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