Folder Structure for Research Audio + Transcripts (Lab Template)

A lab-ready folder structure for research audio and transcripts keeps interviews, focus groups, consent files, and analysis outputs easy to find years later. The best template separates “raw” from “clean,” stores consent/IRB paperwork outside shareable data folders, and uses consistent file names with stable IDs. Below is a practical blueprint you can copy, plus permission tiers and naming examples to keep everything discoverable over multi-year projects.

Primary keyword: folder structure for research audio and transcripts.

• Key takeaways
• Separate raw (never edited) from working (cleaned/coded) files so you can always trace decisions.
• Keep consent/IRB documents in a restricted area and do not mix them with de-identified datasets.
• Use stable participant/session IDs and consistent naming so files stay searchable across years and staff turnover.
• Design permission tiers first, then build folders that match them.
• Write a short README so new team members know what goes where.

What this template is (and why it works)

This template fits most interview and focus group workflows: record audio, create transcripts, clean/de-identify text, code/analyze, then publish or share parts of the dataset. It works because it treats your project like a lab asset: it protects originals, documents decisions, and makes outputs reproducible.

Before you create folders, pick two rules and stick to them: (1) never overwrite raw files, and (2) every “working” file should point back to an original source (via ID, date, or both).

Lab-ready folder structure blueprint (copy/paste)

Use one top-level folder per study or grant, then keep the same subfolders across studies. If you manage many studies, you can also add a “00_Lab_Admin” folder at the lab level for shared policies and templates.

Top-level structure

• 01_Admin-IRB (restricted)
• 02_Raw-Data (restricted)
• 03_Working-Data (restricted or limited)
• 04_Analysis
• 05_Outputs
• 06_Publications
• 07_Archive (locked / read-only)

01_Admin-IRB (restricted)

This folder holds documents that can directly identify participants or expose sensitive recruitment details. Limit it to the smallest set of people who must access it.

• 01_Admin-IRB/IRB_Protocol
• 01_Admin-IRB/IRB_Approvals-Amendments
• 01_Admin-IRB/Consent-Templates
• 01_Admin-IRB/Signed-Consents (highest restriction)
• 01_Admin-IRB/Recruitment-Materials
• 01_Admin-IRB/Data-Management-Plan
• 01_Admin-IRB/Staff-Training (e.g., human subjects)

02_Raw-Data (restricted; never edited)

Store original recordings and any original exports here. Treat this folder as “write once,” then set it to read-only for most team members.

• 02_Raw-Data/Audio
• 02_Raw-Data/Video (if applicable)
• 02_Raw-Data/Field-Notes (scans/photos)
• 02_Raw-Data/Transcripts-Raw (verbatim from tool/vendor)
• 02_Raw-Data/Metadata (intake forms, session logs)

03_Working-Data (your day-to-day workspace)

This is where you clean, de-identify, and prepare data for coding. Keep versions clear so someone can reconstruct what changed.

• 03_Working-Data/Transcripts-Clean (spelling fixes, speaker labels)
• 03_Working-Data/Transcripts-Deidentified (PII removed or masked)
• 03_Working-Data/Audio-Working (trimmed copies for coding; never the only copy)
• 03_Working-Data/Codebooks
• 03_Working-Data/Participant-Key (if you must keep one; highest restriction)

04_Analysis (coded outputs and logs)

Keep analysis outputs separate from the “clean data” so you can rerun or review coding without confusing the dataset itself.

• 04_Analysis/Coding-Exports (e.g., NVivo/ATLAS.ti exports)
• 04_Analysis/Memos
• 04_Analysis/Interrater-Reliability
• 04_Analysis/Scripts (if you use R/Python)
• 04_Analysis/Analysis-Logs (what ran, when, by whom)

05_Outputs (shareable deliverables)

This is where you place approved items to share with collaborators, funders, or internal stakeholders. Only place materials here once they meet your de-identification and approval standards.

• 05_Outputs/Quotes-Excerpts (approved)
• 05_Outputs/Tables-Figures
• 05_Outputs/Presentations
• 05_Outputs/Data-Share-Package (if applicable)

06_Publications (manuscripts and submission assets)

• 06_Publications/Manuscripts
• 06_Publications/Supplement
• 06_Publications/Journals-Submissions (cover letters, responses)
• 06_Publications/Preprints (if used)

07_Archive (locked)

Use this for frozen milestones: final datasets, final codebooks, and “as-submitted” publication packages. Lock it so files cannot change without an explicit process.

• 07_Archive/Raw-Data-Snapshots (optional checksums)
• 07_Archive/Final-Transcripts
• 07_Archive/Final-Analysis
• 07_Archive/Final-Outputs

Permission tiers: who can see what (and how to map folders to access)

Design permissions around the most sensitive items: signed consent forms, participant identity keys, and unredacted recordings. Then build your folder structure so people can collaborate without needing access to restricted materials.

Suggested tiers

• Tier 0 (Public): published papers, slides, approved excerpts, and any public data-share package.
• Tier 1 (Lab-wide): de-identified transcripts, codebooks, analysis memos, and coding exports.
• Tier 2 (Project team): raw transcripts and raw audio/video, plus session metadata.
• Tier 3 (Limited access): signed consents, participant key, recruitment logs with identifying details.

Folder-to-tier mapping (simple and enforceable)

• 01_Admin-IRB → Tier 2–3 depending on subfolder (Signed-Consents and Participant-Key should be Tier 3).
• 02_Raw-Data → Tier 2 (or Tier 3 if recordings contain highly sensitive content).
• 03_Working-Data → Tier 1–2 (Deidentified can be Tier 1; Participant-Key should be Tier 3).
• 04_Analysis → Tier 1 (unless excerpts include identifiers, then restrict).
• 05_Outputs and 06_Publications → Tier 0–1 depending on approval status.

If you work at a university or health system, align access controls with your institution’s policy and IRB requirements. If your study involves protected health information, follow HIPAA privacy and security rules (see the HHS HIPAA Privacy Rule overview for official guidance).

Naming conventions that stay searchable for years

Good names reduce mistakes and make handoffs easier. Your naming system should work even if someone only sees a single file outside the folder tree (for example, in an email attachment or export).

Use stable IDs (not names)

• Study ID: short code (e.g., “CARE24”)
• Participant or group ID: “P###” for interviews, “FG##” for focus groups
• Session ID: “S##” if a participant has multiple sessions
• Date: ISO format YYYY-MM-DD so it sorts correctly
• File stage: RAW, CLEAN, DEID, CODED, FINAL

Recommended file name pattern

[StudyID]_[Method]_[ParticipantOrGroupID]_[Session]_[YYYY-MM-DD]_[Stage]_[Version]

Concrete examples

• CARE24_INT_P014_S01_2026-02-17_AUDIO_RAW_v01.wav
• CARE24_INT_P014_S01_2026-02-17_TRANSCRIPT_RAW_v01.docx
• CARE24_INT_P014_S01_2026-02-17_TRANSCRIPT_CLEAN_v02.docx
• CARE24_INT_P014_S01_2026-02-17_TRANSCRIPT_DEID_v01.docx
• CARE24_FG_FG03_S01_2026-03-05_AUDIO_RAW_v01.mp3
• CARE24_FG_FG03_S01_2026-03-05_TRANSCRIPT_DEID_v01.docx

Versioning rules (keep them boring)

• Use v01, v02, v03 rather than “final_FINAL_reallyfinal.”
• Only call something FINAL when it is frozen and moved to 07_Archive.
• Record meaningful changes in a simple log (see next section).

Practical workflow: from raw audio to coded outputs

A clear workflow prevents the two most common problems: losing the original source and mixing identified and de-identified materials. Use this step-by-step path and mirror it in your folder structure.

Step 1: Intake and log the session

• Create the participant/group ID and session ID before the interview if possible.
• Save the audio to 02_Raw-Data/Audio with the standard file name.
• Add a short row to a session log in 02_Raw-Data/Metadata (date, interviewer, method, notes).

Step 2: Produce a raw transcript

• Save the vendor or tool output to 02_Raw-Data/Transcripts-Raw.
• Do not edit the raw transcript file; copy it into Working-Data for cleanup.
• If you use automation, keep the process consistent and label outputs clearly (see automated transcription options if you need quick first drafts).

Step 3: Clean and standardize

• Copy raw transcript → 03_Working-Data/Transcripts-Clean.
• Standardize speaker labels (e.g., INT, P014, P015, MOD, FG03_SPK1).
• Fix obvious formatting issues and timestamps if you use them.

Step 4: De-identify for sharing and coding

• Copy clean transcript → 03_Working-Data/Transcripts-Deidentified.
• Replace names and direct identifiers with consistent tags (e.g., [CITY], [CLINIC], [CHILD_1]).
• Store any re-identification key only in the restricted area (Tier 3), if you keep one at all.

Step 5: Code and export outputs

• Import de-identified transcripts into your coding tool.
• Export coded segments, codebook versions, and memos into 04_Analysis.
• Keep exports read-only once you report results, so analysis doesn’t drift.

Step 6: Publish and package

• Move approved quotes/excerpts and figures into 05_Outputs.
• Keep manuscripts and submission files in 06_Publications.
• Freeze the final dataset and analysis snapshot in 07_Archive.

Pitfalls to avoid (and how this structure prevents them)

Most file chaos comes from small habits that feel harmless at the time. These are the mistakes that create major cleanup work later.

• Mixing identified and de-identified data: keep signed consents and participant keys in Tier 3 folders, separate from de-identified transcripts.
• Overwriting raw audio or raw transcripts: treat 02_Raw-Data as write-protected after upload.
• Unclear “final” files: reserve FINAL for archived snapshots, and use simple version numbers during work.
• Inconsistent speaker labels: standardize labels early so coding and quote tracking stay reliable.
• Storing files only inside a coding tool project: export key outputs to 04_Analysis so they survive tool upgrades and team changes.
• Relying on personal laptops as the system of record: keep the canonical dataset in a managed, access-controlled workspace.

If you publish video or audio excerpts, remember that captions and transcripts support accessibility. For general guidance, see the W3C WAI guidance on audio and video accessibility and keep those deliverables in 05_Outputs once approved.

Common questions

• Should I store audio and transcripts together?
  Link them by naming and IDs, but store them in separate subfolders (Audio vs Transcripts) so batch tasks stay safe and predictable.
• Where do I put translated transcripts?
  Add a subfolder like 03_Working-Data/Transcripts-Translated and include the language code in the file name (e.g., _ES_). Keep translations aligned to the de-identified version when possible.
• How do I handle focus groups with many speakers?
  Use a group ID (FG03) plus speaker labels that stay stable (SPK1, SPK2) and keep a separate restricted speaker key only if needed.
• Do I need both “clean” and “de-identified” transcripts?
  Yes, if you want an audit trail; “clean” preserves meaning and fixes formatting, while “de-identified” removes direct identifiers for safer sharing and coding.
• What file formats should we standardize on?
  Use a consistent text format your team can open long-term (often .docx or .txt) and keep originals of any specialized exports in 04_Analysis.
• How can we keep files discoverable when staff change?
  Add a short README in the study root that explains IDs, naming rules, and where to find raw vs working vs outputs.
• When should we lock the archive?
  After a paper submission, a dataset handoff, or the end of a coding phase; treat 07_Archive as read-only and only add new snapshots with clear dates.

If you need help turning recordings into consistent, well-labeled transcripts (and keeping your workflow moving), GoTranscript provides the right solutions, including professional transcription services that fit into the folder structure and naming approach above.