Mathematical research data is vast, complex, and multifaceted. It emerges within mathematical sciences but also in other scientific areas such as physics, chemistry, life sciences and the Arts. Standardised formats, data interoperability and application programming interfaces need to be developed to ease the use of data across disciplines. With this in mind, the Mathematical Research Data...
The Bibliotheca Hertziana (BHMPI) has been involved with the process of conceiving a Consortium for the cultural-historic sciences in the Humanities, which led to the foundation of NFDI4Culture in June 2020. The NFDI4Culture mission is to systematically develop, make accessible, and sustainably secure research data from art, music, architecture, theatre, dance, film, and media studies into a...
PUNCH4NFDI is the NFDI consortium of particle, astro-, astroparticle, hadron and nuclear physics, representing about 9.000 scientists with a Ph.D. in Germany, from universities, the Max Planck Society, the Leibniz Association, and the Helmholtz Association. PUNCH physics addresses the fundamental constituents of matter and their interactions, as well as their role for the development of the...
To exploit data generated in the fields of condensed-matter physics and chemical physics of solids as well as catalysis research, a FAIR data infrastructure is necessary. FAIRmat’s goal is to provide this infrastructure. FAIRmat integrates synthesis, experiment, theory, digital infrastructure and applications to pursue this goal.
From an experimental point of view, the tasks include the...
The NFDI4BIOIMAGE consortium was approved for funding in November 2022 and starts in March 2023. Led by Heinrich-Heine University Düsseldorf it brings together researchers, data stewards, and research software engineers from various German Universities and Research Institutes. The Max Planck Institute for Evolutionary Biology has Participant status. NFDI4BIOIMAGE is focused on the development,...
The 4Memory consortium [1] focuses on the field of history and those disciplines that make use of historical data as part of their methodology.
Historical data includes “texts ranging from antiquity to the modern era, images, photos, audio and video recordings, statistics, structured data, metadata, ontologies, and hypertexts” as well as “personal data, spatial structures, and changes in...
Data management in catalysis is currently organised mainly at institutional or working group level and based on local conventions. However, catalysis is complex and interdisciplinary research, so it would be important to create an overarching interface in the area of data management so that FAIR data can be easily exchanged between disciplines. Through this interface and the integration of...
The performance of any engineering material depends critically on its strongly heterogeneous and process-dependent microstructure, ranging from crystal defects at the atomic level, through microscale secondary phases up to macroscale pores. Furthermore, processes on timescales ranging from picoseconds up to centuries need to be addressed. This inherent multiscale character of materials needs...
DataPLANT's main goal is to provide its community with the tools and infrastructure to store, process, and share data in a FAIR (Findable, Accessible, Interoperable, Reusable) manner. The core element of DataPLANT's Research Data Management (RDM) system is the Annotated Research Context (ARC), which has a single-entry point logic starting with the input of data and metadata, allowing the...
In the past decade, much of the progress in human neuroscience has come under scrutiny due to issues related to reproducibly of findings, low powered studies and also the large degree of flexibility in the analysis pipelines. Several mechanisms have been put in place to address those challenges, e.g., preregistrations and registered reports. We have focused on another neglected aspect, the...
