Research Data Sharing Policy

The Digital Health & Telemonitoring Advances supports responsible sharing of research data as an important element of transparent, reproducible, and accountable scholarly communication. The journal encourages authors to make the data underlying their published findings available whenever sharing is ethically appropriate, legally permissible, technically feasible, and consistent with obligations relating to privacy, confidentiality, informed consent, intellectual property, contractual restrictions, and information security.

Research data arising from digital health, telemedicine, telemonitoring, remote patient monitoring, mobile health, artificial intelligence, wearable technologies, electronic health systems, and related areas may contain sensitive personal or health information. Accordingly, this policy does not require unrestricted public disclosure of data where doing so could compromise participant privacy, confidentiality, legal obligations, cybersecurity, or ethical requirements.

Authors are expected to balance research transparency with responsible protection of individuals, communities, institutions, and data providers.

1. Purpose of the Policy

The purpose of this policy is to establish clear expectations regarding the management, documentation, availability, sharing, citation, and responsible reuse of research data associated with manuscripts submitted to and published by the journal.

The journal seeks to encourage data practices that:

  • strengthen transparency in research reporting;

  • support verification of published findings where appropriate;

  • improve reproducibility and methodological clarity;

  • facilitate responsible reuse of research outputs;

  • promote appropriate recognition of data creators;

  • preserve the evidentiary basis of published research; and

  • protect confidential, sensitive, restricted, and legally protected information.

Research data sharing should be considered throughout the research process rather than only after manuscript acceptance.

2. Scope of Research Data

For the purposes of this policy, research data refers to information, observations, measurements, records, files, or other materials generated, collected, obtained, processed, analyzed, or used as evidence in support of scholarly findings.

Depending on the study design, research data may include:

  • clinical or health-related measurements;

  • remote patient monitoring records;

  • physiological signals;

  • wearable-device data;

  • sensor data;

  • telemonitoring records;

  • survey responses;

  • questionnaire data;

  • interview records;

  • behavioral data;

  • mobile health application data;

  • electronic health record extracts;

  • medical imaging data;

  • diagnostic information;

  • laboratory measurements;

  • digital biomarkers;

  • algorithmic outputs;

  • artificial intelligence training, validation, or test datasets;

  • computational outputs;

  • statistical datasets;

  • simulation data;

  • coded qualitative data;

  • source data underlying tables and graphs;

  • analytical files;

  • data dictionaries;

  • codebooks;

  • annotations and labels; and

  • relevant metadata.

Research data do not necessarily include every file generated during a research project. Authors should identify and preserve materials reasonably necessary to understand and evaluate the findings reported in the manuscript.

3. General Data-Sharing Expectation

Authors are encouraged to make underlying research data available where appropriate.

Where data can be shared without violating ethical, legal, privacy, security, confidentiality, intellectual-property, or contractual obligations, authors are encouraged to deposit relevant datasets in an appropriate repository.

Public data sharing is not mandatory where legitimate restrictions apply.

Where research data cannot be made openly available, authors should explain the reason for the restriction through an appropriate Data Availability Statement.

The existence of legitimate restrictions will not, by itself, make a manuscript unsuitable for publication.

4. Data Availability Statement

Manuscripts involving research data should include a Data Availability Statement where applicable.

The statement should identify whether supporting data are:

  • openly available in a repository;

  • included within the article;

  • included in supplementary materials;

  • available from the corresponding author upon reasonable request;

  • available through controlled access;

  • subject to institutional approval;

  • obtained from a third party;

  • restricted for privacy or confidentiality reasons;

  • restricted because of legal or contractual requirements;

  • unavailable because public disclosure would create an unreasonable risk; or

  • not applicable because no new research data were generated or analyzed.

The statement should accurately represent the actual accessibility of the data.

5. Selection of Research Data Repositories

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

Where possible, repositories should provide:

  • persistent identifiers;

  • stable access;

  • clear access conditions;

  • metadata;

  • version information;

  • data citation;

  • licensing information; and

  • appropriate preservation arrangements.

Temporary personal websites or unstable file-sharing links should not ordinarily be used as the sole location for research data intended to remain available as part of the scholarly record.

6. Data Documentation and Metadata

Research data made available to readers should include sufficient documentation to support meaningful interpretation.

Depending on the nature of the dataset, this may include:

  • variable definitions;

  • measurement units;

  • collection periods;

  • sampling procedures;

  • inclusion and exclusion criteria;

  • coding methods;

  • preprocessing procedures;

  • anonymization methods;

  • missing-data conventions;

  • file descriptions;

  • software requirements;

  • device specifications;

  • algorithm versions;

  • data dictionaries;

  • annotation procedures; and

  • other relevant metadata.

Providing access to an undocumented dataset may not provide sufficient transparency for meaningful scholarly evaluation or reuse.

7. Human Participant Data

Research involving human participants requires careful management of privacy, confidentiality, informed consent, and ethical responsibilities.

Authors must not publicly disclose identifiable or potentially re-identifiable participant information where disclosure would conflict with:

  • informed consent;

  • ethics approval;

  • privacy protections;

  • confidentiality obligations;

  • institutional requirements; or

  • applicable law.

Where participant-level data cannot be shared openly, controlled access, aggregated data, de-identified data, or another appropriately protected form of access may be considered.

The journal does not require authors to violate ethical or legal obligations in order to comply with this policy.

8. Health and Medical Data

Health-related information may be particularly sensitive.

Authors should exercise heightened care when sharing:

  • clinical records;

  • diagnostic information;

  • treatment histories;

  • physiological measurements;

  • biometric information;

  • medical imaging;

  • genetic or genomic data;

  • medication records;

  • patient-reported outcomes; or

  • other information related to an individual's health.

Removal of direct identifiers may not always eliminate re-identification risk, particularly when detailed digital or longitudinal datasets are involved.

Authors should therefore evaluate privacy risks carefully before depositing health-related data in an unrestricted repository.

9. Telemonitoring and Remote Patient Data

Studies involving telemonitoring or remote patient monitoring may generate continuous or high-frequency data capable of revealing sensitive information concerning health status, behavior, location, routines, or personal circumstances.

Authors should clearly describe appropriate safeguards used to protect such data.

Where relevant, manuscripts should indicate:

  • the type of data collected;

  • the monitoring technology used;

  • duration of monitoring;

  • storage arrangements;

  • access controls;

  • anonymization or pseudonymization procedures;

  • consent arrangements; and

  • restrictions governing further use.

Raw telemonitoring data should not be made publicly available where doing so could create unreasonable privacy or re-identification risks.

10. Wearable and Sensor Data

Research involving wearable technologies, connected sensors, smart devices, or Internet of Medical Things systems may involve sensitive physiological and behavioral information.

Authors should consider whether data generated by such technologies contain:

  • location information;

  • timestamps;

  • movement patterns;

  • sleep data;

  • heart-rate information;

  • activity records;

  • biometric identifiers; or

  • other information that could contribute to participant identification.

Where necessary, authors should use controlled-access mechanisms rather than unrestricted public release.

11. Electronic Health Record Data

Electronic health record data are commonly subject to strict privacy, ethical, institutional, and legal restrictions.

Authors using such data should not publicly redistribute identifiable or restricted records unless they possess clear authorization to do so.

The manuscript should nevertheless provide sufficient methodological detail concerning:

  • data source;

  • study population;

  • extraction period;

  • inclusion criteria;

  • variables;

  • preprocessing;

  • missing-data handling; and

  • relevant limitations.

Where data access is restricted, the Data Availability Statement should explain the restriction transparently.

12. Artificial Intelligence and Machine-Learning Datasets

Research involving artificial intelligence, machine learning, deep learning, automated decision systems, or predictive models should provide appropriate transparency concerning the data used for model development and evaluation.

Where relevant, authors should describe:

  • dataset source;

  • data provenance;

  • inclusion and exclusion criteria;

  • sample characteristics;

  • preprocessing procedures;

  • annotation or labeling;

  • training data;

  • validation data;

  • test data;

  • class distribution;

  • missing-data handling;

  • demographic characteristics;

  • known biases;

  • external validation datasets;

  • licensing conditions; and

  • access restrictions.

Claims concerning model fairness, generalizability, reliability, or performance should be interpreted in relation to the characteristics and limitations of the underlying data.

13. Generative AI and Synthetic Health Data

Where synthetic or AI-generated data are used, their nature must be disclosed clearly.

Synthetic data must not be described as actual patient, participant, clinical, or observational data.

Authors should explain, where relevant:

  • how synthetic data were generated;

  • model or tool used;

  • parameters;

  • assumptions;

  • validation procedures;

  • relationship to real-world datasets; and

  • limitations.

Where synthetic datasets are shareable, authors are encouraged to make them available with appropriate documentation.

14. Medical Imaging Data

Medical images may contain embedded identifiers or other information capable of identifying individuals.

Before sharing imaging data, authors should ensure that identifiable metadata and visible identifiers have been appropriately removed where required.

Where the risk of re-identification remains significant, controlled access may be more appropriate than unrestricted public deposition.

Any image modification undertaken for anonymization should not alter the scientific meaning of the research evidence.

15. Informed Consent and Data Sharing

Where human participant data are involved, sharing arrangements should be compatible with the consent obtained from participants.

Consent to participate in research does not automatically imply consent for unrestricted public sharing of individual-level data.

Authors should consider whether participant information and consent documents address:

  • storage of research data;

  • future reuse;

  • repository deposition;

  • controlled sharing;

  • international transfer; and

  • secondary research use.

Where original consent does not permit public sharing, the journal does not require authors to disregard those restrictions.

16. Ethics Approval and Data Access

Data sharing should remain consistent with the requirements of the approving research ethics committee, institutional review board, or equivalent body.

Where ethics approval restricts redistribution or secondary use, authors should comply with those conditions.

Editors may request clarification regarding data-sharing restrictions where they affect interpretation or verification of the reported findings.

17. Third-Party Data

Authors using data controlled by hospitals, healthcare providers, government organizations, technology companies, research consortia, commercial databases, or other third parties must comply with the applicable terms of access.

Authors should not redistribute third-party data unless they possess authorization to do so.

Where readers may request access directly from the original provider, the Data Availability Statement should identify the relevant access mechanism where permitted.

Publication by the journal does not override third-party ownership or contractual restrictions.

18. Proprietary and Commercial Data

Some digital health studies involve proprietary systems, commercial devices, private datasets, or confidential technical information.

Authors should disclose relevant access restrictions where possible.

The inability to distribute proprietary data does not automatically prevent publication, provided that the manuscript contains sufficient methodological information to support meaningful scholarly evaluation.

Where appropriate, authors may provide anonymized, aggregated, derived, or non-proprietary data instead of restricted raw data.

19. Privacy and Confidentiality

Authors are responsible for protecting confidential and sensitive information throughout the data lifecycle.

Research data should be shared only when doing so is consistent with relevant:

  • privacy protections;

  • confidentiality commitments;

  • participant expectations;

  • institutional policies;

  • contractual obligations; and

  • legal requirements.

Authors should not assume that anonymization is complete merely because names or direct identifiers have been removed.

Digital datasets may permit re-identification through combinations of demographic, temporal, geographic, physiological, or behavioral variables.

20. Cybersecurity and Data Protection

Digital health research may involve information systems vulnerable to unauthorized access, misuse, or security breaches.

Authors should consider appropriate safeguards for sensitive research data, including where relevant:

  • secure storage;

  • access controls;

  • encryption;

  • authentication;

  • controlled transfer;

  • de-identification;

  • logging; and

  • restricted permissions.

The journal does not require data sharing that would create an unreasonable cybersecurity risk.

21. Research Code and Analytical Materials

Where software code, statistical scripts, machine-learning pipelines, computational notebooks, model configurations, or other analytical materials are central to the reported findings, authors are encouraged to share these materials where legally and technically feasible.

Relevant materials may include:

  • preprocessing scripts;

  • statistical code;

  • source code;

  • notebooks;

  • model architecture details;

  • parameter settings;

  • evaluation scripts; and

  • simulation procedures.

Where code cannot be shared because of intellectual-property, proprietary, security, or contractual restrictions, authors should explain material limitations where appropriate.

22. Data Used During Peer Review

Editors and reviewers may request access to underlying data where such access is reasonably necessary to assess the validity, accuracy, methodology, or integrity of a submitted manuscript.

Research data supplied for confidential editorial evaluation must be treated as confidential material.

Reviewers must not:

  • share the data;

  • reuse the data;

  • publish the data;

  • incorporate them into personal research; or

  • use them for competitive or commercial advantage.

Sensitive data should be transferred only through an appropriate secure mechanism.

23. Data Integrity

Authors are responsible for ensuring that research data accurately represent the underlying research.

The journal does not permit:

  • fabrication;

  • falsification;

  • inappropriate manipulation;

  • deliberate omission;

  • misleading alteration;

  • selective suppression; or

  • intentional misrepresentation

of research data.

Where credible concerns arise, the journal may request supporting records necessary to evaluate the integrity of the published or submitted research.

24. Preservation of Research Records

Authors should retain research records for an appropriate period consistent with institutional requirements, funder requirements, applicable law, disciplinary expectations, and ethical obligations.

Retention arrangements should be sufficient to support legitimate questions concerning published research where reasonably possible.

The journal does not impose a universal retention period where different legal, institutional, or disciplinary requirements apply.

25. Data Sharing After Publication

Where authors state that data are available upon reasonable request, they should make reasonable efforts to honor legitimate scholarly requests subject to applicable restrictions.

Authors may require appropriate conditions such as:

  • identification of the requesting researcher;

  • research-purpose description;

  • ethics approval;

  • institutional authorization;

  • data-use agreement;

  • confidentiality agreement; or

  • security safeguards.

Such requirements should not be used solely to prevent legitimate scholarly scrutiny.

26. Changes in Data Availability

If the availability of data changes materially after publication, authors should notify the journal where the change makes the published Data Availability Statement inaccurate.

Changes may include:

  • repository closure;

  • loss of data;

  • newly imposed access restrictions;

  • changes in consent requirements;

  • replacement of a dataset;

  • changes in third-party permissions; or

  • revision of controlled-access procedures.

Where appropriate, the journal may issue a correction or clarification.

27. Correction of Deposited Data

If authors identify errors in a publicly deposited dataset associated with a published article, they should use transparent versioning or correction procedures where available.

Authors should not silently replace data where doing so would obscure the evidentiary basis of the published research.

Where a change materially affects published findings or conclusions, the journal should be informed promptly.

28. Data Citation

Publicly available datasets that form a substantive source of research evidence should be cited appropriately.

Where available, data citations should identify:

  • dataset creator;

  • dataset title;

  • repository;

  • year;

  • version;

  • persistent identifier; and

  • other relevant access information.

Appropriate citation supports transparency and scholarly recognition of data creators.

29. Data Licensing and Reuse

Authors depositing original datasets should identify appropriate reuse conditions where applicable.

The license governing a journal article does not automatically apply to an independently deposited dataset.

Authors should ensure that they possess the rights necessary to apply a license to shared data.

Third-party data remain subject to the rights and restrictions established by the relevant data provider.

30. Restrictions on Data Sharing

Public sharing may be inappropriate where research data:

  • contain identifiable personal information;

  • contain protected health information;

  • are covered by confidentiality obligations;

  • are subject to ethics restrictions;

  • are governed by participant consent limitations;

  • contain proprietary information;

  • are controlled by a third party;

  • are legally restricted;

  • create cybersecurity risks;

  • contain sensitive location or behavioral information; or

  • cannot be adequately anonymized.

Authors should disclose legitimate restrictions transparently to the extent reasonably possible.

31. Data Unavailability

The inability to provide research data does not automatically constitute research misconduct.

Data may become unavailable because of:

  • lawful destruction;

  • technical failure;

  • institutional retention limits;

  • privacy restrictions;

  • contractual requirements;

  • loss of access; or

  • circumstances outside the authors' reasonable control.

Where data are unexpectedly unavailable, authors should provide a clear explanation if the absence materially affects evaluation of the research.

32. Misleading Data-Sharing Claims

Authors must not knowingly provide false or misleading information regarding the existence or availability of research data.

Examples include claiming that:

  • data are publicly available when they are restricted;

  • a repository contains data that are not actually deposited;

  • data are available on request when the authors cannot provide access;

  • real patient data were used when the study relied on synthetic data; or

  • authors have rights to redistribute third-party data when they do not.

Material discrepancies may be evaluated under the journal's Research Misconduct Policy.

33. Responsibilities of Authors

Authors are responsible for:

  • accurately describing data provenance;

  • obtaining necessary ethics approvals;

  • obtaining appropriate consent;

  • protecting participant confidentiality;

  • respecting privacy and legal requirements;

  • complying with third-party agreements;

  • preserving appropriate supporting records;

  • providing an accurate Data Availability Statement;

  • documenting relevant processing procedures;

  • citing data sources;

  • explaining restrictions;

  • sharing research data where appropriate; and

  • cooperating with legitimate editorial inquiries concerning data integrity.

Authors should not make data-access commitments that cannot reasonably be fulfilled.

34. Responsibilities of Editors

Editors should evaluate data-related issues fairly and proportionately.

Editors may request clarification concerning:

  • data provenance;

  • data availability;

  • ethical approval;

  • consent;

  • restrictions;

  • inconsistencies;

  • analytical procedures; or

  • the relationship between reported results and underlying data.

Editors should not require disclosure that would violate legitimate privacy, ethical, legal, security, or contractual obligations.

35. Responsibilities of Reviewers

Reviewers may assess whether manuscripts provide sufficient information concerning research data to permit meaningful scholarly evaluation.

Reviewers should identify concerns confidentially and should not independently contact research participants, institutions, or data providers unless authorized by the journal.

Unpublished data encountered through peer review must remain confidential.

36. Research Data and Misconduct

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

Editorial action will depend on:

  • seriousness of the concern;

  • available evidence;

  • effect on the reliability of the research;

  • status of the manuscript; and

  • findings of any relevant institutional investigation.

Where necessary, the journal may consider correction, Expression of Concern, retraction, or another appropriate action.

37. Relationship With the Data Availability Policy

The Research Data Sharing Policy establishes broad principles concerning responsible data stewardship, sharing, documentation, repository use, privacy, controlled access, and research transparency.

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

The two policies are complementary and should be interpreted together.

38. Policy Review

The journal may periodically revise this policy to reflect developments in:

  • digital health research;

  • telemonitoring technologies;

  • privacy and data-protection practices;

  • research ethics;

  • artificial intelligence;

  • cybersecurity;

  • repository infrastructure;

  • health-data governance; and

  • scholarly publishing standards.

Material revisions will be reflected transparently on the journal website.

Commitment to Responsible Research Data Practices

Digital Health & Telemonitoring Advances supports research-data practices that strengthen transparency, accountability, reproducibility, and responsible scholarly exchange while protecting individuals and respecting legitimate ethical, legal, privacy, confidentiality, cybersecurity, contractual, and intellectual-property obligations.

The journal encourages authors to consider responsible data stewardship throughout the entire research lifecycle, including collection, storage, analysis, documentation, sharing, reuse, and preservation.

Open research practices are encouraged where appropriate, but transparency must not come at the expense of participant rights, health-data confidentiality, or responsible data governance.