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.
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