Git is used across the world to help developers keep track of changes while working on different parts of the same project. Teams are not the only ones to benefit from version control: lone researchers can benefit immensely. Keeping a record of what was changed, when, and why is extremely useful for all researchers if they ever need to come back to the project later on (e.g., a year later, when memory has faded).
DOME is a set of community-wide guidelines, recommendations and checklists, spanning these four areas (Data, Optimization, Model and Evaluation in Machine Learning) aiming to help establish standards of supervised machine learning validation in biology. The work was initially published in 2021 with more details on the DOME-ML page. This activity is part of the Machine Learning Focus Group.
Date: 24 September 2024, 13:00 CEST
In this course, participants will learn to utilize the ggplot2 package, a widely-used tool in R for creating visually appealing and highly customizable graphs.
This course will not make the participant an absolute expert in the complex and dynamic world of Deep-Learning. Still, it will aim to “break the ice” through the explaination and implementation of simple yet concrete, deep-learning models using the PyTorch library. Participants will be introduced to the basic building blocks of deep-learning models and how the main parameters are tuned and monitored to ensure the training of large models. This course is aimed at PhD students, post-docs and researchers in life sciences who already know about Machine Learning and would like to discover and start practising Deep Learning with PyTorch.
Application deadline: 31 October 2024
Date: 07 - 08 November 2024
The Swedish Bioinformatics Workshop (SBW) is an annual event that has been organized by different universities in Sweden since 2000. It is the biggest meeting of bioinformaticians and computational biologists in Sweden. Senior scientists and non-academic participants are welcome. In addition, the SBW2024 is part of the Nordic Computational Biology (Nordic CompBio) week and is open to participants from other Nordic countries (Estonia included).
Application deadline: 1st of October 2024
Date: 4-5 November 2024
This Tableau course is designed to equip learners with the essential skills required to navigate the software’s core features. Students will learn to create compelling visualisations, manipulate data, develop dashboards, and share insights effectively.
Researchers often face overwhelming raw data, making it crucial to utilize data visualization effectively. This skill transforms complex datasets into clear, insightful narratives, avoiding the pitfalls of misinterpretation through poor design. In this course, we’ll explore optimal chart types for different data, engage in interactive exercises, discuss best practices, and learn about the data-ink ratio and color selection, all illustrated with practical examples.
R is a free software environment for statistical computing and graphics. It compiles and runs on various UNIX platforms, Windows and MacOS.
RStudio is an essential tool for anyone involved in R programming. It plays a vital role in data analysis by facilitating data import, access, transformation, exploration, visualization, and modeling tasks. If you’re embarking on your journey to learn R, now is the perfect moment to familiarize yourself with RStudio. Discover what RStudio is, how to install it, and begin harnessing its capabilities to enhance your data analysis and programming skills.
It would be beneficial to participate in OpenRefine course, so your data would be clean before you start analysing it.
The title of the episode is “Teadustarkvara arendaja roll akadeemias” - “The role of a research software engineer”. Uku spoke mostly of his personal experiences developing software in academia. Viewable at https://youtu.be/watch?v=2oLt1NcrROg and listenable anywhere you consume podcasts.
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.