Research Data Policy | Global Scholar Publications

📊 Research Data Policy

🎯 Purpose of the Research Data Policy

The purpose of this Research Data Policy is to establish consistent standards for the responsible management and reporting of data supporting manuscripts submitted to journals published by Global Scholar Publications. The policy promotes research transparency, reproducibility, accountability, and integrity while recognising that certain datasets may legitimately be restricted because of privacy, ethical, legal, contractual, intellectual property, commercial, security, or third-party access requirements.

This policy helps Global Scholar Publications and its journals:

• strengthen the reliability and credibility of published research;
• promote accurate and transparent reporting of research data;
• support reproducibility and independent verification where appropriate;
• encourage responsible preservation and documentation of research records;
• protect confidential, sensitive, personal, proprietary, and security-related information;
• clarify author responsibilities concerning research data ownership and access;
• promote appropriate data sharing and citation practices;
• address concerns involving unreliable, fabricated, falsified, or manipulated research data;
• support responsible use of computational, software, artificial intelligence, and machine-learning data;
• maintain confidence in the scholarly record across the Global Scholar Publications journal portfolio.

📚 Journal Portfolio Covered by This Policy

This Research Data Policy applies across the journals published by Global Scholar Publications. Authors submitting research to any journal in the portfolio are expected to comply with the applicable requirements for data integrity, documentation, preservation, availability, confidentiality, and responsible data sharing.

Although individual journals may have additional subject-specific requirements, this publisher-level policy establishes the common minimum expectations for responsible research data management throughout the Global Scholar Publications portfolio.

📘 Definition of Research Data

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

Depending on the discipline, research data may include:

• experimental observations and laboratory measurements;
• clinical, medical, biological, pharmaceutical, and health-related datasets;
• engineering test results and technical performance records;
• numerical datasets and statistical outputs;
• survey responses, questionnaires, and interview records;
• field observations and environmental measurements;
• simulation results and computational outputs;
• software source code, algorithms, scripts, and computational models;
• artificial intelligence and machine-learning datasets;
• images, videos, audio files, and sensor recordings;
• technical drawings, models, design files, and experimental documentation;
• instrument logs and calibration records;
• supporting calculations, tables, spreadsheets, and supplementary materials.

⚖️ Principles of Responsible Research Data Management

Research data should be managed according to principles of accuracy, transparency, traceability, security, confidentiality, ethical responsibility, reproducibility, and legal compliance.

The principal expectations of Global Scholar Publications include:

• research data should represent the underlying research honestly and accurately;
• data collection, processing, analysis, and transformation should be appropriately documented;
• original research records should be preserved for a reasonable period;
• changes to datasets should be traceable and scientifically justified;
• confidential and sensitive information should be adequately protected;
• third-party datasets should be used according to applicable permissions and licences;
• supporting data should be available for editorial verification where reasonably necessary;
• legitimate restrictions on data access should be explained transparently;
• data should not be fabricated, falsified, deceptively manipulated, or selectively reported;
• authors remain responsible for the integrity of data supporting their published findings.

👨‍🔬 Author Responsibilities for Research Data

Authors submitting manuscripts to any journal published by Global Scholar Publications are responsible for ensuring that research data supporting their work are accurate, complete, ethically obtained, lawfully used, appropriately documented, and presented without fabrication, falsification, deceptive manipulation, or misleading selective reporting.

Authors should:

• maintain reliable records of data collection and analysis;
• preserve original data or valid source records;
• describe data sources, collection procedures, and processing methods clearly;
• identify relevant exclusions, transformations, preprocessing, and analytical decisions;
• protect confidential and personally identifiable information;
• obtain appropriate permission for third-party or restricted datasets;
• verify consistency between the manuscript, figures, tables, datasets, and supplementary files;
• provide an appropriate Data Availability Statement;
• retain sufficient supporting evidence for reasonable editorial verification;
• cooperate with reasonable requests concerning data integrity and research records.

🧾 Data Management Planning

Authors are encouraged to consider research data management before beginning a study. A suitable data management approach should address how data will be collected, documented, stored, backed up, analysed, preserved, shared, and protected throughout the research lifecycle.

A research data management plan may address:

• types, sources, and formats of data to be collected;
• roles and responsibilities within the research team;
• storage, backup, and recovery procedures;
• file naming and version control;
• confidentiality and access controls;
• ethical, legal, institutional, and regulatory requirements;
• data preservation and repository deposit;
• ownership, licensing, and reuse conditions;
• data retention requirements;
• procedures for dealing with data loss or corruption.

🗂️ Research Data Documentation

Research data should be documented sufficiently to enable the research team, editors, reviewers, and qualified researchers to understand how the data were collected, processed, analysed, and interpreted.

Appropriate documentation may include:

• variable definitions and measurement units;
• data collection dates and locations;
• experimental or observational conditions;
• instrument, equipment, and software information;
• coding and classification systems;
• missing-value explanations;
• data cleaning and preprocessing procedures;
• analysis methods and software scripts;
• dataset versions and changes;
• known limitations or quality concerns.

✅ Accuracy and Completeness of Research Data

Authors should verify that data presented in their manuscripts accurately reflect the underlying records. Figures, tables, graphs, percentages, statistical outputs, datasets, and conclusions should be internally consistent and scientifically supportable.

Authors should not:

• omit relevant observations without appropriate explanation;
• change values merely to support a preferred conclusion;
• duplicate data points or experimental results;
• combine unrelated datasets without disclosure;
• report incomplete results as comprehensive findings;
• misrepresent sample size, controls, participants, or testing conditions;
• alter research records in a way that prevents appropriate verification.

🧪 Experimental, Laboratory and Scientific Data

Experimental research should retain sufficient records to explain the conditions under which data were generated. Depending on the discipline, laboratory notebooks, instrument outputs, calibration records, sample descriptions, experimental protocols, validation results, and related research records may be necessary to support the reported findings.

Authors should document, where applicable:

• experimental procedures and conditions;
• materials, reagents, equipment, and instruments used;
• instrument models and calibration information;
• sample preparation and selection;
• control and comparison conditions;
• repeated tests and excluded results;
• sources of uncertainty and measurement error.

💻 Computational, Software and Simulation Data

Research involving software, algorithms, simulations, computational models, numerical analysis, or digital systems should include sufficient information to explain how the reported results were generated.

Where applicable, authors should preserve:

• source code and analytical scripts;
• software names and versions;
• input data and parameters;
• model assumptions;
• simulation configurations;
• output files;
• validation and sensitivity analyses;
• known software or computational limitations.

🤖 Artificial Intelligence and Machine Learning Data

Research involving artificial intelligence, machine learning, deep learning, computer vision, natural language processing, predictive modelling, or other computational methods should describe datasets and model-development procedures transparently.

Where relevant, manuscripts should explain:

• the source, size, and characteristics of datasets;
• training, validation, and test partitions;
• data labelling and annotation procedures;
• preprocessing and augmentation methods;
• class imbalance and potential dataset bias;
• model selection and evaluation procedures;
• performance metrics;
• ethical, legal, licensing, privacy, and access restrictions.

Artificial intelligence must not be used to fabricate research observations, invent datasets, manipulate results, or create misleading evidence. Authors remain responsible for verifying AI-assisted analyses and outputs and for ensuring that generated outputs are not presented as genuine observations.

📊 Survey, Interview and Observational Data

Research involving surveys, interviews, questionnaires, observations, or participant-generated information should be managed according to applicable ethical approval, informed-consent, privacy, and data-protection requirements.

Authors should document, where applicable:

• participant recruitment and selection;
• consent procedures;
• survey or interview instruments;
• data coding and analysis methods;
• anonymisation or de-identification procedures;
• limitations affecting representativeness or interpretation;
• conditions governing future access and reuse.

🖼️ Images and Visual Research Data

Photographs, microscopy images, screenshots, diagrams, maps, scans, technical drawings, medical images, and other visual research data should accurately represent the underlying research.

Authors should retain original image files and relevant metadata where possible. Image adjustments should not:

• add or remove scientific features;
• hide relevant information;
• duplicate or relocate visual elements deceptively;
• alter contrast selectively to misrepresent findings;
• present generated or simulated content as genuine observations;
• combine images without clear disclosure.

🗄️ Data Storage and Backup

Research data should be stored securely and protected from accidental loss, unauthorised access, corruption, alteration, or destruction.

Appropriate data-protection practices may include:

• regular backups;
• secure institutional or research storage;
• access controls and authentication;
• encryption where appropriate;
• version control;
• separation of identifiable and research data;
• documented disaster-recovery procedures;
• secure disposal after the applicable retention period.

⏳ Research Data Retention

Authors should preserve original research data and supporting records for a reasonable period after publication, taking into account institutional, disciplinary, funder, contractual, ethical, and legal requirements.

The appropriate retention period may depend on:

• the nature of the research;
• institutional policy;
• funding conditions;
• participant consent;
• patent or intellectual property requirements;
• legal or regulatory obligations;
• the likelihood of future verification or responsible reuse.

🔐 Confidential and Sensitive Research Data

Confidential or sensitive research data should not be shared or disclosed in a manner that violates privacy, consent, law, contractual obligations, institutional policy, security requirements, or legitimate commercial interests.

Sensitive data may include:

• personally identifiable information;
• health or medical records;
• private survey or interview responses;
• confidential industrial data;
• proprietary software or technical designs;
• patent-sensitive information;
• security-sensitive infrastructure data;
• government-restricted information;
• data covered by non-disclosure agreements.

👥 Human Participant Data

Authors must ensure that collection, storage, sharing, and reuse of human participant data comply with applicable ethical approval, informed-consent conditions, privacy requirements, and data-protection laws or institutional requirements.

Before sharing participant data, authors should consider:

• whether consent permits data sharing;
• whether direct and indirect identifiers have been removed;
• whether anonymisation is sufficient;
• whether controlled access is required;
• whether legal or institutional restrictions apply;
• whether re-identification remains reasonably possible.

🕵️ Anonymisation and De-identification

Removing names alone may not sufficiently protect participant identity. Individuals may sometimes be identified through combinations of age, location, occupation, institutional affiliation, technical activity, demographic information, or other contextual details.

Authors are responsible for evaluating re-identification risks and applying suitable safeguards before sharing or publishing research data.

🏭 Proprietary and Commercial Research Data

Industry-sponsored or commercially relevant research may involve proprietary datasets, trade secrets, confidential product information, restricted software, unpublished technical specifications, or commercially sensitive research records.

Where data access is restricted, authors should explain, where appropriate:

• the general basis of the restriction;
• who controls the data;
• whether limited access may be granted;
• whether a data-use agreement is required;
• whether anonymised or aggregated data can be shared;
• whether the restriction affects independent verification.

🧠 Intellectual Property and Patent-Related Data

Research data connected to patent applications, licensing, inventions, proprietary methods, commercial development, or intellectual property may require temporary or continuing access restrictions.

Legitimate intellectual property restrictions should not be used to conceal information that is essential for evaluating the scientific or technical reliability of the published work.

📚 Third-Party Research Data

Authors using data created, licensed, or controlled by another person or organisation must comply with the original access, citation, redistribution, privacy, copyright, and licensing conditions.

Authors should:

• identify the original source;
• describe how the data were obtained;
• provide appropriate citation;
• confirm permission for the reported use where required;
• avoid redistributing restricted data without authority;
• explain how qualified researchers may access the original dataset when appropriate.

🧾 Data Availability Statement

Authors should provide a clear Data Availability Statement describing how the data supporting their article may be accessed, where applicable. The statement should accurately reflect the availability, location, conditions, and restrictions associated with the underlying research data.

A Data Availability Statement may indicate that data are:

• included within the article;
• provided as supplementary material;
• deposited in a public or institutional repository;
• available from the corresponding author upon reasonable request;
• available through controlled access;
• restricted because of privacy, legal, contractual, commercial, intellectual property, or security requirements;
• not applicable because no new data were created or analysed.

🌐 Public Research Data Repositories

Where responsible and appropriate, authors are encouraged to deposit research data in a trusted institutional, disciplinary, national, or general-purpose repository.

A repository record should preferably include:

• a clear dataset title;
• author and contributor information;
• a description of the dataset;
• version details;
• metadata and documentation;
• access conditions;
• licensing information;
• a persistent identifier or stable link;
• a reference to the associated published article.

📂 Data Included in Supplementary Materials

Supporting research data may be provided through tables, appendices, spreadsheets, source files, software archives, images, datasets, or other supplementary materials.

Supplementary files should:

• be clearly labelled;
• correspond with the published manuscript;
• exclude confidential or inappropriate personal information;
• include sufficient documentation for interpretation;
• use appropriate and accessible file formats where possible;
• comply with copyright, licensing, privacy, and intellectual property requirements.

🔑 Controlled Access to Research Data

When public access is not appropriate, research data may be made available through a controlled-access process where feasible and lawful.

Controlled access may require:

• a formal written request;
• ethical or institutional approval;
• verification of the requester’s identity or affiliation;
• a data-use agreement;
• secure access conditions;
• limitations on redistribution or commercial use;
• agreement to protect participant confidentiality.

📨 Research Data Requests During Peer Review

Journals published by Global Scholar Publications may request supporting data during editorial screening or peer review when reasonably necessary to evaluate the accuracy, validity, originality, reproducibility, or reliability of a manuscript.

A request may arise when:

• reported values appear inconsistent;
• figures or tables do not correspond with the manuscript;
• statistical or technical methods require clarification;
• image manipulation is suspected;
• the conclusions cannot reasonably be assessed from the submitted information;
• duplicate or fabricated data may be involved;
• reviewers require limited supporting evidence to evaluate the research.

Requests for research data should remain proportionate to the editorial or scientific concern and should respect legitimate confidentiality, privacy, intellectual property, and legal restrictions.

📢 Research Data Requests After Publication

Editors, readers, institutions, funders, or qualified researchers may request supporting data after publication. Authors should respond according to the published Data Availability Statement and any legitimate restrictions governing access.

A request may be declined when:

• participant privacy could be compromised;
• the request conflicts with consent or ethical approval;
• data are controlled by a third party;
• sharing would violate a contract or legal obligation;
• security-sensitive or proprietary information is involved;
• the request is abusive, excessively broad, or unrelated to legitimate scholarly verification.

🔍 Editorial Verification of Research Data

When legitimate concerns arise about the reliability or integrity of research data, a journal or its editorial representatives may request appropriate supporting records.

Depending on the nature of the concern, requested evidence may include:

• raw or original data;
• laboratory notebooks;
• instrument outputs;
• analysis scripts;
• original images and metadata;
• statistical calculations;
• software and simulation files;
• ethical approvals and participant-consent records;
• repository records;
• appropriate institutional verification.

Editorial verification requests should be proportionate to the concern and should take legitimate confidentiality, privacy, intellectual property, contractual, and legal restrictions into account.

🚫 Data Fabrication

Data fabrication involves creating observations, measurements, participants, experiments, calculations, datasets, or results that did not exist.

Examples may include:

• inventing experimental results;
• creating false participant responses;
• reporting simulations that were never performed;
• manufacturing instrument readings;
• generating false research records using artificial intelligence or other tools;
• presenting hypothetical or invented data as genuine observations.

⚠️ Data Falsification

Data falsification involves changing, omitting, manipulating, or selectively presenting research information in a manner that misrepresents the underlying research.

Examples may include:

• altering measurements without scientific justification;
• removing inconvenient observations solely to change the conclusion;
• misrepresenting sample size;
• changing statistical outputs;
• duplicating observations or images;
• concealing relevant failed experiments;
• modifying graphs, scales, or visualisations deceptively.

📉 Selective Reporting and Data Suppression

Authors should not report only favourable outcomes while concealing relevant negative, contradictory, null, or failed results without appropriate scientific explanation.

Where exclusions are scientifically justified, the manuscript should explain:

• which data were excluded;
• why exclusion was necessary;
• whether the decision was made before or after analysis;
• how exclusion affected the results;
• whether sensitivity or additional analyses were performed.

🤖 Artificial Intelligence and Research Data Integrity

Artificial intelligence may assist with data processing, classification, modelling, prediction, visualisation, coding, or other legitimate research activities. However, AI tools must not be used to fabricate, falsify, manipulate, or misrepresent research data or supporting evidence.

Authors remain responsible for:

• verifying AI-assisted analyses and outputs;
• documenting relevant models, methods, and parameters;
• identifying limitations, uncertainty, and potential bias;
• preserving original research data and appropriate source records;
• ensuring generated outputs are not presented as authentic observations;
• disclosing significant AI involvement where appropriate under the applicable journal policies.

⚠️ Failure to Provide Supporting Data

Failure to provide requested data does not automatically establish research misconduct. Legitimate data loss, privacy restrictions, third-party ownership, intellectual property protection, contractual obligations, or legal limitations may prevent disclosure.

However, unexplained refusal, contradictory explanations, missing central records, or an inability to reasonably support important findings may affect editorial confidence in the reliability of the research.

Depending on the circumstances, possible actions may include:

• requesting clarification or supporting documentation;
• requiring additional disclosure or revision;
• suspending editorial processing;
• rejecting the manuscript;
• issuing a correction or expression of concern;
• contacting the authors’ institution or other relevant authority;
• retracting a published article when its principal findings cannot be considered reliable.

✏️ Corrections Related to Research Data

A correction may be appropriate when a limited error affects data, calculations, figures, tables, repository links, supplementary files, or Data Availability Statements while the principal findings remain reliable.

A correction may:

• replace incorrect data values;
• update a figure or table;
• correct statistical calculations;
• add an omitted dataset citation;
• replace an incorrect repository link;
• clarify legitimate access restrictions;
• update supplementary research files.

⚠️ Expressions of Concern

An Expression of Concern may be issued when serious questions about research data or the reliability of a published article remain unresolved and further investigation is necessary.

This may occur when:

• original data are unavailable without adequate explanation;
• authors provide materially inconsistent explanations;
• an institutional investigation is ongoing;
• data reliability cannot yet be determined;
• readers need to be alerted while a concern is being investigated.

⛔ Retraction for Unreliable Research Data

Retraction may be required when fabricated, falsified, manipulated, missing, or fundamentally unreliable research data invalidate the principal findings or conclusions of a published article.

Retraction is generally unnecessary when a limited and correctable data error can be addressed transparently and the central findings remain reliable. Decisions concerning corrections, expressions of concern, or retractions will depend on the evidence available and the seriousness of the issue.

📣 Complaints and Appeals

Authors, readers, reviewers, institutions, funders, or other affected parties may raise concerns or appeal editorial decisions involving research data. Complaints and appeals should provide relevant evidence and clearly explain the basis of the concern.

An appeal may address whether:

• legitimate data restrictions were misunderstood;
• important documentation was overlooked;
• an editorial request was disproportionate;
• a procedural error affected the decision;
• available evidence supports a different corrective action.

🛠️ Enforcement of the Research Data Policy

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

Possible actions include:

• requesting clarification or supporting records;
• requiring a Data Availability Statement;
• requesting appropriate data deposit or documentation;
• requiring manuscript revision or additional disclosure;
• rejecting or withdrawing a manuscript;
• issuing a correction or Expression of Concern;
• retracting an unreliable published article;
• contacting an institution, funder, ethics committee, repository, or appropriate authority;
• restricting future submissions in serious or repeated cases of research misconduct.

🌟 Why the Research Data Policy Matters

A clear Research Data Policy helps authors understand how to collect, document, store, analyse, preserve, report, and share the evidence supporting their scholarly conclusions. It also helps editors, reviewers, institutions, readers, and other researchers assess whether published findings are transparent, reproducible, and reliable.

The journals published by Global Scholar Publications cover a broad range of scientific, technological, multidisciplinary, biological, pharmaceutical, chemical, life-science, engineering, medical, and interdisciplinary research. Responsible research data management is therefore important across different research methods, including laboratory experiments, clinical and health research, surveys, field studies, engineering investigations, computational research, simulations, software studies, artificial intelligence, and data-driven research.

This Research Data Policy helps Global Scholar Publications:

• promote accurate and transparent data reporting;
• support reproducibility and appropriate research verification;
• protect confidential, sensitive, and proprietary information;
• encourage responsible storage and preservation of research records;
• clarify data availability and legitimate access restrictions;
• discourage fabrication, falsification, manipulation, and selective reporting;
• support appropriate corrections and retractions;
• strengthen confidence in the published scholarly record.

🔗 Related Global Scholar Publications Policies

Authors, reviewers, editors, readers, institutions, and other research stakeholders are encouraged to review the relevant editorial and publishing policies of Global Scholar Publications for a complete understanding of research integrity, data sharing, artificial intelligence, publication ethics, peer review, authorship, plagiarism, corrections, retractions, and scholarly accountability.

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