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Welcome to NFDI4Ing – the National Research Data Infrastructure for Engineering Sciences!
Engineering sciences play a key role in developing solutions for the technical, environmental, and economic challenges imposed by the demands of our modern society. The associated research processes as well as the solutions themselves will only be sustainable if being accompanied by a proper research data management (RDM) that implements the FAIR data principles: data has to be findable, accessible, interoperable, and re-usable. NFDI4Ing brings together the engineering communities to work towards that goal. As part of the German National Research Data Infrastructure (NFDI), the consortium aims to develop, disseminate, standardise and provide methods and services to make engineering research data FAIR. As one of the first consortia funded as part of the NFDI, NFDI4Ing has actively engaged engineers across all engineering research areas as well as experienced infrastructure providers since 2017. It now has more than 50 active members and participants and continues to grow.
our mission
Research in engineering ranges from basic research (analysis), i.e. the production, use and processing of data, to applied research and technology development (synthesis). Consequently, the engineering community shows a high affinity and ability to develop and adapt IT systems, familiarity with standardisation (e.g. industry standards), know-how in quality management, and last but not least an established practice of systematic approaches in big projects with many stakeholders.
However, the complexity of information in the focus of research (sub-)communities has resulted in highly specialised approaches towards research data management and in a wide variety of engineering research profiles. Methodically, engineering sciences rely on the understanding of fundamental research (theories) and their application on and validation in particular domains (experiments), enriched by additional information derived from domain-specific models (simulation). These sources provide a diverse set of types, objects, amount, and quality of data.
Despite this diversity in research, engineers share common needs and approaches on the methodological level. Our mission as NFDI4Ing is to leverage these similarities in order to consolidate key objectives and to develop solutions that benefit the engineering community at large. During its first phase, NFDI4Ing identified the most commonly recurring needs, methods, and workflows in engineering research, and classified corresponding challenges for research data management (RDM) in the engineering sciences. On this basis, the consortium then delevoped and validated seven typical research profiles that map to these challenges. We call these research profiles “archetypes” and use them throughout NFDI4Ing to structure our work programme. To learn more about the Archetypes, please visit their profile pages.
our goals
One key task of NFDI4Ing is to consolidate the wide variety of engineering research approaches and expectations towards RDM into a limited number of shared standards and goals. This is an ongoing process. To begin with identifying the specific needs of the engineering sciences community, we used a mixed-method approach including the collection of qualitative and quantitative data. This initial explorative phase had the objective to provide a broad overview of the current state and needs regarding RDM in engineering sciences. To further our understanding and validate the results, we conducted semi-standardised face-to-face interviews with representatives from the five DFG research areas of interest and organised workshops with different foci, each ranging from 12 to 50 attendees. This work resulted in the eight key objectives shown below.
retrace & reproduce
research software
auxiliary information
sharing & integration
authorised access
machine readability
data literacy
data publications
retrace & reproduce
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.
research software
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.
auxiliary information
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 & integration
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.
authorised access
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.
machine readability
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.
data literacy
Engineers profit from an improved data- and software-related education (data literacy) and available domain and application specific best practices.
data publications
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.
These goals integrate perspectives of engineers as well as infrastructure providers and offered guidance in further structuring the NFDI4Ing work programme. The NFDI4Ing work programme is sub-divided in different task areas. At the centre are the seven task areas focussed on addressing the requirements of the archetypes. Due to this close link, we often use the names of the archetypes and those of the corresponding task areas synonymously.
These task areas are flanked by task areas that crosslink them with research-area-centred communities (Community Clusters), or develop and provide specific services and tools (Base Services). The task area management provides administrative support, offers consulting and coordinates the different processes in the consortium. All NFDI4Ing task areas work together to generate and establish sustainable and comprehensive solutions for engineering research data management. You can visit our site on the individual task areas and the NFDI4Ing work programme as a whole to learn more details.
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cross-cutting topics
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, leading to the Leipzig-Berlin Declaration on NFDI Cross-Cutting Topics (in German, only). 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.
- Governance
- 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
- literature
- structured data
- Community-based training on enabling data-driven science and FAIR data
- Training and dissemination comprising
- education
- training and training materials