分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-18 合作期刊: 《数据智能(英文)》
摘要: The industry sector is a very large producer and consumer of data, and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services. As many of the data sources needed are distributed and outside the company, FAIR data will have a major impact, both by reducing the existing internal data silos and by enabling the efficient integration with external (public and commercial) data. Many companies are still in the early phases of internal data FAIRification, providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships. For a global Internet of FAIR Data Services to thrive, also involving industry, professional tools and services are essential. FAIR metrics and certifications on individuals, data, organizations, and software, must ensure that data producers and consumers have independent quality metrics on their data. In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding Industry GOing FAIR.
分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-16 合作期刊: 《数据智能(英文)》
摘要: In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated. Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data. The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. This paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.
分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-16 合作期刊: 《数据智能(英文)》
摘要: Since their publication in 2016 we have seen a rapid adoption of the FAIR principles in many scientific disciplines where the inherent value of research data and, therefore, the importance of good data management and data stewardship, is recognized. This has led to many communities asking What is FAIR? and How FAIR are we currently?, questions which were addressed respectively by a publication revisiting the principles and the emergence of FAIR metrics. However, early adopters of the FAIR principles have already run into the next question: How can we become (more) FAIR? This question is more difficult to answer, as the principles do not prescribe any specific standard or implementation. Moreover, there does not yet exist a mature ecosystem of tools, platforms and standards to support human and machine agents to manage, produce, publish and consume FAIR data in a user-friendly and efficient (i.e., easy) way. In this paper we will show, however, that there are already many emerging examples of FAIR tools under development. This paper puts forward the position that we are likely already in a creolization phase where FAIR tools and technologies are merging and combining, before converging in a subsequent phase to solutions that make FAIR feasible in daily practice.
分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-16 合作期刊: 《数据智能(英文)》
摘要: The FAIR guiding principles aim to enhance the Findability, Accessibility, Interoperability and Reusability of digital resources such as data, for both humans and machines. The process of making data FAIR (FAIRification) can be described in multiple steps. In this paper, we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards. The FAIRification workflow should be applicable to any type of data and has been developed and used for Bring Your Own Data (BYOD) workshops, as well as for the FAIRification of e.g., rare diseases resources. The steps are: 1) identify the FAIRification objective, 2) analyze data, 3) analyze metadata, 4) define semantic model for data (4a) and metadata (4b), 5) make data (5a) and metadata (5b) linkable, 6) host FAIR data, and 7) assess FAIR data. For each step we describe how the data are processed, what expertise is required, which procedures and tools can be used, and which FAIR principles they relate to.
分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-16 合作期刊: 《数据智能(英文)》
摘要: In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be as open as possible and as closed as necessary, open in order to foster the reusability and to accelerate research, but at the same time they should be closed to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.