Going further

Looking to deepen your understanding of research data management or enhance your current practices? This page provides curated resources and tools to help you move from basic data handling to advanced, FAIR-compliant workflows.

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Key topics to explore

Here are some important areas you may wish to explore further:

  • Raw Data Preservation
    Even preliminary or unprocessed data (raw data) are essential to conserve, document, and back up. They form the foundation of your analysis and are critical for reproducibility.

  • Data Integration
    Learn how to merge or align datasets from different sources—whether temporal, geographical, or disciplinary—while preserving structure and metadata integrity.

  • Data Anonymization and Curation
    Understand how to prepare datasets for reuse by others, especially when dealing with sensitive or personal data. Curation involves cleaning, enriching, documenting, and formatting data for long-term usability.

Tools and platforms for enhanced data management

Several tools and platforms have been designed to support researchers through different stages of the data lifecycle.

1. Software selection for interoperability

To maximize interoperability, choose software that adheres to open standards and is widely supported.

Browse recommended tools and repositories in the RDM Tools Catalogue or DMPTool’s software guide

2. European Open Science Cloud (EOSC)

The EOSC Portal provides a central access point to a wide range of research data services, including data storage, processing, analysis, and publishing. It’s particularly useful if you’re looking for:

  • Domain-specific data services

  • Cross-border collaboration tools

  • Open data repositories and platforms

3. Open Science Framework (OSF)

For collaborative and open research projects, the OSF platform offers a powerful suite of features:

  • Version control

  • Shared storage and metadata documentation

  • Private/public project management

  • Integration with GitHub, Zotero, and other tools

4. Data Visualization

Early-stage data visualization helps you explore your dataset, detect anomalies, and generate preliminary insights. Tools like:

  • Tableau Public

  • RAWGraphs

  • Flourish

  • Python libraries: matplotlib, seaborn, plotly
    can support you in producing clear, interactive outputs.

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