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Providing Research Data Management (RDM) support and education is a central goal of NFDI4Ing. Together, the Base Service measure S-6, the Community Cluster measure CC-2,
While experimental data and methods are commonly reported as “best outcome” figure of merits, the systematic process parameters (while being crucial for achieved outcome, in particular in combination and chains of different process steps) are often neglected or buried in hard-to-access lab books and similar sources. This complicates and prolongs process optimization, especially when different printing and post-processing steps need to be combined for a specific application. The project ReCIPE aimed at setting up a tool for researchers and users of printed electronics that can grow into a broad open database on process parameters and connected outcomes for the wider academic and industry-based community.
Data processing is usually not a single task, but in general relies on a chain of tools. To achieve transparency, adaptability, and reproducibility of (computational)
From its inception, members of NFDI4Ing have been working on a concept for the publication and organization of standards in the field of research data. By now, the concept is quite advanced and ready for the next step. The working group “Standardisation” (CC-5) is looking for interested persons from the community to support the work and to get the implementation underway.
ield experiments mostly take place under difficult conditions. Due to the large number of sensors and computers, large amounts of data are usually generated, and data acquisition can be affected by small errors or external events. For further processing and analysis of field data, verification of data quality is essential. To assist researchers, TA Golo has documented a website – the Data Quality Metrics Website.
The recently published “Guidelines zum Text und Data Mining für Forschungszwecke in Deutschland” describe under which conditions text and data mining may be carried out for scientific purposes and what risks exist. An online workshop, “Urheberrechtliche Fragestellungen bei der Gestaltung von Dienstleistungen zum Text und Data Mining” will take place on April 25th to address copyright issues in designing services to support text and data mining.
NFDI4Ing cordially invites you to participate with a contribution on this years conference! The motto of the NFDI4Ing conference 2023 is “Innovation in Research Data Management: Bridging the gaps between disciplines and opening new perspectives for research in engineering science”.
The international online journal ing.grid is now accepting submissions addressing FAIR data management in engineering sciences. With an open access policy, the journal bridges a gap in the field, offering a platform and recognition for sound scientific practice in generating research data, developing reusable tools for processing that data and curating the data to make it findable, accessible, interoperable, and reusable (FAIR).
Getting and staying in touch with the community is the essence of NFDI4Ing. That’s why the consortium offers a broad variety of exchange formats like community meetings, conferences, and workshops. To meet the needs of the community, we continuously work to improve our engagement processes.
It was a close contest, but in the end two nominees prevailed in the NFDI4Ing Awards 2022 against 41 other contributors. We happily congratulate the winners, Wendy (Pengyin) Shan from the University of Alberta Library in Canada, and Rory Macneil from Edinburgh, Scotland. The awards were endowed with 500€ each, sponsored by the WZL Aachen Stiftung.
Virtually all scientific output is communicated through text publications, only suitable for humans and precluding searches beyond simple text matching. By contrast, SciMesh disseminates results as machine-actionable, comprehensive knowledge graphs.
The NFDI4Ing Task Area DORIS designs transferable research-data management concepts and tools for data from high-performance measurement and computation (HPMC), enables, and supports the community to apply these solutions. In order to standardize and facilitate RDM for our peer group, we provide support for the complete data life cycle.
Manual development and maintenance of ontologies are tedious tasks and require extra training to use ontology modelling tools. Therefore, a semi-automatic process to enrich ontologies can assist domain experts, who are not necessarily ontology experts, to map knowledge into ontologies. OntoHuman pursues a Human-in-the-Loop (HiL) approach to address this, which requires humans to provide feedback to an automated system for information extraction from technical documents.
In recent years, concepts, tools and services have been developed that enable good research data management. However, in most disciplines there are not yet any established standards. See how we want to solve this issue for the community.
The workgroup Identity and Access Management finished a first analysis of requirements towards authentication and authorization infrastructures and proposed an AAI architecture for the whole NFDI. Results are available on the continuously updated documentation website.
The availability of terminologies is a critical component of research data management. Without terminologies, meaningful descriptions of research data would not be possible, and the reusability of these data and associated information would be compromised. Accordingly, researchers and funding agencies have a vested interest in the availability of sophisticated and stable terminologies.
The NFDI4Ing task area BETTY envisions a future in which the engineering sciences produce verified, high-quality software that can be reused and extended. Betty wants to identify and provide the missing tools, teaching material and recommendations to make that vision reality.
For most researchers, working with code is common practice. However, many researchers lack knowledge and experience in how to write good code. In the Task Area Alex, we addressed this problem and developed a set of guidelines currently being tested at the Chair of Fluid Systems, TU Darmstadt.
RDMO is a software that assists in planning the data management for your research project. In NFDI4Ing, we adapted and complemented the Open Source software to better support the specific needs of researchers working in the engineering sciences. The service is available at rdmo.nfdi4ing.de.
In NFDI4Ing, various services and solutions are being developed to improve and simplify (research) data management in engineering. To bring these solutions into practice and validate them based on the subject-specific requirements of researchers, NFDI4Ing regularly organizes Community Meetings. Researchers, infrastructure operators and industry partners from various fields of engineering meet to discuss ideas and to network.
You are interested in (research) data management, its current and future challenges, and topics like terminologies, metadata, ontologies, data repositories? Then join us on the NFDI4Ing conference on Wednesday and Thursday, October 26 and 27, 2022!
NFDI4Ing is running a community survey on the state of research data management (RDM) in the engineering sciences. One of the goals of the survey is to tailor the consortium’s services precisely to the needs of engineers. To obtain a detailed picture of the state of RDM in the individual engineering disciplines, we hope for numerous participants from all fields of engineering.
Analysing large collections of documents using text-and-data-mining methods can yield entirely new scientific insights. In the NFDI4Ing task area Automated data and knowledge discovery in engineering literature (S-7), services are developed to enable researchers to more easily apply these innovative methods in practice.
In NFDI4Ing, we develop engineering-specific RDM trainings and educational material and make them publicly available. Learn more about the formats, contents, and our platform.