Embedded in the Cluster Forschungsdaten and partner projects, project participants from six Austrian universities and 23 associated partners spent three years developing and implementing FAIR RDM services, tools, and training materials. FAIR Data Austria has received funding from the Federal Ministry of Education, Science and Research (BMBWF) within the "Digital and Social Transformation in Higher Education" call. The results of the project are currently being further expanded and disseminated in the follow-up project Shared RDM Services and Infrastructure with a significantly larger group of university project partners.

The FAIR Data Austria project has contributed to strengthening knowledge transfer between universities, industry, and society and has supported the sustainable implementation of the European Open Science Cloud (EOSC). The implementation of the FAIR principles (findable, accessible, interoperable and reusable) has played a major role in this. Compliance with the FAIR principles was achieved through

1. integrated research data management (RDM) tailored to the discipline-specific and generic needs of research groups,
2. the development of a DMP tool to support the creation of machine-actionable data management plans (DMPs), taking into account the recommendations of the Research Data Alliance (RDA) and ensuring interoperability with existing systems
3. the establishment and development of next-generation repositories for research data (InvenioRDM), code (Git) and databases (DBRepo), and
4. the development of training and support services for efficient research data management through the design and implementation of training programmes, the introduction of data stewards and the implementation of the GO FAIR Austria Office

FAIR Data Austria has thus formed complementary building blocks in the field of RDM for the cluster projects Austrian DataLab and Services and RIS Synergy.

For efficient research data management in accordance with the FAIR principles, it is essential to support the entire life cycle of research data – from generation to archiving and reuse – with expertise and the appropriate tools. This cannot be done in isolation. The project has successfully promoted cooperation among Austrian universities in developing coherent, robust research data services. This secures the role of Austrian universities in the international research landscape.

Organisationally, the project was divided into the following work packages:

WP 1 – Project management
WP 2 – Development & implementation of RDM tools
WP 3 – Next-generation repositories
WP 4 – Deployment and rollout of repository systems
WP 5 – Process development, RDM training & support

In the Vienna Declaration on the European Open Science Cloud (2018), the EOSC is closely linked to the FAIR principles for research data in order to ensure permanent, easy, efficient, and cross-disciplinary access to research data, its storage, retrieval, use, and further processing. This project has enabled the sustainable implementation of the EOSC in Austria through the development of innovative, FAIR-compliant tools for planning and archiving data, as well as the necessary support services.

The implementation of the FAIR principles results in the following benefits:

• professionally developed RDM platforms and tools support researchers in storing, analysing, and publishing data
• researchers receive recognition for publishing (DOI) their research data and can benefit from other researchers' public data
• sharing data from even "failed" experiments saves time and money
• funding bodies require sustainable data stewardship programmes, which were developed as part of this project
• the data science community gains access to large amounts of data for its exploratory analyses

For efficient RDM in accordance with the FAIR principles, it is essential that the entire life cycle of research data (from generation to archiving) is structured and well organised through processes. In order to provide the best possible support to researchers at universities, various organisational units must therefore be involved in the process.

To ensure the best possible integration into everyday research, this process must be set up in such a way that those affected become participants and proactively contribute to the process. The implementation of FDM at universities must be structured. Digital transformation is therefore a key focus of the digitisation strategies of the participating universities, with a human-centred approach. This is ensured in the long term through the transformation process and innovative training and support measures (e.g. Digital University Hub Cluster programming platform/digital administration).

Data-intensive disciplines require efficient research data management. This includes the efficient use of different infrastructures and methods and contributes to scientific progress (contribution to University 4.0). To this end, generic infrastructures were developed and supplemented by pilot projects with lead communities with discipline-specific requirements (e.g. workflows, tools used, specific standards for metadata).

The aim was to enable and sustainably establish a visible national development boost (opening up scientific processes). In addition, this project strengthened and utilised connections to leading associated international partners (CERN, EOSCHub, EOSC-Pillar, EOSC Secretariat, RDA, GO-FAIR, OpenAIRE, FAIRsFAIR, EUA, Open Science MOOC, COAR). This ensured that the latest findings were incorporated into the project and that the results were visible to the international community.

For general RDM based on the FAIR principles, steps were defined for the entire RDM process, roles were assigned to institutional organisational units (e.g. libraries, research services, IT services), FAIRification of existing repositories and services, and the development of new FAIR infrastructures were pursued, and training, support and incentive systems for FAIR data management and open science were offered via data stewards to accompany the research process throughout the entire data lifecycle.

Discipline-specific RDM supports Austrian universities in introducing and stabilising profile-building and structure-developing measures. To this end, discipline-specific requirements were analysed in lead communities, pilot studies were conducted with researchers, services to support the research process were introduced, IT solutions for discipline-specific RDM were created, and training and support for IT solution training was offered by data stewards.

At the same time, e-accessibility also had to be considered. Data that complies with the FAIR principles must be accessible to everyone, including people with different disabilities. This implies barrier-free access to repositories, barrier-free metadata and barrier-free content.

FAIR Data Austria has thus also created complementary building blocks for higher-level analysis tools and services in the field of RDM, which were developed in the Austrian Data Lab and Services project.