Adding Data Visitation language to ICFs

Page 1 DV4RDA Adding Data Visitation language to ICFs Template Version: 1.1c Template Date: Nov 24, 2025 Prepared by: Patricia Buendia , Seonyoung Kim , DV4RDA project, EOSC Future/RDA Artificial Intelligence and Data Visitation Working Group (AIDV WG), Research Data Alliance(RDA) Description Incorporating Data Visitation language into an Informed Consent Form (ICF) requires clear, nontechnical language that explains the concept and, importantly, how it benefits the participant by protecting their privacy. INSTRUCTIONS This language should be added primarily within the "Confidentiality" or "How Your Information Will Be Kept Private" section of the ICF. You may also briefly mention it in the "Study Procedures" or "Data Handling" sections if those exist, but the core explanation and privacy implications belong in the Confidentiality section. Key things to ensure when using this language: ● Simplicity: Avoid jargon. Explain the core concept (analysis goes to data, data stays put) clearly. ● Focus on Benefit: Emphasize that this method is used to protect their privacy and minimize risk to their data. ● Accuracy: Ensure the description accurately reflects how your specific implementation of data visitation works. ● Consistency: The language here must be consistent with the more detailed description provided in the IRB protocol submitted to the review board. ● Context: Place this text logically within the Confidentiality section, ensuring it flows with the surrounding text about data coding, secure storage, etc. Below is proposed language, structured to fit within a standard ICF format. Remember to adapt this to the specific details of your study, including the type of data, where it's stored, and the specific platform used. Proposed additions for key sections are presented below for two sections: Confidentiality and Data Handling. DOI: 10.15497/RDA00143 DV4RDA Page 2 PROPOSED ADDITIONS TO THE INFORMED CONSENT FORM Section: Confidentiality / How Your Information Will Be Kept Confidential Insert this text after the standard language about coding data, secure storage, and who has access (e.g., study team, regulatory bodies): Additional Privacy Protection Method: Using Data Visitation We may use a method called “Data Visitation” to help protect your privacy when your data is analyzed. Here's how it works and how it helps protect you: 1. Your Information Stays Secure: Your sensitive study data (such as test results or health history) will be safely stored on protected computer systems at [e.g., the hospital, university, or secure data center]. It will not be copied or transferred elsewhere for analysis. 2. Analysis Happens Inside the Secure System: Instead of transferring your data elsewhere for analysis, the approved analysis tools are run directly within the secure system where your data is stored. 3. Only Safe Results Are Shared: Once the analysis is complete, only summaries or results that cannot identify you personally (such as averages across many participants) may leave the secure system. These summaries are carefully reviewed to ensure they cannot be used to determine your identity. 4. Why We Use Data Visitation: This approach adds an extra layer of privacy protection. By keeping your sensitive data securely stored in one location and only allowing de-identified results to leave the secure research environment, we ensure that no one outside the study team ever sees your name or private details. Optional: Brief mention in Study Procedures or Data Handling Section If there's a section describing how data is handled or analyzed, you could add a brief sentence referring to the main explanation: Data Handling & Analysis: DV4RDA Page 3 For the analysis of the collected data, we will use a secure method called Data Visitation. You can read more about how your privacy is protected in the “Confidentiality” section of this form. Cite as: P. Buendia, S. Kim. “Adding Data Visitation language to ICFs.” DV4RDA Project of the EOSC-Future/RDA Artificial Intelligence and Data Visitation Working Group. Research Data Alliance. November 14, 2025. DOI: 10.15497/RDA00143 Acknowledgements This DV4RDA project has received funding through RDA TIGER from the European Union’s Horizon Europe framework programme under grant agreement No. 101094406. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or institutions represented here. Neither the European Union nor the institutions can be held responsible for them.