Skip to main content

data-management-protocols

Use when creating data management plans for qualitative research — storage, security, anonymization, retention, and sharing.

Informations de source

Dépôt
ccashwell/qualitative-research-pro
Dernière activité de la source
11 avril 2026 à 02:10
Langue détectée de SKILL.md
anglais
Étoiles
1
Forks
0

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
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).
Voir sur GitHub