This is the first use case from the initial funding period and serves as an excellent example, as data sharing remains particularly challenging between universities, research institutes, and industry.

What this use case is about:
CARBO-DIOL2.0 is a BMFTR-funded industry- and academia-driven use case that targets the development of new catalyst materials for the conversion of carbohydrate-rich side and waste streams into functional chemicals, namely α-ω-diols. It brings together BASF, KIT, TU Berlin - BasCat - UniCat BASF JointLab, Ruprecht-Karls-Universität Heidelberg - CaRLa, FHI MPG, MPI CEC, University of Bayreuth, hte GmbH and ETH Zürich.

Why it matters for NFDI4Cat:
The use case emphasizes a holistic, big-data approach in catalysis that bridges high-throughput experimentation, simulation/modeling, and data-driven methods (including statistical learning), across broad time and length scales.
That combination creates exactly the kind of complex data landscape where FAIR practices and shared standards determine whether results can be integrated, compared, and reused.

NFDI4Cat angle:
Carbodiol illustrates how use cases can drive the need for interoperable data structures that connect experimental and computational evidence, so insights can be transferred and scaled rather than remaining isolated within one sub-workstream.

CARBO-DIOL2.0

What this use case is about:
This industry-mentored use case brings together two industrial partners: BASF and hte GmbH. It focuses on converting syngas into higher alcohols. It spans multiple catalysis routes and operating regimes, from thermo-catalytic heterogeneous catalysis to electro-catalytic, homogeneous, and bio-catalytic approaches.

Why it matters for NFDI4Cat:
The scientific space is broad: different catalyst types, different conditions, different data modalities, and different communities. This makes it an ideal “stress test” for FAIR catalysis data, because reuse only works if datasets are described consistently and remain comparable across experiments, institutions, and methods.

What kinds of data are in scope:
The use case explicitly considers a wide range of primary and secondary sources, including publications, patents, theses, electronic lab journal entries, LCA/TEA-related information, and secondary materials such as proposed mechanisms, reaction schemes, and proposed active centers.

NFDI4Cat angle:
A key outcome is making such heterogeneous information more interoperable e.g., by connecting datasets with semantic descriptions (vocabularies/terminologies are referenced in the deck) and enabling structured reuse via repository-based publication and linking.

What this use case is about:
This industry-led use case is driven by Evonik and addresses a combined chemical and biotechnological process route for producing sustainable acetone from bio-ethanol. The process concept couples different scientific domains and is organized in multiple work packages, each producing distinct data types and requiring cross-institutional collaboration.

Why it matters for NFDI4Cat:
This use case highlights a common real-world bottleneck: exchanging data across disciplines and partner organizations. In many projects, data is still shared within work packages via tools like shared platforms and spreadsheet templates, making consistent terminology and cross-package reuse difficult.

NFDI4Cat angle:
NFDI4Cat supports the transition from “file exchange” to FAIR data exchange, through guidance and tools that make datasets understandable and reusable beyond their original context, and by supporting vocabulary/ontology extensions with project-relevant terms.

BETA

What this use case is about:
This industry-mentored use case brings together two industrial partners with complementary roles: Clariant (catalyst development) and Linde (process engineering). The shared goal is to advance the sustainable production of olefins through catalysis, while making collaboration and learning across organizational boundaries easier and more reliable.

Why it matters for NFDI4Cat:
If you collaborate across companies (or between industry and academia), you often face a dilemma:

• You want to learn from data together and iterate faster,
• but the data may be confidential, internally restricted, and governed by legal agreements.

This use case is important because it makes these real constraints explicit and turns them into actionable community learning: how can partners exchange and interpret data responsibly, without losing the ability to reuse insights later?

The setting described in the use case includes confidential data that is not publicly accessible, internal restrictions between departments, and data sharing based on secure infrastructure and cooperation agreements that need legal review for coverage of sharing via NFDI4Cat.

NFDI4Cat angle:
It demonstrates that “FAIR in practice” must also work for real-world collaboration models, especially when data is sensitive. By capturing requirements and lessons learned, the use case strengthens the community’s ability to build shared approaches that work across organizations, not only within one lab.