- About the DFG
- Alliance of Science Organisations in Germany
- Statutory Bodies
- Head Office
- International Cooperation
One currently pivotal global challenge for scientific research in the digital age concerns the potential contradiction between (largely) automatized processing of an ever-growing amount of data and the need for validating, verifying and securing results. This two-day conference will illustrate how these essential challenges regarding data provenance, collection, storage, processing and interpretation are tackled in a number of different disciplines such as physics, bioinformatics, materials science and the digital humanities as represented by computational linguistics. In addition to gathering state-of-the-art facts and insights from these different subjects, the conference aims at promoting exchange and reflection from a broader, interdisciplinary perspective. The focus will thereby lie on methodological issues and deliberately refrain from addressing -equally essential- ethical and legal aspects.
The conference is part of the Digital Turn in the Sciences and Humanities project launched by the Head Office of the German Research Foundation with the aim to assess and respond to key developments in the sciences and humanities in the digital age. For further information please visit
University of Bonn)
|Can knowledge inform Machine Learning?
(National Institute of Standards and Technology)
|Traceability in materials design: A use case from molecular simulation
(University of Edinburgh)
|Mastering complex data processing procedures: from particle detector measurements via machine learning algorithms to physics results
|What is a measurement record?
|Towards (more) transparent Natural Language Processing technologies: How teaching others about our tools forces us to ask the right questions
|FAIR data: The European Galaxy Server
(University of Tübingen)
|Robust and reliable machine learning
|Traceability, Reproducibility, Replicability...What It Means for Computational Linguistics
|Towards Reproducibility in Machine Learning and AI
(Max-Planck-Institut für Eisenforschung)
|Automizing work flows in computational materials design