[Neuroimaging] Repronim Training Workshop on Reproducible Research Nov 10-11 before SFN

Satrajit Ghosh satra at mit.edu
Thu Sep 14 18:41:19 EDT 2017

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

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

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/#training>) that will give the students
the information, tools and practices to perform repeatable and efficient

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.

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.

*Location:* George Washington University, Marvin Center, Room 402-404
*Dates:* November 10-11, 2017.
*Costs:* Free - but space is limited - please apply for approval.
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.

*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

*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

*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*
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