ReproNim Training Workshop: Nov 10 full day - Nov 11 morning - George Washington
University

Register and information at: ​https://tinyurl.com/repronim-sfn17

Purpose:
The issue of lack of reproducibility has been described in several scientific
domains for several years, raising concerns specifically in the life science
community. ReproNim has developed a curriculum
(http://www.reproducibleimaging.org/#trai...) that will give the students the
information, tools and practices to perform repeatable and efficient research.

This training workshop will introduce material on the critical aspects
of reproducible brain imaging and will orient attendees using a hands on and
practical experience to conduct neuroimaging analyses with the next generation
of tools.

By the end of this course, the student will be aware of training materials and
concepts necessary to perform reproducible research in neuroimaging. The student
will be able to reuse these materials to conduct local workshops and training
and be able to customize the training for their specific scenario.

Prerequisites:
If you are a student, postdoc or researcher in life science who directly works
with neuroimaging data - or wish to work with these data, and you have some
basic computational background, this training workshop is for you. For instance,
you should have already done either some R, or Python, or Matlab or Shell scripting,
or have used standard neuroimaging tools (SPM, FSL, Afni, FreeSurfer, etc) and
be engaged in neuroimaging research projects. You should have already completed
a neuroimaging analysis or know how to do one.

Logistics:
Location: George Washington University, Marvin Center, Room 402-404
https://events-venues.gwu.edu/meeting-ro...
Dates: November 10-11, 2017.
Costs: Free - but space is limited - please apply for approval.
Schedule:
Friday November 10th:
    8:30-9am: Introduction to the course and participants "setup"
    9am-10:45: Reproducibility Basics (Module 0)
    10:45-11am : Coffee break
    11am-12:45 : FAIR data (Module 1)
    12:45-2pm : Lunch+coffee
    2pm-3:45: Data Processing (Module 2)
    3:45-4pm: coffee break
    4pm-5:45pm: Statistics for reproducible analyses (Module 3)
    5:45-6:15: Questions and answers and feedback session
Saturday November 11th:
    9am-12pm: The Re-executable Micro Publication Challenge
During this time, we will propose a small challenge around producing
an entirely re-executable neuroimaging analysis from fetching data to
producing statistical results. This will also be a time with close
interactions with neuroimaging experts in data handling and analysis.
    12pm-12:30: Closing session: feedback and future: "become a trainer".
Weekly online office hours will be held prior to the workshop. Registered attendees
will receive information via email.

Modules:
Module 0 - Reproducibility Basics: Friday Nov. 10. 9am-10:45am.
This module guides through three somewhat independent topics, which are in the
heart of establishing and efficiently using common generic resources: command
line shell, version control systems (for code and data), and distribution package
managers. Gaining additional skills in any of those topics could help you to
not only become more efficient in your day-to-day research activities, but also
would lay foundation in establishing habits to make your work actually more
reproducible.

Module 1 - FAIR Data: Friday Nov. 10. 11am-12:45.
This module provides an overview of strategies for making research outputs available
through the web, with an emphasis on data. It introduces concepts such persistent
identifiers, linked data, the semantic web and the FAIR principles. It is designed
for those with little to no familiarity with these concepts. More technical
discussions can be found in the reference materials.

Module 2 - Data Processing: Friday Nov. 10. 2pm-3:45pm.
This module teaches you to perform reproducible analysis, how to preserve the
information, and how to share data and code with others. We will show an example
framework for reproducible analysis, how to annotate, harmonize, clean, and
version brain imaging data, how to create and maintain reproducible computational
environments for analysis and use dataflow tools to capture provenance and perform
efficient analyses (docker) and other tools.

Module 3 - Statistics: Friday 4am-5:45
The goal of this module is to teach brain imagers about the statistical aspects
of reproducibility. This module should give you a critical eye on most of the
current literature and the knowledge to do solid work, understand exactly what
is a p-value and its limitation to represent evidence for results, practical
notion of power and associated tools, etc.

Instructors: J. Bates, S. Ghosh, J. Grethe, Y. Halchenko, C. Haselgrove, S.
Hodge, D. Jarecka, D. Keator, D. Kennedy, M. Martone, N. Nichols, S. Padhy,
JB Poline, N. Preuss, M. Travers

This workshop is brought to you by ReproNim: A center for Reproducible Neuroimaging Computation NIH-NIBIB P41 EB019936