It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

Os princípios FAIR, um acrônimo para Findable, Accessible, Interoperable e Reusable, estão presentes nas discussões e práticas contemporâneas da ciência de dados, desde o início de 2014, e tiveram sua aplicação consolidada em 2017, quando a Comissão Europeia passou a exigir a adoção de plano de gestão de dados, com base nesses princípios, por projetos financiados por seus recursos. Desde então, tais princípios passaram a ser norteadores da descoberta, do acesso, da interoperabilidade, do compartilhamento e da reutilização dos dados de pesquisa. No entanto, quando colocados em prática levantam dúvidas e imprecisões, gerando diferentes interpretações, o que dificulta sua aplicação. Por essa razão buscou-se elucidar seu entendimento, utilizando-se de conceitos esclarecedores, apresentando métricas específicas que medem o nível de FAIRnessdos objetos digitais; disseminando a proposta do ecossistema dos dados FAIR e as tecnologias Data FAIRPort e FAIR Data Point. Apresentamos, ainda, estudos realizados na Europa que comprovam o impacto e o potencial desses princípios, em diferentes áreas disciplinares, dando destaque às necessidades e aos exemplos de aplicação. A abordagem metodológica desta pesquisa é de natureza bibliográfica e de caráter qualitativo dando ênfase na descrição conceitual dos elementos necessários para a compreensão do ecossistema FAIR, o que permitiu, neste estudo, trabalhar a fundamentação teórica e conceitual, bem como o uso das práticas do FAIR em diferentes contextos e dimensões. As considerações finais corroboram as mudanças culturais e tecnológicas que vêm ocorrendo, no mundo informacional, relacionadas às novas práticas de gestão de dados e às interações e parcerias necessárias para a sua complexaimplementação.

An early warning system (EWS) is a distributed system that monitors the physical world and issues warnings if it detects abnormal situations. The Internet of Things (IoT) offers opportunities to improve monitoring capabilities of EWS and to realize (near) real-time warning and response. This paper presents the development of an interoperable IoT-based EWS to detect accident risks with trucks that deliver goods at the Valencia port area. Our solution addresses the semantic integration of a variety of data sources with processing in safety-critical applications for effective emergency response. The solution considers existing domain-specific ontologies and standards, along with their serialization formats. Accident risks are assessed by monitoring the drivers' vital signs with ECG medical wearables and the trucks' position with speed and accelerometer data. Use cases include the detection of health issues and vehicle collision with dangerous goods. This EWS is developed with the SEMIoTICS framework, which encompasses a model-driven architecture that guides the application of data representations, transformations, and distributed

Over the last years, numerous ICT applications with mechanisms to detect situations have been developed to support disaster management (DM), which is a field of a great societal and economic importance. Those applications are termed situation-aware (SA) because they try, in near real-time, to perceive and comprehend a situation of some type (e.g. disease epidemics) and project a reaction to the detected situation (e.g. isolate diseased people). An obstacle to the modelling of SA applications is the lack of well-founded structural and temporal constructs, which is inherent to conventional design techniques. Ontology-driven conceptual modelling has been successfully applied to overcome this issue, where ontological analysis based on a foundational ontology supports the modelling of concepts within a specific field as a well-founded core ontology. In this paper we discuss the importance of a well-founded core ontology for DM to support the specification of SA applications. We give an overview of the comprehensive framework we are developing, in which the DM core ontology plays a prominent role in the development of SA applications. In particular, we discuss the challenge of harmonizing concepts related to the modelling of situations in a foundational ontology in the lights of the Barwisean situation theory, Situoid theory and Situation awareness theory. This challenge has to be addressed to properly support SA applications in DM.

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the first attempt to extend SAREF for specific e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Unified Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identification to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.