Information infrastructure and SciOps towards reproducible science

ⓘ Image creditPhoto courtesy of Petra Ritter

The INF project will continue to foster the collaboration between CRC1315 sub-projects and advance further the capacity of the CRC1315 consortium to integrate human, rodent and fly data, leading to the generation of neuroscientific insights and clinical applications. We will build on the infrastructure for research data management (storage, documentation, processing, sharing and publication) that has been developed and used by the consortium in period 2. We will continue providing tools and training to enable the self-consistent organization of cross-species multiscale neuroscience data and models in a common reference space. The INF sub-project is central to the CRC. It consulted with the CRC sub-projects on a regular basis in period two and will continue this frequent exchange in period three. In period two our focus was twofold. First, it was directed to our internal data management capacities, i.e. we implemented tools for data management for heterogeneous datasets within the consortium, providing training and support to maintain an up-to-date data management plan (DMP), and educate about the publication of research outputs other than classical journal contributions such as datasets, software, reagents and hardware. Second, we contributed in and took leadership of local, national and international data management activities, where we contributed actively towards the development of new standards and best practices for sharing research objects and for conduction reliable and reproducible research. The INF project has led to the production of FAIR data (Findable, Accessible, Interoperable, Reusable) data in our consortium. Scientific discovery through knowledge integration by formalizing knowledge as computational models that can be simulated and generate falsifiable predictions related to memory consolidation. In the third funding period– in addition to the continuation of data management infrastructure maintenance and data management proficiency training for the consortium – we plan to advance our consortium members capabilities with respect to principles of rigorous scientific operations to achieve higher levels of operational maturity which necessitates the adoption of new, technology-enabled methodologies and best practices. These involve digital research environments that seamlessly integrate computational, automation, and AI-driven efforts throughout the research cycle—from experimental design and data collection to analysis and dissemination, ultimately leading to closed-loop discovery. Thus, the INF sub-project will continue to guide the consortium towards greater efficiency and effectiveness in scientific research.

Archive – INF (SFB1315/2)

ⓘ Image creditImage courtesy of INF

Graphic abstract: The scientific discovery loop towards an understanding of memory consolidation is supported by the INF project through fostering the proficiency for Science Operations according to the Capability Maturity Model for Science Operations ‘SciOps’ (Johnson et al. 2024).