A5067103208- Research Data Management Practices
- Scientific Computing and Data Management
- Biomedical Text Mining and Ontologies
- Data Quality and Management
- Cancer Genomics and Diagnostics
- Big Data and Business Intelligence
- Semantic Web and Ontologies
- Cerebrovascular and Carotid Artery Diseases
- Renal cell carcinoma treatment
- Genomics and Rare Diseases
- Bariatric Surgery and Outcomes
- Renal and Vascular Pathologies
- Colorectal Cancer Screening and Detection
- Artificial Intelligence in Healthcare
- Topic Modeling
- Electronic Health Records Systems
- Libraries and Information Services
- Nutritional Studies and Diet
University of Amsterdam
2019-2025
Amsterdam University Medical Centers
2019-2025
Public Health Service of Amsterdam
2022-2023
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...
This study is part of an initiative to improve the FAIRness (Findability, Accessibility, Interoperability, Reusability) metabolic bariatric surgery (MBS) registries globally. It explores extent which European registry data can be manually integrated without first making them FAIR and assesses these registries' current level FAIRness. The findings establish a baseline for evaluation provide recommendations enhance MBS management practices. Data dictionaries from five national in Germany,...
Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted collected. De-novo FAIRification, on other hand, incorporates FAIRification steps process of project. In medical research, often collected stored via electronic Case Report Forms (eCRFs) Electronic Data Capture (EDC) systems. By implementing de novo such system, reusability and, thus, scalability across projects can be...
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...
Abstract Background Patient data registries that are FAIR—Findable, Accessible, Interoperable, and Reusable for humans computers—facilitate research across multiple resources. This is particularly relevant to rare diseases, where often scarce scattered. Specific questions can be asked FAIR disease other resources without physically combining the data. Further, implies well-defined, transparent access conditions, which supports making sensitive as open possible closed necessary. Results We...
The FAIR Data Principles are being rapidly adopted by many research institutes and funders worldwide. This study aimed to assess the awareness attitudes of clinical researchers support staff regarding data FAIRification. A questionnaire was distributed in six Dutch University Medical Centers Electronic Capture platform users. 164 21 members completed questionnaire. 62.8% 81.0% currently undertaking at least some effort achieve any aspect FAIR, 11.0% 23.8%, respectively, address all aspects....
The International Society for the Study of Vascular Anomalies (ISSVA) provides a classification vascular anomalies that enables specialists to unambiguously classify diagnoses. This is only available in PDF format and not machine-readable, nor does it provide unique identifiers allow structured registration. In this paper, we describe process transforming ISSVA into an ontology. We also structure ontology, as well two applications ontology using examples from domain rare disease research....
Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) medicine, aims at extracting structured information from text, and less expensive time-consuming than manual extraction. However, most algorithms MLP are institution-specific address only one clinical need, thus cannot be broadly applied. In addition, systems do not detect concepts misspelled text attribute relationships between...
Abstract Background Patient data registries that are FAIR - Findable, Accessible, Interoperable, and Reusable for humans computers facilitate research across multiple resources. This is particularly relevant to rare diseases, where often scarce scattered. Specific questions can be asked disease other resources without physically combining the data. Results We successfully developed implemented a process of making registry vascular anomalies from its conception de novo . Here, we describe...
Introduction Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post-hoc manner: after the research project is conducted collected. De-novo FAIRification, on other hand, incorporates FAIRification steps process of project. In medical research, often collected stored via electronic Case Report Forms (eCRFs) Electronic Data Capture (EDC) systems. By implementing de-novo such system, reusability and, thus, scalability across...
Background: Connecting currently existing, heterogeneous rare disease (RD) registries would greatly facilitate epidemiological and clinical research. To increase their interoperability, the European Union developed a set of Common Data Elements (CDEs) for RD registries.
The FAIR Principles are supported by various initiatives in the biomedical community. However, little is known about knowledge and efforts of individual clinical researchers regarding data FAIRification. We distributed an online questionnaire to from six Dutch University Medical Centers, as well using Electronic Data Capture platform, gain insight into their understanding experience with 164 completed questionnaire. 64.0% them had heard Principles. 62.8% spent some or a lot effort achieve...
ABSTRACT 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, Reusable (FAIR, FAIRification process). The BYOD workshops’ content format differ depending on their goal, context, background needs participants. Data-focused BYODs educate domain experts how make data FAIR find new answers research questions. Management-focused promote instruct project managers...