> For the complete documentation index, see [llms.txt](https://data-champions.renew-platforms.dk/renew-data-champions/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://data-champions.renew-platforms.dk/renew-data-champions/research-data-management/data-life-cycle/analyze-and-collaborate-stage.md). # Analyze & Collaborate Stage

Analyze & Collaborate Stage

## **Analyze and Collaborate Stage – Summary Table** | **Category** | **Details & Best Practices** | | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Stage Purpose** | Inspect, clean, transform, and model data to generate insights, draw conclusions, and support research decision‑making, while ensuring data remains high‑quality, reproducible, and reusable. | | **Importance** | Decisions and practices at this stage directly affect the quality of analysis, long‑term usability, FAIR compliance, and EU funder reproducibility requirements. | | **Document Every Step** | - Record all data processing and transformation steps for reproducibility.- Use Electronic Lab Notebooks (ELNs), collaborative tools, or workflow‑tracking software (Jupyter, R Markdown, Galaxy, OMERO).- Capture: • Scripts, pipelines, and commands• Software versions and dependencies• Parameters or filtering applied- Thorough documentation supports UCPH Open Science and Horizon Europe/ERC compliance. | | **Ensure Data Safety & Organization** | - Annotate datasets as you analyze them; maintain clear folder structures and file naming conventions.- Store data in secure UCPH/reNEW solutions: ERDA, high‑security servers, SharePoint for collaboration.- Separate raw vs processed datasets to protect originals.- Export or convert proprietary formats to open formats (CSV, TIFF, JSON) for interoperability and future reuse. | | **Facilitate Collaboration & Sharing** | - Share intermediate data using controlled, collaborative platforms (internal or approved external).- Implement explicit permissions and version control to prevent accidental overwriting.- Comply with GDPR for sensitive or personal data.- Aligns with EU funder (Horizon Europe, ERC) reproducibility and sharing expectations. | | **Benefits** | - Ensures data integrity and transparency.- Enhances reproducibility and team collaboration.- Prepares datasets for publication or repository deposition, supporting long‑term impact and reuse. |