Achieving genuinely open and reusable research data requires structured data management and robust documentation to ensure interoperability and practical reuse. Many openly published datasets remain underutilised due to inadequate documentation, incomplete metadata, or unresolved sensitivities.
To address these challenges, we implemented a structured data management framework within...
Introduction: Researchers and scientists are increasingly using programming languages for data processing, visualisation, and analysis. Advancement in machine learning (ML) and artificial intelligence (AI) helps accelerate the process of analysing complex research data and conducting experiments, leading to the discovery of hidden patterns in the data. However, installing and configuring the...
Natural history collections are increasingly recognised as critical infrastructure for addressing complex ecological challenges, yet their full potential can only be realised through strategic integration with other ecological data streams. While FAIR principles and the Digital Extended Specimen concept provide theoretical frameworks for data integration, practical implementation requires...
Despite efforts to improve the availability and accessibility of research datasets, interoperability remains a serious barrier to reuse. Data harmonization, the process of aligning data from disparate sources to a standardized schema, plays a key role in addressing data heterogeneity and enabling integration and reuse. Consider a data scientist curating patient blood-glucose measurements as a...
Mappings are an essential component in making research data interoperable across infrastructures, domains and disciplines. Correspondences between official and de facto standards related to conceptual models, structures and vocabularies are required to share meaning and transfer information between both humans and machines. Despite their importance, these correspondences, mappings or...
Background: Longitudinal studies are necessary for tracking the progression of mental health disorders such as depression, anxiety, and psychosis. However, the integration of diverse mental health data from different sources and waves—especially in low- and middle-income countries—remains complex due to variability in instruments, socio-cultural expressions, and data structural formats. This...
As global communities continue to adopt the FAIR Principles, many organizations face the challenge of not just FAIRifying individual datasets, but entire ecosystems of data repositories and services. The many tools and assessments developed for FAIR tend to be customized to a particular data architecture, especially data organized in a file with a DOI and listed on a website. The National...
Arctic research faces persistent challenges, including limited accessibility, geopolitical complexities, and a general scarcity of high-quality data. In response, several international consortia—such as INTAROS and Arctic PASSION—have initiated collaborative efforts to address these issues through the development of integrated Earth observing systems (e.g., SIOS and GIOS). The Svalbard...
