ask reviewers to adhere to the following guidelines:
Confidentiality : All manuscripts and materials related to the review process must be treated as confidential. Reviewers should not share, discuss, or use any information from the manuscript for personal or professional advantage. The manuscript should not be discussed with anyone outside the review process.
Objectivity : Reviews should focus solely on the quality and scientific merit of the manuscript. Reviewers should avoid letting personal preferences or relationships with the authors influence their judgment. Constructive feedback, not personal criticism, should be the focus of the review.
Timeliness : Reviewers are expected to complete their reviews in a timely manner, typically within two to three weeks of receiving the manuscript. If a reviewer is unable to meet the deadline or feels unable to assess the manuscript adequately, they should inform the editor as soon as possible so that alternative reviewers can be found.
Constructive Feedback : Reviewers should provide clear, detailed, and constructive feedback that helps the authors improve their work. This includes addressing the strengths of the manuscript as well as areas where improvement is needed. Specific suggestions for revision are encouraged, particularly in areas of methodology, clarity, or presentation.
Expertise : Reviewers should only accept manuscripts for review in areas where they have sufficient expertise. If, after reviewing the manuscript, a reviewer feels they are not qualified to assess it, they should immediately notify the editor and recuse themselves from the review process.
By following these guidelines, reviewers help maintain the integrity of the *Data Science Journal* and ensure that published articles are of the highest quality.
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2. Conflict of Interest and Financial Disclosures (specific to reviewers)
In order to preserve the integrity and impartiality of the peer review process, the *Data Science Journal* requires that all reviewers disclose any potential conflicts of interest (COIs) that may bias their judgment. A conflict of interest arises when a reviewer has a personal, professional, or financial relationship with the authors, the institutions involved, or the topic of the manuscript under review, which could be perceived to affect their objectivity.
Reviewers must disclose the following:
Financial Conflicts: Any financial relationships that could be perceived as influencing the review, such as funding from competing research projects, consultancies, or investments in related companies.
Personal Relationships : Close personal relationships with the authors or institutional affiliations that may bias their review. This includes, but is not limited to, familial relationships, close friendships, or professional rivalries that could impact objectivity.
Professional Ties : Any past or ongoing collaborations with the authors, such as co-authorship on publications, participation in joint research projects, or significant academic or professional collaborations in the past five years.
Competing Interests : If the reviewer is currently working on similar research or competing projects, they should disclose this to avoid potential bias in evaluating the manuscript.
If a conflict of interest is identified, the reviewer must recuse themselves from the review process, and the editor will assign another qualified reviewer. The editor will carefully assess any disclosed conflicts of interest to determine whether they impact the impartiality of the review process.
Reviewers are expected to provide a full disclosure of any potential conflicts when agreeing to review a manuscript. The *Data Science Journal* strives for transparency and fairness, and by requiring this disclosure, we aim to prevent biased or unfair evaluations and ensure a trusted review process.
In cases where a conflict of interest is not disclosed but is later identified, the *Data Science Journal* reserves the right to take appropriate action, including reassigning the manuscript to another reviewer, retracting published articles, or imposing other penalties in line with journal policies.
