diff --git a/user-doc/tutorials/a-trieste-1.txt b/user-doc/tutorials/a-trieste-1.txt
index c9e3b1b1662ad6c6141a96359ebfe9c1bcf07c35..0b20d690aba6fc5fe0ce5c967d5621bddaabb314 100644
--- a/user-doc/tutorials/a-trieste-1.txt
+++ b/user-doc/tutorials/a-trieste-1.txt
@@ -36,7 +36,7 @@ Please only look at these files after you have tried to solve the problems yours
 \section trieste-1-intro Introduction
 
 This tutorial asks you to compute a variety of different collective variables using PLUMED for a particular trajectory and to compare the files and graphs that you obtain with the correct ones that are shown online.  Compared to some of the other tutorials that are available here this tutorial contains considerably less guidance so in doing this tutorial you will have to
-learn how to consult the manual.  If you would like a more guided introduction to PLUMED it might be better to start with the tutorials \ref belfast-1 or \ref lugano-1.  
+learn how to consult the manual.  If you would like a more guided introduction to PLUMED it might be better to start with the tutorials \ref belfast-1 or \ref marvel-1.
 Also notice that, whereas this tutorial was tested using a pre-release
 version of PLUMED 2.4, it should be completely feasible using
 PLUMED 2.3.
diff --git a/user-doc/tutorials/lugano-1.txt b/user-doc/tutorials/marvel-1.txt
similarity index 95%
rename from user-doc/tutorials/lugano-1.txt
rename to user-doc/tutorials/marvel-1.txt
index 693cf9196c7ab4702b4f29b119be8ab18e85833d..9733ed092c34e187820b71b852d067d0b21c75ac 100644
--- a/user-doc/tutorials/lugano-1.txt
+++ b/user-doc/tutorials/marvel-1.txt
@@ -1,13 +1,13 @@
 /**
-\page lugano-1 Lugano tutorial: Analyzing CVs
+\page marvel-1 MARVEL tutorial: Analyzing CVs
 
-\section lugano-1-aims Aims
+\section marvel-1-aims Aims
 
 The aim of this tutorial is to introduce you to the PLUMED syntax. We will go through the writing of input files to calculate
 and print simple collective variables. We will then discuss how we can use PLUMED to analyze a trajectory by calculating ensemble 
 averages, histograms and free energy surfaces. 
 
-\section lugano-1-lo Learning Outcomes
+\section marvel-1-lo Learning Outcomes
 
 Once this tutorial is completed students will:
 
@@ -16,16 +16,16 @@ Once this tutorial is completed students will:
 - Know how to write a PLUMED input file that can be used to calculate an ensemble average.
 - Know how to write a PLUMED input file that can be used to calculate a histogram.  Students will also learn how this histogram can be converted into a free energy surface.   
 
-\section lugano-1-resources Resources
+\section marvel-1-resources Resources
 
-The <a href="tutorial-resources/lugano-1.tar.gz" download="lugano-1.tar.gz"> tarball </a> for this project contains the following files:
+The <a href="tutorial-resources/marvel-1.tar.gz" download="marvel-1.tar.gz"> tarball </a> for this project contains the following files:
 
 - trajectory-short.xyz : a (short) trajectory for a 16 residue protein in xyz format. All the calculations with plumed driver that will be performed during this tutorial will use this trajectory.
 - template.pdb : a single frame from the trajectory that can be used in conjunction with the \ref MOLINFO command
 - in : An input file for the simplemd code that forms part of PLUMED 
 - input.xyz : A configuration file for Lennard-Jones solid with an FCC solid structure
 
-\section lugano-1-instructions Instructions
+\section marvel-1-instructions Instructions
 
 PLUMED2 is a library that can be incorporated into many MD codes by adding a relatively simple and well documented interface.
 Once it is incorporated you can use PLUMED2 to perform a variety of different analyzes on the fly and to bias 
@@ -56,7 +56,7 @@ a PLUMED input file.  Before we get on to writing input files for PLUMED there i
 on what the other PLUMED2 tools do and instructions for how to interface PLUMED with an MD code.  You may like to look at this information
 now or you might prefer to return after you have finished the exercises here.
 
-\subsection lugano-1-units PLUMED2's internal units
+\subsection marvel-1-units PLUMED2's internal units
 
 By default the PLUMED inputs and outputs quantities in the following units:
 
@@ -66,7 +66,7 @@ By default the PLUMED inputs and outputs quantities in the following units:
 
 If you want to change these units you can do this using the \ref UNITS keyword. 
 
-\subsection lugano-1-introinput Introduction to the PLUMED input file
+\subsection marvel-1-introinput Introduction to the PLUMED input file
 
 Many input files for PLUMED provides specifications for one or more CVs.  These specifications are then followed by an instruction to PLUMED
 to \ref PRINT these CVs and a termination line.
@@ -96,7 +96,7 @@ plumed driver --plumed plumed.dat --ixyz trajectory-short.xyz --length-units 0.1
 \endverbatim
 
 Notice the --length-units 0.1 flag here.  This tells PLUMED to convert the positions in the xyz file here, which are in Angstroms, into nm, which
-remember are \ref lugano-1-units  
+remember are \ref marvel-1-units  
 
 When this command finishes running you should have a file called COLVAR. If you look at it's contents (using the command more COLVAR for instance) you will 
 find that the first two lines read:
@@ -119,7 +119,7 @@ as the atoms in our system move about in accordance with the various forces that
 
 Right so hopefully that wasn't too hard.  What we are going to next is we are going to try to understand the input file that we have written a bit better.
 
-\subsection lugano-1-io The PLUMED input syntax
+\subsection marvel-1-io The PLUMED input syntax
 
 The input file that we issued in the last section looked something like this:
 
@@ -168,7 +168,7 @@ This input demonstrates the key idea of the PLUMED syntax.  Quantities calculate
 they can be reused when performing other commands.  This idea is discussed in more depth in the following video https://www.youtube.com/watch?v=PxJP16qNCYs.  
 If you understand this idea though you are 90% of the way to understanding how to used PLUMED.  Well done. 
 
-\subsection lugano-1-com Center of mass positions
+\subsection marvel-1-com Center of mass positions
 
 When calculating many collective variables it is useful to not think in terms of calculating them directly based on the positions of a number of atoms.
 It is useful to instead think of them as being calculated from the position of one or more virtual atoms whose positions are generated based on the position
@@ -190,7 +190,7 @@ ENDPLUMED
 \endverbatim
 
 Make a PLUMED input containing the above input and execute it on the trajectory that you downloaded at the start of the exercise by making use of the commands
-in section \ref lugano-1-introinput
+in section \ref marvel-1-introinput
 
 Before we turn to analyzing what is output from this calculation there are a few things to note about this input file.  Firstly, I should describe what this file
 instructs PLUMED to do.  It tells PLUMED to:
@@ -246,7 +246,7 @@ Lets summarize what we have learned from these sections thus far.  We have seen
 
 Now, obviously, PLUMED can do much more than calculate the distances between pairs of atoms as we will start to see that in the following sections.  
 
-\subsection lugano-1-torsions Calculating torsion angles
+\subsection marvel-1-torsions Calculating torsion angles
 
 In the previous sections we have seen how we can use PLUMED to calculate distances and how by plotting these distances we can begin to simplify the 
 high dimensional data contained in a trajectory.  Obviously, calculating a \ref DISTANCE is not always the best way to simplify the information contained 
@@ -269,7 +269,7 @@ ENDPLUMED
 \endverbatim
 
 Copy this input to a PLUMED input file and use it to analyze the trajectory you downloaded at the start of this exercise by using the commands
-described in section \ref lugano-1-introinput then plot the CV output using gnuplot.
+described in section \ref marvel-1-introinput then plot the CV output using gnuplot.
 
 As you can hopefully see calculating \ref TORSION values and other CVs is no more difficult than calculating \ref DISTANCE values.  In fact it is 
 easier as generally when you calculate the torsion angles of a protein you often wish to calculate particular, named torsion angles (i.e. the \f$\phi\f$ and \f$\psi\f$
@@ -284,10 +284,10 @@ PRINT ARG=phi6,psi8 FILE=colvar
 \endverbatim
 
 Copy this input to a PLUMED input file and use it to analyze the trajectory you downloaded at the start of this exercise by using the commands
-described in section \ref lugano-1-introinput then plot the CV output using gnuplot.  Notice that you will need the template.pdb file you downloaded 
+described in section \ref marvel-1-introinput then plot the CV output using gnuplot.  Notice that you will need the template.pdb file you downloaded 
 at the start of this exercise in order for this to run.
 
-\subsection lugano-1-gyration An exercise with the radius of gyration
+\subsection marvel-1-gyration An exercise with the radius of gyration
 
 Lets now see if you can use everything you have learned to setup a PLUMED input file of your own.  What I would like you to do is to write
 a PLUMED input file that measures the Radius of Gyration \ref GYRATION for the configurations in each of the frames in the trajectory that
@@ -303,7 +303,7 @@ ca: GROUP ATOMS=9,16,31,55,69,90,102,114,124,138,160,174,194,208,224,238
 
 Now 'ca' is not a virtual atom but a simple list of atoms.
 
-\subsection lugano-1-multicol Coordination numbers
+\subsection marvel-1-multicol Coordination numbers
 
 In the previous sections we have learned how PLUMED can be used to calculate simple functions of atomic positions such as the 
 \ref DISTANCE between pairs of atoms.  As discussed here (https://www.youtube.com/watch?v=iDvZmbWE5ps) many of the more complicated 
@@ -330,12 +330,12 @@ PRINT ARG=d1.* FILE=colvar
 \endverbatim  
 
 Copy this input file to a PLUMED input file. Before using it to analyze the trajectory that you downloaded at the start of the exercise using the commands 
-described in section \ref lugano-1-introinput try to guess what value this coordination number will take.  Hint: what element is atom 9? 
+described in section \ref marvel-1-introinput try to guess what value this coordination number will take.  Hint: what element is atom 9? 
 
 Now see if you can adjust the above input to calculate the coordination number of atom 5.  What is the coordination number of this atom and why does it take 
 this value?
 
-\subsection lugano-1-multicol2 Multicolvar
+\subsection marvel-1-multicol2 Multicolvar
 
 In the previous section we exploited a feature of PLUMED known as multicolvar when calculating the coordination number.  When using this feature
 we are not confined to simply calculating coordination numbers.  For instance the input below allows us to calculate a number of distances and to then
@@ -352,12 +352,12 @@ ENDPLUMED
 \endverbatim
 
 Try to copy this input now and to use it to analyze the trajectory you downloaded at the start of the exercise using the commands 
-described in section \ref lugano-1-introinput.  
+described in section \ref marvel-1-introinput.  
 
 Multicolvar is not just for \ref DISTANCES though.  The infrastructure of multicolvar has been used to develop many PLUMED2 collective variables. 
 One interesting example is the set of Secondary Structure CVs (\ref ANTIBETARMSD, \ref PARABETARMSD and \ref ALPHARMSD).  You can use the input 
 below to calculate the degree of anti-beta secondary structure in each of the trajectory frames by copying this input to a PLUMED input file
-and by exploiting the commands to run driver that were described in section \ref lugano-1-introinput. 
+and by exploiting the commands to run driver that were described in section \ref marvel-1-introinput. 
 
 \verbatim
 MOLINFO STRUCTURE=template.pdb
@@ -371,7 +371,7 @@ ENDPLUMED
 We can do a large number of other things with multicolvar.  If you are interested this topic is described in more detail in the tutorial:
 \ref belfast-10.
 
-\subsection lugano-1-averagesintro Understanding the need for ensemble averages
+\subsection marvel-1-averagesintro Understanding the need for ensemble averages
 
 In the previous sections we have learned how we can use PLUMED to calculate collective variables from simulation trajectories and have seen how,
 by plotting how these collective variables change as a function of time, we can get a handle on what happens during the trajectory.  Generally
@@ -464,7 +464,7 @@ where the sum runs over the set of \f$T\f$ microstates, \f$q(t)\f$, in our traje
 of the collective variable from the positions of the atoms in the microstate.  When we do so we find that the values of the ensemble averages from different 
 trajectories are reasonably consistent and certainly much more consistent than the set of instantaneous CV values.
 
-\subsection lugano-1-averages Calculating ensemble averages using PLUMED
+\subsection marvel-1-averages Calculating ensemble averages using PLUMED
 
 Repeat the steps from the previous section that were used to run the two MD calculations with slightly different input configurations.  This time, however, your 
 PLUMED input should look like this:
@@ -510,7 +510,7 @@ PRINT ARG=d1,d1a FILE=colvar STRIDE=10
 
 Based on what you see when you plot the colvar file and the information on the page about the \ref AVERAGE command what is the CLEAR=10 keyword telling PLUMED to do?
 
-\subsection lugano-1-histograms Calculating histograms
+\subsection marvel-1-histograms Calculating histograms
 
 Most of the time, we are not really interested in calculating ensemble averages for particular collective variables.  What we would really like 
 is the probability that the collective variable takes a particular value or set of values.  In other words, and as discussed in the following video,
@@ -557,7 +557,7 @@ familiar with PLUMED you will find that these labels can refer to a range of dif
 Lets now see if we can bring together everything we have learned in this tutorial in order to analyze the protein trajectory that was downloaded at the start of 
 the exercise.
 
-\subsection lugano-1-analysis A histogram for the protein trajectory
+\subsection marvel-1-analysis A histogram for the protein trajectory
 
 We are going to calculate the \ref HISTOGRAM from our protein trajectory as a function of two different collective variables:
 \ref ANTIBETARMSD and the average distance between the ca atoms of our protein backbone.  The input that allows us to 
@@ -579,7 +579,7 @@ hh: HISTOGRAM ARG=abeta.lessthan,dd.mean KERNEL=DISCRETE GRID_MIN=0,0.8 GRID_MAX
 DUMPGRID GRID=hh FILE=histo
 \endverbatim
 
-Try running the input above on the trajectory that you downloaded at the start of this exercise by using the commands detailed in section \ref lugano-1-introinput.
+Try running the input above on the trajectory that you downloaded at the start of this exercise by using the commands detailed in section \ref marvel-1-introinput.
 You can plot the two dimensional histogram output using the following commands:
 
 \verbatim
@@ -617,18 +617,18 @@ DUMPGRID GRID=fes FILE=fes.dat
 
 Notice though that even when we do this complicated looking calculation we are still, underneath it all, calculating functions of a large number of ensemble averages.
 
-\section lugano-1-conc Conclusions and further work
+\section marvel-1-conc Conclusions and further work
 
 If you have worked through all of this tutorial make sure that you have understood it by ensuring that you understand what the list of learning outcomes
-in section \ref lugano-1-lo means and that you can use PLUMED to perform all these tasks.  In terms of further work you should investigate issues related 
+in section \ref marvel-1-lo means and that you can use PLUMED to perform all these tasks.  In terms of further work you should investigate issues related 
 to the convergence of estimates of ensemble averages such as block averaging.  You might like to investigate how long your simulations have to be in order 
 to obtain reliable estimates of the ensemble average for a collective variable and reliable estimates for the free energy as a function of a collective variable.
 Alternatively, you might like to explore other collective variables that could be used to analyze the protein trajectory that you have worked on in this tutorial.
 
 */
 
-link: @subpage lugano-1
+link: @subpage marvel-1
 
 description: This tutorial explains how to use PLUMED to analyze CVs
 
-additional-files: lugano-1
+additional-files: marvel-1
diff --git a/user-doc/tutorials/lugano-1/in b/user-doc/tutorials/marvel-1/in
similarity index 100%
rename from user-doc/tutorials/lugano-1/in
rename to user-doc/tutorials/marvel-1/in
diff --git a/user-doc/tutorials/lugano-1/input.xyz b/user-doc/tutorials/marvel-1/input.xyz
similarity index 100%
rename from user-doc/tutorials/lugano-1/input.xyz
rename to user-doc/tutorials/marvel-1/input.xyz
diff --git a/user-doc/tutorials/lugano-1/template.pdb b/user-doc/tutorials/marvel-1/template.pdb
similarity index 100%
rename from user-doc/tutorials/lugano-1/template.pdb
rename to user-doc/tutorials/marvel-1/template.pdb
diff --git a/user-doc/tutorials/lugano-1/trajectory-short.xyz b/user-doc/tutorials/marvel-1/trajectory-short.xyz
similarity index 100%
rename from user-doc/tutorials/lugano-1/trajectory-short.xyz
rename to user-doc/tutorials/marvel-1/trajectory-short.xyz
diff --git a/user-doc/tutorials/lugano-2.txt b/user-doc/tutorials/marvel-2.txt
similarity index 94%
rename from user-doc/tutorials/lugano-2.txt
rename to user-doc/tutorials/marvel-2.txt
index 7c492082e20129f4ada8edb8380f63f49f6f6691..534cc46af4272ecfee8e9c8c853195be9a49af8c 100644
--- a/user-doc/tutorials/lugano-2.txt
+++ b/user-doc/tutorials/marvel-2.txt
@@ -1,12 +1,12 @@
 /** 
-\page lugano-2 Lugano tutorial: Path CVs
+\page marvel-2 MARVEL tutorial: Path CVs
 
-\section lugano-2-aims Aims
+\section marvel-2-aims Aims
 
 Consider the two overlain protein structures that are shown in the figure below.  
 Can you see the difference between these two structures?  Can you think of a collective variable that could be used to study the substantial change in structure?
 
-\anchor lugano-2-cdk-fig
+\anchor marvel-2-cdk-fig
 \image html belfast-2-cdk.png "CDK2 conformational change, PDB code 2C5X and 2C5Y."
 
 Your answers to the questions posed above are hopefully: yes I can see the difference between the two structures - the upper loop is radically different in the two 
@@ -26,7 +26,7 @@ tutorial study how these coordinates work in a two-dimensional space as this wil
 you will be able to use what you learn from this tutorial to generalize these ideas so that you can use \ref PATH and \ref PCAVARS in 
 higher-dimensional spaces.  
 
-\section lugano-2-lo Learning Outcomes
+\section marvel-2-lo Learning Outcomes
 
 Once this tutorial is completed students will:
 
@@ -36,9 +36,9 @@ Once this tutorial is completed students will:
 - be able to write PLUMED input files that calculate \ref PATH collective variables for a range of different metrics.
 - be able to measure the quality of a transition state by calculating the isocommittor.
 
-\section lugano-2-resources Resources
+\section marvel-2-resources Resources
 
-The <a href="tutorial-resources/lugano-2.tar.gz" download="lugano-2.tar.gz"> tarball </a> for this project contains the following files:
+The <a href="tutorial-resources/marvel-2.tar.gz" download="marvel-2.tar.gz"> tarball </a> for this project contains the following files:
 
 - transformation.pdb : a trajectory that shows the transition between the \f$C_7ax\f$ and \f$C_7eq\f$ conformers of alanine dipeptide.
 - pca-reference.pdb : a file that gives the start and end points of the vector that connects the \f$C_7ax\f$ and \f$C_7eq\f$ conformers.  This file contains the positions of the atoms in these two structures.
@@ -46,29 +46,29 @@ The <a href="tutorial-resources/lugano-2.tar.gz" download="lugano-2.tar.gz"> tar
 - PATH-isocommittor : a directory containing the files required to run isocommittor simulations that monitor the values of the \ref PATH collective variable \f$S(X)\f$.
 - 2CV-isocommittor : a directory containing the files required to run isocommittor simulations that monitor the values of the \ref PATH collective variables \f$S(X)\f$ and \f$Z(X)\f$ 
 
-\section lugano-2-instructions Instructions
+\section marvel-2-instructions Instructions
 
 In this tutorial we are going to be considering a conformational transition of alanine dipeptide.  In particular we are going to be considering the transition
 between the two conformers of this molecule shown below:
 
-\anchor lugano-2-transition-fig
+\anchor marvel-2-transition-fig
 \image html belfast-2-transition.png  "Two metastable states of alanine dipeptide are characterized by their Ramachandran dihedral angles."
 
 Alanine dipeptide is a rather well-studied biomolecule (in fact it is an over studied molecule!).  It is well known that you can 
 understand the inter-conversion of the two conformers shown above by looking at the free energy surface as a function of the \f$\phi\f$ and \f$\psi\f$ 
 Ramachandran angles as shown below:
 
-\anchor lugano-2-rama-fig
+\anchor marvel-2-rama-fig
 \image html belfast-2-rama.png  "The Free energy landscape of alanine dipeptide in Ramachandran angles in the CHARMM27 force field."
  
 In this tutorial we are not going to use these coordinates to study alanine dipeptide.  Instead we are going to see if we can find a single collective 
 variable that can distinguish between these two states.
 
-\subsection lugano-2-pca1 PCA coordinates
+\subsection marvel-2-pca1 PCA coordinates
 
-Consider the free energy surface shown in figure \ref lugano-2-rama-fig.  It is clear that either the \f$\phi\f$ (\f$x\f$-axis) or the 
+Consider the free energy surface shown in figure \ref marvel-2-rama-fig.  It is clear that either the \f$\phi\f$ (\f$x\f$-axis) or the 
 \f$\psi\f$ (\f$y\f$-axis) angle of the molecule can be used to distinguish between the two configurations shown in 
-figure \ref lugano-2-transition-fig.  Having said that, however, given the shape of landscape and the associated thermal fluctuations we would 
+figure \ref marvel-2-transition-fig.  Having said that, however, given the shape of landscape and the associated thermal fluctuations we would 
 expect to see in the values of these angles during a typical simulation, it seems likely that \f$\phi\f$ will do a better job
 at distinguishing between the two configurations.   \f$\psi\f$ would most likely be a bad coordinate as when the molecule is in the \f$C_7eq\f$ 
 configuration the \f$\psi\f$ angle can fluctuate to any value with only a very small energetic cost.  If we only had information on how the \f$\psi\f$
@@ -98,7 +98,7 @@ monotonically as the transition progresses.
 We can perhaps come up with a better coordinate that incorporates changes in both \f$\phi\f$ and \f$\psi\f$ by using the coordinate illustrated in the figure below.
 
 \anchor pca-figure
-\image html lugano-2-pca-coordinates.png "An illustration showing how PCAVARS coorditates work.  The vector connecting some reference state to any state the system is in can be in (purple and orange points) can be projected onto the vector connecting the two states of interest (black arrow) by using the dot product of the vectors shown here."
+\image html marvel-2-pca-coordinates.png "An illustration showing how PCAVARS coorditates work.  The vector connecting some reference state to any state the system is in can be in (purple and orange points) can be projected onto the vector connecting the two states of interest (black arrow) by using the dot product of the vectors shown here."
 
 We can even use PLUMED to calculate this coordinate by using the input shown below:
 
@@ -147,7 +147,7 @@ We cannot, however, do this in practice (we also shouldn't really use the previo
 to define our vectors here are periodic.  In this next section we will thus look at how we can avoid this problem of periodicity by working 
 in a higher dimensional space. 
 
-\subsection lugano-2-metric1 PCA with the RMSD metric
+\subsection marvel-2-metric1 PCA with the RMSD metric
 
 In the previous section I showed how we can use the projection of a displacement on a vector as a collective variable.  I demonstrated this
 in a two dimensional space as this makes it easy to visualize the vectors involved.  We are not forced to work with two dimensional vectors,
@@ -176,10 +176,10 @@ s = \sum_{i=1}^{3N} (x^{(2)}_i - x^{(1)}_i ) (x^{(3)}_i - x^{(1)}_i )
 \f]
 in a manner of speaking.  The point is that we would not want to calculate exactly this quantity because the vectors of displacements that
 are calculated in this way includes both rotational and translational motion.  This is a problem as the majority of the change
-in moving from the \f$C_7ax\f$ configuration shown in figure \ref lugano-2-transition-fig to the \f$C_7eq\f$ configuration shown in figure 
-\ref lugano-2-transition-fig comes from the translation of all the atoms.  To put this another way if I had, in figure \ref lugano-2-transition-fig,
+in moving from the \f$C_7ax\f$ configuration shown in figure \ref marvel-2-transition-fig to the \f$C_7eq\f$ configuration shown in figure 
+\ref marvel-2-transition-fig comes from the translation of all the atoms.  To put this another way if I had, in figure \ref marvel-2-transition-fig,
 shown two images of the \f$C_7ax\f$ configuration side by side the displacement in the positions of the atoms in those two structures would be
-similar to the displacement of the atoms in \ref lugano-2-transition-fig as as the majority of the displacement in the vector of atomic positions 
+similar to the displacement of the atoms in \ref marvel-2-transition-fig as as the majority of the displacement in the vector of atomic positions 
 comes about because I have translated all the atoms in the molecule
 rightwards by a fixed amount.  I can, however, remove these translational displacements from consideration when calculating these vectors.  In addition,
 I can also remove any displacements due rotation in the frame of reference of the molecule.  If you are interested in how this is done in practice you can 
@@ -188,7 +188,7 @@ use the OPTIMAL metric we are calculating a vector which tells us how far the at
 B in a way that excludes any displacements due to translation of the center of mass of the molecule or any displacements that occur due to rotation
 of the Cartesian frame. 
  
-\subsection lugano-2-iso The isocommittor surface
+\subsection marvel-2-iso The isocommittor surface
 
 In the previous sections I have been rather loose when talking about better and worse collective variables in that I have not been clear in my distinction
 between what makes a collective variable good and what makes a collective variable bad.  In this section I thus want to discuss one method that we can use
@@ -261,18 +261,18 @@ close to 50%.  In fact the CV is not even particularly good at capturing the dif
 it is perhaps clear why.
 
 \anchor ala-tstate
-\image html "lugano-2-trans-state.png" "The PCAVARS coordinate and the transition state are highlighted on this figure.  As you can see the coordinate does not pass through the transition state" 
+\image html "marvel-2-trans-state.png" "The PCAVARS coordinate and the transition state are highlighted on this figure.  As you can see the coordinate does not pass through the transition state" 
 
 You can see the location of the saddle point between these two states in this surface and it is very clear that the vector connecting the \f$C_7eq\f$ state to the 
 \f$C_7ax\f$ state does not pass through this point.  In fact it would be extremely fortuitous if a vector connecting an initial state and a final state also passed
 through the intermediate transition state between them.  We can, after all, define the equation of straight line (a vector) if we are given only two points 
 on it.  In the next section we are thus going to see how we can resolve this problem by introducing a non-linear (or curvilinear) coordinate.
 
-\subsection lugano-2-pathcvs Path collective variables
+\subsection marvel-2-pathcvs Path collective variables
 
 Consider the black path that connects the \f$C_7ax\f$ and \f$C_7eq\f$ states in the free energy shown below:
 
-\anchor lugano-2-good-bad-path-fig
+\anchor marvel-2-good-bad-path-fig
 \image html belfast-2-good-bad-path.png "Examples of good and bad paths:  the black path follows the minimum free energy path connecting the two metastable states, while the red path connects the two states directly via a linear path that passes through high energy"
 
 This black pathways appears to be the "perfect" coordinate for modelling this conformational transition as it passes along the lowest 
@@ -305,7 +305,7 @@ configurations that moved to \f$C_7ax\f$ from those that moved to \f$C_7eq\f$.
 trajectories that move rightwards and the number that move leftwards.  We are thus still a long way from unambiguously identifying the location of the 
 transition state ensemble for this system.
 
-\subsection lugano-2-pathcvs2 The mathematics of path collective variables  
+\subsection marvel-2-pathcvs2 The mathematics of path collective variables  
 
 Let's now take a moment to discuss the mathematics of these coordinates, which is not so complicated if we think about what they do through an analogy.
 Suppose that you were giving your friend instructions as to how to get to your house and lets suppose these instructions read something like this:
@@ -331,10 +331,10 @@ In this expression \f$\vert X-X_i \vert\f$ is the distance between the instantan
 will thus be the one that corresponds to the point that is closest to where the system currently lies.  In other words, \f$S(X)\f$, measures the position 
 on a (curvilinear) path that connects two states of interest as shown in red in the figure below:
 
-\anchor lugano-2-ab-sz-fig
+\anchor marvel-2-ab-sz-fig
 \image html belfast-2-ab-sz.png "The S variable can be thought as the length of the red segment, while the Z variable is the length of the green one." 
 
-\subsection lugano-2-pathz The Z(X) collective variable
+\subsection marvel-2-pathz The Z(X) collective variable
 
 You may reasonably ask what the purpose these \ref PATH collective variables variables serve given that in this case they seem to do no better than 
 \f$\phi\f$ when it comes to the tests we have performed on calculating the isocommittor.  To answer this question we are going to run one final set of 
@@ -368,7 +368,7 @@ on the y-axis:
 Z(X)=-\frac{1}{\lambda}\log (\sum_{i=1}^{N} \ \exp^{-\lambda \vert X-X_i \vert })
 \f]
 
-What this quantity measures is shown in green in the figure \ref lugano-2-ab-sz-fig.  Essentially it measures the distance between the instantaneous configuration 
+What this quantity measures is shown in green in the figure \ref marvel-2-ab-sz-fig.  Essentially it measures the distance between the instantaneous configuration 
 the system finds itself in and the path that is marked out using the way markers.  If you plot the data using script_path.gplt what you thus see is that the 
 system never moves very far from the path that is defined using the \ref PATH command.  In short the system follows this path from the transition state back to 
 either the \f$C_7eq\f$ or \f$C_7ax\f$ configuration.
@@ -392,7 +392,7 @@ this distance can be calculated using the \ref RMSD distance or it can be calcul
 in collective variable values (see \ref TARGET).  Changing the manner in which the distance between path way points is calculated thus provides a 
 way to control the level of detail that is incorporated in the description of the reaction \ref PATH.
 
-\subsection lugano-2-pathcv-find Optimizing path collective variables
+\subsection marvel-2-pathcv-find Optimizing path collective variables
 
 Hopefully the previous sections have allowed you to understand how \ref PATH collective variables work and the sorts of problems they might be used to 
 solve.  If you have one of these problems to solve the next reasonable question to ask is: how to collect the set of reference frames that serve as the 
@@ -405,25 +405,25 @@ this that we will focus on in this section.  The first thing that you will need
 distances between way markers.  That is to say you will have to calculate the distance \f$\vert X_j - X_i \vert\f$ between each pair of frames.
 The values of the distance in this matrix for a good \ref PATH are shown in the figure below:
 
-\anchor lugano-2-good-matrix-fig
+\anchor marvel-2-good-matrix-fig
 \image html belfast-2-good-matrix.png "A good distance matrix for path variables has the gull wing shape shape shown here." 
 
 For contrast the values of the distances in this matrix for a bad \ref PATH are shown in the figure below:
 
-\anchor lugano-2-bad-matrix-fig
+\anchor marvel-2-bad-matrix-fig
 \image html belfast-2-bad-matrix.png "A bad distance matrix for path variables is rather irregular."
 
 If the distance matrix looks like the second of the two figures shown above this is indicates that the frames in the \ref PATH that have been chosen 
 are not particularly effective.  Lets suppose that we have a \ref PATH with four way markers upon it.  In order for the \f$S(x)\f$ CV that was defined 
 earlier to work well frame number 3 must be further from frame number 1 than frame number 2.  Similarly frame number 4 must be still further
-from frame number 1 than frame number 3.  This is what the gull wing shape in \ref lugano-2-good-matrix-fig is telling us.  The order of the frames in the 
+from frame number 1 than frame number 3.  This is what the gull wing shape in \ref marvel-2-good-matrix-fig is telling us.  The order of the frames in the 
 rows and columns of the matrix is the same as the order that they are run through in the sums in the equation for \f$S(X)\f$.  The shape of the surface 
 in this figure shows that the distance between frames \f$i\f$ and \f$j\f$ increases monotonically as the magnitude of the difference between \f$i\f$ and 
 \f$j\f$ is increased, which is what is required.
 
 A second important requirement of a good \ref PATH is shown in the figure below:  
 
-\anchor lugano-2-good-vs-bad-fig
+\anchor marvel-2-good-vs-bad-fig
 \image html belfast-2-good-vs-bad.png "Comparison between the distances between neighboring frames on the PATH.  A good PATH will have a set of frames that are all approximately equally spaced along it."
 
 A good \ref PATH has an approximately equal spacing between the neighboring frames along it.  In other words, the distance between frame 1 and frame 2 
@@ -434,10 +434,10 @@ suitable \f$\lambda\f$ parameter to use is:
 \lambda=\frac{2.3 (N-1) }{\sum_{i=1}^{N-1} \vert X_i-X_{i+1} \vert }
 \f]
 
-\section lugano-2-conc Conclusions and further work
+\section marvel-2-conc Conclusions and further work
 
 If you have worked through all of this tutorial make sure that you have understood it by ensuring that you understand what the list of learning outcomes
-in section \ref lugano-2-lo means and that you can use PLUMED to perform all these tasks.  You might then want to read the original paper on the \ref PATH
+in section \ref marvel-2-lo means and that you can use PLUMED to perform all these tasks.  You might then want to read the original paper on the \ref PATH
 collective variable method as well as a few other articles in which these coordinates have been used to analyze simulations and to accelerate sampling.
 
 - Davide Branduardi and Francesco Luigi Gervasio and Michele Parrinello <a href="http://aip.scitation.org/doi/10.1063/1.2432340"> From A to B in free energy space </a> J. Chem. Phys., 126, 054103 (2007)
@@ -446,8 +446,8 @@ If you are interested in learning more about isocommittor surfaces and the trans
 
 */
 
-link: @subpage lugano-2
+link: @subpage marvel-2
 
 description: This tutorial explains how to use various kinds of path collective variables
 
-additional-files: lugano-2
+additional-files: marvel-2
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