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
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
This course is aimed at PhD students, post-docs and researchers in life sciences wanting to make computational inferences about gene regulation from gene expression data, chromatin accessibility and epigenetic mark data. The course is targeted to a wide audience ranging from computational biologists and bioinformatics researchers who regularly analyse transcriptome and epigenome data, to experimental researchers interested in inferring key regulatory interactions in their own data.
Application deadline: 28 August 2024
Date: 30 August 2024
This workshop will present a general overview of data sensitivity in relation to sharing and reuse; two presentations on aspects of sensitive data in different research areas; a focus on Persistent Identifiers and data handling of sensitive data; and an opportunity to join in the discussion with the presenters and with each other.
Date: 25th June at 14:00-15:15 CEST
This workshop is designed for trainers, anyone developing or maintaining repositories, standards, and policies, librarians, data stewards, and those assisting researchers. However, all Life Science research data community members and beyond – including researchers themselves – are most welcome.
Date: 27 June 2024
This course is addressed to computational biologists, bioinformaticians, researchers, scientists and trainers working in the life sciences who want to learn how to make their research and training FAIRer with reproducible notebooks and websites.
Application deadline: 28 June 2024
Date: 12 July 2024
This 1-h webinar will introduce the enzyme-related data in UniProt and how to find it.
Registration deadline: 30 May 2024
Date: 06 June 2024
Join us for a two-session workshop designed specifically for busy biologists looking to enhance their productivity through effective data management practices.
Dates: March 20th and 21st
Here we offer a two-day workshop with the primary aim of introducing participants to good enough practices for managing their data. On the first day, participants will learn about the basics of data management, good research practices, Common European Data Spaces, data management and governance in industry and data management plans. On the second day, participants will learn how to organise their data, how to make it FAIR, about electronic lab notebooks and how to make their computational results reproducible.
Registration deadline: February 25, 2024
On September 1st (2021), 10-11am CEST, Open Science Coffee will be holding an online seminar "Research Code Review".
More researchers are writing code to process, analyse and visualise their data. For reproducible and robust research, it is increasingly important that this code is reviewed. Join this inaugural Open Science Coffee to learn more and discuss best practices, challenges and opportunities in reviewing research code.
Register before seminar in here: https://tudelft.zoom.us/meeting/register/tJwtcOypqzwpHNZPRVlObq8dK_DIGy0yWLrs
More information: https://osc-delft.github.io/events