This policy explains JLPCS’s expectations for data availability statements, research material transparency, analytical reproducibility, code and documentation sharing, and responsible restrictions on access to data.

The purpose is to help readers, reviewers, and editors understand how research findings were produced, what materials support the analysis, and whether the underlying data or materials can be accessed, verified, reused, or reproduced.

本政策说明 JLPCS 对数据可得性声明、研究材料透明度、分析可重复性、代码与文档共享以及合理访问限制的要求。其目的在于帮助读者、审稿人和编辑理解研究结果如何形成、哪些材料支持分析，以及相关数据或材料是否可以访问、核查、复用或重复分析。

JLPCS encourages authors to make research data, analytical code, instruments, legal source lists, interview protocols, survey materials, coding schemes, supplementary tables, and other supporting materials available whenever this is ethically, legally, and practically possible.

Data sharing is encouraged but not required when sharing would violate privacy, consent, confidentiality, legal obligations, intellectual property rights, platform terms, institutional restrictions, security requirements, or protection of vulnerable participants.

JLPCS 鼓励作者在伦理、法律和实践上可行的情况下提供研究数据、分析代码、研究工具、法律来源清单、访谈提纲、问卷材料、编码方案、补充表格和其他支持材料。但当共享数据会违反隐私、知情同意、保密义务、法律要求、知识产权、平台规则、机构限制、安全要求或脆弱群体保护要求时，作者可以合理限制访问。

Authors should include a Data Availability Statement in the manuscript or declaration file. The statement should clearly explain whether data, code, materials, or supporting documents are available, where they can be accessed, and whether any restrictions apply.

A Data Availability Statement should normally answer:

• What data or materials support the findings?
• Where are the data or materials stored?
• Are the data publicly available, available on request, restricted, or unavailable?
• Are there ethical, legal, privacy, institutional, or commercial restrictions?
• Who should be contacted for reasonable access requests?
• What conditions apply to reuse, citation, confidentiality, or permissions?

作者应在稿件或声明文件中提供数据可得性声明，说明支持研究结论的数据、代码、材料或文档是否可获取、存放位置、访问方式、限制条件和联系人。

中文稿件可使用中文数据可得性声明。作者应根据真实研究情况修改示例，不得作出不准确或无法履行的数据共享承诺。

JLPCS expects authors to report methods, materials, procedures, and analytical decisions with sufficient clarity for scholarly assessment. Reproducibility requirements may differ across legal studies, psychology, communication studies, qualitative research, doctrinal analysis, and interdisciplinary work.

Authors should provide sufficient information about:

• Research design, research questions, hypotheses, propositions, or analytical framework;
• Sampling, recruitment, inclusion and exclusion criteria, data sources, or legal source selection;
• Survey instruments, interview protocols, experimental procedures, coding schemes, or document analysis procedures;
• Variables, measures, scales, coding categories, reliability checks, statistical models, or qualitative analytical steps;
• Software, packages, version numbers, analytical tools, databases, archives, legal search platforms, or AI-assisted tools used;
• Limitations, uncertainty, missing data, exclusions, robustness checks, and interpretive boundaries.

作者应充分说明研究设计、样本、资料来源、变量、测量、编码、分析步骤、软件工具和限制条件，使读者能够理解研究如何完成，并在可行范围内进行核查、复算或理论检验。

For quantitative, survey, psychological, experimental, computational, or statistical studies, authors should provide enough information to allow readers to understand and evaluate the analysis.

• Describe sample size, sampling method, recruitment procedure, inclusion and exclusion criteria, and missing data handling;
• Report measurement instruments, scale items, reliability, validity, coding, and variable construction where applicable;
• Describe statistical models, assumptions, software, packages, version numbers, and estimation procedures;
• Report effect sizes, confidence intervals, robustness checks, model diagnostics, or sensitivity analyses where appropriate;
• Provide data and code where ethically and legally possible, or explain access restrictions;
• Identify any pre-registration, protocol, registered report, or analysis plan where applicable.

定量、问卷、心理学、实验、计算或统计研究应提供样本、变量、测量工具、统计模型、软件版本、效应量、稳健性检验和缺失数据处理等信息。可行时应提供数据和代码；不可公开时应说明限制原因。

For qualitative research, reproducibility often means transparency of research design, data generation, interpretive procedure, reflexivity, and evidence traceability rather than exact replication.

• Describe participant recruitment, interview or observation procedures, fieldwork context, data collection period, and analytical approach;
• Provide interview guides, coding frameworks, category definitions, or analytical memos where appropriate and ethically possible;
• Explain how themes, categories, interpretations, or theoretical claims were developed;
• Protect participant identity when using quotations, field notes, images, screenshots, or contextual details;
• Explain why full transcripts, recordings, or raw field notes cannot be shared when confidentiality or consent restrictions apply;
• State whether de-identified excerpts, coding summaries, or supplementary methodological materials are available.

质性研究的透明度重点在于研究设计、资料生成、分析过程、研究者反思和证据链，而非简单复制。作者应在保护参与者隐私和同意范围的前提下，尽可能说明访谈、观察、编码和解释过程。

For doctrinal, comparative, legal-empirical, policy, governance, institutional, or documentary research, authors should identify sources and analytical procedures clearly enough for readers to verify the legal and documentary basis of the argument.

• Identify statutes, regulations, cases, policy documents, institutional records, reports, archives, or datasets used;
• Explain jurisdiction selection, case selection, document selection, period covered, and search strategy where relevant;
• Provide citations, official source names, dates, document numbers, URLs, archives, or database identifiers where available;
• Explain comparative criteria, doctrinal method, interpretive framework, coding, or policy analysis procedure;
• Distinguish primary legal sources, secondary literature, commentary, media materials, and empirical evidence;
• Explain restrictions when legal files, institutional documents, court materials, or administrative data are confidential or access-limited.

法律、政策、治理、制度和文献研究应清楚说明法律来源、案例选择、政策文件、资料库、检索策略、比较标准和解释方法。涉及保密法律文件、机构资料或受限档案时，应说明限制原因。

Authors should provide analysis code, scripts, software details, model specifications, coding files, or computational notebooks when these materials are necessary to verify or reproduce results and can be shared responsibly.

• Identify software names, packages, libraries, version numbers, operating environment, and settings where relevant;
• Provide code with sufficient comments or documentation to allow interpretation;
• Separate confidential data from shareable code where possible;
• Remove credentials, access tokens, personal data, platform keys, confidential identifiers, or proprietary information before sharing;
• Indicate whether code is original, adapted, licensed, or based on third-party tools;
• Explain why code cannot be shared when proprietary, confidential, security-sensitive, or legally restricted.

当代码、脚本、模型设定、软件环境或计算笔记本对复算结果具有重要作用时，作者应在可行范围内提供或说明其可得性。共享前应删除个人信息、访问密钥、平台凭证和保密内容。

JLPCS recognizes that some data cannot be made publicly available. Authors should explain restrictions clearly and provide as much transparency as possible without breaching ethical, legal, or confidentiality obligations.

Legitimate restrictions may include:

• Participant privacy, informed consent limits, or risk of re-identification;
• Sensitive psychological, behavioral, health, criminal justice, legal, employment, educational, or family information;
• Data involving minors, vulnerable populations, victims, witnesses, clients, patients, or institutional records;
• Confidential legal files, court materials, administrative data, commercial data, platform data, or government records;
• Contractual, platform, intellectual property, security, or national/local legal restrictions;
• Third-party ownership or archive rules that prohibit redistribution.

无法公开数据时，作者不应仅写“数据不可得”，而应说明合理限制原因，并尽量提供聚合数据、去标识化摘要、方法细节、变量说明、代码或其他可验证材料。

Authors may deposit data, code, protocols, instruments, appendices, and supplementary materials in a trusted repository, institutional archive, funder-designated platform, subject-specific database, or as journal supplementary materials where appropriate.

Supplementary or repository materials should include, where applicable:

• Dataset description, variable dictionary, codebook, or data collection documentation;
• Survey instruments, interview protocols, consent forms, or experimental materials where shareable;
• Analysis scripts, statistical code, coding schemes, or model files;
• Supplementary tables, robustness checks, search strategies, or source selection lists;
• README files explaining file structure, software requirements, and reuse conditions;
• License, citation instructions, access restrictions, and contact details.

补充材料应清楚标注文件内容、版本、变量说明、代码说明、访问限制和引用方式。作者应避免上传含个人身份、保密信息或未经授权第三方材料的文件。

Authors must not use generative AI tools to fabricate data, interview transcripts, survey responses, legal materials, participant quotations, statistical outputs, images, references, or research findings. Any substantive AI assistance in data processing, coding, translation, transcription, summarization, or analysis must be disclosed where applicable.

• Authors remain responsible for the accuracy, legality, ethics, and integrity of AI-assisted outputs;
• AI-assisted coding, classification, translation, transcription, or analysis should be verified by the authors;
• Confidential, personal, sensitive, or unpublished data must not be uploaded to external AI tools unless permitted by consent, law, ethics approval, and data protection requirements;
• AI tools, model versions, prompts, workflows, and verification steps should be reported when they materially affect the research process;
• AI tools cannot replace expert judgment, legal interpretation, psychological assessment, or communication analysis.

作者不得使用生成式 AI 伪造数据、访谈、问卷、法律材料、图像、参考文献或研究结论。AI 如实质性参与数据处理、编码、翻译、转录、摘要或分析，应在方法或声明中说明工具、用途、范围和人工核查方式。

Reviewers and editors may evaluate whether the data availability statement, methods, analytical materials, and reproducibility information are sufficient for the manuscript type. Confidential manuscript data and materials must be protected during peer review.

• Reviewers may ask whether data, code, instruments, or source materials are sufficiently described;
• Reviewers may recommend clarification when data availability statements are missing, vague, or inconsistent with the manuscript;
• Reviewers must not request access to confidential data unless necessary and approved through appropriate editorial channels;
• Reviewers must not use, copy, distribute, or reuse unpublished data or materials for personal advantage;
• Reviewers must not upload manuscripts, datasets, tables, figures, code, or confidential materials into AI tools or public platforms;
• Editors may request additional data availability information, supplementary materials, or clarification before review, during revision, or before publication.

审稿人和编辑可评估数据可得性声明、方法说明和可重复性信息是否充分，但必须保护未发表稿件和数据的保密性，不得擅自使用、复制、传播或上传至外部工具。

JLPCS may request clarification when data availability, reproducibility, methods, source documentation, or analytical transparency is incomplete, inconsistent, or raises research integrity concerns.

Possible editorial actions include:

• Requesting a missing or revised Data Availability Statement;
• Requesting additional methodological details, code, source lists, appendices, or supplementary files;
• Requesting clarification of access restrictions, consent limits, confidentiality, or legal barriers;
• Pausing peer review, production, or publication while research integrity concerns are assessed;
• Rejecting a manuscript if data, methods, or evidence cannot support the claims made;
• Issuing a correction, expression of concern, or retraction after publication where data integrity problems affect the reliability of the article.

如数据可得性、方法透明度、证据链或可重复性信息不足，编辑部可要求补充说明、修改声明、提交补充材料，或在出现研究诚信问题时暂停处理、拒稿或采取出版后措施。

Questions about data availability statements, reproducibility, supplementary materials, data restrictions, code sharing, or research transparency may be directed to the JLPCS Editorial Office.

有关数据可得性声明、可重复性、补充材料、数据访问限制、代码共享或研究透明度的问题，可联系 JLPCS 编辑部。

Email: [email protected]