DATA SHARING & RESEARCH TRANSPARENCY POLICY

The Link journal of Speech, Language and Audiology (JSLA) supports responsible research transparency and encourages authors to make research data accessible, reusable, and verifiable where this is appropriate, lawful, and ethically permissible. JSLA recognizes that transparent practices strengthen scientific credibility and reproducibility, but also acknowledges that data sharing must not compromise privacy, confidentiality, consent conditions, intellectual property, contractual obligations, or legal restrictions. Authors remain responsible for ensuring that any shared data, code, instruments, or supplementary files comply with ethical approvals, informed consent commitments, and applicable data protection principles.

Where feasible, authors are encouraged and where justified by study type, funder expectations, or research norms may be expected to share materials that allow verification and reuse, including raw data, processed datasets, statistical code or syntax, questionnaires and measurement tools, study protocols and analysis plans, metadata and data dictionaries, and supplementary materials that are necessary to interpret or replicate the work. When sharing is not possible, authors must explain the restriction transparently through a Data Availability Statement.

Where Data Can Be Deposited

Authors may deposit supporting data and materials in any recognized repository that provides stable access, appropriate governance, and a persistent identifier or permanent link. Acceptable repositories include general-purpose repositories such as Zenodo, Figshare, Dryad, and the Open Science Framework (OSF), as well as institutional repositories including university repositories and national digital archives. Where an appropriate domain-specific repository exists, authors are encouraged to use it, particularly for specialized datasets common to precision medicine and allied disciplines; examples may include repositories and registries relevant to clinical trials, physiological data, genomics, or other structured biomedical datasets. Repository selection should reflect the sensitivity of the data and the need for open, restricted, or controlled access, and authors should ensure that repository terms align with ethics approvals and consent provisions.

Data Availability Statement (DAS)

A Data Availability Statement (DAS) is required for all research articles published in JSLA so that readers can understand whether data is available and under what conditions. The statement must be accurate, consistent with the manuscript, and aligned with ethical and legal constraints. When data are publicly available, the DAS should name the repository and provide a DOI or permanent link using clear wording such as: “The datasets generated and/or analysed during the current study are available in the [repository name] repository at [DOI/permanent link].” When data are available upon reasonable request, authors should state the request pathway and any access conditions, for example: “The data supporting the findings of this study are available from the corresponding author upon reasonable request.” When data cannot be shared publicly due to confidentiality, ethical, legal, or contractual restrictions, authors must state the restriction and, where feasible, indicate whether controlled access may be possible with approvals, such as: “Data cannot be shared publicly due to confidentiality and ethical restrictions, but may be available upon request with institutional approval.” If no new data were generated or collected, authors should state this explicitly, for example: “No new data were collected or created in this study.”

Confidentiality & Ethical Considerations

For studies involving human participants, clinical records, sensitive personal information, or any data with a meaningful risk of re-identification, data must be de-identified and shared only with appropriate safeguards. Authors must ensure that data sharing aligns with informed consent requirements, ethics committee approvals, and applicable data protection principles and local regulations. Where de-identification is insufficient to mitigate risk, authors should use restricted or controlled-access repositories and describe the access process and safeguards in the Data Availability Statement. JSLA expects authors to avoid sharing any data that could reasonably enable identification of individuals or expose participants or communities to harm.

Data Citation Requirements

When datasets or other reusable research outputs are shared, they should be cited in a way that enables attribution and discovery. JSLA expects that shared datasets are cited in the reference list where appropriate, including the dataset creator(s), year, dataset title, repository name, and DOI or permanent link. This practice supports proper credit for data creation and ensures that readers can locate the exact version of the dataset associated with the published findings.

Code, Software, and Algorithm Transparency

JSLA encourages authors to share analysis code and computational workflows whenever feasible and safe, including scripts and syntax used for reproducible analyses (for example R, Python, and SPSS syntax) and key algorithm parameters relevant to primary findings. Where possible, code may be shared through established version-controlled platforms and/or archived in repositories that provide persistent identifiers and versioning. If code cannot be shared due to licensing, security, or confidentiality constraints, authors should still describe the analysis procedures and parameters in sufficient detail to allow informed evaluation and, where feasible, replication using equivalent tools.

Research Reproducibility

Authors must describe methods and procedures with sufficient clarity for readers to evaluate the work and, where feasible, reproduce key elements. This includes transparent reporting of study design, data sources, inclusion and exclusion criteria, measurement tools, and the analysis workflow, including how data were cleaned, transformed, and handled for missingness and sensitivity analyses where relevant. For precision medicine studies, authors should also report any governance constraints relevant to reuse, such as consent limitations, controlled-access conditions, or restrictions related to genomic or high-dimensional datasets.

Misconduct Related to Data

If concerns arise regarding falsified, fabricated, manipulated, selectively withheld, or otherwise unverifiable data, JSLA may request supporting documentation, de-identified raw data where ethically permissible, analysis outputs, or other materials required to evaluate integrity. Where authors cannot provide reasonable assurance of data integrity, the journal may reject the manuscript or, if issues are discovered post-publication, may issue a correction, expression of concern, or retraction as appropriate. In serious cases, the journal may notify relevant institutions or oversight bodies where necessary to protect the integrity of the scholarly record.

Journal Responsibilities

JSLA supports responsible data sharing by requiring Data Availability Statements for research articles, encouraging deposit in appropriate repositories, and linking published articles to datasets and supporting materials where provided. The journal reviews Data Availability Statements for clarity and consistency and may request revisions where statements are incomplete, ambiguous, or inconsistent with the reported study. JSLA aims to promote transparency while respecting ethical and legal constraints, particularly where clinical, sensitive, or precision datasets require careful governance.

Exceptions

Data sharing is not required when confidentiality or ethical restrictions apply, when legal agreements or contractual obligations prohibit disclosure, or when sensitive security considerations make public release inappropriate. However, even when data cannot be shared, authors must still include a Data Availability Statement explaining the restriction and, where feasible, describing whether controlled access or mediated access may be possible through institutional or ethics-approved processes.