Luiz Olavo Bonino da Silva Santos

University of Twente | UT

This paper explores the design and creation of metadata schemas based on the FAIR Data Principles. We provide a clear interpretation of these principles, focusing on how they apply to metadata schemas. Leveraging the OntoUML language, we developed a conceptual model that explains the key components of a FAIR-compliant metadata schema. Through detai...

The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different...

The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the principles is that they would lead to a scenario where autonomous computational agents are capable of performing a “se...

Background The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests or...

Since 2014, “Bring Your Own Data” workshops (BYODs) have been organised to inform people about the process and benefits of making resources Findable, Accessible, Interoperable, and Reusable (FAIR, and the FAIRification process). The BYOD workshops’ content and format differ depending on their goal, context, and the background and needs of participa...

In the context of enterprises, a wide range of models is developed and used for diverse purposes. Due to the investments involved in modeling, these models should ideally be used in projects in which their benefits outweigh their costs. The analysis of modeling benefits and costs requires an in-depth understanding of the goals of modeling and the p...

In the context of enterprises, a wide range of models is developed and used for diverse purposes. Due to the investments involved in modeling, these models should ideally be used in projects in which their benefits outweigh their costs. The analysis of modeling benefits and costs requires an in-depth understanding of the goals of modeling and the p...

The FAIR Principles provide guidance on how to improve the findability, accessibility, interoperability, and reusability of digital resources. Since the publication of the principles in 2016, several workflows have been proposed to support the process of making data FAIR (FAIRification). However, to respect the uniqueness of different communities,...

The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the principles is that they would lead to a scenario where autonomous computational agents are capable of performing a "se...

Guidelines to improve the Findability, Accessibility, Inter-operability, and Reuse of datasets, known as FAIR principles, were introduced in 2016 to enable machines to perform automatic actions on a variety of digital objects, including datasets. Since then, the principles have been widely adopted by data creators and users worldwide with the 'FAIR...

The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the principles is that they would lead to a scenario where autonomous computational agents are capable of performing a ``s...

In this paper, we discuss how we are using metadata schemas and controlled vocabularies to improve interoperability between Brazilian agriculture and livestock trading data providers. A new metadata schema is being created based on a community-based approach. This method relies on knowledge from specialists to define a list of relevant metadata pro...

Scientific advances, especially in the healthcare domain, can be accelerated by making data available for analysis. However, in traditional data analysis systems, data need to be moved to a central processing unit that performs analyses, which may be undesirable, e.g. due to privacy regulations in case these data contain personal information. This...

Beginning in 1995, early Internet pioneers proposed Digital Objects as encapsulations of data and metadata made accessible through persistent identifier resolution services (Kahn and Wilensky 2006). In recent years, this Digital Object Architecture has been extended to include the FAIR Guiding Principles (Wilkinson et al. 2016), resulting in the co...

While the FAIR Principles do not specify a technical solution for ‘FAIRness’, it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources. The FAIR Data Point is a metadata repository that follows the DCAT(2) schema, and utilizes the...

Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present...

Background: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests o...

Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of a more generalized concept of a DT that is made FAIR, that is, universally machine actionable. This methodological overview is a first step toward this clarification. We present a...

Explainable Machine Learning comprises methods and techniques that enable users to better understand the machine learning functioning and results. This work proposes an ontology that represents explainable machine learning experiments, allowing data scientists and developers to have a holistic view, a better understanding of the explainable machine...

In recent years, implementations enabling Distributed Analytics (DA) have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in cas...

Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solut...

Introdução: o desenvolvimento de modelos conceituais como artefatos de referência para compreensão de domínios do conhecimento contribui para auxiliar na redução da distância semântica entre a representação e a interpretação das informações. Tomando por base o contexto do compartilhamento de dados nos esforços do acesso aberto à produção em Ciência...

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. T...

ABSTRACT Significant effort is required to find, make sense and reuse research data. To tackle this problem, the Findable, Accessible, Reusable and Interoperable (FAIR) data principles describe a minimal set of requirements for data management and stewardship, considered as the technological basis for the European Open Science Cloud. The FAIR data...

ABSTRACT This article proposes to discuss the role of data management plans as a tool to facilitate data management during researches life cycle. Today, research data opening is a primary agenda at scientific agencies as it may boost investigations’ visibility and transparency as well as the ability to reproduce and reuse its data on new researches...

The FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, are recognised worldwide as key elements for good practice in all data management processes. To understand how the Brazilian scientific community is adhering to these principles, this article reports Brazilian adherence to the GO FAIR initiative through the creati...

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...

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 di...

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...

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...

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...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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 proje...

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to a...

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to a...

Este artigo tem o objetivo de apresentar os princípios FAIR e a iniciativa Global Open FAIR que busca disseminar esses princípios em todos os países interessados na aplicação dos dados FAIR (Findable, Accessible, Interoperable, Reusable) em seus serviços de informação. Propõe ainda a divulgação e capacitação de instituições de ensino e pesquisa nes...

With the increased adoption of the FAIR Principles, a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers, are seeking ways to transparently evaluate resource FAIRness. We describe the FAIR Evaluator, a software infrastructure to register and execute tests of compliance with the recently published FAIR Metr...

“FAIRness” - the degree to which a digital resource is Findable, Accessible, Interoperable, and Reusable - is aspirational, yet the means of reaching it may be defined by increased adherence to measurable indicators. We report on the production of a core set of semi-quantitative metrics having universal applicability for the evaluation of FAIRness,...

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and...

This proposal is a response to NIH's call for creation of a Data Commons (RM-17-026). The Commons must support use cases of many stakeholders who need access to scholarly process, content, and outcomes in pursuit of knowledge. Moreover, the Commons must be flexible enough to respect researchers’ idiosyncratic workflows, yet specific enough to solve...

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity...

The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As sup...

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity...

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, or EUDat). These data have widely different levels of sensitivity and securi...

With the evolution and widespread adoption of contemporary information technologies, data has taken an increasingly central role in almost all areas of human activity. While on one hand this protagonism of data brings significant benefits for the individuals and organizations, on the other hand it is accompanied by a number of challenges. In the do...

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent...

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, or EUDat). These data have widely different levels of sensitivity and securi...

Business Process Management (BPM) has gained a lot of popularity in the last two decades, since it allows organizations to manage and optimize their business processes. However, purchasing a BPM system can be an expensive investment for a company, since not only the software itself needs to be purchased, but also hardware is required on which the p...

Nowadays, many organizations use BPM for capturing and monitoring their business processes. The introduction of BPM in an organization may become expensive, because of the upfront investments on software and hardware. Therefore, organizations can choose for a cloud-based BPM system, in which a BPM system can be used in a pay-per-use manner. Opting...

Service-Oriented Computing (SOC) is a paradigm for the design, use and management of distributed system applications in the form of services. The vision of SOC is that services represent distributed pieces of functionality that can be combined (or composed, in SOC terms) to generate new functionality with more added-value. In an ideal scenario base...

Recently, paradigms such as Service-Oriented and Pervasive Computing are being combined and applied in scenarios where users are surrounded by a plethora of computing devices and available services. Dealing with a potentially large number of devices and services can become overwhelming to users without appropriate software support. Moreover, in the...

Service-Oriented Computing (SOC) builds upon the intuitive notion of service already known and used in our society for a long time. SOC-related approaches are based on computer-executable functional units that often represent automation of services that exist at the social level, i.e., services at the level of human or organizational interactions....

Recently paradigms such as Service-Oriented and Pervasive Computing are merging in scenarios where users are surrounded by a plethora of computing devices and available services. Dealing with this potentially large number of devices and services can become overwhelming to users without appropriate software support. Moreover, in the case of non-tech...

Service-Oriented Computing (SOC) builds upon the intuitive notion of service already known and used in our society for a long time. SOC-related approaches are based on computer-executable functional units that often represent automation of services that exist at the social level, i.e., services at the level of human or organizational interactions....

Recently paradigms such as Service-Oriented and Pervasive Computing are merging in scenarios where users are surrounded by a plethora of computing devices and available services. Dealing with this potentially large number of devices and services can become overwhelming to users without appropriate software support. Moreover, in the case of non-tech...

Goals are often used to represent stakeholder's objectives. The intentionality inherited by a goal drives stakeholders to pursuit the fulfillment of their goals either by themselves or by delegating this fulfillment to third parties. In Service-Oriented Computing, service client's requirements are commonly expressed in terms of inputs, outputs, pre...

A pragmatic and straightforward approach to semantic service discovery is to match inputs and outputs of user requests with the input and output requirements of registered service descriptions. This approach can be extended by using pre-conditions, effects and semantic annotations (meta-data) in an attempt to increase discovery accuracy. While on o...

Service-Oriented Computing allows new applications to be developed by using and/or combining services offered by different providers. Service discovery and composition are performed aiming to comply with the client’s request in terms of functionality and expected outcome. In this paper we present a framework for dynamic service discovery and compos...

Service-Oriented Computing allows new applications to be developed by using and/or combining services offered by different providers. Service discovery and composition are performed aiming to comply with the client’s request in terms of functionality and expected outcome. In this paper we present a framework for dynamic service discovery and compos...

A pragmatic and straightforward approach to semantic service discovery is to match inputs and outputs of user requests with the input and output requirements of registered service descriptions. This approach can be extended by using pre-conditions, effects and semantic annotations (meta-data) in an attempt to increase discovery accuracy. While on o...

Service-oriented computing allows new applications to be developed by using and/or combining services offered by different providers. In several cases a service needs sensitive information from the clients in order to execute. The existence of a trust relationship between the client and the provider determines which restrictions the service has con...

Service-oriented computing allows new applications to be developed by using and/or combining services offered by different organizations. Service composition can be applied when a client request cannot be satisfied by any in- dividual service. In this case, the creation of a composite service from a number of available services could be pursued. Th...

Context awareness has emerged as an important element in distributed computing. It offers mechanisms that allow applications to be aware of their environment and enable these applications to adjust their behavior to the current context. Considering the dynamic nature of context, the data flow of relevant contextual information can be significant. I...

In the last few years context awareness has emerged as an important element in distributed computing. It offers mechanisms that allow applications to be aware of their environment and enable these applications to adjust their behavior to the current context. Considering the dynamic nature of context, the data flow of relevant contextual information...