This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines.
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.
This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.
This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.
This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML).
This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.
This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.
In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.
This lecture focuses on the rationale for employing neuroimaging methods for movement disorders.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.
In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them.
This lecture covers multiple aspects of FAIR neuroscience data: what makes it unique, the challenges to making it FAIR, the importance of overcoming these challenges, and how data governance comes into play.
This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.
This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.
This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.