Research Data and Transparency

Purpose and Scope

The SDEWES Journals Research Data and Transparency Policy sets out the principles and procedures for the responsible reporting, availability, accessibility, citation, preservation and transparency of research data, code, software, models, methods and other materials supporting manuscripts submitted to journals published by SDEWES Centre.

This policy applies to all submitted, accepted and published manuscripts, including regular articles, review articles, special issue papers, article collections, conference-linked submissions and supplementary materials. It also applies to datasets, code, software, models and other research outputs associated with a manuscript.

It applies to authors, reviewers, Editors-in-Chief, Associate Editors, Guest Editors, Editorial Board members, SDEWES Centre staff or representatives involved in editorial or publication activities, and other individuals participating in the editorial, peer-review or publication process.

The policy covers research data and supporting materials, Data Availability Statements, repositories, access conditions, data and software citation, reproducibility information, data-processing transparency, legitimate restrictions and concerns involving data integrity, availability, provenance or responsible reuse.

In this policy, the responsible editor means the Editor-in-Chief or another editor authorised by the journal to assess the matter, provided that the person has no relevant competing interest.

Related guidance, statement templates, declaration forms and checklists relevant to this policy are listed in Appendix A: Related COPE and International Guidance and Appendix B: Practical Forms, Statements and Checklists.

General Principles

SDEWES journals support research transparency, reproducibility, verification and responsible reuse of research outputs.

Authors are responsible for accurately reporting the data, code, software, models, methods and other materials supporting their findings and for complying with applicable ethical, legal, institutional, contractual, funder and disciplinary requirements.

Authors should make supporting research outputs available wherever this is ethically, legally, contractually and technically possible. Open sharing is encouraged but is not required where a legitimate restriction applies.

Research transparency does not require unrestricted public access to every dataset. Controlled, authenticated or restricted access may be appropriate where personal data, confidentiality, intellectual property, security or another valid consideration prevents open access.

Research data and supporting materials must not be fabricated, falsified, selectively omitted, misrepresented or inappropriately manipulated. Restrictions on access must not be used to conceal data integrity problems or prevent reasonable assessment without a valid reason.

Every applicable manuscript must include an accurate Data Availability Statement, including where no new data were generated or where access is restricted.

Research Data, Code and Supporting Research Materials

For the purposes of this policy, research data and research materials may include raw data, processed data, measurement results, laboratory data, pilot-plant data, fieldwork data, environmental monitoring data, survey data, interview data, questionnaire responses, model inputs and outputs, simulation outputs, statistical outputs, software, code, algorithms, scripts, computational workflows, protocols, methods, materials, questionnaires, interview guides, metadata, images, maps, GIS layers, databases, supplementary files and other materials necessary to understand, verify or reuse the research.

In SDEWES journals, research data may be produced through engineering experiments, energy-system modelling, water-system analysis, environmental monitoring, life-cycle assessment, techno-economic analysis, industrial case studies, circular-economy studies, social-acceptance research, policy analysis, stakeholder engagement, field sampling, laboratory testing, computational modelling or other forms of sustainability research.

Research data may also include data derived from public databases, institutional repositories, third-party datasets, project datasets, municipal data, utility data, industrial data, proprietary datasets or previously published sources.

The materials required to support a manuscript will depend on the type of research. Authors are not necessarily expected to provide every intermediate file or working record, but they must identify and preserve the materials reasonably necessary to understand, evaluate and verify the reported work.

Data Availability Statements

All research articles and review or synthesis articles submitted to SDEWES journals must include a Data Availability Statement. Other article types may also require a statement where they generate, analyse or rely on research data, code, software, models or other supporting outputs.

The statement must accurately identify, as applicable:

• whether data or other supporting outputs were generated or analysed; 
• what data, code, software, models or materials support the findings; 
• whether they are included in the article or supplementary material; 
• whether they are deposited in a repository; 
• the repository name, persistent identifier, accession number or stable link; 
• whether access is open, controlled, restricted, embargoed or available on request; 
• the applicable access conditions; 
• any ethical, privacy, legal, commercial, contractual, confidentiality, security or technical restrictions; and 
• whether no new data were generated or analysed. 

A statement that materials are available on reasonable request should identify the responsible contact and any applicable access conditions. It should not be used merely to avoid repository deposition where no legitimate restriction exists.

Where data or code are unavailable, the statement must explain the reason clearly. A Data Availability Statement must not claim that supporting outputs are available where they cannot, in practice, be accessed.

Authors must review and, where necessary, update the statement during revision and production. Standard wording is provided in the Data Availability Statement Template listed in Appendix B.

Data Sharing, Access and Legitimate Restrictions

Authors are expected to share the data, code and other materials supporting the findings in an appropriate repository wherever this is ethically, legally, contractually and technically possible.

Sharing is particularly important where supporting outputs are necessary to verify results, reproduce analyses, evaluate experimental findings, understand model assumptions, assess scenarios or enable responsible reuse.

Legitimate restrictions may arise from:

• personal, identifiable or human-participant data; 
• consent, research-ethics or privacy requirements; 
• confidential industrial, commercial, municipal, utility or company information; 
• intellectual-property, copyright, database-right or software-licence restrictions; 
• third-party data-use conditions; 
• contractual, project, funder or institutional obligations; 
• security-sensitive infrastructure or operational information; 
• protected species, habitats, sites or environmental locations; 
• legal or regulatory restrictions; or 
• technical constraints that cannot reasonably be addressed through an appropriate repository. 

Where open sharing is not possible, authors should consider controlled access, an embargo, a non-sensitive subset, anonymised or aggregated data, metadata describing the restricted dataset, or another proportionate arrangement.

Synthetic or simulated data provided in place of restricted underlying data must be clearly identified. Authors must explain how those data were generated and must not present them as the original observed, measured or participant-derived data.

Access conditions must be realistic and must not mislead readers. Where access depends on a data owner, ethics committee, institution, repository or other responsible body, the statement should identify the appropriate route rather than implying that the corresponding author alone can grant access.

Personal and identifiable information is governed by Privacy and Research Ethics. Copyright, database rights, permissions and licensing are governed by Copyright & Licensing. Funding, partner involvement and related institutional or commercial interests must be disclosed under Competing Interests and Funding Disclosure.

Repositories, Persistent Identifiers, FAIR Principles, Licensing and Versioning 

Where data, code, software, models or other research outputs are shared, authors should use an appropriate disciplinary, institutional or general-purpose repository that supports stable access, preservation, metadata and persistent identifiers.

Personal websites, temporary file-sharing services, expiring cloud links and personal accounts should not be used as the sole location for materials necessary to support the published findings.

Shared outputs should be made as findable, accessible, interoperable and reusable as reasonably possible. Authors should provide:

• accurate and sufficiently detailed metadata; 
• persistent identifiers where available; 
• clear file and variable descriptions; 
• units, assumptions and methodological context; 
• provenance information; 
• version or release information; 
• applicable access and reuse conditions; and 
• a clear licence where the authors have the authority to apply one. 

FAIR data are not necessarily openly accessible. A repository may use authentication, authorisation, embargoes or controlled-access arrangements where restrictions are justified. Metadata should remain available where possible, even when access to the underlying data is restricted or later withdrawn.

Where a dataset, code release or model is updated after publication, the original version supporting the article should remain identifiable. The article and Data Availability Statement should refer to the version used for the reported analysis.

Data and Software Citation

Datasets, code, software, models and other research outputs that support the manuscript should be cited where appropriate, preferably using persistent identifiers.

Data citations should be included in the reference list or another location required by the journal and should provide enough information to identify and access the dataset or research output.

The required format and placement of data citations may be specified in the relevant journal’s Guide for Authors, reference style or data policy.

A data citation may include:

Author(s), year, dataset or software title, repository or archive, version where applicable, and persistent identifier such as DOI, accession number or stable URL.

Authors must properly acknowledge data, code, software, models or materials created by others and must respect any licence, permission or attribution requirements.

Where a dataset, software release or model has a formal citation recommended by the repository or developer, authors should use that citation. Citation of a general website does not replace citation of the specific version or research output used, where a persistent identifier is available.

Code, Software, Models and Computational Workflows

Where code, software, algorithms, models or computational workflows are central to the findings, authors should provide sufficient information to allow readers to understand, verify, and, where possible, reproduce the analysis.

This may include, where applicable:

• name and version of software, model, solver, package or programming language; 
• code or script availability; 
• repository link or persistent identifier; 
• model assumptions, system boundaries and parameter values; 
• input datasets and data sources; 
• scenario definitions; 
• calibration and validation procedures; 
• computational settings; 
• uncertainty or sensitivity analysis; 
• dependencies, libraries, solvers or external tools used; 
• explanation of restrictions where code or models cannot be shared;
• software, model or dataset version used; 
• random seeds or stochastic settings, where materially relevant; 
• preprocessing and post-processing procedures; 
• provenance or workflow information; and 
• licence and reuse conditions, where applicable.

Where proprietary or commercial software is used, authors should still describe the methodology, assumptions, input data and settings sufficiently for scientific assessment.

Where code cannot be shared because of confidentiality, commercial restrictions, licences, security concerns or other valid reasons, authors must explain this in the Data Availability Statement or another appropriate section.

Where generative AI or AI-assisted technologies contribute to code development, data processing, modelling, analysis, synthetic data or figure generation, the use must be reported and validated under Generative AI Policy. AI-generated code or output does not remove the authors’ responsibility to test, document and substantiate the resulting analysis.

Methods, Materials and Reproducibility

Authors must describe methods, assumptions, materials, system boundaries, experimental conditions, modelling choices and analytical procedures in sufficient detail to allow the research to be assessed, verified, and, where possible, reproduced.

For modelling and simulation studies, authors should clearly describe model structure, input assumptions, constraints, scenarios, temporal and spatial resolution, boundary conditions, optimisation criteria, validation methods and key limitations.

For experimental studies, authors should describe materials, equipment, conditions, procedures, measurement methods, calibration, uncertainty, sample preparation and relevant safety or quality-control procedures.

For life-cycle assessment, techno-economic analysis, energy-system modelling, environmental assessment or policy-analysis studies, authors should provide sufficient transparency on data sources, assumptions, indicators, calculation methods and scenario design.

Where exact replication is not possible, authors should still provide enough information to support critical assessment and reasonable reuse of the approach.

Reproducibility requirements should be applied proportionately. Exact computational reproduction may be achievable for some studies, while experimental, field-based, qualitative or restricted-data research may instead require sufficient transparency to support critical evaluation, methodological understanding and reasonable reuse.

Supplementary Materials

Supplementary materials should be relevant, clearly labelled, accurate and consistent with the manuscript.

Supplementary materials may include datasets, tables, model inputs, equations, extended methods, questionnaires, interview guides, additional figures, maps, code, calculations, scenario assumptions or supporting documentation.

Authors are responsible for the integrity, permissions, accuracy and reliability of supplementary materials in the same way as for the main manuscript.

Supplementary materials must not be used to hide essential methodological information that should be included in the main manuscript.

Supplementary materials must not contain personal data, confidential information, copyrighted third-party content, commercially sensitive or security-sensitive information, or other restricted material unless publication is authorised, ethically justified and subject to appropriate safeguards.

Supplementary materials must be reviewed by the authors before submission and again before publication. Files should be clearly named and described, and the manuscript should indicate where each supplementary item is relevant.

Preservation, Retention and Access for Editorial Assessment

Authors should preserve the original data, code, source files, methodological records and other materials reasonably necessary to substantiate the reported work for the period required by applicable institutional, funder, contractual, disciplinary or legal requirements.

This policy does not establish a single universal retention period. Authors must comply with the requirements applicable to their study and institution.

During editorial assessment, peer review or assessment of a concern, the journal may request proportionate access to supporting data, code, images, source files, repository records or methodological documentation.

Such a request does not necessarily require public release. Confidential, personal, proprietary or restricted materials may be provided through an appropriate secure or controlled process where this is lawful and authorised.

Requests must be limited to information reasonably necessary for the assessment. Personal data and confidential information must be handled under Privacy, while confidential peer-review handling is governed by Peer Review Policy.

Inability to provide the requested material does not automatically establish misconduct. The responsible editor must consider the reason, applicable retention requirements, age of the research and effect on the reliability or verifiability of the manuscript.

Data Integrity and Transparent Processing

Authors must not fabricate, falsify, selectively omit, misrepresent or inappropriately manipulate data, code, images, models, statistical outputs or other research materials.

Data cleaning, correction, filtering, exclusion, transformation, aggregation, imputation, anonymisation and other processing may be legitimate, but procedures that materially affect the results must be described transparently.

Figures, tables, visualisations, maps, images and model outputs must accurately represent the underlying data and methods. Image enhancement, combination, cropping or other adjustment must not obscure, remove or misrepresent relevant information.

AI-generated or synthetic data must be clearly identified and must not be represented as original observed or experimental data. Relevant use of generative AI must comply with Generative AI Policy.

The Data Availability Statement, repository record, supplementary material and manuscript must be mutually consistent.

Authors who identify a material error after submission or publication must notify the journal promptly and cooperate with any necessary clarification or amendment.

Suspected fabrication, falsification, inappropriate manipulation or deliberate misrepresentation is assessed under Publishing Ethics.

Research Data Concerns and Post-Publication Issues

Research data concerns must be assessed fairly, confidentially and proportionately. A missing Data Availability Statement, inaccessible link, or incomplete repository record does not by itself establish data fabrication or misconduct.

The responsible editor should determine whether the concern primarily involves:

• scientific rigour, including errors, gaps, incomplete datasets, insufficient documentation or reproducibility problems; 
• legal or regulatory restrictions, including privacy, consent, confidentiality, intellectual property, licensing, security or data-use restrictions; 
• risk of harm, including possible harm to individuals, communities, animals, ecosystems, protected sites, infrastructure or wider society; or 
• possible fabrication, falsification, inappropriate manipulation or deliberate misrepresentation. 

The assessment should also distinguish concerns identified before publication from those affecting a published article or associated dataset.

The journal may request proportionate clarification, data, code, source files, repository records, licences, permissions, documentation or institutional confirmation. Confidential or restricted data should not be requested for public release where controlled assessment is sufficient.

Before publication, the journal may:

• request a corrected Data Availability Statement; 
• require additional methodological or repository information; 
• request correction, replacement or removal of problematic material; 
• continue review after satisfactory clarification; 
• pause handling while institutional or other clarification is obtained; or 
• reject or discontinue handling where the work cannot be adequately substantiated or a serious integrity concern remains unresolved. 

Where the concern cannot be resolved with the authors, the journal may contact an institution, repository, data owner, funder or other responsible body where appropriate. Any such contact should be factual, confidential and limited to the information reasonably necessary to clarify or assess the concern.

Where published content is affected, any correction, expression of concern, retraction, removal or other action must be assessed under Corrections, Retractions and Withdrawals. Serious suspected misconduct is assessed under Publishing Ethics.

Privacy, consent or participant-data concerns are additionally assessed under Privacy and Research Ethics. Copyright, licensing or third-party data concerns are assessed under Copyright & Licensing.

Concerns about authorship or contributorship of datasets, code, software, models or other research outputs are assessed under Authorship and Contributorship.

Where the responsible editor has a competing interest, it must be managed under Competing Interests and Funding Disclosure, and independent handling must be arranged under Editorial Independence.

Formal appeals or complaints concerning the journal’s handling of the matter are assessed under Editorial Decision Appeals and Complaints.

Policy Review and Updates

SDEWES Centre may review and update this policy periodically to reflect developments in research data management, open science, data repositories, data citation, reproducibility standards, publication ethics, privacy, data protection, copyright, artificial intelligence, research software, funder requirements, legal requirements and international standards.

Updated policies apply from the date of publication on the SDEWES Journals Policies website unless otherwise stated.

Appendix A. Related COPE and International Guidance

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Scientific rigour - Unpublished data

Relevant where concerns arise before publication about the scientific validity, reliability, completeness or reproducibility of data supporting a submitted manuscript.

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Scientific rigour - Published data

Relevant where concerns arise after publication about the scientific validity, reliability, completeness or reproducibility of data supporting a published article.

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Concerns involving legal and regulatory restrictions - Pre-publication

Relevant where unpublished or submitted data may be subject to legal, regulatory, privacy, consent, confidentiality, security or access restrictions.

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Concerns involving legal and regulatory restrictions - Post-publication

Relevant where already published data may raise legal, regulatory, privacy, consent, confidentiality, security or access-restriction concerns.

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Concerns about risk (e.g. potential harm or privacy breach) - Pre-publication

Relevant where data connected with a submitted manuscript may create a risk of harm to individuals, communities, animals, ecosystems, infrastructure, security or wider society.

FORCE11 & COPE Research Data Publishing Ethics Working Group flowchart: Concerns about risk (e.g. potential harm or privacy breach) - Post-publication

Relevant where data connected with a published article may create risk of harm to individuals, communities, animals, ecosystems, infrastructure, security or wider society.

COPE Council. COPE Flowcharts and infographics - Fabricated data in a submitted manuscript

Relevant where editors suspect before publication that data supporting a submitted manuscript may have been fabricated.

COPE Council. COPE Flowcharts and infographics - Fabricated data in a published article

Relevant where editors suspect, after publication, that data supporting a published article may have been fabricated.

COPE Council. COPE Flowcharts and infographics - Inappropriate image manipulation in a published article

Relevant where concerns arise after publication that images, figures or visual data may have been inappropriately manipulated.

GO FAIR. FAIR Principles

Relevant to making research data and metadata findable, accessible under clearly stated conditions, interoperable and reusable, including where authentication, authorisation or other access restrictions apply.

Appendix B. Practical Forms, Statements and Checklists

The documents below provide practical support for author declarations, preparation of Data Availability Statements and proportionate assessment of potentially missing, inaccessible, restricted, misleading or unreliable research data, code and supporting materials.

Research Data and Code Availability Declaration Form

Required author submission form, where applicable – Used to declare whether data, code, software, models or other supporting materials were generated or analysed; where they are available; applicable versions, repositories and identifiers; and whether ethical, legal, privacy, commercial, contractual, confidentiality, security or technical restrictions apply. The corresponding author coordinates the submission after obtaining and confirming the relevant information from all authors.

Data Availability Statement Template

Author guidance template – Used to prepare manuscript statements describing whether data, code, software, models, supplementary materials or other supporting outputs are available, where and under what conditions they can be accessed, and any legitimate restrictions on access or reuse.

Research Data and Transparency Concern Checklist for Editors

Internal editor and publisher checklist – Used by the responsible editor and, where appropriate, SDEWES Centre where potentially missing, incomplete, inaccessible, inconsistent or misleading Data Availability Statements, unavailable datasets, inaccessible code, unclear restrictions, reproducibility problems, legal or risk concerns, or possible data-integrity issues are identified before or after publication.