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 remaining barriers.

• Introduction
  Sharon Crook, Arizona State University 
• Considerations and challenges for FAIR in large and heteregeneous sets of personalized large-scale models
  Speaker: Kelly Shen, Rotman Research Institute at Baycrest Health Sciences
• Do we have the tools and resources to develop FAIR large-scale brain circuit models?
  Speaker: Salvador Dura-Bernal, State University of New York (SUNY) Downstate
• Structured validation processes in neural network simulations and analysis
  Speaker: Michael Denker, Institute of Neuroscience and Medicine
• Discussion

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.

• Introduction
  Satra Ghosh, MIT
• Enhanced dataset search with semantic annotations and NIDM
  Speaker: David Keator, University of California, Irvine
• The village that can climb the hill: perks & hurdles of designing, collecting, and sharing a FAIR dataset in neuroscience
  Speakers: Valentina Borghesani, CNeuromod & Julie A. Boyle, CRIUGM
• Open science efforts for PET imaging - From guidelines over BIDS PET to OpenNeuroPET
  Speaker: Melanie Ganz-Benjaminsen, University of Copenhagen
• Open for research: challenges and opportunities in re-using publicly available datasets
  Speaker: Elizabeth Dupré, McGill University
• Ethics of FAIR in neuroimaging
  Speaker: Gustav Nilsonne, Karolinska Institutet
• Discussion

• Introduction
  Thomas Wachtler, Ludwig-Maximilians University
• Curating electrophysiology data for reuse in EBRAINS
  Speaker: Andrew Davison, CNRS, Gif sur Yvette
• How standards and use-cases shape up the FAIR DANDI archive
  Speakers: Yaroslav Halchenko, Dartmouth College
• AEDAPT: Developing an open, portable, scalable Australian platform for FAIR electrophysiology and MEG data analytics
  Speaker: Tom Johnstone, Swinburne University of Technology
• Towards a standardized data organization for electrophysiology
  Speaker: Sylvain Takerkart, CNRS-AMU
• Discussion

This session will include presentations of infrastructure that embrace the FAIR principles developed by members of the INCF Community.

• Introduction
  Helena, INCF
• The Virtual Brain
  Speaker: Petra Ritter, Berlin Institute of Health
• Addgene
  Speaker: Joanne Kamens, Addgene
• Canadian Open Neuroscience Platform
  Speaker: Tristan Glatard, Concordia University
• EBRAINS
  Speaker: Steven Vermeulen and Jan Bjaalie, HBP
• The China-Cuba-Canada neuroinformatics ecosystem for QEEG
  Speaker: Pedro Valdés Sosa, Cuban Neuroscience Center
• INCF TrainingSuite
  Speaker: Mathew Abrams, INCF

The objective of this session is to present the current and future activities of the INCF councils and committees and provide a forum in which the community can provide feedback and recommend new directions for the INCF network.

• Introduction
  Mathew Abrams, INCF
• Council for Training, Science, and Infrastructure (CTSI)
  Speaker: Jean Baptiste Poline, McGill University
• Industry Advisory Council (IAC)
  Speaker: Vijay Iyer, MathWorks
• Training and Education Committee (TEC)
  Speaker: William Grisham, University of California
• Infrastructure Committee (IC)
  Speaker: Wojtek Goscinski, Monash University
• Standards and Best Practices Committee (SBP)
  Speaker: Maryann Martone, University of California, San Diego
• Discussion

Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools. Rather than rigid distinctions, in the data science of neuroinformatics, these activities and approaches intersect and interact in dynamic ways. Together with a panel of cutting-edge neuro-data-scientist speakers, we will explore these dynamics.

• Introduction
  Ariel Rokem, University of Washington
• Mine your own view: A self-supervised approach for learning representations from neural activity
  Speaker: Eva Dyer, Georgia Institute of Technology
• Using deep learning for the study of behavior
  Speaker: Mackenzie Mathis, EPFL
• Building an interoperable cloud ecosystem to support research assets sharing and scientific reproducibility in neuroimaging
  Speaker: Franco Pestilli, University of Texas, Austin
• Discussion

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.

• Introduction to the International Brain Initiative
  Jan Bjaalie, University of Oslo
• Introduction to the IBI Data standards and sharing working group
  Speaker: Kenji Doya, Okinawa Institute of Science and Technology
• Keynote: Title TBD
  Speaker: Kenneth Harris, University College London
• Keynote: Key to fruitful sharing of data is fruitful data
  Speaker: Kristen Harris, The University of Texas at Austin
• Introduction of three Task Forces of the Data Standards and Sharing WG
  - Data Governance White paper Task Force
  Speaker: Franco Pestilli, The University of Texas at Austin
  - Data Catalog Task Force
  Speaker: Sean Hill, The Centre for Addiction and Mental Health
  - Training Task Force
  Speaker: Sharon Crook, Arizona State University
• Discussion on prospects and needs in neural data sharing
• Closing remarks
  Speaker: Maryann Martone, University of California, San Diego

In recent years, leading academic institutes have begun to embrace and define Open Science. Some puzzles have emerged, including the role of intellectual property protection for innovations emerging in academia. Meanwhile, industry has been grappling with these puzzles for years. A notable milestone was the publication of Open Innovation in the 2000s, which highlighted limitations of closed innovation and benefits of more open innovation for companies to consider.

- What does open innovation mean for commercial entities? How does it help and challenge operations? 

- How do we balance intellectual property protection and open innovation? What is the relationship between intellectual property and open innovation?

- How do we incentivize open innovation for industry and academics?

- What are the risks and benefits of open innovation? For companies and for academics.

- Academia and industry often misunderstand each other. From an industry perspective, what’s one thing about open innovation that might surprise academic researchers? 

- Innovation is generally situated within a “supply chain” with the promise of benefits for both the innovators and the downstream consumers. Where do companies fit into a supply chain and how does that affect open innovation? 

- How can academia & industry collaborate in an open innovation context?

• Introduction
  Vijay Iyer, MathWorks, and Randy McIntosh, Baycrest
• Title - TBD
  Speaker: Joe Artuso, OpenBCI
• Title - TBD
  Speaker: Sue Tappan, MBF Bioscience
• Title - TBD
  Speaker: Markus Butz-Ostendorf, Biomax Informatics
• Title - TBD
  Speaker: Ken Evans, InDoc
• Discussion