Graduate School of health and Medical Sciences

HPC Launch
Provider: Faculty of Health and Medical Sciences

Activity no.: 3948-24-00-00	There are 10 available seats
	Enrollment deadline: 08/09/2024

Date and time	30.09.2024, at: 08:45 - 16:30
Regular seats	20
Course fee	1,800.00 kr.
Lecturers	Anders Krogh
ECTS credits	0.80

Contact person	HeaDS Administration E-mail address: heads-admin@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Explain the purpose and technical components of High-Performance Computing (HPC) resources to support project design and troubleshooting 2. Identify relevant HPC resources and data storage infrastructure for individual project needs 3. Assess the challenges of effective management of large-scale data projects and propose solutions 4. Demonstrate how to meet data documentation and metadata standards for RDM using Cookiecutter project templates 5. Explain how RDM fosters collaboration and reproducibility in research projects Content The goal of the course HPC-Launch is to support the launch (and/or reconfiguration) of health data projects from an efficient and modern computing and data management perspective. Targeting trainees and researchers in bioinformatics and large-scale health records, the course will consist of two modules: High-Performance Computing (HPC) and Research Data management (RDM). With the HPC module, we want to expand understanding and efficient use of HPC resources for complex health data science projects. We will fill gaps in technical understanding for beginner to intermediate users of supercomputing platforms and share up-to-date information on computing resources available to Danish researchers and how to get access. With the RDM module, we will introduce the importance of research data management practices and demonstrate practical tips and tools for its implementation at a local research group level. Overall, the course will be a mix of theory, discussion of real-world use cases and participant needs, and active practice/exercises conducted on the HPC platform UCloud (SDU) using bash and relevant IDEs. In summary, HPC-Launch will provide participants with an overview of current best computing practices for management of health data analysis projects. The two modules are designed to enhance participants’ ability to handle large datasets, conduct sophisticated analyses of biodata, and preserve data and analyses for future projects and project members. HPC-Launch is the first course of a series we are developing to elevate practical technical skills in health data science, and is the intended prerequisite for HPC-Pipes (focused on environment management and bioinformatics pipeline languages) and/or HPC-ML (focused on set-up and optimization of large-scale machine learning projects on HPC platforms). Participants The course is intended for PhD students, postdocs, and junior faculty at SUND who are interested in learning how to use high-performance computing resources and implementing biodata best practices and tools for RDM. Requirements The workshop is for PhD students at SUND who seek to acquire skills in effectively managing data and computing projects. The course is targeted to students performing data analysis on biological research or within the field of data science, therefore some computational experience is required, and basic knowledge of R/Python and bash is highly recommended for the demo session. Relevance to graduate programs The course is relevant to PhD students from the following graduate programs at the Graduate School of Health and Medical Sciences, UCPH: - All graduate programmes Language English Form Lectures with active discussion sessions, interactive demos using the UCloud platform, and group work and exercises navigating UCloud and practicing with tools for RDM-compliant project set-up. Course director Anders Krogh, Professor, Head of Center for Health Data Science, Head of Health Data Science Sandbox Center for Health Data Science, anders.krogh@sund.ku.dk Teachers The workshop is provided by project members of the Health Data Science Sandbox, a national training and research infrastructure project. The Sandbox team is building training resources and guides for learning bioinformatics, predictive modeling in precision medicine, high performance computing and data carpentry. These resources are accessible to all Danish university employees (PhD students and up) via academic supercomputing infrastructure. Jennifer Bartell PhD, Senior consultant and Sandbox project manager Center for Health Data Science, KU bartell@sund.ku.dk Alba Refoyo Martinez PhD, Data Scientist, Sandbox Team Center for Health Data Science, KU alba.martinez@sund.ku.dk Stefano Pupe PhD, Senior Consultant Center for Health Data Science, KU stefano.pupe@sund.ku.dk Dates 30 September 2024 Course location Faculty of Health and Medical Sciences, Panum, Blegdamsvej 3B, 2200 København. Room 7.15.149 Registration Please register by 08 September 2024 Expected frequency This course will be repeated in Spring 2025. Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrollment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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