Scientists of all disciplines are able to retrace or reproduce all steps of engineering research processes. This ensures the trustworthiness of published results, prevents redundancies, and contributes to social acceptance.
Engineers are enabled to develop validated quality-assured engineering research software. They treat software as research data that possibly connects the different stages of stored data.
Recording and linking of auxiliary information and provenance is automated and optimised to reduce the manual data handling tasks as much as possible. This ensures the interpretability of data in the context of a specific project and for a hitherto unknown repurposing.
Sharing and integration of possibly large amounts of data is facilitated and employed by engineers across single studies, projects, institutions, or disciplines via networked technical infrastructure (repositories), open metadata standards, and cultural change.
Collaborative research is unhindered, while preventing unauthorised access to confidential data. Because engineering sciences are close to industry, this calls for sophisticated means in authentication, intellectual property, and license management.
Engineers are able to generate machine-processable representations of auxiliary information based on open standards by means of easily accessible tools. This paves the way to further reuse by data-driven analysis methods such as machine learning and artificial intelligence approaches.
Engineers profit from an improved data- and software-related education (data literacy) and available domain and application specific best practices.
Publication of data is standardised and acknowledged by the engineering community in the same way as publication of scientific documents, including peer-review measures and effects on the scientific reputation.
building our community
NFDI4Ing has signed the Berlin Declaration on NFDI Cross-Cutting Topics and thereby joins the other signatories in the effort of addressing cross-cutting topics in a coordinated fashion. The discussions sparked by the Berlin Declaration have been further advanced by seven cross-cutting initiatives on workshops held October 1st, 2019, in Leipzig, and February 25th, 2020, in Berlin. NFDI4Ing is welcoming these discussions and acknowledges the highly desirable goal of building a research data infrastructure that transcends the confines of single disciplines.
In order to foster the open, transparent inter-consortia exchange currently underway, we offer our view on the list of cross-cutting topics. According to point three of the common vision of the Berlin Declaration, all of the listed topics are relevant for NFDI, but not every topic has the same relevance for each consortium. We share the topics that we identify as relevant in the NFDI4Ing proposal.
- Community integration
- Community support
- Interaction with already existing research data management services in institutions
- Digital twins
- Technical infrastructure
- Quality assurance in research data management processes and metrics for FAIR data
- Research software management
- Metadata and terminology services
- Repositories and storage
- Persistent identifier (PID)
- Authentication and Authorisation Infrastructure (AAI)
- Overall NFDI software architecture – data security and sovereignty
- Automated data and knowledge discovery in
- structured data
- Community-based training on enabling data-driven science and FAIR data
- Training and dissemination comprising
- training and training materials
As outlined by the DFG, the national research data infrastructure (NFDI) aims at systematically managing scientific and research data, providing long-term data storage, backup and accessibility, and networking the data both nationally and internationally. The NFDI will bring multiple stakeholders together in a coordinated network of consortia tasked with providing science-driven data services to research communities. The NFDI’s programme aims for consortia include:
- Establishment of data handling standards, procedures and guidelines in close collaboration with the community of interest
- Development of cross-disciplinary metadata standards
- Development of reliable and interoperable data management measures and services tailored to the needs of the community of interest
- Increased reusability of existing data, also beyond subject boundaries
- Improved networking and collaboration with partners outside the German academic research system with expertise in research data management
- Involvement in developing and establishing generic, cross-consortia services and standards in research data management together with other consortia