- Initial Situation
Running Time: January 2020 to December 2022
Project Lead: TU Graz
The FAIR Data Austria project is designed to strengthen knowledge transfer between universities, industry, and society und supports the sustainable implementation of the European Open Science Cloud (EOSC). Within the project, implementation of the FAIR principles (which mandate that research data be Findable, Accessible, Interoperable, and Reusable) plays a major role. Observation of the FAIR principles is secured through 1) integrated data management aligned with generic and discipline-specific needs of researchers, 2) development of next-generation repositories for research data, code, and other research outputs, and 3) development of training and support services for efficient research data management. FAIR Data Austria thereby offers tools to complement the Austrian Data Lab and Services as well as RIS Synergy projects.
Supporting the entire data lifecycle - from data generation all the way to data archiving - with the appropriate tools and expertise is essential to achieve efficient research data management according to the FAIR principles, a process that can only be successful when supported by all Austrian HEIs. The FAIR Data Austria project therefore supports the collaboration of Austrian universities in developing coherent services for research data, thereby securing Austria's position within the international research landscape.
The Vienna Declaration on the European Open Science Cloud (2018) argues for a close link between the EOSC and the FAIR principles (mandating that research data be Findable, Accessible, Interoperable, Reusable) to ensure that research data remain accessible efficiently, permanently, and across disciplines to store, find, and reuse. The FAIR Data Austria project enables the sustainable implementation of EOSC in Austria through the development of innovative, FAIR-compliant tools for RDM planning and data archiving as well as appropriate support services.
- Professional implementation of the FAIR principles comes with the following benefits:
- Professional platforms and tools for research data management support researchers in archiving, analysing, and publication of research data
- Researchers profit from the ability to receive credit for publishing data sets (which receive a DOI) and profit from other researchers sharing their data
- Sharing data from "failed" experiments saves time and money
- The project develops and implements data stewardship programmes, a prerequisite to receiving funding in many instances
- The Data Science community is given access to large amounts of data for their exploratory analyses
It is essential for efficient and FAIR-compliant Research Data Management that the entire data lifecycle (from generation to archiving) be structured and organized through (documented) processes. In order to best support researchers in data management, a diverse array of organisational units/departments needs to be part of the conversation.
In order to secure optimal integration into the research process, the research data management process needs to be set up in such a way as to proactively involve researchers. The Implementation of RDM in Austrian universities therefore needs to follow a structured process. Human-centered digital transformation is therefore a priority of participant universities' digitisation strategies. The transformation process along with innovative training and support measures (e.g. Digital University Hub - Cluster "Coding Platform/Digital Administration") is designed to ensure just that.
Data-intensive research fields need efficient research data management, including the efficient use of verious infrastructures and methods as a contribution to scientific progress. The project develops generic infrastructures and pilots them in lead communities with discipline-specific requirements (such as workflows, tools, metadata standards).
The overall project aim is to enable sustainable national development that remains internationally visible (opening of scientific processes). In addition, the project will exploit connections to and collaborations with leading associated international partners (CERN, EOSCHub, OSC-Pillar, EOSC Secretariat, RDA, GO-FAIR, OpenAIRE, FAIRsFAIR, EUA, Open Science MOOC, COAR) to ensure that state-of-the-art results are being implemented in the project and results be visible to the community.
The project will define I) steps for general FAIR-compliant RDM, II) roles for organisational units (libraries, research services, IT services), III) make extant infrastructures and servcies (e.g. repositories) FAIR and build new ones, and IV) develop training, support, and incentive structures for FAIR data management and Open Science, with data stewards attending to the entire data lifecycle.
Discipline-specific RDM will support Austrian universities in developing and introducing core competencies. In this instance, FAIR Data Austria will I) analyse discipline-specific requirements in lead communities, II) conduct pilot studies with researchers, III) introduce services for supporting the research process, IV) develop solutions for discipline-specific RDM and V) offer training and support for IT solutions via data stewards.
At the same time, the project will observe issues of e-accessibility. FAIR data need to be accessible to everyone including persons with disabilities, implying barrier-free access to repositories, metadata, and content.
FAIR Data Austria thereby builds complementary building blocks for RDM for those higher-level analytical tools and services developed in the project Austrian Data Lab and Services.
- Establishment of processes and definition of tasks for organisational units to support RDM throughout the data lifecycle
- Integrated RDM for discipline-specific and general needs of research groups
- Development of repositories for research data, code, and databases
- Development of training and support services for efficient RDM (development of digital skills)
- Development of incentives and reward structures
- Strengthening of RDM at Austrian universities through bundling activities and visibility
- Tools for sensitive data
- Barrier-free tools and services