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How to make your messy data usable? / OpenRefine - 24.09.2024 - Registration OPEN

The practical workshop on cleaning your messy data with OpenRefine software.

First, we will cover spreadsheet best practices. Then, we will put that knowledge into practice with OpenRefine. This course will explore the depths of OpenRefine software and see what it can offer. This will include cleaning the data in bigger batches and unifying the data in one sweep (transforms and expressions). Additionally, we will introduce the possibility of downloading additional data from other databases and different extensions OpenRefine software has.

Virtual 2 Day Workshop SABIO-RK and FAIRDOMHub / FAIRDOM-SEEK

SABIO-RK is a manually curated database for biochemical reactions and their kinetic properties. FAIRDOMHub is a free research data management platform built upon the FAIRDOM-SEEK software. FAIRDOM-SEEK contains specific features for systems biologists for data and model management. This is an introductory course for SABIO-RK and FAIRDOMHub. You have the option to participate in either one or both of the courses.

SABIO-RK: 25-NOV-2024 2:00 pm - 5:00pm (CET / UTC +1)

FAIRDOM-SEEK: 26-NOV-2024 2:00 pm - 6:00pm (CET / UTC +1)

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Crash Course in Data Management - 18.09.2024 - Registration OPEN

Data management is an essential part of scientific research and is crucial for maximizing the value of data. Crash course in Data Management is an introductory course that equips researchers with the necessary skills for effective organization and management of research data. Participants will gain knowledge of best practices in data documentation, backup, and sharing. We will go through exercises and assignments to reinforce new knowledge.

Online: Ensuring More Accurate, Generalisable, and Interpretable Machine Learning Models for Bioinformatics

This course is addressed to life scientists, bioinformaticians, and computational biologists who would like to learn more about general best practices in Machine Learning and get more out of their Machine Learning models: more precise hyper-parameters, more generalizable models, and more interpretable models.

Application deadline: 29 September 2024

Date: 15 October 2024

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