Research Data Policy | GJETA

📊 Research Data Policy | Global Journal of Engineering and Technology Advances (GJETA)

🎯 Purpose of the Research Data Policy

The purpose of this Research Data Policy is to establish clear standards for the responsible management and reporting of data supporting manuscripts submitted to GJETA. The policy promotes transparency and research integrity while recognising that some data cannot be made publicly available because of privacy, ethical, legal, contractual, intellectual property, commercial, or security restrictions.

The policy helps GJETA:

• strengthen the reliability of published findings;
• promote accurate and transparent data reporting;
• support reproducibility and independent verification;
• encourage responsible preservation of research records;
• protect confidential, sensitive, and proprietary information;
• clarify author responsibilities for data ownership and access;
• address concerns involving unreliable or manipulated data;
• preserve confidence in engineering and technology research.

📘 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, or conclusions presented in a manuscript.

Research data may include:

• experimental observations and laboratory measurements;
• engineering test results and technical performance records;
• numerical datasets and statistical outputs;
• survey responses and interview records;
• field observations and environmental measurements;
• simulation results and computational outputs;
• software source code, algorithms, and scripts;
• machine learning training, validation, and test datasets;
• images, videos, audio files, and sensor recordings;
• technical drawings, models, and design files;
• instrument logs and calibration records;
• supporting calculations, tables, 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, and legal compliance.

The principal expectations include:

• data should represent the research honestly and accurately;
• data collection and processing methods should be documented;
• original records should be preserved for a reasonable period;
• changes to datasets should be traceable and scientifically justified;
• confidential and sensitive information should be protected;
• third-party data should be used according to applicable licences and permissions;
• supporting data should be available for editorial verification where appropriate;
• restrictions on access should be explained transparently.

👨‍🔬 Author Responsibilities for Research Data

Authors submitting manuscripts to the Global Journal of Engineering and Technology Advances (GJETA) are responsible for ensuring that the data supporting their work are accurate, complete, ethically obtained, lawfully used, and presented without fabrication, falsification, selective reporting, or misleading manipulation.

Authors should:

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

🧾 Data Management Planning

Authors are encouraged to plan how research data will be collected, stored, documented, analysed, preserved, shared, and protected before beginning the study.

A research data management plan may address:

• the types and formats of data to be collected;
• roles and responsibilities within the research team;
• storage and backup procedures;
• file naming and version control;
• confidentiality and access controls;
• ethical and legal requirements;
• data preservation and repository deposit;
• ownership, licensing, and reuse conditions.

🗂️ Research Data Documentation

Research data should be documented sufficiently to allow authors, editors, reviewers, and qualified researchers to understand how the information was collected, processed, and analysed.

Useful documentation may include:

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

✅ Accuracy and Completeness of Data

Authors should verify that the data presented in the manuscript accurately reflect the original records and that figures, tables, graphs, percentages, statistical outputs, and conclusions are internally consistent.

Authors should not:

• omit relevant observations without explanation;
• change values 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, or testing conditions.

🧪 Experimental and Laboratory Data

Experimental research should retain sufficient records to explain the conditions under which data were generated. Laboratory notebooks, instrument outputs, calibration records, sample descriptions, protocols, and validation results may be important for verifying the reported findings.

Authors should document:

• experimental procedures and conditions;
• materials and equipment 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, or numerical analysis should include enough information to explain how results were generated.

Where applicable, authors should preserve:

• source code and 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

Authors reporting artificial intelligence, machine learning, deep learning, computer vision, natural language processing, or predictive modelling research should describe the datasets and model-development process transparently.

The manuscript should explain, where applicable:

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

📊 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:

• 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, and other visual data should represent the original research accurately.

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

⏳ Research Data Retention

Authors should preserve original research data and supporting records for a reasonable period after publication according to 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 reuse.

🔐 Confidential and Sensitive Research Data

Confidential or sensitive 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 ethical approval and informed-consent conditions.

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 protect participant identity. Individuals may sometimes be identified through combinations of age, location, occupation, institutional affiliation, technical activity, or other contextual information.

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

🏭 Proprietary and Commercial Research Data

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

Where data access is restricted, authors should explain:

• 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, or commercial development may require temporary or continuing access restrictions.

Such restrictions should not be used to conceal information 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, and licensing conditions.

Authors should:

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

🧾 Data Availability Statement

Authors should include a clear Data Availability Statement describing how the data supporting the article may be accessed.

The statement may indicate that data are:

• included within the article;
• provided as supplementary material;
• deposited in a public repository;
• available from the corresponding author upon reasonable request;
• available through controlled access;
• restricted because of privacy, legal, contractual, commercial, 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 GJETA article.

📂 Data Included in Supplementary Materials

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

Supplementary files should:

• be clearly labelled;
• correspond with the published manuscript;
• exclude confidential information;
• include enough documentation for interpretation;
• use appropriate file formats;
• comply with copyright and licensing requirements.

🔑 Controlled Access to Research Data

When public access is not appropriate, data may be made available through a controlled process.

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

GJETA may request supporting data during editorial screening or peer review when necessary to evaluate the accuracy, validity, originality, or reliability of a manuscript.

A request may arise when:

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

📢 Research Data Requests After Publication

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

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 scholarly verification.

🔍 Editorial Verification of Research Data

When concerns arise, the journal may request:

• 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;
• institutional verification.

Requests should remain proportionate to the concern and respect legitimate confidentiality or legal restrictions.

🚫 Data Fabrication

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

Examples 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 data as genuine observations.

⚠️ Data Falsification

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

Examples include:

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

📉 Selective Reporting and Data Suppression

Authors should not report only favourable outcomes while hiding relevant negative, contradictory, null, or failed results without 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 analyses were performed.

🤖 Artificial Intelligence and Research Data Integrity

Artificial intelligence may assist with data processing, classification, modelling, prediction, or visualisation, but it must not be used to fabricate, manipulate, or misrepresent research data.

Authors remain responsible for:

• verifying AI-assisted analyses;
• documenting relevant models and parameters;
• identifying limitations and bias;
• preserving the original input data;
• ensuring that generated outputs are not presented as authentic observations;
• disclosing significant AI involvement where appropriate.

⚠️ Failure to Provide Supporting Data

Failure to provide requested data does not automatically prove misconduct because legitimate loss, privacy restrictions, third-party control, or legal limitations may exist.

However, unexplained refusal, contradictory responses, missing central records, or inability to support key findings may affect editorial confidence.

Possible actions include:

• requesting clarification;
• requiring revision or additional disclosure;
• suspending editorial processing;
• rejecting the manuscript;
• issuing a correction or expression of concern;
• contacting the authors’ institution;
• retracting an article when the 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, or availability statements but the main 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 access restrictions;
• update supplementary files.

⚠️ Expressions of Concern

An expression of concern may be issued when serious questions about research data remain unresolved and further investigation is necessary.

This may occur when:

• original data are unavailable;
• authors provide inconsistent explanations;
• an institutional investigation is ongoing;
• data reliability cannot yet be determined;
• readers should be alerted while the matter is reviewed.

⛔ Retraction for Unreliable Research Data

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

Retraction is generally unnecessary when limited data errors can be corrected transparently without changing the central conclusions.

📣 Complaints and Appeals

Authors, readers, reviewers, institutions, or other affected parties may raise concerns or appeal decisions involving research data.

An appeal should explain whether:

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

🛠️ Enforcement of the Research Data Policy

The editorial response will depend on the seriousness of the concern, the available evidence, the stage of publication, the nature of applicable restrictions, and the cooperation of the authors.

Possible actions include:

• requesting clarification or supporting records;
• requiring a Data Availability Statement;
• requesting data deposit or documentation;
• 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 authority;
• restricting future submissions in serious or repeated cases of 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 reviewers, editors, institutions, and readers assess whether published findings are transparent, reproducible, and reliable.

For the Global Journal of Engineering and Technology Advances (GJETA), responsible research data management is particularly important because engineering and technology studies may involve complex experiments, software, simulations, algorithms, artificial intelligence models, technical designs, sensor records, industrial systems, and large computational datasets.

The Research Data Policy helps GJETA:

• promote accurate and transparent data reporting;
• support reproducibility and research verification;
• protect confidential and sensitive information;
• encourage responsible storage and preservation;
• clarify data availability and access restrictions;
• prevent fabrication, falsification, and selective reporting;
• support corrections and retractions where necessary;
• strengthen confidence in published engineering and technology research.

🔗 Related GJETA Editorial and Publishing Policies

Authors, reviewers, editors, readers, institutions, and repository managers are encouraged to review the following policies of the Global Journal of Engineering and Technology Advances for a complete understanding of research data, data sharing, artificial intelligence, publication ethics, corrections, retractions, confidentiality, and scholarly accountability.

🚀 Publish Data-Driven Research with GJETA

❓ Frequently Asked Questions About the Research Data Policy

1. What is the Research Data Policy of GJETA?

The Research Data Policy of the Global Journal of Engineering and Technology Advances (GJETA) explains how authors should collect, document, store, analyse, preserve, report, protect, and provide appropriate access to data supporting published research.

2. What types of information are covered by the Research Data Policy?

The Research Data Policy of the Global Journal of Engineering and Technology Advances (GJETA) covers experimental measurements, numerical datasets, surveys, software, source code, simulations, images, technical designs, machine learning datasets, sensor records, and supporting calculations.

3. Does the Research Data Policy require authors to preserve original data?

Yes. The Research Data Policy requires authors publishing with the Global Journal of Engineering and Technology Advances (GJETA) to preserve sufficient original data and research records for a reasonable period according to institutional, ethical, legal, funder, and disciplinary requirements.

4. Does the Research Data Policy require all data to be publicly shared?

No. The Research Data Policy of the Global Journal of Engineering and Technology Advances (GJETA) recognises that privacy, consent, law, contracts, intellectual property, commercial confidentiality, and security concerns may justify restricted or controlled access.

5. What is a Data Availability Statement under the Research Data Policy?

Under the Research Data Policy, authors submitting to the Global Journal of Engineering and Technology Advances (GJETA) should state whether supporting data are included with the article, deposited in a repository, available on request, subject to controlled access, restricted, or not applicable.

6. Can GJETA request supporting records under the Research Data Policy?

Yes. The Research Data Policy allows the Global Journal of Engineering and Technology Advances (GJETA) to request original data, images, calculations, code, laboratory records, ethical approvals, or other supporting information when necessary to evaluate or verify published findings.

7. How does the Research Data Policy address fabrication and falsification?

The Research Data Policy of the Global Journal of Engineering and Technology Advances (GJETA) prohibits inventing, altering, suppressing, duplicating, selectively reporting, or manipulating data in ways that misrepresent the research or support misleading conclusions.

8. What happens when unreliable data are identified under the Research Data Policy?

When unreliable, fabricated, falsified, manipulated, or unsupported data are identified, the Global Journal of Engineering and Technology Advances (GJETA) may request clarification, reject the manuscript, issue a correction or expression of concern, contact an institution, or retract the article.