# How to Handle Data Sharing in International Collaborations ## Quick Answer - Start data sharing discussions before data collection begins, and document every agreement in a written data sharing agreement that names the data, the permitted uses, and the parties. - Build your sharing plan around the legal requirements of each participating country, the ethical standards of your institutions, and the cultural expectations of your research partners. - Recognize that no single template fits every collaboration, and you must verify your specific obligations with your institution, funder, and legal counsel before you share any data. ## At a Glance International research collaborations create shared value through pooled data, but they also create shared risk. The table below summarizes the main decision points you will face when you plan data sharing across borders. | Decision Point | What to Consider | Practical Next Step | | --- | --- | --- | | Legal jurisdiction | Which laws apply to the data in each participating country | Identify the data protection and research regulations in every partner country before you draft agreements | | Ethical obligations | How consent, privacy, and community expectations shape permissible data uses | Review your institutional review board approvals and consent forms for cross-border sharing language | | Cultural norms | How partner communities view data ownership, benefit sharing, and publication | Discuss data governance expectations with local partners and community representatives early | | Data sensitivity | Whether the data contains identifiable human information, proprietary findings, or restricted materials | Classify each dataset by sensitivity level and document the classification | | Funder requirements | What your funder expects for data management, sharing, and access | Read your funder's data sharing policy and align your plan with its deadlines | | Authorship and credit | How contributors will be recognized in publications and datasets | Agree on authorship criteria and data citation practices before analysis begins | | Long-term stewardship | Who maintains the data after the project ends and under what conditions | Name a data repository or steward in your agreement and specify access terms | ## Understanding the Core Problem International data sharing fails most often because partners assume alignment that does not exist. Researchers from different countries bring different legal frameworks, ethical traditions, and institutional expectations to the same table. A data set that is freely shareable in one country may be restricted in another. A consent form that satisfies one ethics board may not satisfy another. A publication practice that is standard in one discipline may be considered inappropriate in another. The practical problem is not whether to share data. The practical problem is how to share data in a way that respects every partner's obligations and expectations. You need a structured approach that treats legal, ethical, and cultural factors as equally important inputs into every decision. ### Why Legal Factors Are Only Part of the Answer Legal compliance is necessary but insufficient. You must know which regulations apply to your data, but you also must understand that regulations change, that they differ by country, and that they interact with institutional policies. Your institution may have its own data governance rules that go beyond national law. Your funder may impose additional requirements. Your journal may require data availability statements that commit you to specific sharing arrangements. The [National Institutes of Health](https://sharing.nih.gov/data-management-and-sharing-policy) expects researchers to plan for data management and sharing at the proposal stage, which means you should address cross-border questions before you collect any data. The [NIH Grants and Funding](https://grants.nih.gov/) pages describe the broader grant lifecycle, including the expectations that apply to funded projects. These official sources show that data sharing is not an afterthought. It is a planned activity with documented decisions. ### Why Ethical Factors Shape What You Can Share Ethical obligations come from multiple sources. Your institutional review board or research ethics committee approves your study based on the consent documents and protocols you submit. Those approvals may limit how you can use the data, who can access it, and where it can be stored. Your professional community may have its own ethical standards. The [Committee on Publication Ethics](https://publicationethics.org/core-practices) describes core practices that cover data, authorship, peer review, and conflicts of interest. These practices apply to the entire research lifecycle, beyond the publication stage. When you share data internationally, you must verify that your sharing plan is consistent with every ethical approval you have received. If your consent form did not mention international sharing, you may need to seek additional approval or amend your consent process. If your data includes information from vulnerable populations, you may need to consider whether sharing is appropriate at all. ### Why Cultural Factors Determine Whether Sharing Works Cultural norms affect how partners interpret data ownership, credit, and benefit sharing. In some research communities, data is treated as a common resource that should be openly available. In others, data is treated as a community asset that requires ongoing consultation before any external use. Indigenous communities, agricultural cooperatives, and patient advocacy groups may have specific expectations about how their data is used and who benefits from it. You cannot resolve these differences by reading a policy document. You must discuss them directly with your partners. These conversations should happen early, they should be documented, and they should inform your written agreements. A data sharing arrangement that ignores cultural expectations will fail even when it is legally sound. ## Building a Data Sharing Decision Matrix A decision matrix helps you move from general principles to specific actions. The matrix below organizes the questions you should answer for each dataset in your collaboration. You can adapt it to your project by adding rows for your specific data types, partner countries, or funder requirements. | Data Set | Legal Classification | Ethical Constraints | Cultural Considerations | Sharing Decision | | --- | --- | --- | --- | --- | | Human participant survey responses | Identify applicable privacy and data protection laws in each country | Review consent language and ethics approvals for sharing restrictions | Consult community representatives about acceptable uses and benefits | Share only if all legal, ethical, and cultural conditions are met | | Laboratory measurements from animal studies | Determine whether any export or transfer restrictions apply | Confirm that institutional animal care approvals permit data sharing | Discuss authorship and credit expectations with all contributors | Document the sharing terms in a written agreement | | Genomic sequence data | Verify repository and funder requirements for deposition | Confirm that consent covers genomic data sharing | Agree on benefit sharing and publication timing with partners | Deposit in an approved repository with controlled access if needed | | Field observations from agricultural trials | Check whether any national or regional restrictions apply | Confirm that landowner and community permissions cover sharing | Discuss how local knowledge will be credited and protected | Share with attribution and documented provenance | ### Step 1: Classify Your Data Begin by listing every dataset your collaboration will produce or use. For each dataset, record the type of data, the source, the sensitivity level, and the restrictions that apply. This classification work is the foundation of every later decision. You should classify data at the start of the project and revisit the classification whenever the project changes. New data types may be added. Existing data may become more sensitive as the project evolves. Your classification should be a living document that the whole team can access and update. ### Step 2: Identify Applicable Legal Frameworks For each dataset, identify the legal frameworks that apply in every country where the data is collected, stored, processed, or shared. This includes national data protection laws, research regulations, and any sector-specific rules that apply to your data type. You should not attempt to interpret foreign law on your own. Ask your partners to identify the relevant regulations in their countries and to explain how those regulations apply to your project. If your institution has international research offices or legal counsel, involve them early. The [National Library of Medicine](https://www.ncbi.nlm.nih.gov/books) provides access to authoritative biomedical reference materials that can help you understand research methods and regulatory contexts, but it cannot replace legal advice for your specific project. ### Step 3: Review Ethical Approvals and Consent Documents Gather every ethical approval, consent form, and protocol document associated with your project. Review each document for language that addresses data sharing, cross-border transfer, secondary use, and long-term storage. If your documents do not mention international sharing, you have three options. You can seek an amendment to your approvals, you can limit your sharing to what the current documents permit, or you can collect new consent for the sharing activity. The right option depends on your project timeline, your participant population, and the nature of the data. ### Step 4: Discuss Cultural and Community Expectations Schedule dedicated conversations with your partners about data governance. These conversations should cover data ownership, access rights, publication credit, benefit sharing, and long-term stewardship. You should document the outcomes of these conversations and incorporate them into your agreements. For projects involving community data, you should also consult with community representatives. This consultation may be required by your ethics approvals, or it may be an expectation of your partner institutions. Either way, it is a practical step that prevents misunderstandings later. ### Step 5: Draft a Data Sharing Agreement A data sharing agreement is the written record of your decisions. It should name the parties, describe the data, specify the permitted uses, define the access terms, and address publication and authorship. It should also describe what happens when the project ends. Your agreement should be drafted with input from all partners and reviewed by institutional counsel where available. The [Committee on Publication Ethics](https://publicationethics.org/core-practices) core practices emphasize the importance of clear agreements about data and authorship, and your agreement is the mechanism for making those expectations explicit. ### Step 6: Implement and Monitor the Agreement Once your agreement is signed, you must implement it. This means training team members on the terms, establishing secure data storage and transfer procedures, and creating records of data access and use. You should also schedule regular reviews to confirm that the agreement still reflects the project's needs. Monitoring is especially important in long collaborations. Personnel change, institutions merge, and regulations evolve. Your agreement should include a process for updating its terms when circumstances change. ## Drafting Data Sharing Agreements That Work A data sharing agreement is a practical document, not a legal formality. It protects your project by making expectations explicit and by providing a mechanism for resolving disagreements. The sections below describe the components that should appear in every agreement. ### Defining the Data and Its Permitted Uses The agreement must describe the data with enough specificity that there is no ambiguity about what is covered. This description should include the data types, the time period of collection, the format, and any identifiers or variables that make the data sensitive. The permitted uses section should state exactly what each party may do with the data. This includes the research questions that may be addressed, the analyses that may be performed, and the publications that may result. It should also state what is not permitted, such as commercial use, further sharing, or re-identification attempts. ### Establishing Access and Security Requirements The agreement should specify who may access the data, how access is granted, and what security measures must be in place. This includes storage locations, encryption requirements, and restrictions on portable devices. For sensitive data, you may want to require that access occurs only in approved environments or through approved analysis platforms. You may also want to require that any data breaches be reported within a defined time frame. ### Addressing Publication, Authorship, and Credit Publication expectations should be explicit in the agreement. This includes the order of authorship, the criteria for authorship, and the process for approving manuscripts. It should also address how the data itself will be cited and credited. The [Committee on Publication Ethics](https://publicationethics.org/core-practices) core practices describe the responsibilities of authors and the importance of accurate attribution. Your agreement should align with those practices and should name the person responsible for managing authorship decisions. ### Planning for Data Retention and Destruction The agreement should state how long the data will be retained, where it will be stored, and what happens when the retention period ends. This includes provisions for archiving, destruction, or transfer to a repository. Retention planning is especially important for international collaborations because different countries may have different requirements for record keeping. Your agreement should identify the requirements that apply and should name the party responsible for compliance. ### Creating a Dispute Resolution Process Disagreements will happen. The agreement should describe how they will be resolved. This includes the steps for informal resolution, the escalation path, and the governing law or arbitration mechanism. A clear dispute resolution process is not an admission that the collaboration will fail. It is a practical tool that helps partners address problems before they become project-ending conflicts. ## Implementing Your Data Sharing Plan A written plan is only useful if you implement it. The steps below describe how to put your data sharing decisions into practice across the project lifecycle. ### Before Data Collection Finalize your data sharing agreement before you collect any data. Confirm that your consent forms and ethics approvals address the sharing arrangements you have planned. Train your team on the agreement terms and on the security procedures that apply. You should also confirm that your funder's requirements are reflected in your plan. The [NIH Data Management and Sharing Policy](https://sharing.nih.gov/data-management-and-sharing-policy) describes expectations for planning and implementation, and your plan should align with those expectations if your project is NIH-funded. ### During Data Collection Maintain records of all data collection activities, including consent documentation, data quality checks, and any deviations from your approved protocol. These records support your later sharing decisions and provide evidence of responsible conduct. You should also monitor for changes that affect your sharing plan. If you add a new data type, recruit a new population, or change your analysis approach, you may need to update your agreement and seek additional approvals. ### During Analysis and Publication Apply your agreement's terms when you prepare data for analysis and publication. This includes de-identifying data where required, documenting data transformations, and preparing data availability statements that match your sharing commitments. Before you submit a manuscript, confirm that all authors meet the authorship criteria in your agreement and that all contributors are acknowledged appropriately. The [Committee on Publication Ethics](https://publicationethics.org/core-practices) core practices provide context for these responsibilities. ### After the Project Ends Transfer data to the repository or steward named in your agreement. Confirm that access controls are in place and that the data is documented sufficiently for future users. Notify your partners of the transfer and provide them with the access information they need. You should also schedule a final review of the collaboration. This review should assess what worked, what did not, and what you would do differently in future projects. This reflection improves your practice and benefits your future collaborations. ## Records and Measurements That Support Responsible Sharing Responsible data sharing depends on documentation. The records you keep during the project become the evidence that your sharing decisions were deliberate, informed, and consistent with your obligations. ### Essential Records to Maintain Maintain a data governance file that includes your data sharing agreement, your ethics approvals, your consent documents, your data classification records, and your correspondence about data governance decisions. This file should be accessible to all partners and should be updated whenever decisions change. You should also maintain access logs that record who accessed the data, when they accessed it, and what they did with it. These logs support accountability and help you respond to questions about data use. ### Measuring Compliance and Impact Track your compliance with the agreement terms by reviewing access logs, publication outputs, and data repository records. This review should occur at regular intervals and should be documented. You should also track the impact of your sharing by recording how the data is used, who cites it, and what publications result. This information supports your reporting to funders and demonstrates the value of the collaboration. ## Common Failure Patterns in International Data Sharing Understanding how collaborations fail helps you prevent the same failures in your project. The patterns below are common across international research partnerships. ### Assuming One Country's Rules Apply Everywhere Researchers often assume that the rules of their home institution apply to the entire collaboration. This assumption fails when partners are subject to different legal or ethical requirements. The result is a sharing plan that one partner cannot implement. Prevent this failure by asking each partner to document the requirements that apply to them. Compare those requirements and build your plan around the most restrictive applicable terms. ### Delaying Agreement Until Data Is Collected Some collaborations begin with informal understandings and postpone formal agreements until publication time. This delay creates problems because the partners have already made decisions about data use, authorship, and credit without a shared framework. Prevent this failure by drafting your agreement before data collection begins. The agreement can be revised as the project evolves, but the initial framework should be in place from the start. ### Ignoring Cultural and Community Expectations Legal and ethical compliance does not guarantee cultural acceptance. A sharing plan that is legally sound may still fail if it ignores community expectations about data ownership, benefit sharing, or publication credit. Prevent this failure by including cultural and community consultation in your planning process. Document the outcomes of those consultations and incorporate them into your agreements. ### Treating Data Sharing as a One-Time Decision Data sharing decisions are not made once and forgotten. They must be revisited whenever the project changes, when new partners join, or when regulations evolve. Prevent this failure by scheduling regular reviews of your data governance arrangements. These reviews should be documented and should result in updated agreements when needed. ## Limitations and Professional Escalation Criteria Data sharing guidance has limits. The sources cited in this article describe expectations and practices, but they cannot resolve every question about your specific project. You must know when to seek professional help. ### When to Consult Institutional Counsel Consult your institution's legal counsel when your project involves data from multiple countries, when the data is highly sensitive, or when your partners have conflicting legal requirements. Legal counsel can help you understand the implications of your agreement and can draft or review the terms. You should also consult counsel when a dispute arises that you cannot resolve informally. The dispute resolution process in your agreement should name the escalation path, and legal counsel should be part of that path. ### When to Consult Your Research Ethics Office Consult your research ethics office when you are uncertain whether your consent documents and approvals cover your planned sharing. The ethics office can help you determine whether you need amendments or new approvals. You should also consult the ethics office when you plan to share data that was not originally collected for your project. Secondary use raises ethical questions that your ethics office can help you address. ### When to Consult Your Funder Consult your funder when you are uncertain about data management and sharing requirements. Funders publish policies and provide guidance, and their program officers can answer questions about specific requirements. You should also consult your funder when your project changes in ways that affect your data sharing commitments. Funders expect to be informed about significant changes to approved plans. ## Welfare and Safety Context for Data Sharing Data sharing affects people, animals, and communities. Your sharing decisions should reflect a commitment to protecting all of them. ### Protecting Human Participants Human participant data requires the highest level of care. Your sharing plan must protect participant privacy, honor consent commitments, and prevent re-identification. If your data includes vulnerable populations, you should consider whether sharing is appropriate and what safeguards are needed. ### Protecting Animal Subjects Animal research data may be subject to institutional approvals and reporting requirements. Your sharing plan should confirm that your animal care and use approvals permit the planned sharing and that the data is documented sufficiently for responsible reuse. ### Protecting Communities and Local Knowledge Community data and local knowledge require respect for the communities that provided them. Your sharing plan should address benefit sharing, attribution, and the protection of culturally sensitive information. Consultation with community representatives is essential. ## Frequently Asked Questions ### What is the first step in planning international data sharing? The first step is to classify your data and identify the legal, ethical, and cultural requirements that apply in every participating country. This classification work should happen before data collection begins and should be documented in a written data sharing agreement. ### Do I need a separate data sharing agreement for each partner? You should have a written agreement that covers all partners, but the agreement should address the specific obligations of each partner. If partners have conflicting requirements, you may need separate agreements or additional provisions that reconcile the differences. ### How do I handle data that was collected before my collaboration began? You must review the consent documents and ethics approvals that apply to the existing data. If they do not permit the planned sharing, you may need to seek amendments, obtain new consent, or limit your sharing to what is permitted. ### What should I do if my partners have different publication expectations? Discuss publication expectations early and document them in your agreement. The agreement should name the authorship criteria, the order of authorship, and the process for approving manuscripts. The [Committee on Publication Ethics](https://publicationethics.org/core-practices) core practices provide context for these decisions. ### How do I know if my funder has data sharing requirements? Read your funder's published policies and contact your program officer with specific questions. The [NIH Data Management and Sharing Policy](https://sharing.nih.gov/data-management-and-sharing-policy) describes expectations for NIH-funded projects, and other funders publish similar guidance. ### What happens if a partner violates the data sharing agreement? The agreement should include a dispute resolution process that describes the steps for addressing violations. This process typically begins with informal discussion and escalates to formal review or legal action if needed. ### Can I share data that includes identifiable human information? You can share identifiable human information only if your consent documents and ethics approvals permit it and if the sharing complies with applicable law. In most cases, you should de-identify the data before sharing or use controlled access arrangements. ### How long should I retain data after the project ends? Retention periods depend on your funder requirements, institutional policies, and applicable law. Your data sharing agreement should specify the retention period and the arrangements for archiving, destruction, or transfer to a repository. ## Related Articles - [Ethical Considerations in Research with Vulnerable Populations: A Practical Guide](/blog/guides/ethical-considerations-in-research-with-vulnerable-populations-a-practical-guide) - [Ethical Considerations in Research: A Practical Guide for Scientists](/blog/guides/ethical-considerations-in-research-a-practical-guide-for-scientists) - [Ethical Data Collection in Research: Principles and Best Practices](/blog/guides/ethical-data-collection-in-research-principles-and-best-practices) - [Ethical Considerations in Research: A Practical Checklist for Study Design](/blog/guides/ethical-considerations-in-research-a-practical-checklist-for-study-design) - [Ethical Considerations in Psychological Research: A Guide for Students and Early-Career Researchers](/blog/guides/ethical-considerations-in-psychological-research-a-guide-for-students-and-early-career-researche) ## References and Further Reading - [Research Methods Resources](https://www.ncbi.nlm.nih.gov/books). National Library of Medicine. - [EQUATOR Network](https://www.equator-network.org/). EQUATOR Network. - [Core Practices](https://publicationethics.org/core-practices). Committee on Publication Ethics. - [NIH Grants and Funding](https://grants.nih.gov/). National Institutes of Health. - [ORCID for Researchers](https://info.orcid.org/researchers). ORCID. - [Data Management and Sharing Policy](https://sharing.nih.gov/data-management-and-sharing-policy). National Institutes of Health. - [NCBI Literature Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information. - [PubMed](https://pubmed.ncbi.nlm.nih.gov/). National Library of Medicine. > This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.