This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.

This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

This lecture focuses on ontologies for clinical neurosciences.

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data. 

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs. 

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

This lesson describes the fundamentals of genomics, from central dogma to design and implementation of GWAS, to the computation, analysis, and interpretation of polygenic risk scores. 

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes. 

This lecture discusses the the importance and need for data sharing in clinical neuroscience.

This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.

This lecture gives an overview on the European Health Dataspace. 

This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.