Showing papers by "Carole Goble published in 2023"

FAIR in action - a flexible framework to guide FAIRification

Abstract: Abstract The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

Features of a FAIR vocabulary

Abstract: The Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies.We differentiate data, data resources and vocabularies used for FAIR, examine the application of the FAIR Principles to vocabularies, align their requirements with the Open Biomedical Ontologies principles, and propose FAIR Vocabulary Features. We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how they can be used for evaluating and improving vocabularies using exemplary biomedical vocabularies.Our work proposes features of FAIR vocabularies and corresponding indicators for assessing the FAIR levels of different types of vocabularies, identifies use cases for vocabulary engineers, and guides the evolution of vocabularies.

Workflows Community Summit 2022: A Roadmap Revolution

Abstract: Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enabling the publication of workflows and their associated products according to the FAIR principles. This document reports on discussions and findings from the 2022 international edition of the Workflows Community Summit that took place on November 29 and 30, 2022.

Toward a common standard for data and specimen provenance in life sciences

Abstract: Petr Holub ∗1, Rudolf Wi‹ner1,2, Cecilia Mascia3, Francesca Frexia3, 3 Heimo Müller4, Markus Plass4, Clare Allocca5, Fay Betsou6, Tony 4 Burde‹7, Ibon Cancio8, Adriane Chapman9, Martin Chapman10, 5 Mélanie Courtot7, Vasa Curcin10, Johann Eder11, Mark Elliot12, Katrina 6 Exter13, Elliot Fairweather10, Carole Goble14, Martin Golebiewski15, 7 Bron Kisler16, Andreas Kremer17, Sheng Lin-Gibson18, Anna 8 Marsano19, Marco Ma‹avelli20, Josh Moore21, Hiroki Nakae22, Isabelle 9 Perseil23, Ayat Salman24,25, James Sluka26, Stian Soiland-Reyes14,27, 10 Caterina Strambio-De-Castillia28, Michael Sussman29, Jason R. 11 Swedlow21, Kurt Zatloukal4, and Jörg Geiger30 12

Making Bioinformatics Training Events and Material More Discoverable Using TeSS, the ELIXIR Training Portal

Abstract: Many trainers and organizations are passionate about sharing their training material. Sharing training material has several benefits, such as providing a record of recognition as an author, offering inspiration to other trainers, enabling researchers to discover training resources for their personal learning path, and improving the training resource landscape using data‐driven gap analysis from the bioinformatics community. In this article, we present a series of protocols for using the ELIXIR online training registry Training eSupport System (TeSS). TeSS provides a one‐stop shop for trainers and trainees to discover online information and content, including training materials, events, and interactive tutorials. For trainees, we provide protocols for registering and logging in and for searching and filtering content. For trainers and organizations, we also show how to manually or automatically register training events and materials. Following these protocols will contribute to promoting training events and add to a growing catalog of materials. This will concomitantly increase the FAIRness of training materials and events. Training registries like TeSS use a scraping mechanism to aggregate training resources from many providers when they have been annotated using Bioschemas specifications. Finally, we describe how to enrich training resources to allow for more efficient sharing of the structured metadata, such as prerequisites, target audience, and learning outcomes using Bioschemas specification. As increasing training events and material are aggregated in TeSS, searching the registry for specific events and materials becomes crucial. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Evaluating FAIR Digital Object and Linked Data as distributed object systems

Abstract: FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building a ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.