arXiv subjects

Mete U. Akdogan

Publications and source records attributed to Mete U. Akdogan.

3 recordsLinked to original sources

arXiv2025

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor

High-quality, "rich" metadata are essential for making research data findable, interoperable, and reusable. The Center for Expanded Data Annotation and Retrieval (CEDAR) has long addressed this need by providing tools to design machine-actionable metadata templates that encode community standards in a computable form. To make these capabilities more accessible within real-world research workflows, we have developed the CEDAR Embeddable Editor (CEE)-a lightweight, interoperable Web Component that brings structured, standards-based metadata authoring directly into third-party platforms. The CEE dynamically renders metadata forms from machine-actionable templates and produces semantically rich metadata in JSON-LD format. It supports ontology-based value selection via the BioPortal ontology repository, and it includes external authority resolution for persistent identifiers such as ORCIDs for individuals and RORs for research organizations. Crucially, the CEE requires no custom user-interface development, allowing deployment across diverse platforms. The CEE has been successfully integrated into generalist scientific data repositories such as Dryad and the Open Science Framework, demonstrating its ability to support discipline-specific metadata creation. By supporting the embedding of metadata authoring within existing research environments, the CEE can facilitate the adoption of community standards and help improve metadata quality across scientific disciplines.

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arXiv2025

A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub

In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning heterogeneous data under standardized representations, benefiting both repository curators responsible for upholding data quality standards and consumers who require unified datasets. However, data harmonization is difficult in practice, requiring significant domain and technical expertise. We present a software framework to facilitate principled and reproducible harmonization protocols. Our framework implements a novel strategy of building harmonization transformations from parameterizable primitive operations, such as the assignment of numerical values to user-specified categories, with automated bookkeeping for executed transformations. We establish our data representation model and harmonization strategy and then report a proof-of-concept application in the context of the RADx Data Hub. Our framework enables data practitioners to execute transparent and reproducible harmonization protocols that align closely with their research goals.

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arXiv2025

RADx Data Hub: A Cloud Platform for FAIR, Harmonized COVID-19 Data

The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address these challenges, the U.S. National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative in 2020, with the RADx Data Hub, a centralized repository for de-identified and curated COVID-19 data, as its cornerstone. The RADx Data Hub hosts diverse study data, including clinical data, testing results, smart sensor outputs, self-reported symptoms, and information on social determinants of health. Built on cloud infrastructure, the RADx Data Hub integrates metadata standards, interoperable formats, and ontology-based tools to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data sharing. Initially developed for COVID-19 research, its architecture and processes are adaptable to other scientific disciplines. This paper provides an overview of the data hosted by the RADx Data Hub and describes the platform's capabilities and architecture.

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