This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This talk goes over Neurobagel, an open-source platform developed for improved dataset sharing and searching. 

In this lesson, you will learn about the BRAIN Initiative Cell Atlas Network (BICAN) and how this project adopts a federated approach to data sharing. 

In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience. 

This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

This lesson provides a quick tour of some data repositories and how to download and manipulate data from them.

In this lesson, attendees will learn about the data structure standards, specifically the Brain Imaging Data Structure (BIDS), an INCF-endorsed standard for organizing, annotating, and describing data collected during neuroimaging experiments. 

This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.

This lesson serves a primer on elementary algebra.

This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science. 

In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.

This talk gives a primer on calculus, emphasizing its role in data science.

This lesson clarifies how calculus relates to optimization in a data science context. 

This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.

This lesson serves as a primer on the fundamental concepts underlying probability. 

Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

This compilation is courtesy of freeCodeCamp.

This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

This lesson was created by RootMath.

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.

The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms.  Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.