About MLentory
Introduction
MLentory is centered around information on ML models, how to harmonize that data, and how to make it available and searchable on an FDO (FAIR Digital Object) registry. Our goal is to build a system that extracts ML (Machine Learning) model information from different platforms, normalizes that data in a common format, stores it, and shares it in a FDO registry to facilitate Information Retrieval and comparison/recommendation systems.
Organizations
The non-profit association Nationale Forschungsdateninfrastruktur (NFDI) e.V., based in Karlsruhe, was founded in October 2020 to coordinate the activities to develop NFDI. More than 270 institutions are now members of NFDI. Science organisations, universities, higher education institutions, non-university research institutions, scientific societies and associations are all involved. The future of research data management in Germany is being jointly shaped by a strong network.
The vision of NFDI is data as a common good for excellent research, organised by the scientific community in Germany.
Step by step, we are improving the possibilities for utilising data for science and society. Our collaboration in the NFDI Association is creating an overarching organisation for research data management in all branches of science. In collaboration with national and international partners, we are creating the framework conditions for legally compliant, interoperable and sustainable data infrastructures that are easily accessible for researchers in their day-to-day work. We provide training, strengthen expertise in handling data and open up new career paths.
Visit NFDI WebsiteThe vision of NFDI4DataScience (NFDI4DS) is to support all steps of the complex and interdisciplinary research data lifecycle, including collecting/creating, processing, analyzing, publishing, archiving, and reusing resources in Data Science and Artificial Intelligence.
Task Area 3: Infrastructure and Services
This task area establishes the infrastructure to collect and share all input, which we call Digital Objects (DO) which is required to deliver quality-assured data analytics solutions. The registries and repositories keep track of releases of quality assessed data as well as Data Science solutions required for assessment and the resulting benchmark information. The digital objects will be represented with rich metadata information and assembled as Research Knowledge Graphs for public use. Additional components ensure access to search and recommendation services (e.g., through a portal) and to additional services for public use such as data creation, annotation, and curation as well as an online authoring, publication, and execution platform. It further provides unified access to public HPC infrastructures.
Visit NFDI4DataScience WebsiteAn infrastructure and research centre for data and information in the life sciences. Harnessing research and infrastructure to empower people and benefit the environment.
Semantics Technology Team
Combining semantic technologies and data analytics
We are a multidisciplinary Research and Development team combining semantic technologies and data analytics. We work on the development of software components and services to support and improve research on information retrieval, data science and literature-based knowledge discovery with a particular focus on reproducibility. Our areas of application include the evaluation of experimental retrieval and recommendation systems, practical support to FAIR+R principles for software and data science, and data analytics from the combination of ontologies and literature-extracted data in the Life Sciences domain.