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Privacy & Compliance

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.

Sample questions

A preview of what’s in the template. Every question is editable before you launch.

21 questions · ~10 min
Q01
Message

Welcome to this survey on anonymized data sharing and licensing. The purpose of this survey is to understand your preferences and concerns regarding sharing anonymized research data. Your responses will help inform data management and sharing practices. • Participation is entirely voluntary. You may stop at any time without penalty. • There are no right or wrong answers — we are interested in your honest opinions. • Your responses are confidential and will be reported only in aggregate. • Estimated completion time: 6–8 minutes. By proceeding, you confirm that you have read and understood this information and consent to participate.

Q02
Multiple Choice

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
Q03
Opinion Scale

How comfortable are you with sharing anonymized data from this study with other researchers?

Scale: 17
Min:Not at all comfortableMax:Very comfortable
Q04
Multiple Choice

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
Q05
Ranking

Please rank the following repository types in order of your preference for depositing this anonymized dataset (1 = most preferred).

  1. Institutional repository (university/organization)
  2. Domain/discipline-specific repository
  3. Generalist repository (e.g., Zenodo, Figshare)
  4. Controlled-access repository (application and review required)
  5. Private storage (no public sharing)
Drag to rank
Q06
Multiple Choice

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
Q07
Long Text

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.

Q08
Dropdown

What is your age group?

  • 18–24
  • 25–34
  • 35–44
  • 45–54
  • 55–64
  • 65+
  • Prefer not to say
Q09
Message

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.

Q10
Multiple Choice

Have you previously contributed data to a research study that was later shared or deposited in a public repository?

  • Yes
  • No
  • Not sure
Q11
Opinion Scale

How concerned are you about the possibility that anonymized data could be linked back to you (re-identification)?

Scale: 17
Min:Not at all concernedMax:Extremely concerned
Q12
Opinion Scale

When choosing a data license, how important is it to you that others must give proper attribution when using the data?

Scale: 17
Min:Not at all importantMax:Extremely important
Q13
Multiple Choice

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
Q14
AI Interview

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.

Q15
Multiple Choice

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q16
Multiple Choice

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
Q17
Opinion Scale

How important is it to you that the data cannot be used for commercial purposes?

Scale: 17
Min:Not at all importantMax:Extremely important
Q18
Dropdown

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
Q19
Opinion Scale

How important is it to you that reuse of the data is as easy and unrestricted as possible for other researchers?

Scale: 17
Min:Not at all importantMax:Extremely important
Q20
Multiple Choice

What is your current employment status?

  • Employed full-time
  • Employed part-time
  • Self-employed
  • Student
  • Not employed
  • Retired
  • Prefer not to say
Q21
Dropdown

In which region do you currently reside?

  • Africa
  • Asia
  • Europe
  • North America
  • Oceania
  • South America
  • Prefer not to say

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • 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 Template

This 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.

What it does well

  • 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

Where it falls short

  • 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

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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