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E-learning is a means to provide training accessible 24/7, anywhere, anytime, to an infinite number of participants. In 2012, SIB Swiss Institute of Bioinformatics was asked by Food and Agriculture Organization (FAO) of the United Nations and the International Atomic Energy Agency to prepare e-learning courses for laboratory technicians, veterinarians and molecular epidemiologists from diagnostic and research laboratories of developing FAO and IAEA member states. Following this initial experience in e-learning, we followed training in the appropriate pedagogical approach to apply in e-learning courses in 2021. This led us to explore the types of courses and topics that can be taught. We also determined the time and effort required to develop and maintain such courses up to date. We will report on our recent experience in using e-learning to teach bioinformatics at SIB Swiss Institute of Bioinformatics.

With the ongoing explosion of scientific datasets being generated, 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 educational programs, resulting in a skills gap in many of the researchers tasked with analyzing these big datasets.
To address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has 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 support the instructor community, we added new features 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. The GTN framework has then proven to be an invaluable resource for individual learners as well as educators.

A major aim of the H3ABioNet project has been to design a sustainable approach to build and develop bioinformatics capacity to support genomics research across Africa. An integral component of this approach has focused on developing high quality training materials. H3ABioNet has thus embarked on a large effort to make our training materials (including supporting tools such as containers and workflows) FAIR. H3ABioNet ensures webpages reflect the most relevant information to ensure optimum findability and accessibility of materials. The use of tags and keywords across webpages helps to improve Search Engine Optimisation (SEO) along with submitting materials to a public repository to assign a permanent identifier, and improve data provenance and discoverability. We have also recently begun the process of implementing bioschemas (https://bioschemas.org/) across training webpages, making them more discoverable to web scrapers and training databases and repositories like GOBLET (https://www.mygoblet.org/) and TeSS (https://tess.elixir-europe.org/), thus increasing their findability. Since most materials within H3ABioNet are released under a creative commons license, materials are also free for re-use and distribution, allowing the reach of training materials to extend far beyond H3ABioNet. We aim to highlight our experiences with this talk.

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model—OpenAI's ChatGPT—can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.

Applying the FAIR principles to training material encourages its re-use, positively impacting trainees and course developers. Trainees can find and learn from training materials at their own pace, and trainers can build further upon existing training materials. In the past years, at SIB, we have applied the FAIR principles to our training material, and in this presentation, we will go through our established workflow. Along this process, we realized, like many other organizations, that git repositories provide an excellent framework to share and reuse training material. Because git is created for co-development, it is straightforward to work together both within and outside an organization and share materials that are under constant development. However, material on GitHub or GitLab can be hard to find. Therefore, we created Glittr.org, a web application containing bioinformatics training material on GitHub/Lab. With this application, users can search and sort training material based on topic, popularity, recency, and other characteristics. With these initiatives, we aim to contribute to the re-use and co-development of bioinformatics training material.

The ELIXIR Germany node (ELIXIR DE) represents a subgroup of the German Network for Bioinformatics Infrastructure (de.NBI). de.NBI offers Services, Compute Resources via the de.NBI Cloud, Industry Engagement through the de.NBI Industrial Forum, and Training.

The de.NBI networks supplies researchers with over 100 freely available Tools, the so called Services. In case of higher capacity needed, the de.NBI Cloud is usable for data analysis and storage with its active 561 projects in 2023. The connection to the Industry is established via the de.NBI Industrial Forum holding 39 members from small and medium enterprises (SME) to big Pharma representatives. It is thus obvious that Training – be it connected to a Service, Cloud or Industrial Forum - is an integral part of the de.NBI network and hence the ELIXIR DE node. More than 450 courses training courses with approximately 10.000 attendees comprising a broad variety of bioinformatics and research centric topics have been held. The courses tackle in-depth hands-on training for specific Services or areas of expertise as well as broader topics like introduction to programming languages, workflows or research data management (RDM). Latest efforts entailed the participation in the RDM trainer network established through the ELIXIR CONVERGE project.

Research Data Management is getting more attention within research institutions and funders.
The ELIXIR Research Data Management (RDM) Trainer Network brings together individuals from national (ELIXIR Nodes) and local (research institutes, and higher education) organisations, who are committed to promoting good practices in data management among researchers. Network members collaborate to develop training materials, share best practices and offer support to one another as well as expand their training knowledge and skills in RDM, and connect with peers.
The Network is cemented on three major pillars: establish and sustain the Network, organise and implement knowledge sharing activities to support the development of the network through regular gathering and events, and capture its outcomes and impact utilising ELIXIR’s training impact metrics. As part of the ELIXIR-CONVERGE WP2, the Network has already organised several informational gatherings and a series of hackathons revolving around data management plans, data management / data stewardship training, reproducibility, and FAIR data.
The network is currently establishing itself in a sustainable manner within the ELIXIR network. Further, ELIXIR RDM training network will also stay tightly connected to the ELIXIR Training Platform and other relevant ELIXIR Platforms and Communities.

When the COVID-19 crisis shut down most undergraduate research opportunities, we developed an online, remote, computer-based Research Experiences for Undergraduates on the topic of Macromolecular Structure and Function. The program provided a mentored research experience and training in professional skills to assist the participants in pursuing a degree and future career in STEM. This fully virtual project involved faculty at four geographically-distributed institutions specializing in diverse but complementary approaches to study macromolecular structure and function. This project can also serve as an example for future remote, online projects that would especially be helpful for students who don’t have access to similar programs at their universities, cannot travel to attend a summer program, have physical challenges that make it difficult for them to work in a lab, or students whose research opportunities are limited due to war or civil unrest.

The generation, analysis, and interpretation of -omics data have become essential in modern biological research, but many researchers lack the necessary computational skills. The Bioinformatics Support Hub team has developed a program at the Cushing/Whitney Medical Library at Yale School of Medicine that addresses this need by providing bioinformatics education, personalized consultations, and research assistance. The program guides researchers through a defined cycle of -omics data analysis, from data downloading to data processing, statistical analysis, biological interpretation, and data deposition in public repositories. The population acessing this program is diverse including students, postdoctoral researchers, clinicians, and faculty. Participants in workshops and consultations report the achievement of tangible research outcomes. The Bioinformatics Support Hub team also fosters collaboration and coordination among different departments, acting as a unifying force within the organization. Continuous improvement and feedback collection are integral to the program's success, with peer-to-peer teaching and engagement with developers of new tools and software to address knowledge gaps and challenges. Overall, the program developed by the Bioinformatics Support Hub team at the Yale’s Medical Library, empowers researchers with bioinformatics knowledge, skills, and tools to advance biomedical and clinical research, while fostering a culture of learning and collaboration within the research community.

PerMedCoE is the High Performance Computing (HPC) / Exascale Centre of Excellence for Personalised Medicine in Europe and aims at advancing personalised medicine by adapting cell-level simulation methods to pre-exascale systems to translate omics data into molecular disease models. The training programme contributes to this goal by educating professionals in life sciences and HPC communities about PerMedCoE developments.

PerMedCoE designed a competency-based training programme that focuses on user needs to enable them to run large scale cell-level simulations in HPC environments. The definition of relevant competencies and a training needs analysis supported our decision to prioritise training in modelling, use of HPC resources, and programming.

The PerMedCoE training programme targets consortium partners to increase capacity in tool development, and external users to facilitate the adoption of these tools. A combination of formats such as webinars, online and face-to-face courses allows us to reach a wide audience, including software developers and biomedical researchers.

The PerMedCoE training programme has been well received with positive feedback after the events and people returning to subsequent activities. This contributes to the creation of a community around the project and to expand the use of cell-level simulation tools, which facilitates the development of computational personalised medicine.

Bioinformatics training generally includes instruction in individual tools and databases. However, for responsible research in genomics, bioinformatics training needs to include lessons that incorporate processing data through the various tools and training on responsible conduct of research. A class-based undergraduate experience (CURE) course was developed to combine bioinformatics training with library resources (including free textbook access, and workshops on reproducibility and literature searching) to teach students responsible conduct in bioinformatics research. This study evaluates new modules for bioinformatics and teaching responsible conduct of research (RCR) to measure whether the activities used in teaching improved student knowledge of and attitudes towards these topics. Data is being obtained from students taking the Genomics CURE course. Assignments and materials generated for the class research project will be coded and analyzed qualitatively to identify understanding of learning objective. Aggregated responses on quizzes will be analyzed to quantify the understanding of learning objectives. Additionally, a retrospective survey will be analyzed to assess self-identified learning gains and attitudes about the learning objectives. The results from this study will identify effective methods of teaching bioinformatics and ethical and responsible conduct of research and inform future lessons to improve undergraduate scientific training.

Since 2016, the Bioinformatics and Biostatistics, and Image Analysis Hubs at Institut Pasteur have co-developed a training program for PhD students, financed by INCEPTION. Over 60 specialized engineers share their expertise in next-generation sequencing techniques, bioinformatics, and biostatistics through this program.
Students select from a variety of modules in biostatistics, bioinformatics, and image analysis. The program consists of four tracks: a mandatory common core, and three tracks dedicated to biostatistics, bioinformatics, and image analysis. The common core offers introductory courses on computer science, reproducible research, experimental design, and ethics. The additional tracks compose the bulk of the bioinformatics program with a track dedicated to biostatics (four modules), a track for bioinformatics (11 modules), and a track focused on image analysis (four modules). Overall, the program involves over 30 teachers, and attracts about 70 students annually.
A Moodle server handles registration and teaching material distribution, and Virtual Machines ensure a controlled environment for each class. Hub engineers regularly update the courses, aligning with the rapidly evolving next-generation sequencing techniques.
The program proved to be highly appreciated by students and supervisors alike, greatly improved interactions with the Hub and rapidly became an asset for the research community at Institut Pasteur.
Link: https://research.pasteur.fr/fr/course/bioinformatics-program-for-phd-students-2022-2023/

Across every aspect of work and play we are now asked to consider the environmental impact of our lifestyles and take action where we can. This includes evaluating the impact of our work, and recent studies in computational biology have highlighted the sometimes significant impact of research. Training fits into this landscape through (1) the impact training delivery can create, and (2) the opportunity for trainers to engage and encourage users to think about their compute needs differently.

During the WEB at ISMB 2022, a keynote and panel discussion on the twin topics of green training and green computing started a conversation between trainers from across the globe (in-person and online) which resulted in draft guidance and a wish-list for individuals and organisations to explore this topic further.

At the Bioinformatics Education Summit in May 2023, these initial discussions were expanded to further develop this guidance, identifying where environmental issues may arise, how levels of impact could be assessed and prioritising modes of delivery (whilst not reducing learning impact). Additionally we were able to gauge awareness and knowledge of green compute practices amongst the training community and generate a list of known-unknowns, enabling us to move this important discussion forward.

The ISCB Student Council started the ‘Regional Student Groups’ (RSG) program in 2006 to facilitate interactions between local young scientists in computational biology across the globe. RSGs have developed a platform for young computational biology researchers and students at local regions to nurture networking potential and foster bioinformatics education, thus overcoming the challenges in their regional communities. Led by young researchers and students, our global RSG family has presence in 25 countries, overcoming the difficulties caused by covid-19, and the list is growing. RSGs organize different events, such as symposiums, webinars, podcasts, workshops, courses, panel discussions, and hackathons, to disseminate science and provide training on cutting-edge topics in computational biology. The spectrum of such initiatives organized by RSGs plays an essential role in updating the delegates with recent advances in the field. These events also create an opportunity for national and international collaboration among students and early-career scientists. It provides opportunities for students to foster their management and organizational skills to further their career prospects in academia and industry. This work highlights how RSGs have evolved with different operational styles and workings but with a common mission to present a crucial framework for upcoming computational biologists’ networking and educational potential.

The Harvard Chan Bioinformatics Core (HBC) provides consulting and training services to the Harvard community. Our training program is designed to empower researchers to perform analysis on their own data to alleviate the bottleneck of data analysis. Our workshops cover topics including an Introduction to the Command-line, Introduction to R, Bulk RNA-seq, scRNA-seq, Chromatin Biology and Variant Calling. In addition to our workshops, we offer shorter module workshops covering more specific topics including Advanced bash, Publication-grade Figure Making, Introduction to Python, Git/GitHub and RMarkdown. However, due to the pandemic, we shifted our teaching from in-person to online and addressed the issues associated with online teaching. Here, we outline the difficulties we encountered while shifting our courses from in-person to online and the solutions we employed to resolve these issues. All of our course materials are designed to accommodate self-learning and are freely available for public use.