Participant Preferences for Anonymized Data Sharing & Licensing
Measures research participants' comfort with anonymized data sharing, preferred licensing models, repository preferences, and required safeguards. Designed for IRB-compliant studies where researchers need informed consent data on secondary data use.
샘플 질문
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How familiar are you with Creative Commons or other data-sharing licenses?
- Very familiar
- Somewhat familiar
- Heard of them but not sure what they mean
- Not familiar at all
How comfortable are you with sharing anonymized data from this study with other researchers?
If this anonymized dataset were shared, which license type would you most prefer?
- CC BY (attribution required)
- CC BY-NC (non-commercial use only)
- CC0 (no restrictions / public domain)
- Other (please specify)
- Not sure / no preference
Please rank the following repository types in order of your preference for depositing this anonymized dataset (1 = most preferred).
- Institutional repository (university/organization)
- Domain/discipline-specific repository
- Generalist repository (e.g., Zenodo, Figshare)
- Controlled-access repository (application and review required)
- Private storage (no public sharing)
Which safeguards do you believe should be in place before anonymized data are shared? Select all that apply.
- Data use agreement (DUA) or terms of use
- Access committee review/approval
- Only aggregate or highly de-identified data released
- Embargo period before release
- User authentication (e.g., two-factor authentication)
- Citation and attribution requirements
- Access logs and audit trails
- None of the above
Based on your responses in this survey, please share any additional conditions, concerns, or thoughts that would affect your comfort with anonymized data sharing and licensing.
What is your age group?
- 18–24
- 25–34
- 35–44
- 45–54
- 55–64
- 65+
- Prefer not to say
Thank you for completing this survey. Your responses have been recorded and will be used in aggregate to inform data-sharing policies. If you have any questions, please contact the research team.
Have you previously contributed data to a research study that was later shared or deposited in a public repository?
- Yes
- No
- Not sure
How concerned are you about the possibility that anonymized data could be linked back to you (re-identification)?
When choosing a data license, how important is it to you that others must give proper attribution when using the data?
After your data have been deposited in a repository, which best describes your preference for data withdrawal?
- I should be able to request withdrawal at any time
- Withdrawal should be allowed until public release, then no longer possible
- Withdrawal should not be possible after anonymization
- Not sure / it depends on the circumstances
We'd like to explore your views on anonymized data sharing in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your earlier responses.
How do you describe your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Which types of data would you be comfortable sharing in anonymized form? Select all that apply.
- Survey responses
- Interview transcripts (de-identified)
- Audio or video recordings (voice/face removed or altered)
- Physiological or sensor data
- Location or time-stamped activity data
- Code or analysis scripts
- None of the above
How important is it to you that the data cannot be used for commercial purposes?
What is the highest level of education you have completed?
- Less than high school
- High school or equivalent
- Some college / Associate degree
- Bachelor's degree
- Master's degree
- Doctorate (PhD/EdD)
- Professional degree (MD/JD)
- Prefer not to say
How important is it to you that reuse of the data is as easy and unrestricted as possible for other researchers?
What is your current employment status?
- Employed full-time
- Employed part-time
- Self-employed
- Student
- Not employed
- Retired
- Prefer not to say
In which region do you currently reside?
- Africa
- Asia
- Europe
- North America
- Oceania
- South America
- Prefer not to say
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
다른 서비스와 비교
다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.
이 템플릿을 선택하는 이유
- Captures nuanced preferences with dedicated opinion-scale items on attribution, commercial use restrictions, and reuse openness -- not just a single yes/no consent checkbox
- Includes a ranking question for repository preferences and multiple-choice items on safeguards and license type, giving researchers structured comparative data for IRB documentation
- Adds an AI follow-up interview to probe the reasoning behind stated comfort levels and concerns, producing richer qualitative context than a static form
- Ends with standard demographic classification (age, gender, education, employment, region) alongside an auto-generated report, supporting subgroup analysis for IRB review
Jotform
Personal Data Consent Form TemplateThis is a static consent-capture form for collecting agreement to personal data use, not a research survey exploring licensing preferences or repository choices. It's fielding-ready for basic consent logging but doesn't measure nuanced attitudes toward anonymized data sharing. Useful as a simple signature/checkbox instrument rather than an attitudinal research tool.
잘하는 점
- Purpose-built for consent capture with a familiar Jotform drag-and-drop builder
- Likely supports e-signatures and quick deployment for basic compliance needs
- Free-tier accessible, low setup effort for simple consent logging
아쉬운 점
- No mechanism to probe participant reasoning or follow up adaptively on stated preferences
- No license-model, repository-ranking, or safeguard questions specific to secondary data use
- No automated per-response quality scoring or auto-generated analysis report
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