This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Tutorial on collaborating with Git and GitHub. This tutorial was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Introduction to reproducible research. The lecture provides an overview of the core skills and practical solutions required to practice reproducible research. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience.  FAIR defines a set of high-level principles and practices for making digital objects, including data, software, and workflows, Findable, Accessible,  Interoperable, and Reusable.  But FAIR is not a specification;  it leaves many of the specifics up to individual scientific disciplines to define.  INCF has been leading the way in promoting, defining, and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry, and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been the successes?  What is currently very difficult? Where does neuroscience need to go? This lecture covers the biomedical researcher's perspective on FAIR data sharing and the importance of finding better ways to manage large datasets.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience.  FAIR defines a set of high-level principles and practices for making digital objects, including data, software, and workflows, Findable, Accessible,  Interoperable, and Reusable.  But FAIR is not a specification;  it leaves many of the specifics up to individual scientific disciplines to define.  INCF has been leading the way in promoting, defining, and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry, and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been the successes?  What is currently very difficult? Where does neuroscience need to go? 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.

Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives. This session will focus on the question of FAIRness in neuroimaging research touching on each of the FAIR elements through brief vignettes of ongoing research and challenges faced by the community to enact these principles. This lecture covers the processes, benefits, and challenges involved in designing, collecting, and sharing FAIR neuroscience datasets.

Over the last three decades, neuroimaging research has seen large strides in the scale, diversity, and complexity of studies, the open availability of data and methodological resources, the quality of instrumentation and multimodal studies, and the number of researchers and consortia. The awareness of rigor and reproducibility has increased with the advent of funding mandates, and with the work done by national and international brain initiatives. This session will focus on the question of FAIRness in neuroimaging research touching on each of the FAIR elements through brief vignettes of ongoing research and challenges faced by the community to enact these principles. This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience.  FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible,  Interoperable and Reusable.  But FAIR is not a specification;  it leaves many of the specifics up to individual scientific disciplines to define.  INCF has been leading the way in promoting, defining and implementing FAIR data practices for neuroscience.  We have been bringing together researchers, infrastructure providers, industry and publishers through our programs and networks.  In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience.  We will engage in a discussion on questions such as:  how is neuroscience doing with respect to FAIR?  What have been successes?  What is currently very difficult? Where does neuroscience need to go?

This lecture will provide an overview of Addgene, a tool that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of Addgene, their mission, and available resources.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community

This lecture covers the IBI Data Standards and Sharing Working Group, including its history, aims, and projects.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community. This session covers the framework of the International Brain Lab (IBL) and the data architecture used for this project.

The FOSTER portal has produced a number of guides to help implement Open Science practices in daily workflows, including The Open Science Training Handbook.  It provides many basic definitions, concepts, and principles that are key components of open science, as well as general guidance for developing and implementing these practices in one's own research environments.

Topics include:

• Open Concepts and Principles
• Open Research Data and Materials
• Open Research Software and Open Source
• Reproducible Research and Data Analysis
• Open Access to Published Research Results
• Open Licensing and File Formats
• Collaborative Platforms
• Open Peer Review, Metrics and Evaluation
• Open Science Policies
• Citizen Science
• Open Educational Resources
• Open Advocacy