Building and sustaining a Community of computational biologists at EMBL through Consulting, Training, and Infrastructure
Paladin, Lisanna; Paredes-Cisneros, Isabela; Alves, Renato; Geissen, Eva-Maria; Kaspar, Sarah; Miranda, Jacobo
- Publisher
- Zenodo
- Language
- en
Abstract
Collaboration is essential in computational biology, where interdisciplinary approaches drive innovation and address complex challenges. At the European Molecular Biology Laboratory (EMBL), the Data Science Internal Support (DaSIS) strengthens this collaboration through four key pillars: consulting, training, infrastructure, and community building.Consulting is central to our work, providing expert guidance on challenges ranging from statistical analysis and mathematical modeling to coding and project management. By connecting researchers with domain-specific experts across EMBL, we help facilitate impactful, collaborative projects.Training empowers researchers to thrive in a fast-evolving landscape. We offer hybrid workshops, self-paced resources, and focused sessions on topics like programming, visualization, and machine learning. Our approach balances accessibility with depth, helping scientists stay at the forefront of their fields.Robust infrastructure supports our mission. We maintain tools such as coding platforms, version control systems, and cloud-based environments tailored to bioinformatics. Combined with collaborative and communication platforms, these ensure seamless research workflows.Community unites these efforts. We foster engagement through meetups, clubs, and knowledge-sharing forums that encourage connection, collaboration, and lasting professional networks.While our work is rooted in life sciences, the challenges and strategies are relevant across disciplines. We share our approach at the Open Science Fair to promote broader dialogue on effective research support and to learn from others. Crucially, we advocate for research support as a vital, rewarding career in academia. By highlighting this work, we hope to inspire conversations about career development, institutional priorities, and how to better recognize and integrate support roles into the scientific ecosystem.
Full text
Lisanna Paladin Data Science Community and Internal Support Lead Building and sustaining a Community of computational biologists at EMBL through Consulting, Training, and Infrastructure Image generated with Freepik AI
The European Molecular Biology Laboratory (EMBL) EMBL-EBI Bioinformatics Grenoble Structural biology Barcelona Tissue biology and disease modelling Hamburg Structural biology Heidelberg Life sciences Rome Epigenetics and neurobiology > 2000 employees 28 member states
Wandzik et al. Cell 2020 To offer vital services to scientists in the member states and the world Scientific services To perform fundamental research in molecular biology Excellent research To actively engage in technology transfer and industry relations Innovation and translation To coordinate and integrate European life science research Integrating life sciences To train scientists, students, and visitors at all levels Advanced training EMBL’s five missions advance Life Sciences in Europe
EMBL’s context and related challenges The context ● Internationality (multi-site) ● Dynamic (high turnover) ● Collaborative environment ● Open Science policy The challenges ● Induction of newcomers ● Sustainability of activities ● Distributed work ● Flexible and responsive educational programme ● Easy access to information ● Streamlined advertisement of activities
Image generated with Freepik AI www.embl.org/about/info/data-science-centre The Data Science Centre aims to ●Facilitate research advances in Data Science ● Offer internal service: research support for biologists ● Offer external service: critical tools & data resources ● Develop common representations: data, conventions, workflows ● Contribute to training & career development The Data Science Centre (DSC)
Image generated with Freepik AI The DSC: who
Image generated with Freepik AI 7 The Data Science Internal Support (DaSIS) Our expertise Research Computational Biology integrates biology and computer science to analyze complex data, promote effective data and software practice, and support open science. Statistical methods - spanning experimental design, data visualization, hypothesis testing, and modeling - ensure rigorous, reproducible research. Mathematical modelling simplifies complex biological systems, tests hypotheses and predicts experimental outcomes, helping researchers gain deeper insights. Cloud computing and storage provide secure, scalable solutions for handling large datasets, running compute-intensive tasks, and share resources. as well as: ● Project management ● Software engineering ● Career development ● Infrastructure maintenance ● Usage of AI tools…
Image generated with Freepik AI From challenges Consulting – Expert guidance to overcome computational challenges, connect with domain specialists, and drive research. to skills building Training – Hands-on workshops and self-paced learning as skill-based training in programming, data visualization, machine learning... supported by tools… Infrastructure – Scientific and collaborative tools, e.g. coding platforms, GitLab, and communication platforms. and a sense of community Community – Meetups, clubs, and forums that foster connections, idea exchange, and professional growth. The Data Science Internal Support (DaSIS) Our way of operating
Image generated with Freepik AI The Data Science Internal Support (DaSIS) Our impact
