One focus of the initiative is the call for proposals and funding of groups under the DFG’s Emmy Noether Programme. The aim is for these groups to attract the next generation of highly qualified researchers with a focus on AI methods by opening up career opportunities that offer a high degree of independence at an early stage. The initiative enables up to 45 Emmy Noether Groups to be established across four rounds of calls.

Call for proposals 2025/2026 

Call for proposals (closed)(interner Link)

Call for proposals 2020/2021

Call for proposals (closed)(interner Link) I  Funded Projects(externer Link)

Call for proposals 2019/2020

Call for proposals (closed)(interner Link) I  Funded Projects(externer Link)

Eight Research Units(interner Link) were funded under the call with the aim of promoting closer integration between AI methods and research fields that either apply AI methods for the purpose of generating scientific knowledge or investigate broader questions associated with the use of AI. This initiative has now been completed and no further calls will be issued. New research proposals may be submitted at any time under the Research Units programme.

Funded Project 2020/2021(externer Link)

Methods used in the context of artificial intelligence – such as machine learning (ML) and text and data mining (TDM) – are becoming increasingly relevant in many areas of digital research practice and in the provision of scientific information. Such methods are used to analyse and process large volumes of data, for instance, as well as for language processing and generation. The development and use of these methods rely on a wide range of multimodal data types, such as factual data, measurement data, behavioural data, monolingual and multilingual texts, image data, synthetic data, digitisation and indexing data. The quality and availability of these data types can vary significantly, and their aggregation and cleansing often require considerable effort. As such, there is a need within the research community for systematically prepared, curated, annotated and aggregated data corpora.

The funding aims to support the establishment and development of high-quality, extensive data corpora that can serve as a robust and reliable foundation for the development/advancement and application of AI methods in research. The future use of methods and applications based on the funded corpora may take place both in research and within scientific information infrastructures. The quality, scope and composition of the corpora must be tailored to the relevant needs and must enable research beyond individual projects or locations, or contribute to improving the provision of scientific information. The data corpora should be provided in accordance with established principles and standards (such as FAIR and CARE) and via existing information infrastructures, in particular the National Research Data Infrastructure (NFDI).