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Best Practices

Open Science and Data Accessibility

  • Adhere to Open Science Principles: Strive to make your research data as open as possible, while respecting ethical, legal, and institutional constraints. Open data fosters transparency, collaboration, and reproducibility in research.

  • Maximize Data Accessibility: Share your data openly whenever possible, ensuring it can be accessed, reused, and built upon by others in the scientific community. If certain restrictions apply, clearly document and justify them in the dataset metadata.

  • Licensing for Openness: Use permissive licenses, such as CC-BY, to encourage data sharing and reuse while ensuring proper attribution. If necessary, select an alternative license but ensure it aligns with the principles of open science.

Data Organization

  • File Naming Conventions: Use clear, consistent, and descriptive file names. Include relevant details such as version numbers, dates, and data types. For example, name files like experiment_1_2025-01-01.csv for clarity and easy identification.
⚠️ Use only characters from the sets A-Z, a-z, 0-9, hyphen, or underscore. Avoid using special characters such as &% $ # : or )
  • Folder Structure: Organize your files logically into well-defined folders. Group related files together (e.g., raw data, processed data, scripts). A structured folder organization improves navigation and makes it easier to maintain the dataset over time.

Metadata Standards

  • Descriptive Titles: Choose clear and concise titles for your datasets. The title should accurately reflect the content and make it easy for others to understand the dataset’s scope.

  • Keywords and Abstracts: Include relevant keywords and an abstract that summarizes the dataset’s contents, methodology, and purpose. This information enhances searchability and helps users quickly understand the dataset’s context.

  • Comprehensive Metadata: Provide detailed metadata for each dataset version, including information on data collection methods, file types, and any known limitations of the dataset.

Sensitive Data

  • Handling restricted data: Follow institutional guidelines for managing sensitive or restricted data. This includes anonymizing or encrypting data as necessary to protect privacy and ensure compliance with legal and ethical standards.

  • Permissions and access control: Review and manage access to your datasets carefully. If you notice that your account has more permissions than you are authorized to have, please contact your Department Dataverse Steward to adjust your permissions. Data Upload Policy: Only upload data that is directly linked to your work at BSC. Do not upload personal, confidential, or unrelated data without appropriate authorization.

Licensing and Citation

  • Default License: All datasets should be tagged with a CC-BY license by default, ensuring proper attribution. You may change the license if necessary but ensure it is clearly communicated.

  • Link to BSC Guestbook: Always link your dataset to the BSC Guestbook for proper registration and tracking.

  • Citation: Provide proper citation information for your dataset, and encourage others to do the same when using your data. This helps give proper attribution and ensures the dataset’s academic integrity.

FAIR Principles

  • Adhere to FAIR: Follow the FAIR Principles (Findable, Accessible, Interoperable, and Reusable) when creating and managing datasets. Strive to make your research outputs as open as possible, ensuring that others can discover, access, and use your data in the future.

Reporting Misuse

If you observe any misuse of the BSC Dataverse platform, such as uploading data that violates the Deposit Agreement or institutional guidelines, please report it immediately. Misuse may include uploading personal, sensitive, or unauthorized data, or any activity that compromises the integrity of the platform.

To report such instances, please contact the BSC Data Management Team at datamanagement@bsc.es. Y


By adhering to these best practices, you contribute to a well-organized, secure, and compliant dataset management environment within the Dataverse platform. This not only enhances the discoverability of your work but also promotes collaboration and reproducibility across the research community.