BUG: Post Workbench transition fixes

Post Workbench transition fixes:
- Remove the `permalink: index.html` statement for the index header.
- Rename and move the `lesson_links.md` file to the root so that it can
  be found.
- Fix the section relative links to the episodes in the instructor
  notes.
- Fix the LaTeX equations where the transition has mistakenly introduced
  unnecessary additional backslashes.

episodes/diffusion_tensor_imaging.md

@@ -37,11 +37,11 @@ The tensor models the diffusion signal mathematically as:
 
 ![](fig/diffusion_tensor_imaging/diffusion_eqn.png){alt='Diffusion signal equation'}
 
-Where $\\boldsymbol{g}$ is a unit vector in 3D space indicating the direction
+Where $\boldsymbol{g}$ is a unit vector in 3D space indicating the direction
 of measurement and $b$ are the parameters of the measurement, such as the
-strength and duration of diffusion-weighting gradient. $S(\\boldsymbol{g}, b)$
-is the diffusion-weighted signal measured and $S\_{0}$ is the signal conducted
-in a measurement with no diffusion weighting. $\\boldsymbol{D}$ is a
+strength and duration of diffusion-weighting gradient. $S(\boldsymbol{g}, b)$
+is the diffusion-weighted signal measured and $S_{0}$ is the signal conducted
+in a measurement with no diffusion weighting. $\boldsymbol{D}$ is a
 positive-definite quadratic form, which contains six free parameters to be
 fit. These six parameters are:
 
@@ -50,14 +50,14 @@ fit. These six parameters are:
 The diffusion matrix is a variance-covariance matrix of the diffusivity along
 the three spatial dimensions. Note that we can assume that the diffusivity has
 antipodal symmetry, so elements across the diagonal of the matrix are equal.
-For example: $D\_{xy} = D\_{yx}$. This is why there are only 6 free parameters
+For example: $D_{xy} = D_{yx}$. This is why there are only 6 free parameters
 to estimate here.
 
 Tensors are represented by ellipsoids characterized by calculated eigenvalues
-($\\lambda\_{1}, \\lambda\_{2}, \\lambda\_{3}$) and
-($\\epsilon\_{1}, \\epsilon\_{2}, \\epsilon\_{3}$) eigenvectors from the previously
+($\lambda_{1}, \lambda_{2}, \lambda_{3}$) and
+($\epsilon_{1}, \epsilon_{2}, \epsilon_{3}$) eigenvectors from the previously
 described matrix. The computed eigenvalues and eigenvectors are normally
-sorted in descending magnitude (i.e. $\\lambda\_{1} \\geq \\lambda\_{2}$).
+sorted in descending magnitude (i.e. $\lambda_{1} \\geq \lambda_{2}$).
 Eigenvalues are always strictly positive in the context of dMRI and are
 measured in $mm^2/s$. In the DTI model, the largest eigenvalue gives the
 principal direction of the diffusion tensor, and the other two eigenvectors
@@ -172,7 +172,7 @@ the tensor:
 ![](fig/diffusion_tensor_imaging/fa_eqn.png){alt='FA equation'}
 
 Values of FA vary between 0 and 1 (unitless). In the cases of perfect,
-isotropic diffusion, $\\lambda\_{1} = \\lambda\_{2} = \\lambda\_{3}$, the diffusion
+isotropic diffusion, $\lambda_{1} = \lambda_{2} = \lambda_{3}$, the diffusion
 tensor is a sphere and FA = 0. If the first two eigenvalues are equal the
 tensor will be oblate or planar, whereas if the first eigenvalue is larger
 than the other two, it will have the mentioned ellipsoid shape: as diffusion
@@ -239,10 +239,10 @@ The final two metrics we will discuss are axial diffusivity (AD) and radial
 diffusivity (RD). Two tensors with different shapes may yield the same FA
 values, and additional measures such as AD and RD are required to further
 characterize the tensor. AD describes the diffusion rate along the primary axis
-of diffusion, along $\\lambda\_{1}$, or parallel to the axon (and hence, some
+of diffusion, along $\lambda_{1}$, or parallel to the axon (and hence, some
 works refer to it as the *parallel diffusivity*). On the other hand, RD
 reflects the average diffusivity along the other two minor axes (being named
-as *perpendicular diffusivity* in some works) ($\\lambda\_{2}, \\lambda\_{3}$).
+as *perpendicular diffusivity* in some works) ($\lambda_{2}, \lambda_{3}$).
 Both are measured in $mm^2/s$.
 
 ![](fig/diffusion_tensor_imaging/ax_rad_diff.png){alt='Axial and radial diffusivities'}

index.md

@@ -1,5 +1,4 @@
 ---
-permalink: index.html
 site: sandpaper::sandpaper_site
 ---
 

instructors/instructor-notes.md

@@ -25,7 +25,7 @@ neither does it focus on the importance of visualization for that purpose.
 
 ## Lesson design
 
-### [Introduction to Diffusion MRI data](../episodes/introduction.md)
+### [Introduction to Diffusion MRI data](episodes/introduction.md)
 
 - If your workshop includes the [Introduction to MRI and BIDS](https://carpentries-incubator.github.io/SDC-BIDS-IntroMRI/) lesson,
   learners will have the necessary knowledge to better understand how diffusion
@@ -38,7 +38,7 @@ neither does it focus on the importance of visualization for that purpose.
   [DSI Studio], [ExploreDTI], [MRtrix], or [TrackVis], to analyze or visualize
   diffusion MRI data.
 
-### [Preprocessing dMRI data](../episodes/preprocessing.md)
+### [Preprocessing dMRI data](episodes/preprocessing.md)
 
 - Pre-processing in dMRI depends on the available data (acquisition) and the
   quality of the data, so learners should be encouraged to look at their data to
@@ -50,7 +50,7 @@ neither does it focus on the importance of visualization for that purpose.
   [ANTs]) that are used as command-line tools, so learners should be encouraged
   to check their documentation, and adjust the arguments as necessary.
 
-### [Diffusion Tensor Imaging (DTI)](../episodes/diffusion_tensor_imaging.md)
+### [Diffusion Tensor Imaging (DTI)](episodes/diffusion_tensor_imaging.md)
 
 - Learners should be able to understand the use, relevance and limitations of
   the DTI model, both from the a clinical point of view, and a research setting.
@@ -65,7 +65,7 @@ neither does it focus on the importance of visualization for that purpose.
   DTI mistakenly being used interchangeably, partially due to the extensive use
   of the DTI model in clinical practice).
 
-### [Tractography](../episodes/tractography.md)
+### [Tractography](episodes/tractography.md)
 
 - Make sure to explain the difference between the actual biological white matter
   fibers and streamlines in tractograms, and why tractography is not quantitative