Publications

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4 Publications visible to you, out of a total of 4

Abstract (Expand)

Modern research projects increasingly require hybrid metadata approaches that balance adherence to domain-overarching, as well as domain-specific community standards with flexibility for project- or resource-specific metadata. The FAIRDOM-SEEK platform [1] is a widely used research data management system designed to support diverse domains, from systems biology to health research data, by integrating standardized metadata models (e.g., the ISA framework [2]) with customizable extensions. To address this need, we introduce the Extended Metadata feature in SEEK, which allows researchers to extend core metadata schemas with user-defined fields, hierarchies, and semantic annotations while ensuring interoperability with domain-specific standards. We demonstrate this capability through two use cases: 1. NFDI4Health Local Data Hubs (LDH) [3],[4]: In the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health [5]), we have developed Local Data Hubs (LDH) based on the SEEK platform. These hubs support federated data structuring and sharing for sensitive health data from clinical trials, epidemiological studies, and public health research and allow to connect local platforms to the central metadata repository of NFDI4Health, the German Health Study Hub. Given the complexity of the NFDI4Health metadata schema (MDS) [6], the SEEK-based LDH software utilizes the Extended Metadata feature to fully represent the schema, allowing for flexible project-defined metadata extensions. 2. FAIR Data Station (FAIR-DS) [7]: Based on the ISA-framework, with the addition of Observation units from MIAPPE [8], the FAIR-DS is a web application that enables users to create and manage metadata according to FAIR principles. Using packages and terms configured through the UI, it generates Excel spreadsheets which are then populated to gather the metadata. FAIR-DS is then used to validate the metadata and generates RDF datasets representing the content. SEEK has been updated to allow Extended Metadata and Sample Types to be configured automatically via these RDF datasets, and also the content can be imported, and updated, in a single action. The Extended Metadata feature allows users to define additional metadata attributes to be tailored to specific data types, ensuring compliance with standards. When creating a resource, users can select an Extended Metadata type from a dropdown menu, dynamically triggering the rendering of associated metadata input forms within the web interface. This enables seamless integration of resource-specific metadata (e.g., clinical trial study metadata) alongside core descriptive fields. Currently, only instance administrators can create, manage (enable/disable), and delete additional attributes for specific resource types (e.g., ISA items such as Investigation, Study, Assay, as well as Projects and Models) based on specific schemas (e.g., the NFDI4Health MDS). Attribute types range from simple (e.g., string, text, date, integer, Boolean) to complex (e.g., controlled vocabularies linked to ontologies, nested hierarchical structures), with validation rules for mandatory or optional fields. Regular expressions are introduced to ensure correct input formatting. Metadata schemas can be created through backend seed files, JSON uploads, or FAIR-DS RDF imports. These schemas are programmatically accessible via the SEEK REST API, enabling automated metadata creation and retrieval. This ensures interoperability with external tools while adhering to FAIR data principles.

Authors: Xiaoming Hu, Stuart Owen, Frank Meineke, Finn Bacall, Carole Goble, Wolfgang Müller, Martin Golebiewski

Date Published: 2025

Publication Type: InProceedings

Abstract (Expand)

This EuroScienceGateway report gives an overview of FAIR Digital Objects (FDO), considering their use for computational workflows as scholarly objects. EuroScienceGateway has progressed the technologies Signposting and RO-Crate for implementing Workflow FDOs with the registry WorkflowHub and the workflow system Galaxy, and initiated work with academic publishers to encourage workflow citation practices. Here we document how WorkflowHub supports research software best practices for workflows, and assist building FAIR Computational Workflows. Provenance of workflow executions has been made possible in an interoperable way across many workflow systems using Workflow Run Crate profiles, including from Galaxy.  Finally this report explores how Workflow FDOs are exposed and can be utilised, e.g. gathered in knowledge graphs and having tighter workflow system integration.

Authors: Stian Soiland-Reyes, Eli Chadwick, Finn Bacall, José M. Fernández, Björn Grüning, Hakan Bayındır

Date Published: 2024

Publication Type: Tech report

Abstract (Expand)

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Authors: M. L. Neal, M. Konig, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, M. Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Mar 2019

Publication Type: InBook

Abstract (Expand)

In order to investigate the effect of soybean isoflavones(SI) on the oxidative modification to low-density lipoprotein(LDL) and to differentiate the effect of SI and alpha-tocopherol, in vitro and in vivo test were conducted. An in vitro model of LDL oxidative modification induced by copper-ion was established by monitoring the production of thiobarbituric acid-reactive substances (TBARS) and conjugated dienes after SI or alpha-tocopherol was added. The in vivo test was conducted by feeding rats with a high fat diet supplemented with SI and measured the sensitivity of LDL oxidative modification mediated by Cu2+ in vitro. The results revealed that when SI was added into the in vitro LDL oxidation system, the content of TBARS or conjugated dienes in the system was much reduced with a dose-effect relationship, whether lipid oxidation being initiated or not by copper-ion at 37 degrees C. In comparison with SI, only a significant inhibiting effect on lipid oxidation while alpha-tocopherol was added before the initiation of oxidation. High fat diet induced a rising of LDL sensitivity of oxidative stress, and adding SI to the high fat diet could counteract the sensitivity of LDL oxidative modification significantly. It is concluded that SI is a valuable natural antioxidant different from alpha-tocopherol in inhibiting LDL oxidative modification both in vitro and inv vivo.

Authors: X. Yan, J. Gu, C. Sun, D. Liu

Date Published: No date defined

Publication Type: Not specified

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