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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.
Field experiments are characterized by environmental conditions that cannot be fully controlled by the experimenter. Examples for field experiments include testing driver assistance systems in traffic, submarine robots in open water, or monitoring traffic in cities. Task Area GOLO is developing services and solutions to support engineers conducting field experiments in their work.
NFDI4Ing is yielding results, and these and the documentation of our work want to be organized. But what can FAIR data management look like for research data management work? Maybe like this.
The transformation of energy systems is one of the greatest challenges of the 21st century. We have created a machine-readable comparison of greenhouse gas reduction scenarios for Germany, comparing different studies for a future low-carbon energy system.
The NFDI4Ing measure metadata and terminology services is entering its next phase of development.
The first meeting of the Community Board of the Community Cluster “Systems Engineering, Computer Science and Electrical Engineering and Information Technology” (CC-44) took place on 24 May 2022. The exchange on community-specific issues and practices of research data management served to sharpen the fields of action and needs in community work within NFDI4Ing.