Reproducible NeuroImaging Training before SFNPosted By: David Kennedy - Aug 29, 2018
Tool/Resource: NITRC Community
Hands on reproducible analysis of neuroimaging data
When: November 2-3, 2018
Where: UCSD, La Jolla, CA
ReproNim Training Workshop - Purpose: An increasing body of evidence points to some issues in reproducibility in biomedical or life sciences, raising concerns in the scientific community. ReproNim has developed a curriculum that will give the students the information, tools and practices to perform repeatable and efficient research and a map of where to find the resources for deeper practical training. This training workshop will introduce material on the key aspects of reproducible brain imaging and will orient attendees using a hands on and practical experience to conduct neuroimaging analyses, using 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.
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. 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 a neuroimaging research project. You should have already completed a neuroimaging analysis or know how to do one.
Reproducibility Basics: Friday Nov. 2. 9am-10:45am. This module guides through three 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 will 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 more reproducible.
FAIR Data: Friday Nov. 2. 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.
Data Processing: Friday Nov. 2. 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.
Statistics: Friday 4pm-5:15pm 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 the current literature and the knowledge to do solid statistical analysis, understand the limitations of p-values, the notion of power and of positive predictive values and how to represent evidence for results.
Reproducible publication project - Saturday 9am-12:00 This is an hands on session: small groups will work with the instructors on the steps to deliver a fully reproducible publication.
Location: University of California San Diego (detail of location will be given by email)
Dates: November 02-03, 2018.
How to register: Eventbrite (https://tinyurl.com/repronim-train): $25.
Online office hours will be held prior to the workshop. Registered attendees will receive information via email.
Instructors: J. Bates, S. Ghosh, J. Grethe, Y. Halchenko, M. Hanke, C. Haselgrove, S. Hodge, D. Jarecka, D. Keator, D. Kennedy, M. Martone, N. Nichols, S. A. Abraham, J.-B. Poline, N. Preuss, M. Travers, and others
This workshop is brought to you by ReproNim: A Center for Reproducible Neuroimaging Computation NIH-NIBIB P41 EB019936