A5067310105- Research Data Management Practices
- Scientific Computing and Data Management
- Data Quality and Management
- Big Data and Business Intelligence
- Biomedical Text Mining and Ontologies
- Semantic Web and Ontologies
- Service-Oriented Architecture and Web Services
- Context-Aware Activity Recognition Systems
- Advanced Software Engineering Methodologies
- Business Process Modeling and Analysis
- Multi-Agent Systems and Negotiation
- Bioinformatics and Genomic Networks
- Genetics, Bioinformatics, and Biomedical Research
- Privacy-Preserving Technologies in Data
- Artificial Intelligence in Healthcare
- Sharing Economy and Platforms
- Robotics and Automated Systems
- Model-Driven Software Engineering Techniques
- Image Retrieval and Classification Techniques
- Usability and User Interface Design
- Information Science and Libraries
- Machine Learning in Healthcare
- Big Data Technologies and Applications
- Distributed systems and fault tolerance
- Biosensors and Analytical Detection
University of Twente
2008-2025
Leiden University Medical Center
2017-2025
Leiden University
2018-2025
Universidade Federal de Sergipe
2024
Royal Netherlands Meteorological Institute
2023
Weatherford College
2023
Vrije Universiteit Amsterdam
2016-2019
University of Trento
2005
There is an urgent need to improve the infrastructure supporting reuse of scholarly data. A diverse set stakeholders-representing academia, industry, funding agencies, and publishers-have come together design jointly endorse a concise measureable principles that we refer as FAIR Data Principles. The intent these may act guideline for those wishing enhance reusability their data holdings. Distinct from peer initiatives focus on human scholar, Principles put specific emphasis enhancing ability...
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 behaviours researchers expect from contemporary data resources, how principles manifest in reality was largely open to interpretation.As support for has spread, so breadth these interpretations.In observing this creeping spread interpretation, several original authors felt it now appropriate revisit Principles,...
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 guiding do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability Reusability digital resources. This has likely contributed to adoption principles, because individual stakeholder communities can implement own solutions. However, it also resulted inconsistent...
The FAIR Principles 1 (https:/
Abstract Transparent evaluations of FAIRness are increasingly required by a wide range stakeholders, from scientists to publishers, funding agencies and policy makers. We propose scalable, automatable framework evaluate digital resources that encompasses measurable indicators, open source tools, participation guidelines, which come together accommodate domain relevant community-defined FAIR assessments. The components the are: (1) Maturity Indicators – community-authored specifications...
In recent years, as newer technologies have evolved around the healthcare ecosystem, more and data been generated. Advanced analytics could power collected from numerous sources, both institutions, or generated by individuals themselves via apps devices, lead to innovations in treatment diagnosis of diseases; improve care given patient; empower citizens participate decision-making process regarding their own health well-being. However, sensitive nature prohibits organizations sharing data....
The FAIR guiding principles aim to enhance the Findability, Accessibility, Interoperability and Reusability of digital resources such as data, for both humans machines. process making data (“FAIRification”) can be described in multiple steps. In this paper, we describe a generic step-by-step FAIRification workflow performed multidisciplinary team guided by stewards. should applicable any type has been developed used “Bring Your Own Data” (BYOD) workshops, well e.g., rare diseases resources....
Data in the life sciences are extremely diverse and stored a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG pathway or UniProt protein data) to that general-purpose FigShare, Zenodo, Dataverse EUDAT). These have widely different levels sensitivity security considerations. For example, clinical observations about genetic mutations patients highly sensitive, while species diversity generally not. The lack uniformity models one repository...
In order to provide responsible access health data by reconciling benefits of sharing with privacy rights and ethical regulatory requirements, Findable, Accessible, Interoperable Reusable (FAIR) metadata should be developed. According the H2020 Program Guidelines on FAIR Data, “as open as possible closed necessary”, “open” in foster reusability accelerate research, but at same time they “closed” safeguard subjects. Additional provisions protection natural persons regard processing personal...
ABSTRACT Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all principles. In this paper we present and discuss the Data Point (FDP), software architecture aiming define common approach publish semantically-rich machine-actionable metadata according We core components features of FDP, its provision, criteria evaluate whether application adheres FDP specifications service register, index allow users search for content available FDPs.
Objetivo: Apresentar um panorama das principais iniciativas e serviços relacionados ao uso dos princípios FAIR nas práticas de gestão dados pesquisa. Metodologia: Foi realizada uma pesquisa exploratória com abordagem qualitativa, a partir levantamento bibliográfico documental seguintes bases: Base Dados Referenciais Artigos Periódicos em Ciência da Informação, Scientific Electronic Library Online ferramentas busca Google Acadêmico. Resultados: Foram identificadas importantes iniciativas, que...
The FAIR principles provide guidance on improving the Findability, Accessibility, Interoperability, and Reusability of digital resources. Since publication principles, several workflows have been proposed to support process making data (FAIRification). However, respect uniqueness different communities, both available deliberately designed remain agnostic in terms standards, tools, implementation choices. Consequently, FAIRification needs be properly planned, details must discussed with...
<title>Abstract</title> This paper investigates the impact of restructuring knowl- edge graphs (KGs) with well-founded conceptual models to improve ma- chine learning (ML) predictions, particularly in drug repurposing appli- cations. These were developed using OntoUML, which is grounded Unified Foundational Ontology, and constructed following an established workflow for data FAIRification–a process aimed at making more Findable, Accessible, Interoperable, Reusable. We compared performance a...
Directly or indirectly, the FAIR principles define a number of requirements for data and services ecosystem. Among them, there are identifiers digital objects, including separation between metadata objects they describe, need identifier to be globally unique persistent that record includes object describe. In order pursue an increased level automation, Digital Object Framework defines predictable resolution behaviour not only support access target but also allows client application request...
Numerous implementations of FAIR Digital Objects (FDOs) are actively emerging and being evaluated against FDO specifications. Here, we focus on the Nanopublication Framework as a possible implementation FDOs. Nanopublications unitary, standardised, self-contained RDF-based knowledge graphs consisting three subgraphs: an assertion graph that includes main content, provenance metadata graph, publication information graph. In thorough analysis specifications (represented by efforts Forum) based...
Abstract “FAIRness” - the degree to which a digital resource is Findable, Accessible, Interoperable, and Reusable aspirational, yet means of reaching it may be defined by increased adherence measurable indicators. We report on production core set semi-quantitative metrics having universal applicability for evaluation FAIRness, rubric within additional can generated community. This effort output from stakeholder-representative group, founded FAIR principles’ co-authors drivers. now seek input...
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 emerged over recent years. However, in projects require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging combination multiple ecosystems, including governance, regulatory compliance, modification...
Since their publication in 2016 we have seen a rapid adoption of the FAIR principles many scientific disciplines where inherent value research data and, therefore, importance good management and stewardship, is recognized. This has led to communities asking “What FAIR?” “How are currently?”, questions which were addressed respectively by revisiting emergence metrics. However, early adopters already run into next question: can become (more) question more difficult answer, as do not prescribe...
Este artigo tem o objetivo de apresentar os princípios FAIR e a iniciativa Global Open que busca disseminar esses em todos países interessados na aplicação dos dados (Findable, Accessible, Interoperable, Reusable) seus serviços informação. Propõe ainda divulgação capacitação instituições ensino pesquisa nesses princípios, com intuito promover normalização no tratamento da gestão garantindo interoperabilidade entre eles. Como procedimento metodológico, utiliza revisão bibliográfica documental...
Abstract Transparent evaluations of FAIRness are increasingly required by a wide range stakeholders, from scientists to publishers, funding agencies and policy makers. We propose scalable, automatable framework evaluate digital resources that encompasses measurable indicators, open source tools, participation guidelines, which come together accommodate domain relevant community-defined FAIR assessments. The components the are: (1) Maturity Indicators - community-authored specifications...
Os princípios FAIR, um acrônimo para Findable, Accessible, Interoperable e Reusable, estão presentes nas discussões práticas contemporâneas da ciência de dados, desde o início 2014, tiveram sua aplicação consolidada em 2017, quando a Comissão Europeia passou exigir adoção plano gestão com base nesses princípios, por projetos financiados seus recursos. Desde então, tais passaram ser norteadores descoberta, do acesso, interoperabilidade, compartilhamento reutilização dos dados pesquisa. No...
The industry sector is a very large producer and consumer of data, many companies traditionally focused on production or manufacturing are now relying the analysis amounts data to develop new products services. As sources needed distributed outside company, FAIR will have major impact, both by reducing existing internal silos enabling efficient integration with external (public commercial) data. Many still in early phases “FAIRification”, providing opportunities for SMEs academics apply...