The NFDI4ING work programme is organised into three complementary groups of task areas:

1. Archetype Task Areas – addressing research-data management challenges and methods specific to typical engineering workflows.
2. Overarching Solutions Task Areas – providing cross-cutting infrastructure, standards, services and support usable by all archetypes, and bridging to the wider NFDI ecosystem (e.g. via Base4NFDI).
3. Management & Outreach Task Area – coordinating consortium-wide communication, outreach, community participation, quality assurance, and ensuring long-term sustainability of services.

The NFDI4ING Archetypes:

NFDI4ING’s “archetype” concept clusters typical engineering research workflows into a small number of representative profiles – so-called “archetypes” – to capture recurring needs, methods, and challenges in research data management (RDM).
Based on this classification, NFDI4ING develops tailored services and solutions addressing data handling in engineering workflows (e.g., experiments, simulations, sample tracking, heterogeneous data, field data). A validation via focus groups and a large survey showed that 95% of responding research groups identify with at least one archetype (many mix features of several), demonstrating the concept’s broad relevance across engineering disciplines.

The Archetypes and their key challenges in short:

• ALEX: tailored experiments whose setups often change, requiring flexible data-management strategies.
• BETTY: research driven by software tools, simulations, or code, where data is largely generated/managed via software.
• CADEN: workflows that require tracing of physical samples or data through many processing stages.
• DORIS: simulations or measurements using High-Performance Computing, producing large datasets needing special storage, metadata and reproducibility solutions.
• ELLEN: research combining many different data types/sources, i.e. integrating data from literature, sensors, experiments, etc.
• FIONA: building on existing, often distributed and heterogeneous datasets: re-uses, combines, and enriches data from multiple sources, making it available to other researchers and machines.

The Overarching Solutions:

NFDI4ING’s  “Overarching Solutions” are cross-cutting service areas that provide infrastructure, standards, and support for all engineering research workflows. They offer shared base services such as quality assurance, research-software support, metadata and terminology management, repositories, data security, training, and data/knowledge discovery. By harmonising and scaling these services, NFDI4ING prevents siloed approaches and enables consistent, FAIR- and AI-ready data handling across communities. They also ensure integration with national and international infrastructures, supporting federated identity and access, common information models, and long-term sustainability.

The Overarching Solutions and their key challenges in short:

• TES – Training, Education & Standardisation: Provides standardised RDM training for engineering sciences, integrates RDM into university teaching, and drives standardisation via NFDI-RFCs.
• SKG – Semantic Metadata & Knowledge Graphs: Develops harmonised metadata models for FAIR Digital Objects and enables federated use of engineering knowledge graphs.
• FIS – Federated Interactive Data Space: Provides information services to search and find data, interactive federated services to store and share data across infrastructures.
• CPT – Copilot: Assistance Systems & AI: Creates AI-based assistance for RDM processes, including formal data-quality assessment, chatbots and recommender systems, and AI support for research-software engineers.
• J – Journal ing.grid: Ensures efficient operation of the Open-Access engineering RDM journal ing.grid and supports community-building and outreach.