Spatially-integrated marine benthic data provide the foundation for effective spatial decision-support tools that address critical issues in marine and coastal systems. However, the diversity of benthic data types and forms often presents challenges for proper data integration. Without common standards, these data cannot be easily integrated and reused. Here, we proposed standards for data collection, processing, archiving, and publication, as well as metadata standards, for various types of commonly integrated marine data (i.e., bathymetric, backscatter, biological, chemical, geological, and physical oceanographic data). The standards are aimed at Florida data producers and users and, as a result, leverage existing state, regional, and national infrastructure. The standards recommendations were shared with the Florida-based stakeholder community to quantify the overall level of agreement, the likelihood of their widespread adoption, and the types of tools needed to increase that likelihood. Overall, the Florida community reported varying but positive agreement with the recommended standards and expressed willingness to adopt them, on the condition that easy-to-use tools and comprehensive documentation are provided to facilitate the implementation. We also found that while relatively few individuals or organizations establish standards themselves, most researchers and users are willing to adopt recommended practices when they are easy and efficient to implement. In an era where scientific data are digitally processed and archived, scientists must foster a culture that consistently shares data, workflows, and use cases. However, this requires tools and accessible resources to facilitate the broader adoption of standards within the scientific community.
Effective exchange of health data between healthcare providers, national registries, and European Reference Networks (ERNs) is crucial for advancing research and clinical care of rare diseases. Technical, organisational, and legal barriers persist in many situations that disrupt this process. The Joint Action on Integration of ERNs into National Healthcare Systems (JARDIN) tackles these barriers by identifying such existing solutions. At the technical level, there are existing standards, software, and infrastructures that could be adapted and reused if there were evidence of a method and value in doing so. These need to be identified, matched against known problems, and tested before subsequent work can begin to evaluate and deploy solutions. This paper describes the methodologies and outcomes of a hackathon conducted as a software-oriented workshop to collaboratively identify and implement solutions to technical barriers identified as relevant to rare diseases. The event brought together 47 experts from 10 European countries to combine their knowledge and experience to tackle challenges of data harmonisation, secure data access, and the Findable, Accessible, Interoperable and Reusable (FAIR) description of data services. Motivated by a simple use case, the participants proposed adapting existing infrastructures to create a realistic, secure data-sharing architecture based on existing standards. Solutions included federated querying of rare disease data integrated with a standard for data access conditions, the use of semantic models for rare disease data harmonisation across organisations, and extending a standard metadata model to better describe rare disease data being held by organisations. The process of expert-driven selection of the components yielded approaches relevant for the rare disease community and has provided important momentum. As an output of the JARDIN initiative, this will lead to further real-world evaluation and pilot testing together with rare disease data stakeholders and ultimately EU-wide recommendations that will benefit coordination and focus on improving data exchange.
This paper presents MatSci-YAMZ, a novel implementation of Yet Another Metadata Zoo (YAMZ), a crowdsourced vocabulary application. MatSci-YAMZ is designed to assist vocabulary development for materials science by configuring a large language model (LLM) and provenance tracking into the YAMZ workflow. Implementation of MatSci-YAMZ occurred in three phases: requirements and model selection, development, and fine-tuning. The application utilizes the Gemma3:27b LLM to formulate definitions based upon examples provided with human definitions. Provenance tracking captures the evolution of LLM-generated definition evolution, which occurs in response to user comments. MatSci-YAMZ can improve shared semantics among communities of materials researchers whose terminology may contain nuances or idiosyncratic usages. Improved semantic alignment supports more concise data descriptions and use of metadata, which is foundational to supporting the FAIR principles of findability, accessibility, interoperability, and reusability. Improved provenance tracking also provides greater insight into how researchers engage with LLM-generated definitions as well as the changes that occur. MatSci-YAMZ opens several avenues of research into crowdsourced vocabulary development among materials researchers. Current findings highlight the need for additional user testing and study to better understand human-in-the-loop vocabulary development with existing LLMs.
