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Use Case by Role

DDI is a metadata standard that improves how research data are described, shared, and reused.  It supports the creation of FAIR (Findable, Accessible, Interoperable, and Reusable) data by providing structured, machine-actionable metadata.  Whether you're a researcher, data manager, statistical agency, or developer, DDI provides tools and best practices to enhance your work.  

Choose the guide that best fits your role:

Researchers

DDI helps researchers document and find data in a structured way, ensuring better reproducibility and transparency.

How DDI Helps Researchers

  • Makes Data Easier to Find: Many repositories use DDI to document data, making it easier to search for datasets, including by topic, methodology, time period, geographic coverage, and more.
  • Makes Data Easier to Reuse: Structured DDI metadata makes it easier to understand the context, methodology, and variables used in the original study.  Well-documented datasets using DDI ensure that research can be verified and reused.
  • Improves Data Integration and Interoperability: DDI's structured metadata facilitates interoperability by providing standardized descriptions of variables, methodologies, and data collection processes.  This allows researchers to more easily compare, merge, and analyze data across different studies, disciplines, and repositories.
  • Ensures Data Quality and Compliance: DDI helps ensure metadata completeness and accuracy, supports adherence to data management policies and FAIR principles, and improves the reliability and reproducibility of research data.
  • Preserves Data for Future Use: Data has long-term value beyond the initial research project.  Using DDI to document data ensures they remain understandable and usable decades later. 

How Researchers Can Get Started

  1. Find Data -- Search for datasets in trusted repositories that use DDI metadata standards, such as CESSDA, ICPSR, and others.  Exploring these collections can help you discover well-documented data relevant to your research.
  2. Document Your Own Data -- Use DDI tools to create detailed metadata for your datasets.  This ensures your data are well described, easier to share, and reusable in the future.
  3. Learn About DDI -- Explore training materials to understand the basics of DDI and how it supports data documentation and reuse.
  4. Engage with the Community -- Join mailing lists to connect with other researchers and data managers using DDI, sharing best practices and solutions.
Data Managers

DDI provides a standardized, structured way for data managers to describe, curate, and archive research data.

How DDI Helps Data Managers

  • Improves Data Discovery: DDI makes data easier to search and access based on structured metadata fields (e.g., title, summary, time period, geographic coverage, methodology).
  • Ensures Data Consistency: Standardized documentation improves interoperability across repositories, reducing duplication and errors.
  • Supports Long-Term Preservation and Reuse: DDI-compliant metadata ensures data remain understandable and usable for future researchers -- years or even decades down the line.

How Data Managers Can Get Started

  1. Choose the Right DDI Product for Your Needs -- Explore the full range of DDI products to find the one that fits your workflow and goals.
  2. Structure Your Metadata -- Use standardized fields and controlled vocabularies to describe your data clearly and consistently.
  3. Integrate DDI into Your Workflow -- Implement DDI-compliant tools to improve metadata generation, enhance discoverability, and support curation.
Statistical Agencies

Statistical agencies play a crucial role in collecting, processing, and disseminating high-quality data to inform public policy and research.  DDI provides a standardized approach to describing these data, ensuring consistency, interoperability, and long-term usability.

How DDI Helps Statistical Agencies

  • Standardizes Metadata: DDI helps agencies consistently document surveys, variables, methods, and classifications -- making data more transparent and easier to manage over time. 
  • Enables Data Interoperability and Integration: With machine-readable, standards-based metadata, DDI makes it easier to combine and share data from surveys, censuses, and administrative sources -- both nationally and internationally. 
  • Supports Longitudinal and Historical Comparisons: DDI tracks changes in variables and methods across time, helping agencies harmonize data and support reliable historical comparisons. 
  • Improves Data Access and Reuse: DDI powers search catalogs and detailed documentation, making data easier to find, understand, and reuse by researchers and the public.
  • Automates Workflows: By embedding metadata into processes, DDI reduces manual effort through automation of documentation, validation, and data release tasks. 

How Statistical Agencies Can Get Started

  1. Assess Your Metadata needs -- Conduct an inventory of your current metadata structures and workflows.  Determine which parts of your data lifecycle (e.g., collection, processing, dissemination) would benefit most from DDI.
  2. Map Existing Metadata Practices -- Review how you currently handle metadata -- what's collected, how it's stored, and where gaps exist.  Compare this with the DDI lifecycle to identify areas where DDI can enhance your workflows.
  3. Leverage Tools and Resources -- Explore DDI-compliant tools.  Evaluate whether open-source or vendor-supplied tools best suite your infrastructure.
  4. Review Case Studies -- Review case studies of other statistical agencies using DDI.
Developers

For developers, adopting structured, machine-actionable DDI metadata simplifies integration, enhances interoperability, and enables automation across research data systems.

How DDI Helps Developers

  • Facilitates Integration: DDI's standardized metadata formats make it easier to integrate data across different repositories and discovery platforms.
  • Supports Interoperability: With DDI's shared schemas and vocabularies, developers can build systems that reliably exchange metadata with other platforms using common, well-defined structures.  
  • Automates Data Processing: Structured, consistent metadata supports automation of tasks like transformation, validation, ingestion, and export in metadata-driven pipelines.

How Developers Can Get Started

  1. Explore the DDI Schemas -- Review the product schemas to understand the data model, required elements, and available extensions.
  2. Experiment with Tools and APIs -- Leverage DDI tools to import, integrate, and export DDI-compliant metadata within your data system or repository.
  3. Implement DDI in Your Systems -- Incorporate DDI-compliant metadata into your data repository, catalog, ETL pipelines, or research applications.  This might involve: mapping existing metadata to DDI, creating metadata export/import functions, or validating against DDI schemas.
  4. Join the Developers Community and Stay Updated -- DDI is open and community-driven. Engage with other developers by joining the DDI Developers mailing list, contributing to GitHub DDI-Developers group projects, or joining DDI hackathons.