Findable: Data with identifiers and metadata that facilitate its discovery.

Accessible: To the community and research under clear permissions (i.e Creative Commons Licenses).

Interoperable: Data conforming to specific standards (i.e. standardized vocabularies) and formats (i.e. CSV, TIFF).

Reusable: Data sufficiently described (and licensed) to allow reuse by interested parties.

Planning includes clear perspectives on how researchers will manage, store, and share their data. This step may include:

Identify data needs: Determine what type of data is needed or available to answer the research question.

Create a Data Management Plan (DMP): Outline how data will be collected, documented, and preserved, file formats, appropriate metadata standards, and long-term data sharing and preservation.

Ethical considerations: Address any ethical requirements related to the data privacy, consent, and security.

Researchers focus on collecting research data from:

• Experiments: laboratory procedures or clinical trials.

• Fieldwork: population surveys or environmental measurements.

• Existing datasets: secondary data from repositories or historical archives).

Properly store research data to prevent loss, corruption, or unauthorized access. Some best practices include:

Systematic backups: Having multiple copies of the data in multiple physical and digital locations.

Security: Sensitive data must be protected from unauthorized access by encrypting files or restricting access to authorized personnel.

File organization: Appropriate and consistent naming conventions to facilitate the location and understanding of records.

Analyzing data to draw scientific conclusions. This may include:

Data cleaning: Using reproducible workflows to handle and clean data, removing possible errors, duplicates, and inconsistencies.

Statistical analysis: Use theoretically motivated statistical models to make scientific inference and identify patterns or relationships in the data.

Visualization: Create visualizations to present scientific results.

Document the content of the dataset to make it easier to understand and reuse. This comprises the generation of a readme file containing Metadata schemes (information about the data).

Archiving and sharing research data involves its long-term preservation in trusted data repositories, ensuring that the data are:

Accessible: Data is findable and accessible for reuse.

Preserved: To prevent data loss due to software or hardware obsolescence. Repositories have systematic process to ensure that data is always accessible.

Licensed: to define how others can use the data.

Cited: with a DOI or other associated unique identifier.

When you share data, make sure that it meets these characteristics:

Folders and files are organized in a structured way: Use standardized file formats (e.g., CSV, TIFF) and check for consistency in naming conventions.

The metadata/readme allow the dataset to be understood as a standalone object, providing collection methods, processing steps, and relevant context.

It is desirable that it contains reproducible workflows used to process and generate the research results.