What are the best practices for managing research data in a multi-lingual project?

3 Adopt a common data model

A common data model is a way of organizing and structuring your data so that it can be easily shared, integrated, and analyzed across different languages and systems. A common data model can help you avoid duplication, inconsistency, and confusion in your data, and facilitate your data interoperability and reuse. To adopt a common data model, you need to follow some principles, such as using common identifiers, formats, and vocabularies; aligning your data elements and attributes; and mapping your data to existing standards or ontologies. You can also use tools and platforms that support common data models, such as FAIR (Findable, Accessible, Interoperable, Reusable) principles or DDI (Data Documentation Initiative) standards.

4 Collaborate and communicate effectively

Managing research data in a multi-lingual project requires effective collaboration and communication among your team members, partners, and users. You need to establish clear and regular communication channels, such as meetings, emails, or chats, and use tools and platforms that enable collaboration, such as shared folders, documents, or dashboards. You also need to respect and appreciate the linguistic and cultural diversity of your team, and foster a culture of trust, respect, and feedback. Moreover, you need to communicate your data management practices and outcomes to your external audiences, such as funders, reviewers, or the public, and highlight the value and impact of your multi-lingual data.

5 Ensure data quality and security

Data quality and security are essential for any research project, but especially for a multi-lingual one. You need to ensure that your data are accurate, complete, and consistent across languages and formats, and that they meet the expectations and requirements of your research questions and objectives. You also need to ensure that your data are protected from unauthorized access, use, or disclosure, and that they comply with the relevant ethical and legal standards and regulations. To ensure data quality and security, you need to implement some measures, such as data validation, verification, and cleaning; data backup, encryption, and anonymization; and data access, consent, and retention policies.

6 Share and disseminate your data

Sharing and disseminating your data is not only a good research practice, but also a way of increasing the visibility, impact, and recognition of your multi-lingual project. You can share and disseminate your data through various channels and formats, such as data repositories, journals, reports, or presentations. However, you need to consider some factors, such as the purpose, audience, and language of your data sharing and dissemination; the format, license, and citation of your data; and the feedback and evaluation of your data. You also need to follow some guidelines, such as the FAIR principles or the RDA (Research Data Alliance) recommendations, to ensure that your data are findable, accessible, interoperable, and reusable by others.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?