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Deliverable 9.2 relates to tasks 9.1 and 9.2 on strengthening the central Covid-19 Data Portal and coordinating nascent and established data hubs in mobilising data into the central portal. To achieve this, an active network of regional and national SARS-CoV-2 data hubs managers was established. To get existing data flowing, we first organised a technical workshop on data brokering to the ENA, from which an online tutorial and RDMkit documentation were published. Then, in order to support nascent and more established nodes in mobilising data, we started addressing the various challenges and gaps identified: (i) Importance to build a case for the regional/national data hub and brokering model, also for mid- and long-term funding: a survey was conducted on 11 countries to identify use cases, funding mechanisms and assess sustainability of the developed infrastructures and tools. We aim to publish it in F1000 (ELIXIR Gateway); (ii) Maturity assessment to oversee data hub development and identify potential areas of improvement: a maturity model for pathogen data hubs was developed and is readily available online. (iii) Sensitive data remains siloed and not shared on public open data repositories, calling for the need to federate pathogen data hubs through a central system enabling privacy-preserving data queries and controlled data access following FAIR principles: an opinion paper is being finalised to present and discuss a model of Federated One Health Surveillance Platforms, beyond Covid-19. A proof-of-concept of how a data hub might be “fairified” was also conducted in the Czech Republic. (iv) Training in analytical pipelines and documentation - several actively followed workshops were organised on Galaxy and available pipelines and tools have been documented. (v) Need to better understand the legal aspects of SARS-CoV-2 data sharing: a dedicated workshop was organised to identify the common needs and start addressing them. Legal experts should be further invited to the table given the many open questions remaining. The pandemic has demonstrated the importance of genomic surveillance. This deliverable establishes the foundations to strengthen SARS-CoV-2 data hubs and importantly, scale and expand them to other pathogens and use cases serving both research and surveillance through a federation of hubs.

In this deliverable report, we highlight outreach and training achievements in the period of January 2022 to February 2023 and also summarise overall achievements for the entire CINECA project. In total, we have delivered 59 learning interventions (including staff visits, webinars, workshops and training events, short training videos, and an overarching self-paced learning pathway) and we have reached a large and diverse audience, through both training and dissemination activities and through our website and social media channels. Below we provide further detail in terms of the diverse activities that we have developed and delivered since the beginning of the project, the wide demographic we have engaged, our reach through dissemination and training activities, and the impact that our training has had in the longer term.

This deliverable (D.5.2) allows clinical researchers to access and reuse existing data from public omics databases, to integrate these data with their own data and to enable access to a suite of bioinformatics tools, via the Galaxy workflow platform, for advanced cancer biomarker discovery and for patient stratification. This deliverable (D5.2) provides FAIR data access from GA4GH public genomics data resources (e.g. European Genome Archive (EGA) using the GA4GH htsget protocol directly into Galaxy. We developed an online tutorial to explain our workflow in detail, which is associated with the Galaxy Training Network (GTN). This study shows it is feasible to adopt end-to-end (i.e. data to results) scalable FAIR analysis of clinical data, and ultimately for any future analysis on data available at the EGA. Additionally we have developed a unified resource, Cancer Galaxy, as a landing page for Cancer related workflows and tools1 which includes tools implemented/developed by CINECA and other bioinformatics groups from the EU and worldwide.

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations. Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

In this deliverable document, we report on the activities in task 6.4 - Training Programme, describe the CINECA training activities that took place in months 25-36 of the project and provide the Training Plan for the final year. The CINECA training programme aims to train people within the CINECA consortium as well as external users. Different approaches have been employed, including face-to-face and online courses, hackathons, training videos and staff exchanges. While we waited for CINECA products to be completed, many of the training efforts for the year again focused on internal learning opportunities and knowledge exchanges, but some externally facing events were held to disseminate outputs. All the training and outreach events continued to be heavily impacted by COVID-19, which removed our ability to hold face-to-face workshops and staff exchanges. The staff exchanges were suspended and replaced with virtual meet-ups.

DNA is often referred to as the source code of life; it encodes the proteins that control the functioning of our cells and plays a huge role in our health. The publication of the human reference genome in 2003, combined with sustained technological advances in genome sequencing ever since, have transformed the field of biomedical research, and have led to an explosion of the amount of data being generated. However, scientists typically are not trainedin the skills required to manage and analyse these large datasets. Furthermore, bioinformatics tools and workflows tend to be very complex, and often require programming skills to run.As a result, researchers often rely on bioinformaticians to perform the data analyses for them.

Sharing data has become common practice around the globe, and training is part of this trend. This PLoS 10 simple rules paper published in 2020 introduced tips and tricks on how to make training materials FAIR (Findable, Accessible, Interoperable and Reusable), which encouraged trainers to share their materials with others, and facilitated this process. The WEB (Workshop on Education for Bioinformatics) 2021 was a dedicated forum for training providers, trainers and trainees to share their experiences of making training materials FAIR. The WEB was composed of short presentations from invited speakers who have been through this process, where they have introduced their approaches, challenges and solutions., {"references":["https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854"]}

In this deliverable document, we report on the activities in task 6.4 - Training Programme, describe the CINECA training activities in the first 24 months of the project and provide the Training Plan for the next 12-24 months. As with the first 12 months of the CINECA project, many of the training efforts for the second 12 months also focused on internal learning opportunities and knowledge exchanges. However, all the training and outreach events have been heavily impacted by COVID-19, which removed our ability to hold face-to-face workshops and staff exchanges. For training interventions targeted at a broader audience, we have set up a webinar series, providing quarterly online learning interventions. We ran a total of 6 webinars (3 of these webinars in 2019, and 3 in 2020), with 23 attendees on average, 68% on average of those who registered. In addition, a series of short training videos (https://www.cineca-project.eu/short-videos) was created to facilitate the uptake of CINECA outputs. Eight short videos were produced by work packages on different topics. To increase engagement, the short videos were submitted to ELIXIR’s training portal and disseminated via CINECA’s various communication channels.

Does your facility do bioinformatics training? Would your facility like to do bioinformatics training? The Galaxy Training Network library (https://training.galaxyproject.org/) is an easy way to offer bioinformatics training at your facility with minimal preparation time and startup cost. These materials feature slides, hands-on-tutorials, and training data sets. The library features well over 100 slide sets and hands-on tutorials, created by over 130 Galaxy community members, and covering a wide range of bioinformatics topics. Galaxy (https://galaxyproject.org) is a widely adopted platform for bioinformatics analysis and training, allowing trainers, learners, and researchers to focus on concepts and tools, rather than Linux systems administration and learning command line interfaces. The library of materials is free to use and adapt as needed. The GTN community is supportive and responsive to community needs. All materials are kept in GitHub and are managed in a transparent, community driven manner.

The increase in personal genome data generated in diagnostics and research holds great promise for advancing personalized prevention and medicine. However, valuable genomic and associated clinical data is fragmented across many healthcare providers and research organizations, making it difficult to reuse due to lack of findability, accessibility and interoperability. This prohibits us from exploiting the potential information contained in these genomes for health benefit. FAIR Genomes aims to provide guidelines that should increase reuse of genomic data while considering the needs of all stakeholders and addressing ELSI issues. We present a standardized meta-data schema to harmonize genomic data workflows and their reporting practices. This schema is broadly segmented into five categories: general information; informed consent; personal and clinical information; material information and technical information. In face-to-face and videoconference meetings, we work towards defining the schema, which is a list of common and optional data elements with relationships and values mapped to existing ontologies such as SNOMED, DUO, HPO, UMLS and EDAM. This project aims to make all data and meta-data elements findable and interoperable to increase FAIRness and standardization in capturing genomic data. This meta-data schema provides a strong basis for digital twin data in Dutch hospitals, development of personal genetic lockers, and active Dutch participation in the European '1+ Million Genomes' Initiative. The scope of this schema goes beyond to next-generation DNA sequencing data. We expect to expand into various *omics varieties, as well as capturing analysis pipelines in FAIR terms. Hence, the FAIR Genomes meta-data framework could be used to develop other research-based infrastructures such as X-omics, BBMRI, ELIXIR, Solve-RD and European Joint Programme on Rare Diseases. The FAIR Genomes meetings are open to receive input from anyone to achieve the highest quality and usability of the resulting meta-data framework. Join us at: https://github.com/fairgenomes. &nbsp

In this deliverable document, we report on the activities in task 6.2a - Training Programme and describe the CINECA training activities in the first 12 months of the project. In the first phase of the CINECA project, many of the training efforts are focused on internal learning opportunities and knowledge exchanges. There are many interdependencies between the different CINECA work packages, so to this end we have set up a staff exchange program.For training interventions targeted at a broader audience, we have set up a webinar series, providing online learning interventions. Feedback from these webinars is collected and is used to further increase the utility of the webinars going forward.We have disseminated a survey to identify the training needs of CINECA’s stakeholder community. Additionally, we ran our first stakeholder engagement session at the International Hundred Thousand Cohorts Consortium (IHCC) Meeting in Reykjavik, Iceland in April 2019. During this session, we gathered feedback on the challenges of managing cohorts and cohort data harmonisation. More extensive face-to-face workshops and training events are being planned for 2020.

In this deliverable document, we report on the activities in task 6.1 – Stakeholder analysis, with the outreach and dissemination plan, as well as the training plan presented in this report, we have completed this task. We have used a combination of surveys and face-to-face meetings to verify and extend or identify key stakeholder groups, understand their interest in the project, and identify bottlenecks that can be addressed by outreach and training. Outreach activities will be accomplished by task 6.2 and training by task 6.3.

In this deliverable document, we report on the activities in task 6.1 - Stakeholder analysis, with the outreach and dissemination plan, as well as the training plan presented in this report, we have completed this task. We have used a combination of surveys and face-to-face meetings to verify and extend or identify key stakeholder groups, understand their interest in the project, and identify bottlenecks that can be addressed by outreach and training. Outreach activities will be accomplished by task 6.2 and training by task 6.3. The stakeholder survey was developed and distributed within the CINECA stakeholder community; a total of 54 responses were received. While the response rate was on the low side, it is sufficient for the initial analysis, which is presented below. A CINECA competency framework was developed and validated through the survey. The competency framework has four areas of competence (generating data, finding and accessing data, analysis and clinical interpretation of data and sharing data), reflecting the research lifecycle. A total of 17 competencies have been defined. We consider both the stakeholder analysis and the competency profile to be living documents that will evolve over the course of the project. We plan to obtain additional feedback on both the competency profile as well as update the stakeholder analysis throughout the project through a range of methods, these could include interviews, discussion groups, conference workshops. A dissemination strategy was created aimed at the effective communication of CINECA activities and outcomes to all relevant partners and stakeholders. To this end, a range of dissemination channels have been set up, including a CINECA project webpage, various social media channels, a newsletter, and physical materials such as flyers and brochures. Furthermore, a set of conferences and events have been identified at which a CINECA presence (e.g. workshop, booth, talk) could benefit stakeholder engagement. Consistent branding across these different dissemination channels is achieved through the development of a logo, colour scheme, and the creation of templates for materials such as brochures, posters and slides. Furthermore, a number of CINECA areas of research and expertise have been identified that may be suitable for publication as peer-reviewed articles. A training plan was developed covering a variety of learning interventions, including webinars, staff visits, workshops and hackathons. In the early phase of the project (first ~18 months), training activities will be focused primarily on internal CINECA knowledge exchanges to allow work package interdepencies to be resolved. To this end, a regular webinar series has been started to inform CINECA project members and other interested parties of the various CINECA activities and outcomes. Furthermore, a staff exchange program was set up to allow knowledge exchanges between different interdependent work packages. CINECA members may submit staff visit requests via an online form, and after the visit a blog post about the outcomes will be posted on the CINECA website. As the project progresses, the focus of training activities will gradually shift to dissemination to a wider audience and will consist primarily of workshops (face-to-face or online) and hackathons.

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

International audience; Background: New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies. Findings: We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io). Conclusions: Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.