Collection, Generation and Validation of Datasets suitable for training AI Models for 6G Networks and for AIaaS

The Horizon Europe grant opportunity titled "Collection, Generation and Validation of Datasets suitable for training AI Models for 6G Networks and for AIaaS" (topic ID HORIZON-JU-SNS-2026-STREAM-B-01) is scheduled to open on January 29, 2026, and will close on April 29, 2026. The total allocated budget for this initiative is €8,000,000, focusing on advancing the development of high-quality datasets for artificial intelligence applications in the context of 6G networks.

Eligible applicants include various legal entities established in EU Member States and Associated Countries, such as businesses, research organizations, universities, and industry partners. Consortia are expected to comprise multiple organizations, fostering strong industrial participation with demonstrated expertise in AI and operational networks. The initiative specifically seeks to exclude entities from Russia, Belarus, and non-government-controlled regions of Ukraine, as well as high-risk suppliers of mobile network communication equipment.

The funding type is categorized as a grant under Horizon Europe's Innovation Actions, covering up to 70% of eligible costs for private entities and 100% for non-profits. Applications must follow a single-stage submission process, requiring comprehensive proposals without pre-screening.

Project stages will involve development, validation, and demonstration, centered around creating datasets and tools for training AI models relevant to 6G networks. Expected outcomes include curated collections of real-world and synthetic datasets, open-source simulators for dataset generation, and frameworks for managing the lifecycle of datasets produced.

The call emphasizes the necessity for a strong industrial presence and collaboration among academia, ensuring that standardization and best practices are adhered to. Participants are expected to engage in activities that promote the datasets' use, integrate with existing infrastructures, and comply with EU regulations on data governance and privacy.

Overall, this Horizon Europe opportunity aims to enhance the performance and capabilities of 6G networks through the development of robust AI datasets, facilitating advancements in AI as a Service (AIaaS) applications, while also addressing strategic concerns such as data sovereignty and technological independence within Europe.

This is a Horizon Europe call, specifically HORIZON-JU-SNS-2026-STREAM-B-01, focused on "Collection, Generation and Validation of Datasets suitable for training AI Models for 6G Networks and for AIaaS". It falls under the HORIZON JU Innovation Actions (HORIZON-JU-IA) funding scheme with a HORIZON Action Grant Budget-Based [HORIZON-AG] Model Grant Agreement. The call employs a single-stage deadline model. The planned opening date is 29 January 2026, and the deadline for submission is 29 April 2026, 17:00:00 Brussels time. The total budget allocated for this topic is 8,000,000 EUR, and it is expected that around 1 grant will be awarded.

The expected outcomes of this call are:

1.  A curated collection of high-quality real-world datasets suitable for training AI models that can run in operations, capturing realistic deployment scenarios. These scenarios include user density, mobility patterns, network conditions, and realistic data traffic patterns from various applications, including vertical applications. The collection can be obtained through operational networks, network digital twins, advanced experimental platforms, gen AI, and should be significant in scale and broad representativeness.

2.  An open-source simulator to create synthetic datasets of high quality, correlated between different network layers and across different network points, capturing realistic deployment scenarios. These scenarios include user density, mobility patterns, network conditions, anomalies, network attacks, and realistic data traffic patterns from various applications, including vertical applications.

3.  A modular framework and related methodology for generating and processing high-quality realistic synthetic datasets to train AI models usable by 6G systems. This includes cleaning and preprocessing.

4.  A data space of appropriate scale to manage the datasets, covering the full data lifecycle for managing datasets produced by SNS JU projects, for different reference use-cases at different scales. The data space should follow a framework that supports data/metadata sharing and governance within the SNS JU ecosystem, facilitating collaboration and interoperability, ensuring data sovereignty, privacy, security and compliance with EU regulations. Existing solutions for managing SNS produced data sets including repositories should be considered to avoid duplication with existing initiatives.

5.  A framework to audit any synthetic datasets that the project will create to ensure the validity/credibility of produced datasets.

6.  Activities that will encourage widespread use of the dataset, such as integration with CAMARA/Network APIs/agents, or the design of new control plane functions, by the SNS community, other EC initiatives, and standardization bodies.

The produced datasets are targeted to be used by the SNS community to train AI models that will improve the performance of 6G networks or serve to develop AI solutions for 6G services and applications (AIaaS) for SNS JU verticals.

The scope of this call focuses on:

*  Collecting and making available high-quality real-world datasets from advanced (e.g., 5G Advanced, 6G) operational networks, network digital twins, gen AI tools, experimental platforms, or trials. These datasets should capture realistic deployment scenarios, including user density, mobility patterns, network conditions, and realistic data traffic patterns from various applications, including verticals. Datasets with limitations in coverage, capacity, number of devices, or data and metadata quality should not be considered. The datasets should originate from actors with significant experience from operational networks and network components as well as service providers (including verticals). For real-world datasets, ensure the presence of Metadata definition, methods to verify data value for AI model training, and data reusability.

*  Developing full protocol stack, end-to-end implementations enabling high-fidelity, system-level simulations of 6G networks, building upon and enhancing existing open-source simulators. These simulations will produce correlated reference datasets at different network layers and across different network points, spanning from the physical layer to the application layer. The simulations will include:

 *  Support for multi-radio access technologies, including cellular, Wi-Fi, and non-terrestrial systems.
 *  Disaggregated RAN architectures, enabling flexible and scalable deployment models.
 *  Multi-band operation with accurate propagation and channel modelling across various frequency ranges (e.g., FR3, mmWave, cmWave, sub-THz), incorporating ray tracing-based channel models for enhanced realism.
 *  Realistic traffic patterns that reflect anticipated data flows in future network scenarios.
 *  Anomalies and network attacks that can be eventually used to test the resilience of AI solutions in 6G networks.

*  Designing an open-source framework and toolset for generating high-quality realistic synthetic data, tailored to diverse environmental scenarios (e.g., urban, suburban, rural, indoor, industrial), user densities, security threats, mobility patterns, and node behaviour, including memory, CPU, storage, and energy consumption, as well as traffic profiles from a variety of vertical applications. These datasets should be produced following existing calibration directives from standardization bodies (e.g., 3GPP) and expected traffic patterns from European and international organizations (e.g., 5GAA, 5G-ACIA, etc.). The datasets should be validated from proponents that have significant experience from operational networks and network components as well as service providers (including verticals). Optionally, the tool may consider the use of reliable LLM solutions to enable a user-friendly interface for users and/or to calibrate the simulator and/or create the desired datasets.

*  Creating large-scale, open-source high-quality synthetic datasets, following well-established reference use-cases, containing measurements, channel and network indicators, and performance metrics across multiple protocol layers (RF, physical, MAC, network, transport, and application). These datasets will cover a broad range of network scenarios, architectures, technologies, and system configurations related to smart networks and services.

*  Validating and quality assessing existing SNS JU project datasets, verifying the data’s accuracy, consistency, and completeness, ensuring their alignment with the specific use case and performance requirements of the 6G network.

*  Ensuring the provision of Metadata definition, methods to verify data value for AI model training, and data reusability for real-world datasets.

*  Engaging with standardization bodies and relevant open-source communities to promote the adoption of the framework, the associated simulation tools, and open datasets.

*  Creating tutorials and implementing dissemination activities to encourage widespread use of the framework, its synthetic datasets, and the underlying simulation tools, including the development of APIs and new intelligence control plane functions.

The call expects strong industrial participation with demonstrated AI and operational expertise to ensure credibility, usability, and engagement with standardisation bodies. Academic institutions and RTOs will complement consortia where their expertise adds clear value.

The proposal page limit is 70 pages for RIAs and IAs submitted under Stream B and C.

Participation is limited to legal entities established in Member States and Associated Countries, with specific restrictions for entities directly or indirectly controlled by non-eligible countries or entities, and for entities assessed as high-risk suppliers of mobile network communication equipment. Legal entities established in Russia, Belarus, or in non-government-controlled territories of Ukraine are not eligible to participate.

At least half of the budget should be implemented by the SNS JU member (other than the Union) and their constituent or affiliated entities.

Applicants will be invited to fill a mandatory table of compliance at proposal stage in the Application Form Technical Description (Part B).

Provision of a gender equality plan for public bodies, research organisations or higher education establishments applies as per Part B of the General Annexes to the Horizon Europe Work Programme 2026-2027.

For IAs under Stream B and RIAs under Stream C, the award criteria table is complemented by a sub-criterion assessing the proposal contribution to the overall SME objective and a sub-criterion assessing the proposal contribution to the IKOP objectives.

Participants of selected projects will be requested to cooperate in the SNS JU Programme for topics of common interests by signing a written agreement. AI/ML training data sets created and used in the context of the selected projects have to be made available through a common repository.

A call-specific application form will be used, available in the Submission System.

This Horizon Europe call focuses on developing high-quality datasets and simulation tools to advance AI-driven 6G network technologies. It seeks to create curated collections of real-world and synthetic datasets, open-source simulators, and frameworks for training AI models, all aimed at improving 6G network performance and fostering AI-as-a-Service (AIaaS) applications. The call emphasizes collaboration, data sharing, and the involvement of industry, academia, and standardization bodies to ensure the development of credible, usable, and widely adopted resources for the 6G ecosystem. The goal is to provide the SNS community with the necessary tools and data to train AI models, ultimately leading to more efficient and intelligent 6G networks.

Eligible Applicant Types: The call is open to legal entities established in Member States and Associated Countries. The topic expects proposals with strong industrial participation with demonstrated AI and operational expertise. Academic institutions and RTOs will complement consortia where their expertise adds clear value. The call text mentions SMEs as well.

Funding Type: The funding type is a grant, specifically a HORIZON JU Innovation Action (HORIZON-JU-IA) and HORIZON JU Research and Innovation Actions (HORIZON-JU-RIA) and HORIZON JU Coordination and Support Actions (HORIZON-JU-CSA).

Consortium Requirement: The opportunity requires a consortium of multiple applicants. The call expects proposals with strong industrial participation with demonstrated AI and operational expertise to ensure credibility, usability, and engagement with standardisation bodies. Academic institutions and RTOs will complement consortia where their expertise adds clear value.

Beneficiary Scope (Geographic Eligibility): The geographic eligibility is limited to legal entities established in Member States and Associated Countries.

Target Sector: The program targets the ICT sector, specifically focusing on 6G networks, AI, and related services (AIaaS). It also touches upon areas like telecommunications, software services, and innovation.

Mentioned Countries: The call specifies that participation is limited to legal entities established in Member States and Associated Countries. It also mentions restrictions for entities established in Russia, Belarus, or in non-government-controlled territories of Ukraine.

Project Stage: The project stage encompasses research, development, validation, and demonstration, aiming to create datasets and frameworks that can be used for training AI models for 6G networks. The call also mentions advanced (high TRL) experimental platforms.

Funding Amount: The funding amounts vary by topic: HORIZON-JU-SNS-2026-STREAM-B-01 (Innovation Actions): €8,000,000, HORIZON-JU-SNS-2026-STREAM-C-01 (Research and Innovation Actions): €8,000,000, HORIZON-JU-SNS-2026-STREAM-CSA-01 (Coordination and Support Actions): €3,000,000, HORIZON-JU-SNS-2026-STREAM-CSA-02 (Coordination and Support Actions): €2,000,000, HORIZON-JU-SNS-2026-STREAM-CSA-03 (Coordination and Support Actions): €1,000,000.

Application Type: The application type is an open call, with a single-stage submission process.

Nature of Support: Beneficiaries will receive money in the form of a grant.

Application Stages: The application process is single-stage.

Success Rates: The success rates are not explicitly mentioned, but the indicative number of grants for each topic is specified.

Co-funding Requirement: The text states that at least half of the budget should be implemented by the SNS JU member (other than the Union) and their constituent or affiliated entities. This implies a co-funding or in-kind contribution requirement from the SNS JU members.

Summary: This Horizon Europe call, part of the SNS JU Programme, focuses on "Collection, Generation and Validation of Datasets suitable for training AI Models for 6G Networks and for AIaaS". It seeks to fund projects that will create high-quality, representative datasets (both real-world and synthetic) and open-source tools to support the development and validation of AI models for 6G networks. The call is structured into Innovation Actions, Research and Innovation Actions, and Coordination and Support Actions, each with specific objectives and funding levels. The projects should involve strong industrial participation, with academic institutions and RTOs complementing the consortia. The geographic scope is limited to legal entities established in Member States and Associated Countries, with specific restrictions for entities linked to Russia, Belarus, or non-government-controlled territories of Ukraine, and high-risk suppliers of mobile network communication equipment. The call emphasizes open science, data sharing, and collaboration within the SNS JU ecosystem. The goal is to provide the SNS community with the resources needed to develop AI solutions that will improve the performance of 6G networks and enable new AI-driven services and applications.