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Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre‐analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross‐organizational documentation, traceability, and non‐repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine‐actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard. [ABSTRACT FROM AUTHOR]

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs). [ABSTRACT FROM AUTHOR]

Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability. [ABSTRACT FROM AUTHOR]

We report on our work on the development of a novel holographic display technology, capable of targeting multiple freely moving naked eye viewers, and of a demonstrator, exploiting this technology to provide medical specialists with a truly interactive collaborative 3D environment for diagnostic discussions and/or pre-operative planning. [ABSTRACT FROM AUTHOR]

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

The data produced during a research project are too often collected for the sole purpose of the study, therefore hindering profitable reuse in similar contexts. The growing need to counteract this trend has recently led to the formalization of the FAIR principles that aim to make (meta)data Findable, Accessible, Interoperable and Reusable, for humans and machines. Since their introduction, efforts are ongoing to encourage FAIR principles adoption and to implement solutions based on them. This paper reports on the FAIR-compliant registry we developed to collect and serve metadata describing clinical trials. The design of the registry is based on the FAIR Data Point (FDP) specifications, the state-of-the-art reference for FAIRified metadata sharing. To map the metadata relevant to our use case, we have extended the DCAT-based semantic model of the FDP adopting well-established ontologies in the biomedical and clinical domain, like the Semanticscience Integrated Ontology (SIO). Current implementation is based on the Molgenis software and provides both a user interface and a REST API for metadata discovering. At present the registry is being loaded with the metadata of the 18 clinical studies included in the 'I FAIR Program', a project finalised to the dissemination of FAIR best practices among the clinical researchers in Sardinia (Italy). After a testing phase, the registry will be publicly available, while the new model and the source code will be released open source.

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).