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Target audience: Post Docs 


Ten simple rules for making training materials FAIR

Sharing, reusing and reproducing data, software and other digital objects are the basis for open science practices, and to ease this the scientific community has developed the Findable, Accessible, Interoperable and Reusable (FAIR) principles. FAIR principles are however not so simple to...

Scientific topics: FAIR data

Operations: Data handling

Keywords: FAIR data, FAIR

Resource type: Video

Ten simple rules for making training materials FAIR https://tess.elixir-europe.org/materials/ten-simple-rules-for-making-training-materials-fair Sharing, reusing and reproducing data, software and other digital objects are the basis for open science practices, and to ease this the scientific community has developed the Findable, Accessible, Interoperable and Reusable (FAIR) principles. FAIR principles are however not so simple to understand and adopt, and may be quite convoluted for some. The ELIXIR Training Platform in cooperation with other associated partner ZB MED has put together a Ten Simple Rules for Making Training Materials FAIR which simplify the FAIR principles by breaking them down into very practical steps. This webinar introduced these ten rules and discussed the challenges and ideas to put them into practice. FAIR data FAIR data, FAIR PhD students Post Docs Undergraduate students
Slides from Data Management Planning Workshop

Slides from Data Management Planning online workshop for Life Science Projects for researchers at NTNU 2020-12-01

Scientific topics: Data management

Operations: Data handling, Data retrieval, Deposition

Keywords: Data management plan, data management

Resource type: Slides

Slides from Data Management Planning Workshop https://tess.elixir-europe.org/materials/slides-from-data-management-planning-workshop-08010d67-d503-436b-95b9-6fe29f47ebba Slides from Data Management Planning online workshop for Life Science Projects for researchers at NTNU 2020-12-01 Data management Data management plan, data management PhD students Professors Post Docs All postgraduates
Slides from Data Management Planning Workshop

Slides from Data Management Planning workshop for Life Science Projects in Ås 11 March 2020

Scientific topics: Data management

Operations: Deposition, Data retrieval, Data handling

Keywords: Data management plan, data management

Resource type: Slides

Slides from Data Management Planning Workshop https://tess.elixir-europe.org/materials/slides-from-data-management-planning-workshop-1bb3798d-4e70-423c-87b6-82ac7e5e5c54 Slides from Data Management Planning workshop for Life Science Projects in Ås 11 March 2020 Data management Data management plan, data management PhD students Post Docs Professors
Slides from Data Management Planning Workshop

Slides from Data Management Planning workshop for Life Science Projects in Bergen 04 March 2020

Scientific topics: Data management

Operations: Data handling, Data retrieval, Deposition

Keywords: Data management plan, data management

Resource type: Slides

Slides from Data Management Planning Workshop https://tess.elixir-europe.org/materials/slides-from-data-management-planning-workshop Slides from Data Management Planning workshop for Life Science Projects in Bergen 04 March 2020 Data management Data management plan, data management PhD students Post Docs Professors
Deep Learning using a Convolutional Neural Network

This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not...

Scientific topics: Machine learning

Resource type: Video

Deep Learning using a Convolutional Neural Network https://tess.elixir-europe.org/materials/deep-learning-using-a-convolutional-neural-network This course part focuses on a recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency. It is particularly relevant in research areas, which are not accessible through modelling and simulation often performed in HPC. Traditional learning, which was introduced in the 1950s and became a data-driven paradigm in the 90s, is usually based on an iterative process of feature engineering, learning, and modelling. Although successful on many tasks, the resulting models are often hard to transfer to other datasets and research areas. Machine learning PhD students Post Docs
Introduction to Machine Learning Algorithms

This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data.

Scientific topics: Machine learning

Resource type: Video

Introduction to Machine Learning Algorithms https://tess.elixir-europe.org/materials/introduction-to-machine-learning-algorithms-b1434ce7-b934-4b48-af7c-0274e2c37815 This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data. Machine learning PhD students Post Docs
Introduction to Machine Learning Algorithms

This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data.

Scientific topics: Machine learning

Resource type: PDF

Introduction to Machine Learning Algorithms https://tess.elixir-europe.org/materials/introduction-to-machine-learning-algorithms This course offers basics of analysing datasets with machine learning algorithms and data mining techniques in order to understand foundations of learning from large quantities of data. Machine learning PhD students Post Docs