Vera Matser

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

This document describes how the BioExcel Centre of Excellence has worked with its users to agree on the competencies that they require to make the most of HPC for their research. To enrich the competency profile we have, for each competence, defined what an individual will need to know and what skills they need to have to exhibit competence in a specific area, as well as listed what behaviours are suited and unsuited to an individual with that particular competency. We have performed a training needs analysis to gather the competency requirements for BioExcel’s users. These users include: entry-level users (e.g. bench-based molecular life scientists with a very limited computational background, working in both academic and industrial settings); expert users (e.g. structural bioinformaticians/computational chemists, medicinal chemists and others) and computational service providers, including systems administrators, applications experts and others involved in providing high-end computational services and software to biomolecular scientists. An initial workshop was organised to outline the competencies required by these users to make the most of HPC, and the main outcome of the workshop was a matrix of the different target groups against the agreed competencies. A total of 31 competencies have been defined; 5 Generic Competencies, 13 Scientific Competencies; 8 Generic Computing Competencies and 5 Parallel Computing Competencies. We are in the process of mapping these competencies to the learning outcomes of courses already provided by existing training programmes so that we can focus on developing new training that meets currently unmet needs. Though a large number of the competencies have existing courses mapped against them the coverage is often partial or fragmented. At the interim analysis stage (refers to D4.2 versus final analysis in D4.3 due in month 12) 10 out of the 31 competencies are insufficiently covered; based on having less than 5 training resources mapped against the competencies. Taking into account the partial mapping, the number of insufficiently covered competencies increased to 19. Initial plans for the BioExcel Training Programme are presented with mapping against the relevant competencies. The work (task 4.1) underlying this deliverable formally runs until month 12 and the complete results with recommendations for the BioExcel Training Programme will be presented in D4.3 (PM12).

Leaf shape is an important feature of plant development and is known to be controlled by genetic, hormonal and environmental factors. Leaves are the plants photosynthetic organs and provide the plant with the energy to grow. Leaf size and shape, and especially the alteration of leaf size and shape, in mutants can provide us with valuable insight into the genetic basis of leaf development. Alterations in the regulatory control of early leaf development can be visualised by analysing the mature leaf. However, the human eye is not made to identify subtle differences between shapes and we have therefore used automated quantitative imaging technology to quantify differences in shape. In this thesis we employ landmark-based geometric morphometrics to analyse Arabidopsis leaf size and shape. We have quantified the natural leaf size and shape variation in Arabidopsis and built a Leaf Size and Shape Library using Arabidopsis accessions.
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\nThe Arabidopsis leaf shape library has been applied to the leaf size and shape characterization of a sub-clade of plant specific class I TCP transcription factors (TCP8, TCP14, TCP15, TCP22 and TCP23) in an attempt to better understand their role in leaf development. Functional characterization of class I TCP genes has been hampered by a high degree of redundancy between its family members. We have discovered that TCP14 and TCP15 repress cell proliferation in leaves and thereby modulate leaf shape, combined with work from Kieffer et al., 2011 it constitutes proof that class I TCP genes can activate or repress transcription in a tissue dependent manner. TCP8, TCP22 and TCP23 have a yet to be determined role in modulating leaf shape that may work separately from TCP14/TCP15. TCP8 and TCP23 appear to have a regulatory role that is not limited to leaves.

We include here illustrations created by Scriberia during our workshop activities. The Advancing Management Skills in Biomedical AI Research Project started on 1st October 2025 and has been a rapid research project investigating difficulties in cross-sector collaboration and data sharing for Biomedical AI research. To address this question, it was essential to engage a broad range of stakeholders working in Biomedical AI research so that we could gain a better understanding of current challenges and barriers, surface existing best practices and ongoing initiatives and move towards practical and innovative solutions. We started by forming a working group to begin understanding the challenges and to get suggestions on the structure and focus of our planned workshop. Therefore, informed by input from our working group, we organised a one-day in person workshop (Advancing Collaboration and Data Sharing Agreements in Biomedical AI Research Workshop) that was held on 4th February 2026 at Wallacespace in Spitalfields, London. We brought together a wide range of stakeholders from different sectors, different disciplines and roles within the AI biomedical research community, including the biomedical data science community, research technical professional networks, and research infrastructures. These included individuals working across the management of AI research such as legal, research and strategy managers, knowledge exchange and research culture professionals, as well as data science and AI specialists. See a workshop summary here: To be added Acknowledgements We would like to acknowledge the contributions of the wider Biomedical AI research community to this event and the overall work of this project. These contributions included being part of and attending working group meetings from October 2025 to December 2025, individual meetings with the research team, asynchronous contributions to working group note documents and attendance at our workshop on 4th February 2026. The project and event were shaped, organised and facilitated by the ABDC Team, and we also want to thank our Turing colleagues, Vanessa Forster, Kit Good, Luis Santos, Martin O’Reilly, Mark Saunders for their helpful initial discussions on the project topic and their help with facilitation at working group meetings and our workshop. Funding acknowledgement This workshop was part of the Advancing Management Skills in Biomedical AI Research project, which is funded by the EPSRC Pilot approaches for supporting skills in AI grant (UKRI3180). This project is also supported by the Advancing Biomedical Data Science Project Team, which is a collaboration between The Alan Turing Institute and EMBL-EBI, funded by the Medical Research Council as part of the Biomedical Data Science Leadership Awards.