Enhancing reuse of data and biological material in medical research : from FAIR to FAIR-Health

The known challenge of underutilization of data and biological material from biorepositories as potential resources formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.peer-reviewe

A Semantic Framework to Support AI System Accountability and Audit

The Semantic Web - 18th International Conference, ESWC 2021, Proceedings Springer Science and Business Media Deutschland GmbH ISBN: 9783030773847 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ISSN (Print): 0302-9743 ISSN (Electronic): 1611-3349 Volume: 12731 LNCSPostprintPostprin

Templates as a method for implementing data provenance in decision support systems

AbstractDecision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates – abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project

Trusted Provenance with Blockchain - A Blockchain-based Provenance Tracking System for Virtual Aircraft Component Manufacturing

The importance of provenance in the digital age has led to significant interest in utilizing blockchain technology for tamper-proof storage of provenance data. This thesis proposes a blockchain-based provenance tracking system for the certification of aircraft components. The aim is to design and implement a system that can ensure the trustworthy, tamper-resistant storage of provenance documents originating from an aircraft manufacturing process. To achieve this, the thesis presents a systematic literature review, which provides a comprehensive overview of existing works in the field of provenance and blockchain technology. After obtaining strategies to utilize blockchain for the storage of provenance data on the blockchain, a system was designed to meet the requirements of stakeholders in the aviation industry. The thesis utilized a systematic approach to gather requirements by conducting interviews with stakeholders. The system was implemented using a combination of smart contracts and a graphical user interface to provide tamper-resistant, traceable storage of relevant data on a transparent blockchain. An evaluation based on the requirements identified during the requirement engineering process found that the proposed system meets all identified requirements. Overall, this thesis offers insight into a potential application of blockchain technology in the aviation industry and provides a valuable resource for researchers and industry professionals seeking to leverage blockchain technology for provenance tracking and certification purpose

Big Data Analytics in Static and Streaming Provenance

Thesis (Ph.D.) - Indiana University, Informatics and Computing,, 2016With recent technological and computational advances, scientists increasingly integrate sensors and model simulations to understand spatial, temporal, social, and ecological relationships at unprecedented scale. Data provenance traces relationships of entities over time, thus providing a unique view on over-time behavior under study. However, provenance can be overwhelming in both volume and complexity; the now forecasting potential of provenance creates additional demands. This dissertation focuses on Big Data analytics of static and streaming provenance. It develops filters and a non-preprocessing slicing technique for in-situ querying of static provenance. It presents a stream processing framework for online processing of provenance data at high receiving rate. While the former is sufficient for answering queries that are given prior to the application start (forward queries), the latter deals with queries whose targets are unknown beforehand (backward queries). Finally, it explores data mining on large collections of provenance and proposes a temporal representation of provenance that can reduce the high dimensionality while effectively supporting mining tasks like clustering, classification and association rules mining; and the temporal representation can be further applied to streaming provenance as well. The proposed techniques are verified through software prototypes applied to Big Data provenance captured from computer network data, weather models, ocean models, remote (satellite) imagery data, and agent-based simulations of agricultural decision making

Governance of Autonomous Agents on the Web: Challenges and Opportunities

International audienceThe study of autonomous agents has a long tradition in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities

PROV-Health: método para gerenciamento de dados de proveniência em sistemas de informação em saúde

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências da Educação, Programa de Pós-Graduação em Ciência da Informação, Florianópolis, 2023.A Ciência da Informação e a Ciência da Computação, compactuam aspectos interdisciplinares no que diz respeito à Proveniência de Dados entre diferentes Sistemas de Informação em Saúde (SIS). Os SIS armazenam grandes volumes de dados em seus repositórios descentralizados, tornando um desafio o gerenciamento e a interoperabilidade desses dados. Assim, a Proveniência de Dados associada a tecnologias computacionais contribui para a rastreabilidade dos dados de origem nos SIS, a fim de identificar erros e transformações nos dados e atribuí-los às fontes, sendo útil para tomadas de decisões nos mais variados cenários de saúde. Nesta tese apresenta-se um método para gerenciamento de dados de proveniência em SIS com base no instanciamento do modelo da World Wide Web Consortium Provenance (W3C PROV) em conformidade com os aspectos de interoperabilidade entre diferentes bases de dados de saúde descentralizadas. Para alcançar os objetivos definidos para esta tese, assume-se uma metodologia de pesquisa aplicada em um estudo de caso, caracterizada como experimental e exploratória, com abordagem quantitativa e qualitativa. Quanto aos procedimentos técnicos no desenvolvimento e validação do método, foram realizados experimentos em um cenário de saúde real com dados reais, os quais possibilitaram identificar além de benefícios, desafios e ameaças durante sua experimentação. O método foi avaliado via formulário eletrônico enviado a especialistas da área, os quais destacaram pontos importantes para reflexão na estrutura do método. Conclui-se que ao final dos experimentos e das etapas de análises dos resultados, o método proposto mostrou-se significativo no que se refere ao gerenciamento de dados de proveniência em diferentes SIS, sendo possível adaptá-lo a qualquer cenário de saúde.Abstract: Information Science and Computer Science share interdisciplinary aspects when it comes to Data Provenance between different Health Information Systems (HIS). HIS store large volumes of data in their decentralized repositories, making the management and interoperability of this data a challenge. Thus, Data Provenance associated with computer technologies contributes to the traceability of source data in HIS, in order to identify errors and transformations in the data and attribute them to the sources, being useful for decision-making in the most varied health scenarios. This thesis presents a method for managing provenance data in HIS based on instancing the World Wide Web Consortium Provenance (W3C PROV) model in accordance with interoperability aspects between different decentralized health databases. In order to achieve the objectives defined for this thesis, an applied research methodology was used in a case study, characterized as experimental and exploratory, with a quantitative and qualitative approach. As for the technical procedures for developing and validating the method, experiments were carried out in a real health scenario with real data, which made it possible to identify not only benefits, but also challenges and threats during experimentation. The method was evaluated via an electronic form sent to experts in the field, who highlighted important points for reflection on the structure of the method. The conclusion is that at the end of the experiments and the stages of analysis of the results, the proposed method proved to be significant in terms of managing provenance data in different HIS and can be adapted to any healthcare scenario

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems