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For general questions and information on the consortium you can contact us using either the following contact form or the contact information below. Contact information for indiviual archetypes, base services and community clusters is provided on their respective profile pages. In case you are unsure on who to contact, feel free to use the contact form and we will connect you to the right expert(s). You might also want to check our FAQ or our Q&A platform – your question may already be answered there.
The NFDI4Ing Q&A platform is here to empower researchers in the engineering sciences with a collaborative space to ask and answer questions about their research data management. Whether you’re a seasoned expert or just starting out, this platform is designed to foster knowledge exchange and support your research journey.
Contact
Postal address:
IT Center der RWTH Aachen University
Service building Seffenter Weg 23
52074 Aachen, Germany
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
base services
In the task area base services, all basic RDM services are bundled – both existing and those in development. As such, it provides central services for the research archetype task areas in order to address the archetype-specific requirements. In the same spirit, it develops and maintains services considered as relevant by the Community Clusters. The base services are designed to both deal with the strong interdependencies between different measures and to foster a development mentality focussed on interlocking and consolidating efforts between the partners. Each measure is worked on by multiple partner institutions with a defined lead. Each staff member of the task area participates in at least two measures to support and further develop a holistic view on the service portfolio.
key objectives
NFDI4Ing builds all of its services in a modular and user-centred style. The basic services are delivered by this task area, while the archetype-specific task areas build upon these services their differentiated services tailored to their respective methodological demands from the engineering communities. In the other direction, the RDM implementations in the archetype task areas serve as prototypes for the base services according to our user-centred approach. We have identified seven service objectives relevant for all engineering archetypes and communities and thus for NFDI4Ing’s overarching key objectives:
- All archetypes and communities rely on data quality assurance processes, the respective tools, and data quality metrics to make their data FAIR and to enable engineers to appraise and select data for further curation (measure S-1);
- the support of research software development is by now urgent in all fields of engineering, particularly but not only in computational engineering (measure S-2);
- providing easy-to-use yet comprehensible metadata tools for the engineering research daily routine as well as establishing detailed terminologies for engineering are the common ground for all RDM processes (measure S-3);
- the safe and secure storage and long-term archiving of data as well as the possibility to share or publish data in suitable repositories is relevant to all archetypes and communities, yet in different shape (measure S-4);
- all archetype services participate in NFDI4Ing’s overall software architecture incl. authentication, authorisation, and role management schemes, absolutely necessary for confident data from research projects close to industry (measure S-5);
- concepts and materials for training are needed in all task areas (measure S-6);
- most of the outcomes of engineering research is still hidden in human-readable publications only. Making engineering data FAIR needs advanced techniques of data extraction from and knowledge discovery in the engineering literature (measure S-7).
All services are designed as open, modular, and standardised as possible, in order to foster crossconsortial reusability. Furthermore, we strive for technical and structural connectivity to parallel and prospective developments in RDM on the national and international level (e.g. in the EOSC).
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resources
On the following pages we provide additional resources on the consortium, separated in press information, publications, downloads and job vacancies.
the archetype concept
From its inception, NFDI4Ing opted for a methodical and user-oriented approach to meet the requirements of the engineering sciences (research areas 41-45 according to the DFG classification 2016-2019 [only available in German]). In this heterogeneous community of highly specialised research (sub-)areas, a vast number of individualised approaches and extremely focussed tools and applications have been developed. Primarily due to their often lacking modularity, these tools and approaches can rarely be reused or applied to new problems by engineering research groups with similar, yet slightly different requirements. Additionally, the tools and applications are often not sustainable and easily outdated from the point of IT progress or new scientific demands. We consider this to be highly inefficient. In NFDI4Ing, we decided to lay ground for a systematic solution of this challenge by developing a new solution design: the archetype concept.
approach & results
To identify the specific needs of the engineering science community, we used a mixed-method approach including the collection of qualitative and quantitative data. Since 2017, we conducted semi-standardised face-to-face interviews with representatives from the five DFG research areas of interest and a number of workshops with different foci, each reaching from 12 to 50 attendees.The objective of this exploratory phase was to provide a broad overview of the current state and needs regarding research data management (RDM) in the engineering sciences. Based on this data, we developed 24 key dimensions for describing engineering science as morphological box, a heuristic problem solving method e.g. used for the development of product innovations. Then, this morphological box was used in the structuring process to categorise individual researchers according to their respective characteristics and to identify our most important target groups in terms of prototypical engineering scientists or methodological archetypes. In total, we derived seven archetypes being representative for the majority of the engineering sciences and derived method oriented task areas from each of these archetypes:
- Alex: bespoke experiments with high variability of setups
- Betty: engineering research software
- Caden: provenance tracking of physical samples & data samples
- Doris: high performance measurement & computation
- Ellen: extensive and heterogeneous data requirements
- Frank: many participants & simultaneous devices
- Golo: field data & distributed systems
These archetypes define typical research methods and workflows classifying corresponding challenges for research data management. So, engineers will be reached via their identification with the methodological archetypes. You can find further information on each archetype on their respective profile pages linked below.
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evaluation & further development
NFDI4Ing is and will continue to put emphasis on the identification and harmonisation of engineering research archetypes. Therefore, we are aiming to continually evaluate whether our community of interest (still) feels represented by the deduced archetypes. In a first step to this end we conducted an online survey in mid-2019. This method allowed approaching and involving a significant number of engineering scientists. The survey consisted of two parts: (1) RDM-related questions and (2) evaluation of the archetypes. We used an extensive mailing list including engineering research associations and other potential disseminators to achieve a reasonable sample size. Thus, we reached engineering research groups at all German universities and universities of applied sciences, as well as at all nonuniversity engineering research institutions (e.g. Fraunhofer institutes, Helmholtz centres, governmental research institutes, etc.). In total, 618 engineers completed the survey (each representing one research group from all fields of engineering), providing a solid basis for evaluating our approach.
Analysis of the survey data confirmed the representativeness and relevance of our seven archetypes: 95% of all research groups identify themselves with at least one archetype. The typical engineering research group combines elements of three to four archetypes and considers on average two archetypes as very relevant, showing a good division of demands by the archetypes. These findings are mostly independent of the engineering sub-discipline. These conclusions are further supported by the aforementioned interviews and workshops giving valuable qualitative feedback regarding our method-oriented and user-centred approach.
We are deeply committed to keeping up to date with the developing needs and demands in RDM in the engineering sciences. With our Community Clusters we try to establish a consistent dialogue between the consortium and the individual engineering research areas – both for sharing results and best practices, as well as to stay current on the developements and requirements of the engineering sciences. Via our seed funds programme we offer funding for innovative ideas and projects that further develop our work programme. Should the need arise, we are always open to modify our archetypes (or even add a new one).
