12. Authorship, Publication Ethics, and Intellectual Property

Introduction and Context

Research is communicated through publications, software, datasets, patents, presentations, and other scholarly outputs. These outputs do more than report findings: they allocate credit, establish priority, influence careers, shape institutional reputation, and inform technological and public decisions. For that reason, research communication is an ethical activity rather than a merely technical final stage of research.

Publication ethics concerns the standards that govern the conduct, evaluation, dissemination, correction, and ownership of scholarly work. It asks who deserves authorship, how contributions should be recognized, how reviewers should behave, what constitutes plagiarism or data manipulation, and how errors should be corrected. Intellectual property concerns the legally protected interests associated with creations such as inventions, software, written works, databases, designs, and confidential information. Ethical publication and intellectual property management overlap, but they are not identical. A person may have a legal copyright interest without deserving academic authorship, and an author may be ethically responsible for a paper even when copyright is assigned to a publisher.

These issues are especially important in information technology research. IT projects commonly involve large teams, open-source components, software repositories, automated image and data processing, collaborative datasets, commercial sponsors, and artificial intelligence tools. The boundary between research contribution and technical support may therefore be difficult to define. Doctoral researchers must be able to make defensible decisions, document them transparently, and respond appropriately when misconduct or disagreement arises.

Learning Objectives

By the end of this chapter, the reader should be able to:

1. Distinguish authorship, contributorship, acknowledgment, and intellectual property ownership.

2. Apply principled criteria for assigning authorship and author order.

3. Explain plagiarism, fabrication, falsification, image manipulation, duplicate publication, and citation misconduct.

4. Evaluate ethical responsibilities in peer review and identify risks associated with predatory publishing.

5. Compare copyright, licensing, patents, trade secrets, and related forms of protection.

6. Design practical procedures for maintaining publication integrity in digital and collaborative research.

7. Explain the purpose of corrections, expressions of concern, and retractions.

8. Analyze publication and intellectual-property dilemmas in professional and doctoral research settings.

Credit, Responsibility, and the Meaning of Authorship

Authorship is both a form of credit and a form of accountability. An author is not simply the person who typed the manuscript or occupied the most senior position in a laboratory. Authorship normally requires a substantial intellectual contribution to the work, participation in drafting or critically revising the publication, approval of the final version, and willingness to accept responsibility for the integrity of the work. Different disciplinary bodies express these requirements in somewhat different language, but the central idea is consistent: authorship should reflect meaningful intellectual contribution and responsibility.

A useful distinction is between contribution and authorship. A programmer who implements a routine exactly as specified may have made important technical contributions but may not qualify as an author. By contrast, a researcher who designs the algorithm, justifies its methodological assumptions, interprets its performance, and helps revise the manuscript may qualify. Authorship should be discussed at the beginning of a project and revisited when the work changes.

Contributorship statements improve transparency by describing what each person did. Common contribution categories include conceptualization, methodology, software, data curation, formal analysis, investigation, visualization, supervision, project administration, funding acquisition, and writing. A contributorship statement does not eliminate the need for authorship criteria, but it helps distinguish genuine contributions from honorary or symbolic authorship.

Honorary, guest, or gift authorship occurs when a person is listed because of status, authority, friendship, funding influence, or institutional position without making a qualifying contribution. Ghost authorship occurs when a person who made a substantial contribution is omitted, including professional writers or undisclosed industry researchers. Both practices distort the scholarly record. They can conceal conflicts of interest and make accountability difficult.

Author order has no universal meaning. In some fields the first author performed most of the research and writing; in others, alphabetical order is customary, while the last author may be the senior investigator. Teams should agree explicitly on the meaning of order and record the decision. The corresponding author coordinates communication with the journal and commonly takes responsibility for submission accuracy, disclosure of conflicts, author approval, and responses after publication. Corresponding authorship does not transfer all responsibility from the other authors.

Disputes should be addressed through documented discussion, institutional procedures, or an independent research-integrity office. Journals are usually not well placed to decide complex employment or contribution disputes. Changes in authorship after submission should be supported by written consent from all affected authors and should never be used to pressure a junior researcher.

Publication Ethics as a System of Research Integrity

Publication ethics is best understood as a system rather than a list of prohibitions. It includes responsible study design, accurate record keeping, fair attribution, honest presentation, independent evaluation, transparent correction, and respect for legal and contractual obligations. Several frameworks are useful in practice.

The responsible-publication cycle begins with planning. Researchers should agree on authorship expectations, data-management responsibilities, disclosure requirements, target journals, intellectual-property arrangements, and procedures for resolving disagreements. During research, they should preserve original data, document analysis decisions, maintain version histories, and distinguish exploratory from confirmatory analyses. During writing, they should verify quotations, citations, figures, permissions, and author approval. During review and after publication, they should respond honestly to criticism and correct material errors.

A risk-based approach is particularly useful in doctoral research. At each stage, researchers can ask: What could mislead readers? Who could be harmed? What evidence supports this claim? Who owns or controls the data and software? What information must be disclosed? What independent check could detect an error? These questions convert general ethical principles into operational safeguards.

Plagiarism and Unattributed Borrowing

Plagiarism is the presentation of another person's words, ideas, structure, data, code, images, or creative expression as one's own without appropriate acknowledgment. It includes verbatim copying, close paraphrasing, translation without attribution, mosaic plagiarism, and reuse of distinctive ideas. Text-matching software can identify possible overlap, but similarity is not itself proof of plagiarism. Properly cited quotations, standard technical phrases, references, and legitimately shared methods may produce similarity without misconduct.

Self-plagiarism, or redundant text reuse, involves presenting substantial previously published material as new without disclosure. Limited reuse of technical descriptions may be reasonable when transparently identified and permitted, but substantial undisclosed duplication can mislead readers and violate copyright or journal policy. Good practice includes taking careful notes, marking copied text during drafting, citing sources at the point of use, and preserving the distinction between one's own interpretation and a source's argument.

Fabrication, Falsification, and Questionable Research Practices

Fabrication is making up data, observations, participants, experiments, or results. Falsification is manipulating research materials, procedures, data, images, or reporting so that the research record is misrepresented. Examples include deleting inconvenient observations without justification, changing labels, inventing performance measures, or reporting an experiment that was not conducted.

Not every error is misconduct. Honest mistakes, justified exclusion criteria, failed experiments, and statistically weak findings are part of research. The ethical distinction depends on intent, recklessness, transparency, and the effect on the research record. Questionable practices that may not meet the formal definition of misconduct can still damage integrity. These include selective reporting, p-hacking, undisclosed changes to hypotheses, inappropriate exclusion of outliers, and overstating causal conclusions.

Researchers should preserve raw data, analysis scripts, laboratory or system logs, preregistered protocols where appropriate, and records of deviations from the original plan. Reproducible computational environments, code review, independent replication, and audit trails are especially valuable in IT research.

Image and Digital-Object Manipulation

Digital images are data when they represent experimental results, medical scans, screenshots of system output, or visual evidence. Adjustments that improve legibility may be acceptable if applied equally across the image, disclosed when relevant, and incapable of changing the interpretation. Cropping, contrast adjustment, color balancing, or removal of irrelevant background may be permissible under journal rules.

Unethical manipulation includes selectively enhancing a feature, deleting a band or data point, duplicating part of an image, splicing unrelated results without clear demarcation, or presenting a processed image as an original. The same principle applies to charts, diagrams, code outputs, and generated illustrations. Researchers should retain original files, use non-destructive editing, label composites, and provide processing details. Artificial intelligence tools that generate or modify images require particular caution because they can introduce content that was not observed.

Duplicate, Redundant, and Salami Publication

Duplicate publication occurs when substantially the same work is published more than once without transparent justification. Redundant publication can waste reviewer and reader resources, distort evidence syntheses, and create an inflated record of productivity. Salami publication divides one coherent study into multiple papers with insufficiently distinct research questions or contributions.

Related outputs are not automatically unethical. A conference paper may legitimately be expanded into a journal article if the journal permits it, the earlier work is cited, the new contribution is substantial, and overlap is disclosed. Secondary publication in another language or for a different audience may also be acceptable under explicit conditions. The relevant question is whether readers and editors can understand the relationship among the outputs.

Citation Ethics

Citations should enable readers to identify intellectual influences and verify claims. Citation manipulation includes fabricating references, citing sources not read, misrepresenting a source's conclusion, excessive self-citation, coercive citation, and reciprocal citation agreements designed to inflate metrics. References should be relevant, accurate, sufficiently current for the claim, and balanced across competing perspectives.

Authors should cite original sources where feasible, distinguish primary evidence from reviews, and avoid using citations merely as decoration. Reviewers and editors must not demand irrelevant citations to increase journal or personal metrics. In computational research, citation may also be due to software, datasets, benchmarks, pretrained models, protocols, and open-source projects.

Peer Review: Confidentiality, Fairness, and Independence

Peer review is a quality-control and improvement process, not an opportunity to acquire ideas or exercise personal power. Reviewers should accept assignments only when they possess suitable expertise and have enough time to provide a careful assessment. They should disclose conflicts of interest and decline when personal, financial, institutional, or competitive interests could compromise independence.

Review materials are confidential. A reviewer must not share a manuscript, use unpublished ideas, copy methods, contact authors without permission, or submit the work to an automated tool whose privacy terms are unknown. Reviews should evaluate the work rather than the identity, nationality, gender, institution, or reputation of the authors. Criticism should be specific, evidence-based, and professional.

Editors have additional responsibilities. They must select appropriate reviewers, protect confidentiality, manage conflicts, investigate credible allegations, and avoid using editorial influence to advance personal interests. Artificial intelligence can assist with screening or language support, but automated decisions may reproduce bias, expose confidential manuscripts, or obscure accountability. Human oversight remains essential.

Predatory Publishing and the Evaluation of Venues

Predatory publishing refers to deceptive or exploitative publishing practices that prioritize fees over genuine editorial and peer-review services. Warning signs include aggressive unsolicited invitations, false claims about indexing or impact factors, unclear fees, implausibly broad scope, unverifiable editorial boards, fake metrics, promises of guaranteed acceptance, and poor transparency about peer review or archiving. A journal should not be labeled solely because it charges a fee; legitimate open-access journals may charge article-processing fees and still maintain rigorous standards.

Researchers should verify a journal's publisher, editorial policies, ownership, peer-review process, indexing claims, archiving arrangements, correction policy, license terms, and fee schedule. Institutional librarians, disciplinary associations, recognized indexing services, and established journal directories can assist evaluation. Doctoral candidates should resist the pressure to publish rapidly in venues that cannot protect the scholarly record or provide credible review.

Intellectual Property in Research and Computing

Intellectual property law varies by jurisdiction, contract, and type of work. Researchers should obtain institutional or legal advice for high-value or disputed matters. The main categories are related but distinct.

Copyright protects original expression fixed in a tangible or digital form, including articles, software source code, documentation, photographs, graphics, and some database structures. It generally does not protect ideas, facts, mathematical principles, or methods as such. Ownership may belong to the creator, employer, institution, funder, or another party under employment, collaboration, or publishing agreements. Copyright assignment to a publisher may limit later reuse, whereas a license grants specified permissions without necessarily transferring ownership.

Open licenses make reuse conditions explicit. Permissive software licenses commonly allow reuse with conditions such as attribution and preservation of notices. Copyleft licenses may require derivative software to be distributed under the same or compatible terms. Creative Commons licenses regulate reuse of creative works; conditions may include attribution, noncommercial use, or prohibition of derivatives. License compatibility matters: combining components with incompatible obligations can make distribution unlawful or ethically improper. Researchers must maintain a dependency inventory and comply with notices and license terms.

Patents protect qualifying inventions, subject to jurisdiction-specific requirements such as novelty, inventive step or non-obviousness, and industrial applicability or utility. Public disclosure before filing can damage patent rights in many jurisdictions. Consequently, researchers planning commercialization should coordinate publication with a technology-transfer office before presenting or depositing code. A patent does not automatically prove that an invention is ethically valuable, safe, or commercially successful; it is a legal exclusion right with defined scope.

Trade secrets protect valuable confidential information when reasonable measures are taken to keep it secret. Confidential algorithms, datasets, source code, and business processes may qualify, but disclosure through a paper, repository, thesis, or conference can destroy secrecy. Confidentiality agreements and data-use agreements should be reviewed before research begins.

Data ownership and access are particularly complex. A dataset may contain copyright interests, privacy restrictions, contractual limitations, database rights, or ethical obligations to participants. The ability to access data does not imply the right to redistribute it. Researchers should document provenance, consent conditions, de-identification, retention periods, access controls, and permitted secondary uses.

A Practical Integrity Workflow

A research group can operationalize these principles through a publication and IP protocol:

1. At project initiation, record expected contributions, provisional authorship, funding sources, conflicts, ownership, confidentiality, and intended outputs.

2. During research, preserve raw data, source code, versions, metadata, permissions, and decision logs.

3. Before drafting, identify related publications, patent deadlines, license obligations, and restrictions on data or images.

4. During writing, use citation management carefully, verify every reference, distinguish results from interpretation, and disclose reuse.

5. Before submission, obtain approval from all authors, check figures and supplementary files, confirm ethical approvals, disclose conflicts, and review journal legitimacy.

6. During peer review, maintain confidentiality and respond to criticism with evidence rather than concealment.

7. After publication, monitor for errors, preserve artifacts, share materials where lawful and ethical, and issue corrections when necessary.

This workflow is strengthened by version-control systems, reproducible notebooks, continuous integration tests for research software, checksum-protected archives, data dictionaries, and controlled-access repositories. Tools support integrity but do not replace judgment. A perfectly versioned false result remains false, and a similarity score cannot determine authorship or intent.

Corrections, Expressions of Concern, and Retractions

The scholarly record should be corrected in proportion to the problem. A minor typographical error may require a simple correction. A substantive but non-invalidating error may require an erratum or corrigendum. An expression of concern alerts readers that a publication is under investigation when the available evidence is unresolved. Retraction is appropriate when findings are unreliable because of major error, fabrication, falsification, plagiarism, unethical research, or other serious defects. Retraction is not necessarily a punishment; its primary purpose is to prevent reliance on an unreliable record.

Retractions should be linked clearly to the original article, explain the reason, and preserve the publication history rather than silently deleting the work. Authors who discover a serious error should notify the editor promptly. Concealing an error generally causes greater harm than acknowledging it. Subsequent papers should cite the corrected status where relevant.

Illustrative Case: The Distributed Analytics Study

Background

A doctoral team develops a machine-learning system for detecting equipment failures from industrial sensor data. The project involves a doctoral student, a faculty supervisor, an industrial partner, a data engineer, and a research software developer. The team prepares a journal article and plans to release a cleaned dataset and source code.

Situation and Problem

The supervisor proposes listing the industrial partner's director as an author because the company supplied access to the data. The data engineer, who designed the cleaning pipeline and identified serious measurement bias, is told that acknowledgment is sufficient. The software developer discovers that the manuscript's performance graph contains a duplicated curve created during figure assembly. The team also learns that the source code incorporates a library under a license incompatible with the proposed redistribution. Finally, the industrial contract treats the sensor data as confidential and requires review before publication.

Analysis

Data access alone does not establish authorship, although the director might qualify if they made a substantial intellectual contribution, helped interpret the research, participated in manuscript revision, approved the final version, and accepted responsibility. The data engineer's methodological design and analysis are strong evidence of a qualifying contribution. The duplicated curve must be corrected, and the original figure and processing history should be examined to determine whether the error affects the conclusions. The code cannot be released until license obligations and contractual restrictions are resolved. The team's publication schedule must therefore be adjusted rather than used to justify concealment.

Approach and Solution

The team holds a documented authorship meeting using contribution categories. It agrees that the doctoral student, supervisor, and data engineer qualify as authors; the software developer qualifies after contributing to interpretation and manuscript revision; and the director is acknowledged unless further intellectual participation occurs. The corresponding author obtains written approval from all authors.

The team restores the figure from original files, conducts an independent analysis, and discloses the correction in the submission if needed. A legal and technology-transfer review identifies a compatible replacement library and removes restricted components. The industrial partner reviews the manuscript only for confidentiality and contractual compliance, not for suppression of unfavorable results. The team releases a synthetic example and an access-controlled version of the real data, together with documentation describing limits on reuse.

Outcome

The article is submitted later than planned but with a transparent contribution statement, corrected figures, lawful software terms, and a clear data-access procedure. The research record is more credible, and each participant's responsibility is visible.

Learning Points

The case demonstrates that authorship is not a reward for rank or access; technical work can be intellectually substantive; publication deadlines do not override accuracy; software licenses and contracts affect dissemination; and transparency is often the most practical way to resolve competing obligations.

Challenges, Limitations, and Professional Responsibilities

Publication systems face structural pressures. Hiring and promotion may reward publication counts, journal prestige, citation metrics, or rapid output, creating incentives for salami publication, honorary authorship, exaggerated claims, and predatory venues. Global differences in law, disciplinary conventions, language, and institutional power can make apparently simple rules difficult to apply. Junior researchers may fear retaliation when challenging a supervisor, while collaborators may disagree about ownership across countries.

No checklist can resolve every case. Authorship criteria require interpretation, and legal rights depend on jurisdiction and contract. Similarity detection, image-forensics software, and AI-based screening produce indicators rather than final judgments. Institutions should therefore provide confidential advice, independent investigation, whistleblower protection, authorship mediation, data-management support, and training that treats integrity as a continuing professional responsibility.

Researchers should also consider fairness beyond formal compliance. They should recognize invisible labor, avoid exploiting students or technical staff, make accessibility and reproducibility reasonable goals, and disclose relationships that could influence interpretation. Responsible scholarship protects not only the reputation of a publication but also the people, communities, and systems affected by its claims.

Current Developments and Emerging Issues

Generative AI is changing writing, coding, translation, image creation, peer review, and literature discovery. AI systems should not be listed as authors because they cannot accept responsibility, approve a final manuscript, or disclose conflicts. Human users remain responsible for accuracy, confidentiality, bias, citations, and intellectual-property compliance. Policies differ by venue, so use of AI assistance should be disclosed when required, and confidential manuscripts or restricted data should not be entered into unapproved systems.

Open science is expanding the use of preprints, open data, open code, registered reports, transparent peer review, and persistent identifiers. These practices can improve reproducibility but also create risks involving privacy, premature dissemination, licensing, and version confusion. Research evaluation is increasingly moving toward responsible assessment that values data, software, mentoring, replication, and societal contribution alongside journal metrics. Researchers must understand both the opportunities and the governance requirements of these developments.

Best Practices for Doctoral Researchers

A doctoral researcher should maintain an authorship and contribution record from the beginning of a project, use version control for code and manuscripts, retain unaltered source files, and document all image and data processing. Every borrowed idea, quotation, dataset, software package, model, and figure should be traceable to its source and permission status. Before submission, the researcher should verify author approval, conflicts, ethical approvals, funder requirements, licenses, confidentiality constraints, and the credibility of the journal.

When uncertain, the safest response is not concealment but consultation. Discuss the issue with a supervisor who is not directly involved, research-integrity personnel, a librarian, an institutional review body, or a technology-transfer specialist. Responsible publication is a collective practice, but each researcher remains accountable for the claims and materials they place in the scholarly record.

Chapter Summary

Authorship combines scholarly credit with responsibility and should be based on meaningful intellectual contribution, participation in manuscript development, final approval, and accountability. Contributorship statements clarify individual roles, while honorary and ghost authorship distort the record. Publication ethics requires honest reporting, appropriate citation, confidential and fair peer review, transparent handling of overlap, and prompt correction of serious errors. Plagiarism, fabrication, falsification, deceptive image manipulation, duplicate publication, and citation manipulation undermine research integrity even when they do not all have identical legal consequences.

Intellectual property management requires distinguishing copyright, licenses, patents, trade secrets, and data-access rights. IT researchers must address software dependencies, algorithmic artifacts, contracts, privacy, and AI-assisted production. A practical integrity workflow begins with contribution and ownership agreements, continues through documented and reproducible research, and extends to peer review, publication, correction, and possible retraction. Ethical judgment, institutional support, and transparent documentation are essential because tools and checklists cannot replace professional responsibility.

Key Terms

Authorship: Scholarly credit accompanied by responsibility for a publication.

Contributorship: A transparent description of the specific roles performed by each project participant.

Corresponding author: The author who manages communication with the journal and coordinates submission-related responsibilities.

Honorary authorship: Listing a person as an author despite the absence of a qualifying contribution.

Ghost authorship: Omitting a person who made a substantial contribution to the work.

Plagiarism: Presenting another person's words, ideas, data, code, or creative work without appropriate attribution.

Self-plagiarism: Reusing substantial previously published material without adequate disclosure.

Fabrication: Inventing data, observations, participants, experiments, or results.

Falsification: Manipulating research materials, procedures, data, or reporting to misrepresent the record.

Image manipulation: Altering a research image or digital object in a way that misleads interpretation or conceals evidence.

Duplicate publication: Publishing substantially the same work more than once without transparent justification.

Citation manipulation: Using citations deceptively to inflate metrics, misrepresent evidence, or exert improper influence.

Peer review: Independent scholarly evaluation of research before or after publication.

Predatory publishing: Deceptive publishing activity that prioritizes fees or output over credible editorial and review services.

Copyright: Legal protection for original expression fixed in a tangible or digital form.

Open license: A license that grants specified permissions for reuse subject to stated conditions.

Patent: A jurisdiction-specific legal right that can exclude others from exploiting a qualifying invention for a limited period.

Trade secret: Confidential information protected through reasonable measures because it has economic value.

Retraction: A formal notice that a publication is unreliable and should not be treated as part of the valid scholarly record.

Expression of concern: A public notice indicating that a publication is under investigation or has unresolved reliability concerns.

Review Questions

Short Answer Questions

1. What two dimensions of responsibility are represented by authorship?

2. How does contributorship improve transparency in collaborative research?

3. Distinguish fabrication from falsification.

4. Why is a similarity score insufficient by itself to establish plagiarism?

5. What is the main purpose of a retraction?

Long Answer Questions

1. Explain a defensible process for assigning authorship and author order in a multidisciplinary IT project.

2. Discuss the ethical responsibilities of authors, reviewers, and editors in maintaining publication integrity.

3. Compare copyright, open licensing, patents, and trade secrets in relation to software and research outputs.

4. Analyze the relationship among duplicate publication, salami publication, citation manipulation, and research evaluation pressures.

5. Describe a complete integrity workflow for a doctoral project involving sensitive data, software, and multiple collaborators.

Analytical / Application Questions

1. A researcher removes several sensor readings because they reduce model accuracy but does not report the removal. Determine whether this is an error, questionable practice, or falsification, and justify your assessment.

2. A journal asks a reviewer to upload a confidential manuscript to an external AI tool for rapid language assessment. What ethical and governance questions should the reviewer ask before complying?

3. A research team wants to release code containing components with incompatible licenses while an industry agreement restricts data redistribution. Develop a publication and dissemination strategy that protects both research integrity and legal obligations.

22. Multiple Choice Questions with Answers

Question 1

Which combination most appropriately supports authorship in a collaborative research project?

A. Holding a senior institutional position and approving the budget

B. Providing routine technical assistance and being acknowledged

C. Making a substantial intellectual contribution, revising the manuscript, approving the final version, and accepting responsibility

D. Providing access to data without participating in interpretation or writing

Correct Answer: C. Making a substantial intellectual contribution, revising the manuscript, approving the final version, and accepting responsibility

Question 2

A researcher discovers that a performance curve in a published figure was accidentally duplicated during figure assembly. What is the most responsible first response?

A. Delete the original article from all repositories

B. Notify the editor, investigate whether conclusions are affected, and seek an appropriate correction

C. Leave the figure unchanged because the error was unintentional

D. Create a new analysis without documenting the original error

Correct Answer: B. Notify the editor, investigate whether conclusions are affected, and seek an appropriate correction

Question 3

Which action is most clearly an example of falsification?

A. Reporting a statistically insignificant result

B. Changing labels and selectively deleting observations to produce a preferred result

C. Using a standard statistical method described in a textbook

D. Repeating an experiment after equipment failure

Correct Answer: B. Changing labels and selectively deleting observations to produce a preferred result

Question 4

Why should a reviewer avoid entering a confidential manuscript into an unapproved AI system?

A. AI systems cannot perform language correction

B. The manuscript may be exposed, retained, or processed in ways that breach confidentiality

C. Peer review must always be conducted anonymously by humans only

D. AI systems are legally classified as authors

Correct Answer: B. The manuscript may be exposed, retained, or processed in ways that breach confidentiality

Question 5

Which statement best distinguishes copyright from a patent?

A. Copyright protects qualifying inventions, while patents protect literary expression

B. Copyright generally protects original expression, while patents may protect qualifying inventions

C. Copyright and patents protect exactly the same subject matter

D. Neither copyright nor patents apply to software-related research

Correct Answer: B. Copyright generally protects original expression, while patents may protect qualifying inventions

Question 6

A paper substantially repeats an earlier publication but does not cite or disclose the earlier work. This is most likely:

A. Transparent secondary publication

B. Duplicate or redundant publication

C. Permissible self-citation

D. A patent filing

Correct Answer: B. Duplicate or redundant publication

Question 7

Which is a warning sign of a potentially predatory journal?

A. Clear peer-review procedures and transparent fees

B. A stated correction and retraction policy

C. Guaranteed acceptance, unverifiable editorial claims, and aggressive unsolicited invitations

D. Archiving through a recognized repository

Correct Answer: C. Guaranteed acceptance, unverifiable editorial claims, and aggressive unsolicited invitations

Question 8

A dataset is publicly accessible online. What follows ethically from that fact?

A. Anyone may redistribute it without restriction

B. The dataset automatically becomes public domain

C. Researchers may use it without checking privacy, licensing, provenance, or contractual restrictions

D. Public access does not eliminate the need to check permissions and use conditions

Correct Answer: D. Public access does not eliminate the need to check permissions and use conditions

Question 9

What is the primary purpose of an expression of concern?

A. To permanently erase an article from the scholarly record

B. To alert readers to unresolved reliability concerns while an investigation proceeds

C. To reward authors who correct minor typographical errors

D. To transfer copyright to the journal

Correct Answer: B. To alert readers to unresolved reliability concerns while an investigation proceeds

Question 10

A software developer designs the data-cleaning pipeline, identifies measurement bias, interprets the results, and substantially revises the manuscript. What is the strongest ethical conclusion?

A. The developer can only be acknowledged because software work never supports authorship

B. The developer likely meets authorship criteria, subject to final approval and responsibility

C. The developer must be the sole author

D. The developer should be omitted to avoid conflicts with the supervisor

Correct Answer: B. The developer likely meets authorship criteria, subject to final approval and responsibility