Research repository template: build your own in 2026
Copy this research repository template covering folder structure, study metadata, tagging schema, and governance docs to get your insight archive running in a day.
Research repository template: build your own in 2026
A research repository template gives your team a ready-made structure for storing studies, tagging insights, and retrieving findings without starting from a blank page every time. This post provides a copy-ready template covering folder structure, study metadata fields, insight taxonomy, governance documents, and contribution workflow, with notes on adapting it for tools like Notion, Dovetail, or Airtable.
If you already have a repository and want advice on running it well, see the companion post on research repository best practices.
Why start from a template?
Most research teams build their first repository organically, adding studies ad hoc until the structure becomes impossible to search. A template front-loads the decisions that matter: what to store, how to label it, who owns it, and when to delete it. Getting those decisions right from the start saves weeks of retroactive migration and significantly increases the chance that the repository actually gets used.
The template below is designed for teams of two to twenty researchers. Larger organisations may need to adapt the governance sections, but the structural logic scales.
The five-layer template
Layer 1: folder or workspace structure
Organise your top-level workspace into five sections:
| Section | What goes here |
|---|---|
| Studies | One entry per research project, linked to all artefacts |
| Insights | Atomic insight cards tagged by theme and product area |
| Participants | Anonymised participant profiles with consent records |
| Templates and guides | Screeners, discussion guides, consent forms, tag glossary |
| Governance | Data-retention schedule, access policy, contribution checklist |
Avoid organising the top level by team or quarter. Studies and insights outlive team structures, and date-based folders make cross-study search harder. Product area or research method are more durable top-level axes.
Layer 2: study metadata form
Every study entry should include these fields. Fields marked (required) must be completed before a study is marked active; the others can be filled in progressively.
Required fields:
- Study title (required)
- Completion date (required)
- Primary research method (required): moderated interview, unmoderated test, survey, diary study, contextual inquiry, secondary research
- Target audience segment (required): describe who participated, not just job title
- Key objectives (required): one sentence per objective, maximum three
- Status (required): planned, recruiting, in-field, analysis, complete, archived
Recommended fields:
- Product area or feature
- Researcher owner
- Number of participants
- Recruiting method: internal panel, external panel, social, CRM
- Key findings summary: three to five bullets
- Linked decisions: which product or strategy decisions this study informed
- Related studies: other studies this one should be read alongside
- Data retention expiry: the date after which raw data should be deleted per policy
Storing the recruiting method is often overlooked. When a stakeholder later asks “how did you find those users?”, having it on record lets you assess sample quality and decide whether the study findings apply to a broader population.
Layer 3: raw data storage convention
Raw data, transcripts, recordings, and survey exports should follow a consistent naming convention so files can be matched to study entries without manual lookup:
[YYYY-MM-DD]_[StudyID]_[Method]_[ParticipantID]
Example: 2026-05-14_S042_ModInt_P07Store raw data in a location covered by your data-retention and consent policy. Cloud folders with role-based access (Google Drive, SharePoint, S3) work well. Purpose-built tools like Dovetail store recordings alongside transcripts and link them directly to insight clips.
Never store personally identifiable information in the main studies database. Participant names, emails, and contact details belong in the participants section with appropriate access controls, linked to studies by an anonymised participant ID.
Layer 4: insight taxonomy
An insight is a single, discrete finding stated as a declarative sentence, grounded in at least two participant observations. Each insight card should include:
- Insight statement (one sentence)
- Supporting evidence (quote or observation clips, minimum two)
- Confidence level: low (1 to 2 sources), medium (3 to 5), high (6 or more)
- Tag: theme (from controlled list)
- Tag: product area (from controlled list)
- Tag: audience segment (from controlled list)
- Linked study (relation property)
- Date added
- Researcher owner
Starter theme tag list (customise for your product):
Onboarding, Navigation, Trust and credibility, Performance and reliability, Pricing and value perception, Collaboration, Notifications, Search and discovery, Error recovery, Accessibility, Integration, Mobile experience
Why a controlled list matters: free-text tagging leads to synonyms that split related insights across different tags. “Pricing”, “price”, “cost”, and “too expensive” should all map to the same tag: Pricing and value perception. Write a one-sentence definition for each tag and publish it in your Templates and guides section.
Layer 5: governance documents
A repository without governance decays. The minimum governance set is:
Contribution checklist A one-page checklist researchers complete before marking a study complete. Confirm: metadata form filled out, raw data uploaded and named correctly, insights added and tagged, consent records filed, retention date set, study shared with relevant product team.
Tag glossary A living document defining every tag value across all axes. Include the definition, an example of what belongs under this tag, and an example of what does not. Review quarterly.
Data retention schedule Specifies how long each data type is kept. A common policy: identifiable participant data (names, emails) deleted within 12 months; transcripts and recordings retained for 24 months; insight cards and study metadata retained indefinitely. Align with GDPR if you recruit EU participants, and with your organisation’s legal requirements.
Access policy Defines who can read studies and insights (typically all product and design teams), who can edit or add studies (researchers and research ops), and who can delete data (research ops lead only, with approval).
Adapting the template for popular tools
| Tool | How to implement the template |
|---|---|
| Notion | Create a Studies database with the metadata fields as properties. Add an Insights database with a Relation to Studies. Use Gallery or Table views filtered by product area, method, or tag. |
| Airtable | One base, three tables: Studies, Insights, Participants. Linked records connect them. Use Forms for the contribution checklist. |
| Dovetail | Projects map to studies. Tags map to your theme and product area axes. The evidence library surfaces insight cards across projects. |
| Confluence | Use page templates for study entries. Add labels matching your tag list. A Confluence database (Confluence Cloud) can replicate the metadata fields. |
| EnjoyHQ | Segments map to your audience tags. Collections map to product areas. Import transcripts and apply labels from your controlled list. |
For teams starting from scratch, Notion offers the best balance of flexibility and zero incremental cost. Purpose-built tools like Dovetail or Condens reduce setup time for the synthesis layer and add AI-powered tagging, but require a paid subscription.
For a detailed comparison of analysis tools, see best Dovetail alternatives in 2026.
Connecting recruitment to the repository
The repository only holds value if studies are completed and filed. Slow or failed recruitment is the most common reason studies stall before analysis even begins. When you plan a new study, capture the recruiting method in the study metadata at kick-off, not retrospectively.
Teams running moderated interviews at scale increasingly use platforms with built-in panels so recruiting does not block the research calendar. CleverX connects researchers to a verified B2B and B2C panel across 150 countries, with AI-moderated interview options that can feed transcripts directly into your synthesis workflow, reducing the gap between data collection and repository contribution.
For guidance on planning the study itself before you start recruiting, see how to create a user research plan.
Common template mistakes and how to avoid them
Over-engineering the taxonomy at launch. Start with 10 theme tags and expand only when you have evidence a gap exists. Premature complexity leads to inconsistent tagging and researcher frustration.
Skipping the governance documents. The folder structure and metadata form feel like the “real” work, but without a contribution checklist and a tag glossary the repository drifts within three months.
Separating raw data from insights. When transcripts live in one folder and insight cards live somewhere else with no link between them, stakeholders cannot trace a finding back to its source. Use relation properties or file references to keep the chain of evidence intact.
No designated owner. A repository maintained by everyone is maintained by no one. Assign one research ops person or senior researcher to run quarterly audits, merge duplicate tags, and enforce the contribution checklist.
Nielsen Norman Group’s research on research repositories and knowledge management consistently identifies ownership and taxonomy governance as the two highest-impact factors for repository longevity.
Getting started: a one-week setup plan
Day 1: Choose your tool and create the five-layer structure. Copy the metadata fields listed above as properties or column headers.
Day 2: Write your first tag glossary. Start with theme tags only. Define 10 to 15 values with one-sentence definitions and one example each.
Day 3: Add your five most recent completed studies using the metadata form. This surfaces gaps in the template before you go further.
Day 4: Draft the contribution checklist and data retention schedule. Have your legal or privacy contact review the retention periods.
Day 5: Share the repository with your product and design leads. Walk one stakeholder through a search. Their confusion is your best usability test.
After the first week, schedule a 30-minute quarterly audit and a one-hour annual taxonomy review on your research calendar. The maintenance cost is low if you run it consistently.
Frequently asked questions
What should a research repository template include?
A complete template covers five layers: a folder or workspace structure (by product area or method), a study metadata form (title, date, method, audience, objectives, status), a raw-data storage convention (transcripts, recordings, exports), a synthesis artefact section (themes, insights, clips), and a governance block (data owner, consent records, retention date). Without all five, the repository tends to become an inconsistent archive within months.
Can I build a research repository in Notion?
Yes. Notion is one of the most popular free options for small and mid-size research teams. The key is to create a master Studies database with the metadata fields listed in this template, then use linked databases to surface studies by product area, audience segment, or research method. Relation properties connect studies to participant profiles and insight tags.
What metadata fields are essential for every study?
The six fields no study entry should be without are: study title, completion date, primary research method, target audience segment, key objectives (one sentence each), and status (planned, in-field, complete, archived). Secondary fields that add significant search value include product area, researcher owner, linked decisions, and data-retention expiry date.
How do I tag insights in a research repository?
Use a controlled vocabulary rather than free-text tags. Define tag axes (theme, audience, product area, sentiment), limit each axis to 10 to 20 standard values, write a one-sentence definition for each value, and publish the glossary next to the repository. Run a quarterly tag-merge pass to collapse duplicates. The goal is that two researchers tagging the same insight independently choose the same tags at least 80 percent of the time.
How long does it take to set up a research repository?
A minimal viable repository (folder structure plus metadata template) can be set up in a few hours using a tool you already have, such as Notion, Airtable, or Confluence. A fully governed repository with a taxonomy, contribution workflow, consent-record store, and retention policy takes one to two weeks of focused effort. The governance and tagging schema take longer than the tool setup itself.
Which tools support this research repository template?
The template structure in this post is tool-agnostic and maps cleanly onto Notion (database properties), Airtable (base + linked tables), Dovetail (projects + tags), Confluence (page templates + labels), and EnjoyHQ (projects + evidence library). Purpose-built tools like Dovetail and Condens include some of these fields out of the box; general-purpose tools like Notion require manual setup but offer more flexibility.