About the Leibniz Data Manager

The Leibniz Data Manager (LDM) is a modular research data infrastructure that enables the FAIR-compliant management and exploration of Research Digital Objects (RDOs)—such as datasets, software, documents, and scientific publications. By combining Knowledge Graphs (KGs) with Neuro-Symbolic AI, LDM supports intelligent, interoperable, and reusable data workflows across scientific domains. Key features include:

  • Metadata Representation using standard and domain-specific vocabularies (e.g., DCAT, Datacite, UMLS), ensuring semantic interoperability and alignment with FAIR principles.
  • Knowledge Graph Construction from heterogeneous data sources through a declarative data integration system (DIS), enabling structured and contextual metadata.
  • Entity Linking and Enrichment of RDOs with external KGs such as Wikidata and the Open Research Knowledge Graph (ORKG), using tools like Falcon and BioLinkedAI to automatically expand metadata.
  • On-the-Fly Metadata Updates, maintaining synchronization with evolving knowledge and newly published resources.
  • Federated Query Processing across multiple KGs, allowing users to pose natural language questions that are transformed into SPARQL and executed over LDM and ORKG.
  • Dataset Comparison and Visualization, offering tools to analyze and contrast RDOs based on shared attributes and research contexts.
  • Live Code Execution via Jupyter Notebooks, enabling reproducible scientific workflows and interactive data analysis within a secure, containerized environment.
  • Neuro-Symbolic Reasoning for both query answering and metadata expansion, combining symbolic models with LLM-based contextualization to enhance discoverability and usability of RDOs.

LDM is publicly accessible at https://service.tib.eu/ldmservice/, and a Docker container for local deployment is available at https://github.com/SDM-TIB/LDM_Docker

The upload and description of dataset metadata require registration. More detailed information in https://service.tib.eu/ldmservice/LDM-register.

For more details on using LDM, visit the Demo page or watch the following explanatory video.

 

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