Data Sharing Policy | GJETA

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

🎯 Purpose of the Data Sharing Policy

The purpose of this Data Sharing Policy is to establish clear expectations regarding the availability of data supporting manuscripts submitted to GJETA. The policy promotes transparent research reporting while recognising that unrestricted public sharing is not appropriate or possible in every situation.

The policy helps GJETA:

• strengthen confidence in published findings;
• support research verification and reproducibility;
• encourage responsible preservation of research records;
• promote accurate citation and recognition of datasets;
• protect confidential, sensitive, restricted, or proprietary information;
• clarify author responsibilities regarding data access requests;
• support ethical and legally compliant data management.

📘 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 or conclusions of a manuscript.

Depending on the nature of the study, research data may include:

• experimental measurements and laboratory results;
• engineering test records and performance data;
• survey responses and interview records;
• numerical datasets and statistical outputs;
• simulation files and computational results;
• software source code, scripts, and algorithms;
• 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

Data sharing should be guided by transparency, accuracy, ethical responsibility, respect for participant rights, legal compliance, 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 should be explained transparently;
• sensitive or confidential information should be protected;
• datasets obtained from third parties should be used according to applicable permissions and licences;
• data contributors and original sources should be acknowledged appropriately.

👨‍🔬 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 research are accurate, complete, 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 data;
• describe data sources and analytical procedures clearly;
• identify relevant exclusions, transformations, or preprocessing steps;
• protect confidential and personally identifiable information;
• obtain permissions required for third-party datasets;
• respond reasonably to editorial requests for supporting information;
• provide an appropriate data availability statement.

🧾 Data Availability Statement

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

A Data Availability Statement should be accurate and specific enough to help readers understand how supporting data may 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 permission 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 take into account data preservation, accessibility, persistent identification, licensing, confidentiality, and 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 the Manuscript and Supplementary Files

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

Supplementary files 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

Engineering and technology research frequently depends on software, source code, algorithms, scripts, computational models, machine learning pipelines, or simulation environments. Authors are encouraged to share such materials when possible and when doing so does not violate security, licensing, intellectual property, contractual, 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, or data science research should describe the 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 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.

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.

🕵️ Anonymisation and De-identification

Authors should remove or modify identifying information before sharing data when appropriate. However, 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 public release.

🏭 Proprietary, Commercial and Industry Data

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

Where 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 anonymised 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 and contractual obligations.

Patent-related restrictions should not be used to conceal information essential to evaluating the 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, and licences. 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, data may be made available through a controlled-access process. Access conditions should be reasonable, transparent, and proportionate to the sensitivity of the data.

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 or controlled by third parties must comply with the original access conditions, licences, and citation requirements.

Authors should not redistribute third-party data without 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 data. A licence can help readers understand whether they may copy, analyse, modify, redistribute, or build upon the dataset.

Authors should ensure that they have authority to apply a licence and that the chosen 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 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 data should be accompanied by sufficient documentation to make it 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, GJETA may request supporting data when necessary to evaluate the accuracy, completeness, originality, or reliability of a manuscript.

Such requests may arise when:

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

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

📢 Data Requests After Publication

Readers or researchers 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 any applicable restrictions.

A request may be declined or limited when:

• participant privacy could be compromised;
• the requester cannot meet 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 scholarly verification;
• 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 records.

GJETA may request:

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

🚫 Fabrication, Falsification and Selective Data Reporting

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

Unacceptable practices include:

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

🖼️ Original Images and Visual Data

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

Acceptable adjustments should not alter the scientific meaning of the image or conceal relevant information.

⚠️ Failure to Provide Supporting Data

Failure to provide requested supporting data does not automatically establish misconduct, because legitimate restrictions 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 the manuscript or article.

Possible editorial actions may include:

• requesting clarification;
• requiring revision or additional disclosure;
• suspending manuscript processing;
• rejecting the manuscript;
• issuing a correction or expression of concern;
• contacting an institution or funder;
• retracting an article when the 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 journal may issue an appropriate correction.

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 the 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 manipulated data make the published findings unreliable.

Retraction is not normally required when limited data errors can be corrected without changing the principal conclusions.

📢 Complaints and Appeals

Authors, readers, reviewers, or institutions may raise concerns regarding data availability, access restrictions, data integrity, or editorial handling. Complaints should identify the relevant article 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;
• the available supporting evidence was not considered adequately.

🛠️ Enforcement of the Data Sharing Policy

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

Possible actions may include:

• requesting a Data Availability Statement;
• requiring additional 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 body.

🌟 Why the Data Sharing Policy Matters

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 and technical information.

For the Global Journal of Engineering and Technology Advances (GJETA), data transparency is particularly important because engineering and technology research may involve experiments, simulations, software, algorithms, sensor data, industrial systems, artificial intelligence models, design files, and complex computational workflows.

The Data Sharing Policy helps GJETA:

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

🔗 Related GJETA Editorial and Publishing Policies

Authors, reviewers, editors, and readers are encouraged to review the following policies of the Global Journal of Engineering and Technology Advances for a complete understanding of the journal’s requirements for research transparency, ethical publishing, confidentiality, authorship, originality, and post-publication accountability.

🚀 Publish Transparent Research with GJETA

❓ Frequently Asked Questions About the Data Sharing Policy

1. What is the Data Sharing Policy of GJETA?

The Data Sharing Policy of the Global Journal of Engineering and Technology Advances (GJETA) explains how authors should preserve, document, disclose, share, restrict, and provide access to data supporting published engineering and technology research.

2. Does the Data Sharing Policy require all data to be publicly available?

No. The Data Sharing Policy of the Global Journal of Engineering and Technology Advances (GJETA) encourages responsible sharing but recognises that privacy, confidentiality, legal, contractual, security, intellectual property, or commercial restrictions may prevent open access.

3. What is a Data Availability Statement under the Data Sharing Policy?

Under the Data Sharing Policy, authors submitting to the Global Journal of Engineering and Technology Advances (GJETA) should explain whether supporting data are publicly available, included with the article, available upon reasonable request, or restricted for a legitimate reason.

4. How does the Data Sharing Policy apply to confidential participant data?

The Data Sharing Policy requires authors publishing with the Global Journal of Engineering and Technology Advances (GJETA) to protect participant privacy, follow informed-consent conditions, comply with ethical approval, and use anonymisation or controlled access where appropriate.

5. Does the Data Sharing Policy cover software and source code?

Yes. The Data Sharing Policy of the Global Journal of Engineering and Technology Advances (GJETA) applies to source code, scripts, algorithms, computational workflows, software documentation, simulations, and other technical materials supporting the research.

6. Can editors request data under the Data Sharing Policy?

Yes. Under the Data Sharing Policy, the Global Journal of Engineering and Technology Advances (GJETA) may request supporting data when necessary to assess technical accuracy, verify findings, investigate inconsistencies, or review possible research-integrity concerns.

7. What happens if data cannot be shared under the Data Sharing Policy?

When data cannot be shared, the Data Sharing Policy requires authors submitting to the Global Journal of Engineering and Technology Advances (GJETA) to explain the restriction transparently and describe whether controlled or limited access may be possible.

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

When unreliable, fabricated, falsified, or manipulated 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.