Data Sharing Policy | Global Scholar Publications

📊 Data Sharing Policy

🎯 Purpose of the Data Sharing Policy

The purpose of this Data Sharing Policy is to establish consistent expectations for research data supporting manuscripts submitted to journals published by Global Scholar Publications. The policy promotes research transparency while recognizing legitimate circumstances in which data must remain restricted or controlled.

This policy helps Global Scholar Publications and its journals:

• strengthen confidence in published research findings;
• support research verification and reproducibility;
• encourage responsible preservation of research records;
• promote accurate citation and recognition of research datasets;
• protect confidential, sensitive, personal, restricted, or proprietary information;
• clarify author responsibilities concerning research data access;
• encourage responsible and lawful reuse of research data;
• support ethical and transparent scholarly communication across the journal portfolio.

📚 Journals Covered by This Data Sharing Policy

This publisher-level Data Sharing Policy applies to the journals published by Global Scholar Publications. Individual journals may establish additional data-sharing requirements where appropriate to their subject area, article type, research methodology, ethical requirements, or disciplinary standards.

Authors should review the specific requirements of the relevant journal in addition to this publisher-level policy before submitting a manuscript.

📘 Definition of Research Data

Research data include information collected, generated, observed, measured, processed, analysed, simulated, or compiled during a research project and used to support the findings, interpretations, or conclusions presented in a manuscript.

Depending on the subject and methodology of the research, research data may include:

• experimental measurements and laboratory results;
• clinical, medical, or biological research records where ethically shareable;
• engineering test records and performance measurements;
• survey responses and interview records;
• numerical datasets and statistical outputs;
• simulation files and computational results;
• software source code, scripts, algorithms, and computational workflows;
• images, videos, audio files, and sensor recordings;
• technical drawings, design files, models, and specifications;
• machine learning datasets and model outputs;
• field observations and environmental measurements;
• supplementary tables, calculations, and validation records.

⚖️ Principles of Responsible Data Sharing

Research data sharing should be guided by transparency, accuracy, ethical responsibility, respect for participant rights, legal compliance, research integrity, and protection of legitimate confidentiality or ownership interests.

The principal expectations are:

• data should accurately represent the research performed;
• supporting records should be preserved for a reasonable period;
• data should be shared when ethically, legally, and practically possible;
• restrictions on data access should be explained transparently;
• sensitive or confidential information should be appropriately protected;
• datasets obtained from third parties should be used according to applicable permissions and licences;
• data contributors, original sources, and repositories should be acknowledged appropriately;
• shared data should be sufficiently documented to support responsible interpretation and reuse.

👨‍🔬 Author Responsibilities for Research Data

Authors submitting manuscripts to journals published by Global Scholar Publications are responsible for ensuring that research data supporting their work are accurate, appropriately documented, ethically obtained, and consistent with the findings reported in the manuscript.

Authors should:

• maintain reliable records of data collection and analysis;
• retain original or appropriately processed research data;
• describe data sources and analytical procedures clearly;
• identify relevant exclusions, transformations, or preprocessing procedures;
• protect confidential and personally identifiable information;
• obtain appropriate permissions for third-party datasets;
• preserve supporting materials for a reasonable period;
• respond reasonably to editorial requests for supporting information;
• provide an appropriate Data Availability Statement where required;
• ensure that shared datasets are accurately described and appropriately cited.

🧾 Data Availability Statement

Authors should provide a clear Data Availability Statement describing whether the data supporting the research are publicly available, included within the article or supplementary materials, deposited in a repository, available upon reasonable request, or subject to restrictions.

A Data Availability Statement should be accurate and sufficiently specific to help readers understand whether and how supporting research data can be accessed.

📂 Example: Publicly Available Data

The datasets supporting the findings of this study are available in the identified public repository at the location provided in the manuscript.

📄 Example: Data Included with the Article

The data supporting the findings of this study are included within the article and its supplementary materials.

📨 Example: Data Available on Reasonable Request

The data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to applicable ethical, legal, contractual, or confidentiality requirements.

🔐 Example: Restricted Data

The supporting data are not publicly available because they contain confidential, proprietary, security-sensitive, or personally identifiable information. Access may be considered subject to appropriate permissions and safeguards.

🌐 Public Data Repositories

Where appropriate, authors are encouraged to deposit research data in a trusted subject-specific, institutional, national, or general-purpose repository. Repository selection should consider data preservation, accessibility, persistent identification, licensing, confidentiality, and relevant disciplinary standards.

Repository records should preferably include:

• a clear dataset title;
• author or contributor information;
• a description of the dataset;
• version information where applicable;
• access conditions;
• licensing information;
• a persistent identifier such as a DOI or stable repository link.

📂 Data Included in Manuscripts and Supplementary Files

Authors may provide supporting research data directly within the manuscript, tables, figures, appendices, or supplementary files. Supplementary materials should be organised clearly and should contain sufficient information for readers to understand their relationship to the published article.

Supplementary materials may include:

• extended datasets;
• additional tables and figures;
• technical calculations;
• questionnaires and research instruments;
• software documentation;
• source code or scripts;
• simulation parameters;
• validation and sensitivity analyses;
• additional methodological information.

💻 Sharing Software, Code and Computational Workflows

Many research studies published across the Global Scholar Publications journal portfolio depend on software, source code, algorithms, scripts, computational models, machine learning pipelines, statistical tools, or simulation environments. Authors are encouraged to share relevant computational materials when possible and when doing so does not violate security, licensing, intellectual property, contractual, confidentiality, or commercial obligations.

Shared computational materials should be documented sufficiently to explain:

• the purpose of the software or code;
• the programming language and software version;
• required libraries or dependencies;
• input data formats;
• key parameters and assumptions;
• instructions for execution or reproduction;
• licensing and reuse conditions.

🤖 Data Used in Artificial Intelligence and Machine Learning Research

Authors reporting artificial intelligence, machine learning, deep learning, computer vision, data science, or related computational research should describe datasets used for training, validation, and testing as transparently as possible.

Where applicable, authors should report:

• the source and size of the dataset;
• data collection or acquisition methods;
• inclusion and exclusion criteria;
• preprocessing and labelling procedures;
• training, validation, and test partitions;
• known limitations, imbalances, or biases;
• ethical, legal, or licensing restrictions;
• whether the dataset is publicly accessible.

🔐 Confidential and Sensitive Data

Authors must protect confidential or sensitive information and should not share research data publicly when disclosure could violate participant privacy, legal obligations, contractual agreements, security requirements, intellectual property rights, or legitimate institutional interests.

Sensitive data may include:

• personally identifiable information;
• medical or health-related records;
• confidential industrial data;
• private institutional records;
• unreleased product specifications;
• proprietary software or technical designs;
• security-sensitive infrastructure information;
• government-restricted or defence-related information;
• data covered by non-disclosure agreements.

👥 Human Participant Data

Research involving human participants must comply with applicable ethical approval, informed consent, privacy, and data-protection requirements. Data sharing should be consistent with the permissions granted by participants and the conditions approved by the relevant ethics committee or institutional authority.

Before sharing participant data, authors should consider:

• whether informed consent permits data sharing;
• whether direct and indirect identifiers have been removed;
• whether anonymisation is sufficient;
• whether restricted or controlled access is more appropriate;
• whether applicable laws or institutional policies limit disclosure.

🕵️ Anonymization and De-identification

Authors should remove or modify identifying information before sharing data when appropriate. Removal of names alone may not be sufficient because individuals may sometimes be identified through combinations of demographic, geographic, institutional, technical, or contextual information.

Authors are responsible for evaluating re-identification risks and applying suitable safeguards before releasing data publicly or providing data to third parties.

🏭 Proprietary, Commercial and Industry Data

Research conducted with companies, industrial laboratories, manufacturers, software developers, commercial sponsors, or other organizations may involve proprietary information that cannot be shared openly.

Where research data access is restricted, authors should explain:

• the general reason for the restriction;
• whether limited access may be granted;
• who controls access to the data;
• whether an application, agreement, or permission is required;
• whether aggregated or anonymized data can be provided.

🧠 Intellectual Property and Patent Considerations

Data associated with patent applications, licensing discussions, proprietary inventions, or commercial development may require temporary or continuing restrictions. Authors should ensure that publication and data sharing comply with institutional intellectual property policies, contractual obligations, and applicable legal requirements.

Intellectual property or patent-related restrictions should not be used to conceal information essential to evaluating the reliability or validity of the published findings.

📜 Legal and Contractual Restrictions

Authors must comply with applicable laws, regulations, research agreements, data-use agreements, funding conditions, confidentiality clauses, institutional requirements, and licenses. Where legal or contractual restrictions prevent public sharing, the Data Availability Statement should explain the limitation without revealing protected information.

🔑 Controlled and Restricted Access

When open access is not appropriate, research data may be made available through a controlled-access process. Access conditions should be reasonable, transparent, proportionate to the sensitivity of the data, and consistent with applicable ethical and legal requirements.

Controlled access may involve:

• submission of a formal request;
• approval by an ethics committee or data-access committee;
• signing a data-use agreement;
• verification of institutional affiliation;
• use of a secure research environment;
• limitations on redistribution or commercial use.

📚 Third-Party Data

Authors using datasets created, owned, licensed, or controlled by third parties must comply with the original access conditions, licenses, terms of use, and citation requirements.

Authors should not redistribute third-party data without appropriate permission. Where the dataset cannot be included with the article, the manuscript should explain how qualified readers may obtain it from the original source.

©️ Data Ownership, Licensing and Reuse

Authors should clarify ownership and licensing conditions for shared research data. A suitable license can help readers understand whether they may copy, analyze, modify, redistribute, or build upon a dataset.

Authors must ensure that they have appropriate authority to apply a license and that the selected terms do not conflict with institutional, funder, participant, contractual, or third-party obligations.

🗄️ Data Preservation and Retention

Authors should preserve research data and related records for a reasonable period after publication in accordance with applicable institutional, disciplinary, funder, contractual, legal, and ethical requirements.

Records that may need to be retained include:

• raw or original data;
• processed datasets;
• laboratory notebooks;
• instrument outputs;
• analysis scripts;
• software versions;
• consent and ethical approval documentation;
• data dictionaries and metadata;
• image source files;
• correspondence relating to data access or restrictions.

📋 Data Documentation and Metadata

Shared research data should be accompanied by sufficient documentation to make the information understandable and usable. Appropriate metadata improve discoverability, interpretation, reproducibility, and long-term preservation.

Documentation may include:

• variable names and definitions;
• measurement units;
• data collection dates;
• file formats;
• coding conventions;
• missing-value explanations;
• processing and cleaning procedures;
• software requirements;
• limitations and known errors.

📨 Data Requests from Editors and Reviewers

During editorial screening or peer review, a journal published by Global Scholar Publications may request supporting data when necessary to evaluate the accuracy, completeness, originality, methodology, or reliability of a manuscript.

Such requests may arise when:

• reported values appear inconsistent;
• figures and tables do not correspond with the manuscript text;
• statistical or technical concerns require clarification;
• image manipulation is suspected;
• reported findings cannot be adequately understood from the submitted materials;
• research integrity or misconduct concerns have been raised.

Authors should respond reasonably to legitimate editorial requests and provide relevant information while maintaining legitimate confidentiality, privacy, legal, ethical, or contractual protections.

📢 Data Requests After Publication

Readers, researchers, reviewers, institutions, or other qualified parties may contact authors to request supporting data after publication. Authors should consider reasonable requests in good faith and respond according to the Data Availability Statement and applicable restrictions.

A request may be declined or limited when:

• participant privacy could be compromised;
• the requester cannot meet applicable ethical or security requirements;
• the data are controlled by a third party;
• sharing would breach a contract or legal obligation;
• the request is excessively broad, unclear, abusive, or unrelated to legitimate scholarly purposes;
• the requested data no longer exist despite reasonable preservation efforts.

🔍 Data Verification and Research Integrity

Availability of supporting data may be important when questions arise about fabrication, falsification, selective reporting, image manipulation, computational errors, or inconsistencies between published findings and original research records.

Where appropriate, Global Scholar Publications or the relevant journal may request:

• raw data;
• original image files;
• analysis scripts;
• laboratory records;
• software output files;
• statistical calculations;
• ethical approvals or consent documentation;
• institutional or source verification.

🚫 Fabrication, Falsification and Selective Data Reporting

Authors must not invent, alter, suppress, manipulate, or selectively present research data in ways that misrepresent the research or its findings. Such practices may constitute serious research misconduct.

Unacceptable practices include:

• creating data that were never collected;
• changing observations without scientific justification;
• deleting inconvenient results solely to support a preferred conclusion;
• duplicating data points or images;
• misrepresenting sample size or experimental conditions;
• altering statistical analyses to produce misleading conclusions;
• presenting simulated or fabricated results as genuine observations.

🖼️ Original Images and Visual Data

When concerns arise regarding figures, photographs, graphs, technical drawings, microscopy images, screenshots, charts, or other visual outputs, authors may be asked to provide original files, source data, or relevant metadata.

Image processing or adjustments should not alter the scientific meaning of the image, conceal relevant information, or create a misleading representation of the underlying research.

⚠️ Failure to Provide Supporting Data

Failure to provide requested supporting data does not automatically establish research misconduct because legitimate restrictions, confidentiality requirements, third-party ownership, or loss of historical records may exist. However, unexplained refusal, contradictory explanations, or inability to provide evidence supporting central findings may affect editorial confidence in a manuscript or published article.

Depending on the circumstances, possible editorial actions may include:

• requesting clarification;
• requiring additional disclosure or documentation;
• suspending manuscript processing;
• rejecting the manuscript;
• issuing a correction or expression of concern;
• contacting an institution, funder, ethics committee, or other responsible body;
• retracting an article when the published findings cannot be considered reliable.

✏️ Corrections to Data and Availability Statements

When errors are identified in published data, repository links, access conditions, dataset descriptions, or Data Availability Statements, the relevant journal may issue an appropriate correction in accordance with its post-publication policies.

A correction may:

• replace an incorrect repository link;
• clarify access restrictions;
• add omitted dataset information;
• correct values in tables or supplementary files;
• update licensing or ownership information;
• explain changes to supporting data.

⚠️ Expressions of Concern and Retractions

An expression of concern may be issued when serious data-related questions remain unresolved. Retraction may be considered when fabricated, falsified, unavailable, or materially manipulated data make the published findings unreliable.

Retraction is not normally required when limited data errors can be corrected without materially affecting the principal conclusions of the article.

📢 Complaints and Appeals

Authors, readers, reviewers, institutions, or other affected parties may raise concerns regarding data availability, access restrictions, data integrity, or editorial handling. Complaints should identify the relevant article or manuscript and explain the issue clearly.

Appeals may be considered when an affected party believes that:

• a data restriction was misunderstood;
• important legal, ethical, or contractual evidence was overlooked;
• an editorial request was unreasonable or disproportionate;
• a procedural error affected the decision;
• available supporting evidence was not adequately considered.

🛠️ Enforcement of the Data Sharing Policy

The editorial response to a data-related concern will depend on the seriousness of the issue, the available evidence, the stage of publication, the nature of any applicable restrictions, the potential effect on research reliability, and the authors’ cooperation.

Possible actions may include:

• requesting a Data Availability Statement;
• requiring additional research-data documentation;
• requesting access to supporting data;
• requiring manuscript revision;
• rejecting or withdrawing a manuscript;
• issuing a correction or expression of concern;
• retracting an unreliable article;
• contacting an institution, funder, ethics committee, repository, or other responsible organisation.

🌟 Why Data Sharing Matters in Scholarly Publishing

A clear Data Sharing Policy strengthens confidence in scholarly research by helping readers understand whether and how supporting evidence can be accessed. Responsible data sharing promotes verification, reproducibility, collaboration, efficient reuse of research resources, and long-term preservation of valuable scientific, technical, medical, and interdisciplinary information.

For Global Scholar Publications, responsible data practices are particularly important because its journal portfolio covers diverse research areas, including science and technology, multidisciplinary studies, biology, pharmacy, chemistry, life sciences, engineering, technology, medicine, and dentistry.

This Data Sharing Policy helps the publisher and its journals:

• promote transparent and reproducible research;
• support validation of published findings;
• encourage responsible preservation of research records;
• clarify circumstances in which data may be restricted;
• protect participant privacy and confidential information;
• support appropriate dataset citation and reuse;
• strengthen trust in scholarly publications.

🔗 Related Global Scholar Publications Policies

Authors, reviewers, editors, and readers are encouraged to review the relevant editorial and publishing policies of Global Scholar Publications for a complete understanding of requirements relating to research transparency, publication ethics, authorship, originality, peer review, artificial intelligence, copyright, licensing, corrections, retractions, and post-publication accountability.

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