- name
- data-management-protocols
- description
- Use when creating data management plans for qualitative research — storage, security, anonymization, retention, and sharing.
# Data Management Protocols for Qualitative Research
A data management plan (DMP) specifies how you **collect, organize, secure, retain, and optionally share** qualitative materials. Funders and IRBs increasingly expect DMPs even when data cannot be fully open.
## DMP components (typical)
- Data types (audio, transcripts, field notes, screenshots, photos).
- File naming/versioning conventions.
- Storage locations, backups, encryption, access roles.
- Anonymization/de-identification strategy.
- Retention period and secure destruction procedures.
- Sharing constraints and derived sharing products (e.g., redacted excerpts).
## File naming conventions
Use consistent patterns: `YYYY-MM-DD_siteID_participantPseudonym_interview01_audio.wav`. Avoid real names in filenames. Maintain a **separate encrypted key** linking pseudonyms to identifiers if needed for longitudinal contact (IRB-permitted).
## Storage solutions
Prefer institutional encrypted storage over personal laptops. If cloud storage is used, verify **BAA** or equivalent for health data contexts and institutional approval. Keep **3-2-1 backups** where feasible (three copies, two media types, one offsite).
## Anonymization techniques
Remove direct identifiers; generalize places and dates when small communities enable re-identification; paraphrase highly distinctive stories in publications when necessary. Document **what was altered** for auditability.
## De-identification procedures
Distinguish **de-identified** vs **anonymous** data. Qualitative audio often cannot be truly anonymous without destruction—plan accordingly in consent (what participants agree you may retain/share).
## Retention policies
Align with IRB approvals, institutional rules, and legal holds. After retention ends, use **secure wiping** (not simple delete) for sensitive files.
## FAIR principles (adapted for qualitative data)
FAIR (Findable, Accessible, Interoperable, Reusable) originated for digital objects; qualitative adaptation emphasizes rich **metadata** (who, where, when, how collected), stable identifiers where sharing occurs, and **ethical reuse conditions**.
## Ethical constraints on sharing
Consent may forbid sharing; community harm risk may forbid open archives. Consider **controlled access repositories** or sharing only derived categories with illustrative redacted excerpts.
## DMP templates
Use funder-specific templates (NSF, NIH) as shells, then add qualitative specifics: transcript versioning, memo logs, software exports (NVivo/Atlas.ti), and team collaboration rules.
## Team workflows
Define: who transcribes; QC process; where “gold” transcripts live; how coding exports are versioned; how Slack/email fragments are purged if they contain identifiers.
## Checklist
- [ ] Naming, folder structure, and versioning documented.
- [ ] Encryption and access control match sensitivity.
- [ ] Anonymization map maintained separately from public files.
- [ ] Retention/destruction schedule approved and actionable.
- [ ] Sharing options align with consent and community risk.
## References (starting points)
- Corti, L., et al. *Managing and sharing research data* — qualitative considerations.
- UK Data Service guidance on anonymization of qualitative data.
- NIH/NSF data management policy pages (update with current agency language when applying).
View on GitHub