implement requested changes from review (60)

We need a section to point user how to apply the FAIR principles? And how to apply them at each stage of the project (planning phase, kicking off, ongoing, completed) This must be very PRACTICAL section, and understandable to users. At current state it is too much generic.

Author: annefou

pages/Community and Best Practices/Code Data and Workflow Quality.md

@@ -1,12 +1,50 @@
 # Code, Data and Workflow Quality
 
 ## Applying and maintaining FAIR Principles
-Guidelines on how to implement FAIR principles within your research workflow, data management, and publication process.
 
-Ensuring all research outputs, including code, data, and documentation, are compliant with FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
+The checklist below provides practical guidance for applying the FAIR principles in EarthCODE projects.
+
+- 1. Planning Phase
+    - Choose FAIR-compliant data formats, metadata standards and tools (e.g., STAC, JSON for metadata, Zarr for data).
+    - Select an open license (e.g., CC-BY) to ensure data accessibility and reusability.
+    - Define roles and permissions for team members to control access to data and resources.
+    - Check the available EarthCODE platforms and try to identify the one that best suits your needs.
+- 2. Kicking Off
+    - Whenever possible, choose an EarthCODE platform to make publishing in EarthCODE easier
+    - Create and add metadata to datasets as data is collected to ensure findability.
+    - Document project goals and Open Science practices: Share with the team and ensure alignment with FAIR principles.
+    - Ensure data accessibility: Make sure datasets are stored in a location where they can be easily accessed by the team (e.g., cloud storage, data repository).
+- 3. Ongoing
+    - Monitor data and metadata quality regularly to ensure accessibility and correctness.
+    - Ensure interoperability: Check that datasets are compatible with community standards and formats (e.g., Zarr, GeoTIFF, GeoJSON, NetCDF).
+    - Foster collaboration: Encourage sharing and cross-team cooperation, including sharing links to datasets stored externally if not yet in EarthCODE.
+    - Implement version control for datasets, ensuring that the latest versions are clearly documented.
+- 4. Publishing in EarthCODE (End of Project)
+    - Upload datasets and metadata to EarthCODE for long-term storage and publishing.
+    - Link datasets to related resources (e.g., other datasets, publications) through metadata to facilitate further exploration and reuse.
+    - Verify open licenses: Ensure datasets are published with open licenses for broad reuse (e.g., CC-BY).
+    - Ensure the metadata is up-to-date and complete before publishing in EarthCODE.
+- 5. Completed
+    - Finalize and publish datasets in EarthCODE, ensuring they are accessible, findable, and reusable.
+    - Encourage data reuse: Ensure datasets are easy to find, access, and understand for reuse by others.
 
 ## Best Practices for high-quality Code, Data and Workflows
 
-Tips and guidelines for maintaining high-quality code and data, including automated testing and validation.
+Maintaining high-quality code and data throughout your project ensures that your outputs are reusable, trustworthy, and easier to publish. Below are tips and recommended practices to support quality assurance and reproducibility:
 
+- Code Quality
+     - Use Version Control: Track your development using Git and a shared repository (e.g., GitHub or GitLab).
+     - Automate Testing: Implement unit tests and integration tests using tools like pytest, unittest, or CI/CD workflows.
+     - Follow Coding Standards: Adopt a consistent style (e.g., PEP8 for Python) and use linters (e.g., flake8, black) to maintain code clarity.
+     - Write Documentation: Provide clear usage instructions and inline comments. Consider using Jupyter Notebooks or Markdown README files to explain workflows.
+- Data Quality
+     - Validate Your Data: Apply automated checks for data formats, missing values, and schema consistency.
+     - Document Your Data: Create or maintain metadata alongside your datasets, including descriptions of variables, units, and collection methods.
+     - Use Standard Formats: Choose interoperable, machine-readable formats (e.g., NetCDF, GeoTIFF, Zarr) and community-agreed standards (such as CF-Conventions).
+     - Track Data Changes: when needed, version datasets as they evolve and log processing steps to support reproducibility.
+- Integration with EarthCODE
+     - Use EarthCODE-Compatible Tools: When possible, rely on tools and environments that are natively supported within EarthCODE platforms.
+     - Test Workflows in EarthCODE Early: Validate your workflows in the target platform before final publication to avoid integration issues.
+     - Publish Intermediate Outputs: Store and document intermediate results to help others understand and reuse your work incrementally.
+     - Regularly revisiting these practices during the project lifecycle will reduce last-minute issues and make your results easier to share and build upon.