Merge pull request #64 from annefou/implement-60

implement requested changes from review (60)

Author: sunnydean

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.