BioMedical Study Lifecycle Management (BMS-LM)
Private
Biomedical research data reuse and sharing is essential for fostering research
progress. To...
Ontology Info
| Field | Value |
|---|---|
| Acronyme | BMS-LM |
| Description | Biomedical research data reuse and sharing is essential for fostering research<br>progress. To this aim, data producers need to master data management and reporting<br>through standard and rich metadata, as encouraged by open data initiatives such as<br>the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular<br>terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data<br>Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and<br>aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research. |
| Format | OWL |
| Categories | Health, Other, All Organisms, Biomedical Resources, Experimental Conditions, Upper Level Ontology |
| Groups |
Metrics
| Metric | Value |
|---|---|
| Classes | 154 |
| Individuals | 22 |
| Properties | 107 |
| Max Depth | 2 |
| Max Child Count | 12 |
| Average Child Count | 2 |
| Classes With One Child | 17 |
| Classes With More Than 25 Children | 0 |
| Classes With No Definition | 34 |
Projects using BMS-LM
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