Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.
The CIDOC-CRM standard indicates that common events, actors, places and timeframes are important in linking together cultural material, and provides a framework for describing them. However, merely describing entities in this way in two datasets does not yet interlink them. To do that, the identities of instances still need to be either reconciled, or be based on a shared vocabulary.The WW1LOD dataset presented in this paper was created to facilitate both of these approaches for collections dealing with the First World War. For this purpose, the dataset includes events, places, agents, times, keywords, and themes related to the war, based on over ten different authoritative data sources from providers such as the Imperial War Museum. The content is harmonized into RDF, and published as a Linked Open Data service.While generally basing on CIDOC-CRM, some modeling choices used also deviate from it where our experience dictated such. In the article, these deviations are discussed in the hope that they may serve as examples where CIDOC-CRM itself may warrant further examination.As a demonstration of use, the dataset and online service have been used to create a contextual reader application that is able link together and pull in information related to WW1 from e.g.
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