Syllabus outline

[satra]

Create the order and list of topics to be covered.

[satra]

@jbpoline - please update based on montreal and training meeting discussions

[satra]

from the repronim folder: @mih @jbpoline - let's discuss any changes with respect to the syllabus on this thread:

Introduction to reproducible neuroimaging: motivations
David Kennedy, University of Massachusetts, United States
8:30-10:00
FAIR Data - BIDS datasets
 Jeffrey Grethe [presenting] and Maryann Martone, UCSD, United States

talk 1: Intro to FAIR
exercise: 16 attributes of FAIR - e.g. Is there a clear license, what is a PID, What is meant by metadata, … 
link attributes for 2 modules below 
talk 2: Standardization and BIDS
exercise: dicom to BIDS conversion exercise: basic conversion (tie in w/ ReproIn in next section)
talk 3: FAIR Metadata: searching and using Scicrunch
exercise: BIDS metadata - participants.tsv and semantic annotation
talk 4: Brief Intro to NIDM
exercise: NIDM conversion tool to create sidecar file

10:00-10:15 coffee break
10:15-11:45
Computational basis
Yaroslav Halchenko, Dartmouth College, United States and Michael Hanke, Magdeburg Germany
talk 1: ReproIn : More on this?
Exercise: 
talk 2: Git/GitAnnex/DataLad: 
Exercise: 
talk 3: Everything Else 
Exercise: 
12:00-13:00  Lunch

13:00-14:30  Neuroimaging Workflows
Dorota Jarecka and Satrajit Ghosh, MIT, United States, Camille Maumet, INRIA, France 
talk 1: ReproFlow: Reusable scripts and environments, PROV
Exercise: Run, rinse, and repeat
talk 2: ReproEnv: Virtual machines/ContainersReproPaper, NIDM components
Exercise: Create different environments 
[talk 3: ReproTest: Variability sources (analysis models, operating systems, software versions)]
Exercise: Run analysis with different environments
14:30-14:45  Break

14:45-16:00  Statistics for reproducibility
Celia Greenwood, McGill University, Canada and Jean-Baptiste Poline, McGill University, Canada
Assumes we have a csv file with say 100 subjects and columns like: “age, sex, pheno1, pheno2… “
**Talk : ** Evil p-value : what they are - and are not. Power: what I need to know and understand.
**Exercise**: Let's take a dataset with brain volumes with N =1000 subjects, sample smaller numbers and do some analyses.

**Talk 2: **  Understand what is the Positive Predictive Value (PPV). 
**Exercise 2: ** In this exercise, we will be looking at the different factors that impact the PPV and their effect. 

**Talk 3: **  Understand the effect of choosing what covariates go in the model 
**Exercise 3:** Find some effect with random covariates

16:00-16:30  Conclusion & Getting Feedback
Nina Preuss, Preuss Enterprises, United States

[satra]

meta issue added to track exercises #6 feel free to create individual issues to discuss any specific components.

[jbpoline]

Thanks - just created one issue for the stat section - also checked Michael fsl analyses of the functional data - they run fine :)

[jbpoline]

This schedule is out sync with this

[jgrethe]

Updated mine to be in sync

[satra]

this issue was simply the outline - you should update the metaissue, but more specifically each of your section issues.