Research Data Sharing Policy

The Journal of Circular Economy Innovations supports responsible research data sharing as an important component of transparent, reproducible, and trustworthy scholarly communication. Authors are encouraged to make the data underlying their published findings available whenever such sharing is ethically appropriate, legally permissible, technically feasible, and consistent with obligations concerning privacy, confidentiality, intellectual property, contractual restrictions, and participant protection.

The journal recognizes that research within circular economy scholarship may involve diverse forms of evidence, including experimental measurements, life-cycle assessment datasets, material-flow information, industrial records, environmental observations, survey data, policy datasets, economic indicators, computational outputs, waste-stream data, supply-chain information, and proprietary organizational records. Accordingly, data-sharing expectations must account for both the value of research openness and legitimate restrictions that may apply to particular forms of data.

This policy should be read together with the journal's Data Availability Policy, Publication Ethics Policy, Privacy Policy, Copyright and Licensing Policy, Research Misconduct Policy, and applicable ethical requirements.

Purpose of the Policy

The purpose of this policy is to establish clear principles for the responsible sharing, documentation, preservation, citation, and reuse of research data associated with manuscripts submitted to or published by the Journal of Circular Economy Innovations.

The journal seeks to encourage data practices that:

• strengthen transparency in research reporting;

• improve the traceability of published findings;

• support appropriate verification and reproducibility;

• facilitate responsible reuse of research outputs;

• promote proper recognition of data creators and providers;

• improve methodological clarity;

• support cumulative knowledge development; and

• protect confidential, proprietary, personal, security-sensitive, and legally restricted information.

Research data sharing should be approached as part of responsible research practice rather than as a purely administrative requirement.

Definition of Research Data

For the purposes of this policy, research data refers to information, records, observations, measurements, files, datasets, or other materials generated, collected, obtained, processed, or analyzed as part of the research and used to support the findings or conclusions reported in a manuscript.

Depending on the study, research data may include:

• quantitative datasets;

• qualitative research records;

• experimental measurements;

• laboratory observations;

• survey responses;

• interview data;

• field observations;

• environmental monitoring data;

• material-flow datasets;

• waste-generation and recovery data;

• recycling and reuse statistics;

• life-cycle inventory data;

• supply-chain datasets;

• energy and resource consumption data;

• economic and financial indicators;

• industrial process data;

• computational outputs;

• simulation datasets;

• statistical analysis files;

• geographic or spatial data;

• source data underlying tables and graphs;

• codebooks and data dictionaries;

• relevant metadata; and

• other materials necessary to understand the evidentiary basis of the research.

Not every file generated during a research project must necessarily be shared. Authors should identify the data that are materially relevant to the findings and conclusions reported in the manuscript.

General Data-Sharing Expectation

The Journal of Circular Economy Innovations encourages authors to make relevant research data accessible whenever reasonable and appropriate.

Where research data can be shared without violating ethical, legal, privacy, contractual, commercial, security, or intellectual-property obligations, authors are encouraged to deposit the data in an appropriate repository or provide another reliable means of scholarly access.

Where public sharing is not possible, authors should clearly describe the restriction through an appropriate Data Availability Statement.

Restrictions on data sharing do not automatically make a manuscript unsuitable for publication. The journal recognizes that some research necessarily depends on data that cannot be openly released.

Data Availability Statement

Manuscripts that report research based on collected, generated, processed, or analyzed data should include an appropriate Data Availability Statement where applicable.

The statement should explain whether the supporting data are:

• publicly available in a recognized repository;

• included within the article or supplementary materials;

• available from the corresponding author upon reasonable request;

• available through controlled-access procedures;

• obtained from a third party and subject to access restrictions;

• restricted because of privacy, confidentiality, contractual, commercial, or legal considerations;

• unavailable because disclosure would create ethical or security concerns; or

• not applicable because the article does not rely on a research dataset.

Authors should provide accurate and sufficiently specific information concerning the availability of the data.

Research Data Repositories

Where data are suitable for public sharing, authors are encouraged to use an appropriate institutional, disciplinary, governmental, or general-purpose data repository.

Where possible, repositories should provide:

• stable access to deposited materials;

• persistent identifiers;

• clear metadata;

• version information;

• appropriate access controls;

• citation information;

• licensing information; and

• reasonable provisions for long-term availability.

Authors should avoid relying exclusively on temporary personal webpages, short-term cloud links, or unstable file-transfer services when a more appropriate scholarly repository is available.

The journal does not require the use of a particular commercial data repository unless necessary for a specific research context.

Documentation and Metadata

Shared research data should be accompanied by sufficient documentation to enable qualified readers to understand and interpret the materials.

Depending on the study, documentation may include:

• variable definitions;

• data dictionaries;

• codebooks;

• measurement units;

• collection dates;

• sampling procedures;

• geographic coverage;

• methodological notes;

• inclusion and exclusion criteria;

• preprocessing procedures;

• missing-data conventions;

• anonymization methods;

• file descriptions;

• software requirements;

• dataset versions; and

• information explaining derived variables.

Making a dataset technically accessible without sufficient documentation may limit its scholarly usefulness.

Circular Economy and Sustainability Data

Research published by the Journal of Circular Economy Innovations may involve complex datasets concerning materials, products, industries, environmental systems, economic activities, and resource flows.

For studies involving circular economy analysis, authors should provide appropriate transparency concerning data used for areas such as:

• material-flow analysis;

• life-cycle assessment;

• waste characterization;

• recycling rates;

• resource productivity;

• product life extension;

• remanufacturing;

• reuse systems;

• industrial symbiosis;

• circular supply chains;

• sustainable production;

• sustainable consumption;

• carbon and environmental accounting;

• circular business models;

• resource-efficiency indicators; and

• environmental or socioeconomic impact assessment.

Where important assumptions, conversion factors, system boundaries, allocation methods, or derived indicators influence the findings, these should be documented adequately.

Life-Cycle Assessment Data

For studies involving life-cycle assessment, authors should provide sufficient information to permit scholarly evaluation of the inventory data, system boundaries, functional units, methodological assumptions, allocation procedures, impact-assessment methods, and relevant data sources.

Where complete life-cycle inventory datasets cannot be released because of licensing or commercial restrictions, authors should identify those restrictions and provide sufficient methodological information to support interpretation of the analysis.

Public availability should not be claimed where underlying databases require subscription, institutional access, or contractual authorization.

Material-Flow and Industrial Data

Research involving material-flow analysis, industrial ecology, manufacturing processes, resource consumption, or industrial symbiosis may rely on commercially sensitive or confidential data.

Where such data cannot be openly shared, authors should state the nature of the restriction where possible.

Aggregated or appropriately anonymized information may be shared when this does not compromise legitimate commercial or contractual obligations.

Authors should avoid disclosing confidential business information without appropriate authorization.

Human-Participant and Survey Data

Research involving surveys, interviews, behavioral studies, community research, or other human-participant data must protect privacy and confidentiality.

Authors must not publicly disclose personally identifiable or potentially re-identifiable information where disclosure would be inconsistent with:

• informed consent;

• ethical approval;

• confidentiality obligations;

• institutional policies; or

• applicable law.

Where possible, appropriately anonymized or de-identified datasets may be shared.

Where anonymization is insufficient to protect participants, controlled access or non-disclosure may be necessary.

Informed Consent and Data Sharing

Consent to participate in research should not automatically be interpreted as consent for unrestricted public release of participant-level data.

Where applicable, authors should ensure that planned data-sharing arrangements are consistent with the consent obtained from research participants.

Research using legacy datasets should be evaluated carefully where original consent documentation did not anticipate open data sharing.

Confidential and Proprietary Data

The journal recognizes that certain datasets may be subject to legitimate restrictions.

These may include:

• commercially confidential information;

• proprietary industrial datasets;

• licensed databases;

• confidential organizational records;

• government-restricted data;

• security-sensitive information;

• information protected by nondisclosure agreements;

• contractually restricted datasets; and

• data protected by intellectual-property rights.

Authors should disclose relevant restrictions where possible and should not imply that such data are openly accessible when they are not.

Third-Party Data

Authors using data obtained from third parties must ensure that they have appropriate permission or legal authority to use the data for the purposes described in the manuscript.

Third-party data should be cited appropriately.

Where redistribution is prohibited, authors should not upload or publicly distribute the data without authorization.

If readers may independently obtain the same data from the original provider, authors should provide relevant access information where appropriate.

Publicly Available Data

Where research relies on publicly accessible datasets, authors should identify the original source accurately.

Where available, the manuscript should provide:

• dataset title;

• responsible organization or creator;

• repository or database name;

• version information;

• access date where appropriate; and

• persistent identifier.

Authors should distinguish between genuinely open datasets and resources that require registration, payment, institutional membership, or approval.

Research Code and Analytical Materials

Where computer code, statistical scripts, computational workflows, models, or analytical tools are central to the findings, authors are encouraged to share these materials when technically and legally feasible.

Relevant materials may include:

• statistical scripts;

• simulation code;

• computational notebooks;

• data-processing scripts;

• optimization models;

• life-cycle calculation procedures;

• model parameters;

• environmental assessment workflows; and

• supporting documentation.

Where code cannot be shared because of proprietary, contractual, security, or licensing restrictions, authors should describe the limitation appropriately.

Synthetic and Simulated Data

When a manuscript relies on synthetic, simulated, generated, or hypothetical data, the nature of the data must be stated clearly.

Synthetic or simulated datasets must not be presented as though they represent actual observations from individuals, organizations, industrial systems, environmental monitoring programs, or other real-world sources.

Where possible, authors should describe:

• how the data were generated;

• assumptions used;

• model parameters;

• simulation conditions;

• limitations; and

• procedures necessary to reproduce the dataset.

Data Supporting Tables and Graphs

Where tables, graphs, statistical outputs, or other visual presentations form an important part of the analysis, the underlying data should be identifiable where possible.

Authors should not selectively present only favorable results while withholding information that materially changes the interpretation of the study.

Where data underlying published graphs cannot be shared, the relevant restriction should be disclosed when necessary.

Data Integrity

Authors are responsible for ensuring that research data are reported accurately and honestly.

The journal does not permit:

• fabricated data;

• falsified measurements;

• inappropriate alteration of observations;

• deliberate suppression of relevant results;

• misleading manipulation of datasets;

• selective reporting intended to distort conclusions; or

• misrepresentation of data provenance.

Where credible concerns arise concerning data integrity, the journal may request relevant supporting information or documentation.

Data Requests During Peer Review

Editors or reviewers may request access to supporting data when reasonably necessary to evaluate the validity, methodology, or integrity of a submitted manuscript.

Data provided specifically for confidential editorial or peer-review purposes must be treated as confidential.

Reviewers must not use unpublished datasets obtained through the review process for their own research, commercial advantage, or other unauthorized purposes.

Sensitive data should be transferred only through appropriate and secure procedures.

Data Availability After Publication

Where authors state that data are available upon reasonable request, they should make reasonable efforts to honor legitimate scholarly requests consistent with the conditions stated in the article.

Authors may require appropriate safeguards concerning:

• participant confidentiality;

• ethics approval;

• data-use agreements;

• institutional authorization;

• security requirements;

• commercial confidentiality; or

• intellectual-property restrictions.

The phrase “available upon reasonable request” should not be used where the authors already know that data cannot be made available.

Controlled Access

Some data may appropriately be made available only under controlled-access procedures.

Controlled access may require:

• identification of the requesting researcher;

• a description of the intended use;

• institutional affiliation;

• ethics approval;

• confidentiality agreements;

• data-use agreements; or

• compliance with other legitimate safeguards.

Access conditions should be applied consistently and should not be used merely to prevent legitimate scholarly scrutiny.

Data Citation

Datasets that contribute substantially to the research should be cited where appropriate.

A data citation should include sufficient information to identify the dataset and may include:

• creator or responsible organization;

• dataset title;

• repository;

• year;

• version; and

• persistent identifier.

Appropriate data citation recognizes the intellectual contribution of data creators and improves traceability.

Data Licensing

Authors who deposit original datasets in repositories are encouraged to identify the conditions governing reuse.

The license applying to a journal article does not automatically apply to an independently deposited dataset unless the same license has expressly been assigned to the dataset.

Authors must not apply a reuse license to third-party data where they do not possess the authority to grant those rights.

Changes to Shared Data

Where a dataset associated with a published article is corrected, updated, replaced, or materially revised, authors should maintain appropriate version information where possible.

Changes should not be made in a manner that obscures the evidentiary record supporting the published article.

If a change in the data materially affects the findings or conclusions of the article, authors should inform the journal promptly.

Exceptions to Public Sharing

Public data disclosure may not be appropriate where research data:

• contain sensitive personal information;

• could permit re-identification of participants;

• are protected by confidentiality obligations;

• are subject to legal restrictions;

• are owned by a third party;

• contain confidential commercial information;

• are covered by contractual restrictions;

• contain security-sensitive information;

• are protected by intellectual-property rights; or

• could cause unreasonable harm if publicly released.

The Journal of Circular Economy Innovations does not require authors to violate ethical duties, legal requirements, contractual obligations, or legitimate confidentiality protections in order to satisfy this policy.

Research Misconduct Related to Data

Concerns regarding fabricated, falsified, manipulated, or materially misrepresented research data may be considered under the journal's Research Misconduct Policy and Publication Ethics Policy.

The inability to provide data does not automatically establish misconduct. Data may become unavailable for legitimate reasons.

However, unexplained discrepancies between statements made in the article and the actual existence, provenance, or availability of research data may warrant editorial inquiry.

Where serious data-related concerns materially undermine the reliability of published findings, the journal may consider correction, retraction, expression of concern, or another appropriate editorial action.

Responsibilities of Authors

Authors are responsible for:

• accurately identifying the origin of research data;

• ensuring lawful and ethical use of data;

• protecting confidential information;

• obtaining necessary permissions;

• maintaining appropriate research records;

• providing accurate data-availability information;

• documenting essential processing procedures;

• citing external datasets appropriately;

• explaining legitimate data-sharing restrictions; and

• cooperating with reasonable editorial inquiries.

Authors should not make statements concerning data accessibility that cannot reasonably be fulfilled.

Responsibilities of Editors and Reviewers

Editors should evaluate research-data concerns impartially and proportionately.

Reviewers may assess whether the manuscript provides sufficient information concerning data sources, collection, processing, analysis, transparency, and limitations.

Editors and reviewers must treat unpublished research data as confidential and must not use those data for personal, professional, commercial, or competitive advantage.

Requests for additional data should be relevant to legitimate scholarly assessment and proportionate to the manuscript being evaluated.

Relationship With the Data Availability Policy

The Research Data Sharing Policy establishes the journal's broader principles concerning responsible data sharing, documentation, repositories, controlled access, reuse, confidentiality, and stewardship.

The separate Data Availability Policy addresses how authors should communicate the availability status of the specific data supporting an individual manuscript or published article.

The two policies are complementary and should be considered together.

Commitment to Responsible Research Data Practices

The Journal of Circular Economy Innovations supports research-data practices that promote transparency, accountability, reproducibility, and responsible knowledge exchange while respecting legitimate ethical, legal, privacy, commercial, contractual, and intellectual-property protections.

The journal encourages authors to consider responsible data stewardship throughout the complete research lifecycle—from collection and documentation to analysis, preservation, sharing, and appropriate reuse.

Responsible data sharing strengthens the credibility of scholarly research and can contribute to more reliable evidence for circular economy innovation, sustainable resource management, environmental decision-making, industrial transformation, and the broader transition toward more regenerative and resource-efficient economic systems.