@dyt811 first commit

.gitignore

@@ -8,4 +8,4 @@ _site
 .Rproj.user
 .Rhistory
 .RData
-
+.idea/

AUTHORS

@@ -1 +1,3 @@
+Yang Ding ([email protected])
+Paule-J Toussaint ([email protected])
 Mahad Khan ([email protected])

CITATION

@@ -1 +1 @@
-FIXME: describe how to cite this lesson.
+fixme: must update information

README.md

@@ -1,4 +1,4 @@
-# FIXME Lesson title
+# Data Visualization
 
 [![Create a Slack Account with us](https://img.shields.io/badge/Create_Slack_Account-The_Carpentries-071159.svg)](https://swc-slack-invite.herokuapp.com/)
 
@@ -24,9 +24,7 @@ Look for the tag ![good_first_issue](https://img.shields.io/badge/-good%20first%
 
 Current maintainers of this lesson are 
 
-* FIXME
-* FIXME
-* FIXME
+* Yang Ding
 
 
 ## Authors

_config.yml

@@ -10,7 +10,7 @@
 carpentry: "swc"
 
 # Overall title for pages.
-title: "Neuroimaging 3"
+title: "MRI Data Visualization"
 
 # Life cycle stage of the lesson
 # possible values: "pre-alpha", "alpha", "beta", "stable"
@@ -29,7 +29,7 @@ kind: "lesson"
 repository: <USERNAME>/<PROJECT>
 
 # Email address, no mailto:
-email: "[email protected]"
+email: "[email protected]"
 
 # Sites.
 amy_site: "https://amy.software-carpentry.org/workshops"

_episodes/01-introduction.md

@@ -1,18 +1,60 @@
 ---
 title: "Introduction"
-teaching: 5
+teaching: 10
 exercises: 0
 questions:
-- "Lorem ipsum dolor sit amet, consectetur adipiscing elit?"
+- "Why look at your data?"
+- "When to look at your data?"
+- "What to look for in your data?"
+- "How to look at your data?"
 objectives:
-- "First learning objective. (FIXME)"
+- "Understand the importance of visualization"
+- "Know why, when, what and how to look at your data"
 keypoints:
-- "First key point. Brief Answer to questions. (FIXME)"
+- "Garbage in, Garbage Out"
+- "After major processing steps, especially spatial manipulation"
+- "Motion artefacts, signal dropout, distortion, more motion" 
+- "Open data and notice any abnormal patterns."
 ---
-Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Enim eu turpis egestas pretium aenean pharetra. Purus in mollis nunc sed id. Malesuada pellentesque elit eget gravida cum sociis natoque. Ornare quam viverra orci sagittis eu volutpat odio. Aliquam vestibulum morbi blandit cursus. Facilisi nullam vehicula ipsum a arcu cursus vitae. Semper quis lectus nulla at volutpat. Odio pellentesque diam volutpat commodo sed egestas egestas. Eget mauris pharetra et ultrices neque. Cras tincidunt lobortis feugiat vivamus at augue eget arcu dictum. Ut tortor pretium viverra suspendisse potenti nullam ac tortor vitae. Elit eget gravida cum sociis natoque penatibus et magnis dis. Eu non diam phasellus vestibulum lorem sed. Phasellus vestibulum lorem sed risus ultricies tristique nulla aliquet. Nunc mi ipsum faucibus vitae aliquet nec. Dui ut ornare lectus sit. Urna condimentum mattis pellentesque id nibh tortor id aliquet. Eu ultrices vitae auctor eu augue ut lectus arcu bibendum. Est sit amet facilisis magna etiam tempor orci eu lobortis.
 
-Urna id volutpat lacus laoreet non curabitur gravida. Neque aliquam vestibulum morbi blandit cursus risus at. Mattis aliquam faucibus purus in massa. Id consectetur purus ut faucibus pulvinar elementum integer enim neque. Eu non diam phasellus vestibulum lorem sed risus ultricies tristique. Eu scelerisque felis imperdiet proin fermentum leo vel orci. Integer malesuada nunc vel risus. In eu mi bibendum neque egestas congue quisque. Vitae purus faucibus ornare suspendisse. Condimentum lacinia quis vel eros donec ac odio tempor.
+# Data visualization
+
+
+## Why? 
+
+### Because of GIGO...
+> GIGO: Garbage in garbage out. 
+> - George Fueschsel, IBM Programmer and Instructor
+{: .callout}
+
+Neuroimaging data science is about processing data: localisation (data discovery), accession (local or remote, permissions), cleaning (artifact removal), and transformation before putting them through an analysis pipeline. If at any step the data are bad, your entire analysis can be meaningless and misguided at best, and at worst lead to the wrong conclusions. Visualising your data helps detect potential sources of problem and 
+It is one of the most important steps before any kind of data analysis but it is also one of, if not the most commonly ignored.
+Quality check of data is fundamental, and this applies across all neuroimaging modalities (e.g. different imaging acquisitions such as structural and functional neuroimaging), and all analysis types (e.g. univariate / multivariate statistics, connectivity analysis). 
+
+> Fig 
+> Source: "Echo time dependence of BOLD fMRI studies of the piriform cortex" https://www.ncbi.nlm.nih.gov/pubmed/19936572
+> Just imagine what you can do with awesome orbitofrontal cortex fMRI data like these when... there are massive holes in your data. I am sure the group comparisons would look nice.  /s
+{: .challenge}
+
+### Because it will help you create figures.
+
+The other solution for a less intuitive representation would be to create a table. 
+Ever tried to make figures but not sure how to start? Familiarity with these tools will help you get nice figures, QUICKER, EASIER, and less painful.
+
+### Because it will help with your analyses, regardless of the type of data. 
+
+* functional MRI: task or resting state 
+* Structural MRI: T1, T2, PD, etc.
+* Diffusion weighted MRI
+* PET: metabolism, in vivo receptor
+* M/EEG
+
+
+##  
+
+###  
+
+{: . } 
 
-Quam lacus suspendisse faucibus interdum posuere. Porttitor lacus luctus accumsan tortor posuere ac ut consequat semper. Velit ut tortor pretium viverra suspendisse. Felis donec et odio pellentesque diam volutpat commodo sed. Vestibulum lectus mauris ultrices eros in cursus turpis massa tincidunt. Massa tempor nec feugiat nisl pretium fusce id velit. Nec dui nunc mattis enim ut tellus elementum sagittis. At auctor urna nunc id cursus metus aliquam eleifend mi. Augue neque gravida in fermentum. Nibh sit amet commodo nulla facilisi nullam vehicula ipsum a.
 {% include links.md %}
 

_episodes/02-dataformat.md

@@ -0,0 +1,29 @@
+---
+title: "Common Data Formats"
+teaching: 10
+exercises: 0
+questions:
+- "What is the type of data you are looking at?"
+objectives:
+- "Identify the data format."
+keypoints:
+- "First key point. Brief Answer to questions. (FIXME)"
+---
+
+### Quote from Larobina and Murino, 2014, J Digit Imaging, doi:10.1007/s10278-013-9657-9:
+
+Image file formats provide a standardized way to store the information describing an image in a computer file. A medical image data set consists typically of one or more images representing the projection of an anatomical volume onto an image plane (projection or planar imaging), a series of images representing thin slices through a volume (tomographic or multislice two-dimensional imaging), a set of data from a volume (volume or three-dimensional imaging), or multiple acquisition of the same tomographic or volume image over time to produce a dynamic series of acquisitions (four-dimensional imaging). The file format describes how the image data are organized inside the image file and how the pixel data should be interpreted by a software for the correct loading and visualization.
+
+### DICOM (DCM) : https://www.dicomlibrary.com/dicom/
+
+### BIDS : https://bids.neuroimaging.io/
+
+### NIfTI (NII, HDR/IMG) : https://nifti.nimh.nih.gov/
+
+### Analyze (HDR/IMG) : http://imaging.mrc-cbu.cam.ac.uk/imaging/FormatAnalyze
+
+### MINC, MNC : https://en.wikibooks.org/wiki/MINC/SoftwareDevelopment/MINC2.0_File_Format_Reference
+
+### MGH (Massachussetts General Hospital data format) / MGZ (compressed MGH) :
+
+### NRRD (Nearly Raw Raster Data) : http://teem.sourceforge.net/nrrd/format.html; https://readthedocs.org/projects/pynrrd/downloads/pdf/latest/

_episodes/03-bids.md

@@ -0,0 +1,15 @@
+---
+title: "Brain Imaging Data Structure (BIDS)"
+teaching: 10
+exercises: 0
+questions:
+- "What is BIDS?"
+objectives:
+- "First learning objective. (FIXME)"
+keypoints:
+- "First key point. Brief Answer to questions. (FIXME)"
+---
+
+BIDS specific visualizer.
+
+{% include links.md %}

_episodes/04-neuroimaging.md → _episodes/04-dicom.md

@@ -1,5 +1,5 @@
 ---
-title: "Neuroimaging 3"
+title: "DICOM, oldest format"
 teaching: 10
 exercises: 0
 questions:

_episodes/07-neuroimaging.md → _episodes/05-nii.md

@@ -1,5 +1,5 @@
 ---
-title: "Neuroimaging 6"
+title: "Nifti Files"
 teaching: 10
 exercises: 0
 questions:

_episodes/03-neuroimaging.md → _episodes/06-minc.md

@@ -1,9 +1,9 @@
 ---
-title: "Neuroimaging 2"
+title: "Why do I have to deal with MINC file?"
 teaching: 10
 exercises: 0
 questions:
-- "Consectetur adipiscing elit"
+- "How to even open MINC files?"
 objectives:
 - "First learning objective. (FIXME)"
 keypoints:

_episodes/07-4D.md

@@ -0,0 +1,27 @@
+---
+title: "Beyond 3D Data"
+teaching: 10
+exercises: 15
+questions:
+- "How to check high dimensional data? "
+objectives:
+- "Learn how to inspect check 4D fMRI data"
+- "Learn how to inspect check 3D diffusion data"
+keypoints:
+- "First key point. Brief Answer to questions. (FIXME)"
+---
+
+## SPM Register View
+
+* Convoluted. 
+* But works. 
+
+## FSL View
+
+## Display? 
+
+## Others? 
+
+## Poor Choices
+
+{% include links.md %}

_episodes/06-neuroimaging.md → _episodes/08-stats.md

@@ -1,5 +1,5 @@
 ---
-title: "Neuroimaging 5"
+title: "Staticial Maps"
 teaching: 10
 exercises: 0
 questions:

_extras/about.md

@@ -1,5 +1,8 @@
 ---
 title: About
 ---
+This lesson is organized as part of the NeuroImager training curriculum. 
+
+
 {% include carpentries.html %}
 {% include links.md %}

_extras/guide.md

@@ -2,23 +2,8 @@
 title: "Instructor Notes"
 layout: page
 ---
-Lorem ipsum dolor sit amet, consectetur adipiscing elit,
-sed do eiusmod tempor incididunt ut labore et dolore magna
-aliqua. Enim eu turpis egestas pretium aenean pharetra.
-Purus in mollis nunc sed id. Malesuada pellentesque elit
-eget gravida cum sociis natoque. Ornare quam viverra orci
-sagittis eu volutpat odio. Aliquam vestibulum morbi blandit
-cursus. Facilisi nullam vehicula ipsum a arcu cursus vitae.
-Semper quis lectus nulla at volutpat. Odio pellentesque diam
-volutpat commodo sed egestas egestas. Eget mauris pharetra et
-ultrices neque. Cras tincidunt lobortis feugiat vivamus at 
-augue eget arcu dictum. Ut tortor pretium viverra suspendisse
-potenti nullam ac tortor vitae. Elit eget gravida cum sociis
-natoque penatibus et magnis dis. Eu non diam phasellus vestibulum
-lorem sed. Phasellus vestibulum lorem sed risus ultricies tristique
-nulla aliquet. Nunc mi ipsum faucibus vitae aliquet nec. Dui ut
-ornare lectus sit. Urna condimentum mattis pellentesque id nibh tortor
-id aliquet. Eu ultrices vitae auctor eu augue ut lectus arcu bibendum.
-Est sit amet facilisis magna etiam tempor orci eu lobortis.
+1. Practise. 
+2. Ensure software version match. 
+
 
 {% include links.md %}