diff --git a/user-doc/tutorials/a-trieste-1.txt b/user-doc/tutorials/a-trieste-1.txt
index 1f18b6d2f352b2cac0655de8dfc71cc761b17312..0af1e1d2342877933d78962637e2b2f7de7de7b2 100644
--- a/user-doc/tutorials/a-trieste-1.txt
+++ b/user-doc/tutorials/a-trieste-1.txt
@@ -151,7 +151,7 @@ the command `vmd ref.pdb traj-whole.xtc`.
 
 In the following we will make practice with computing and printing collective variables.
 
-\subsection trieste-1-ex-1 Exercize 1: Computing and printing collective variables
+\subsection trieste-1-ex-1 Exercise 1: Computing and printing collective variables
 
 Analyze the `traj-whole.xtc` trajectory and produce a colvar file with the following collective variables.
 
@@ -249,7 +249,7 @@ of your input file.
 \hidden{Combining collective variables}
 
 
-In this first exercize we only computed simple functions of the atomic coordinates.
+In this first exercise we only computed simple functions of the atomic coordinates.
 PLUMED is very flexible and allows you to also combine these functions to create more complicated
 variables. These variables can be useful when you want to describe a complex conformational change.
 PLUMED implements a number of functions that can be used to this aim that are described in the
@@ -292,9 +292,9 @@ any arbitrarily complex collective variable using just \ref DISTANCE and \ref MA
 Anyway, if the CV combinations that you are willing to use can be computed easily with some
 external program, do it and compare the results with the output of the PLUMED driver.
 
-\subsection trieste-1-ex-1b Exercize 1b: Combining collective variables
+\subsection trieste-1-ex-1b Exercise 1b: Combining collective variables
 
-As an optional exercize, create a file with the following quantities:
+As an optional exercise, create a file with the following quantities:
 - The sum of the distances between Mg and each of the phosphorous atoms.
 - The distance between Mg and the closest phosphorous atom.
 
@@ -356,7 +356,7 @@ be used in the following cases:
 The last point is perhaps the most surpising one. Some of the PLUMED actions can indeed move the stored atoms to
 positions better suitable for the calculation of collective variables.
 
-The previous exercize was done on a trajectory where the RNA was already whole. For the next exercize you will use the
+The previous exercise was done on a trajectory where the RNA was already whole. For the next exercise you will use the
 `traj-broken.xtc` file instead, which is a real trajectory produced by GROMACS. Open it with VMD to understand
 what we mean with broken
 \verbatim
@@ -463,7 +463,7 @@ In case the two molecules can separate from each other this would be rather prob
 
 We will now see what happens when using \ref WHOLEMOLECULES on multiple molecules *incorrectly*.
 
-\subsection trieste-1-ex-2b Exercize 2b: Mistakes with WHOLEMOLECULES
+\subsection trieste-1-ex-2b Exercise 2b: Mistakes with WHOLEMOLECULES
 
 Prepare a PLUMED input file that makes all the water molecules whole. Use the following template
 \plumedfile
@@ -495,13 +495,13 @@ that the system is doing what you expect.
 
 \hidden{Mastering FIT_TO_TEMPLATE}
 
-In an exercize above we used \ref FIT_TO_TEMPLATE. This action uses as a reference a PDB file
+In an exercise above we used \ref FIT_TO_TEMPLATE. This action uses as a reference a PDB file
 which typically contains a subset of atoms (those that are fitted). However,
 when you apply \ref FIT_TO_TEMPLATE with `TYPE=OPTIMAL`, the whole system
 is translated and rotated.
 The whole system here means all atoms plus the vectors defining the periodic box.
 
-\subsection trieste-1-ex-2c Exercize 2c: Mastering FIT_TO_TEMPLATE
+\subsection trieste-1-ex-2c Exercise 2c: Mastering FIT_TO_TEMPLATE
 
 Check how the periodic box rotates when using \ref FIT_TO_TEMPLATE.
 Use the following template
@@ -569,7 +569,7 @@ In summary, in this tutorial you should have learned how to use PLUMED to:
 - Compute collective variables.
 
 All of this was done by just reading an already available trajectory.
-Notice that there are many alternative tools that could have been used to do the same exercize.
+Notice that there are many alternative tools that could have been used to do the same exercise.
 Indeed, if you are familiar with other tools, it might be a good idea to also try them and compare the results.
 The special things of working with PLUMED are the following:
 - PLUMED implements a vast library of useful collective variables.
diff --git a/user-doc/tutorials/a-trieste-3.txt b/user-doc/tutorials/a-trieste-3.txt
index c3939b6338d97b20e5616c06908d529c8a64db12..8586df344dacdb6e75d2d6c46ce4034f8a539aa3 100644
--- a/user-doc/tutorials/a-trieste-3.txt
+++ b/user-doc/tutorials/a-trieste-3.txt
@@ -108,7 +108,7 @@ We will make use of two toy models: the first is a water dimer, i.e. two molecul
 
 \note Create a folder for each exercise and use subfolders if you want to run the same simulation with multiple choices for the parameters
 
-\section trieste-3-ex-1 Exercize 1: converged histogram of the water dimer relative distance
+\section trieste-3-ex-1 Exercise 1: converged histogram of the water dimer relative distance
 
 \image html trieste-3-wdimer.png "A water dimer"
 
@@ -149,7 +149,7 @@ The result should be comparable with the following:
 \image html trieste-3-histo-dimer.png "A histogram of the relative distance (in nm) with errors"
 Notice the peak at 0.9 nm, this is the effect of using cut-off for the calculation of the interactions in the simulation (check the run-dimer.mdp file for the properties of the run)
 
-\section trieste-3-ex-2 Exercize 2: Apply a linear restraint on the same collective variable 
+\section trieste-3-ex-2 Exercise 2: Apply a linear restraint on the same collective variable 
 Now we will try to apply a linear restraint on the relative distance and compare the resulting distribution.
 The new sampling will reflect the effect of the bias.
 Be carefull about the statistics: in the simulation of exercise 1 you were postprocessing a trajectory of 125000 frames accumulating one frame every ten in an histogram and clearing
@@ -221,7 +221,7 @@ Be carefull again about the difference in the way statistics is accumulated on-t
 
 Now the resulting histogram should be comparable to the reference one.
 
-\section trieste-3-ex-3 Exercize 3: Apply a quadratic restraint on the same collective variable 
+\section trieste-3-ex-3 Exercise 3: Apply a quadratic restraint on the same collective variable 
 
 Do you expect a different behaviour? This time we can write the plumed input file in such a way to compare directly the biased and unbiased histograms.
 
@@ -254,7 +254,7 @@ The comparison of the two histograms with the former will show the effect of the
 > python3 do_block_histo.py > histo-reweighted.dat 
 \endverbatim
 
-\section trieste-3-ex-4 Exercize 4: Apply an upper wall on the distance.
+\section trieste-3-ex-4 Exercise 4: Apply an upper wall on the distance.
 In the above cases we have always applied weak biases. Sometimes biases are usefull to prevent the system in reaching some region of the conformational space. In this case instead of using \ref RESTRAINT , we can make use of lower or upper restraints, e.g. \ref LOWER_WALLS and \ref UPPER_WALLS.
 
 What happen to the histogram when we use walls? 
@@ -285,7 +285,7 @@ Run it.
 
 If we have not sampled a region througly enough it is not possible to estimate the histogram in that region even using reweighting (reweighting is not magic!).
 
-\section trieste-3-ex-5 Exercize 5: Evaluate the free energy and use it as an external restraint
+\section trieste-3-ex-5 Exercise 5: Evaluate the free energy and use it as an external restraint
 
 The main issue in sampling rare events is that importance sampling algorithms spend more time in low energy regions and if two low energy regions are separated by a high energy one is unlikely for the sampling algorithm to cross the high energy region and reach the other low energy one. From this point of view an algorithm based on random sampling will work better in crossing the barrier. A particularly efficient sampling can be obtained if one would know the underlying free energy and thus use that to bias the sampling and make the sampling probability uniform in the regions of relavent interest.
 In this exercise we will make use of the free-energy estimate along the distance collective variable to bias the sampling of the same collective variable in the dimer simulation. To do so we will make use of a table potential applied using the \ref Bias \ref EXTERNAL. We first need to get a smooth estimate of the free-energy from our fist reference simulations, we will do this by accumulating a histogram with kernel functions, that is continuos function centered at the value of the accumulated point and added accumulated on the discrete represattion of the histogram, see <a href="https://en.wikipedia.org/wiki/Kernel_density_estimation"> Kernel density estimation </a>.
@@ -374,7 +374,7 @@ Run it.
 
 How do the biased and unbiased histograms look like? In the following we will apply this concept to sample the conformational space of a more complex system.
 
-\section trieste-3-ex-6 Exercize 6: Preliminary run with Alanine dipeptide
+\section trieste-3-ex-6 Exercise 6: Preliminary run with Alanine dipeptide
 
 Alanine dipeptide is characterised by multiple minima separated by relatively high free energy barriers. Here we will explore the conformational space of
 alanine dipeptide using a standard MD simulation, then instead of using the free energy as an external potential we will try to fit the potential using
@@ -414,7 +414,7 @@ gnuplot>rep f(x)
 
 The function and the resulting parameters can be used to run a new biased simulation:
 
-\section trieste-3-ex-7 Exercize 7: First biased run with Alanine dipeptide
+\section trieste-3-ex-7 Exercise 7: First biased run with Alanine dipeptide
 
 \plumedfile
 # vim:ft=plumed
@@ -445,7 +445,7 @@ gnuplot> ...
 
 We can now run a third simulation where both regions are biased.
 
-\section trieste-3-ex-8 Exercize 8: Second biased run with Alanine dipeptide
+\section trieste-3-ex-8 Exercise 8: Second biased run with Alanine dipeptide
 
 \plumedfile
 # vim:ft=plumed
diff --git a/user-doc/tutorials/a-trieste-4.txt b/user-doc/tutorials/a-trieste-4.txt
index 1d3d3f5e12ecfa319d48bfd5a98680f8ee997a2f..590925323cdcb749de63d5dc3719e0949c3cfdd3 100644
--- a/user-doc/tutorials/a-trieste-4.txt
+++ b/user-doc/tutorials/a-trieste-4.txt
@@ -26,7 +26,7 @@ The \tarball{trieste-4} for this project contains the following files:
 This tutorial has been tested on a pre-release version of version 2.4. However, it should not take advantage
 of 2.4-only features, thus should also work with version 2.3.
 
-\note We suggest to run the three exercizes in three separate directories. For Exercize 3, you will need the output of the first two exercizes, so don't delete it!
+\note We suggest to run the three exercises in three separate directories. For Exercise 3, you will need the output of the first two exercizes, so don't delete it!
  
 \section trieste-4-intro Introduction
 
@@ -115,9 +115,9 @@ It is conventional use to characterize the two states in terms of Ramachandran d
 \image html belfast-2-transition.png "Two metastable states of alanine dipeptide are characterized by their Ramachandran dihedral angles."
 
 
-\section trieste-4-ex-1 Exercize 1: my first metadynamics calculation
+\section trieste-4-ex-1 Exercise 1: my first metadynamics calculation
 
-\subsection trieste-4-ex-1a Exercize 1a: setup and run 
+\subsection trieste-4-ex-1a Exercise 1a: setup and run 
 
 In this excercise we will setup and perform a well-tempered metadynamics run using the backbone dihedral \f$ \phi \f$
 as collective variable. During the calculation, we will also monitor the behavior of the other backbone dihedral \f$ \psi \f$.
@@ -209,7 +209,7 @@ of the Gaussian height is higher than the initial height specified in the input
 In fact, this column reports the height of the Gaussian rescaled by the pre-factor that
 in well-tempered metadynamics relates the bias potential to the free energy.
 
-\subsection trieste-4-ex-1b Exercize 1b: estimating the free energy 
+\subsection trieste-4-ex-1b Exercise 1b: estimating the free energy 
 
 One can estimate the free energy as a function of the metadynamics CVs directly from the metadynamics
 bias potential. In order to do so, the utility \ref sum_hills should be used to sum the Gaussians
@@ -264,20 +264,20 @@ of your metadynamics simulation!
 
 \note The two observations above are necessary, but qualitative conditions for convergence.
 A quantitative assessment of convergence can be obtained by performing an error analysis of the
-reconstructed free-energy profile, as explained in the last exercize
+reconstructed free-energy profile, as explained in the last exercise
 
-\section trieste-4-ex-2 Exercize 2: playing with collective variables
+\section trieste-4-ex-2 Exercise 2: playing with collective variables
 
-In this exercize, we will run a well-tempered metadynamics simulation on alanine dipeptide in vacuum, this time
+In this exercise, we will run a well-tempered metadynamics simulation on alanine dipeptide in vacuum, this time
 using as CV the backbone dihedral \f$ \psi \f$. 
-Please complete the template `plumed.dat` file used in the previous exercize to run this calculation.
+Please complete the template `plumed.dat` file used in the previous exercise to run this calculation.
 
 Once your `plumed.dat` file is complete, you can run a 10-ns long metadynamics simulations with the following command
 \verbatim
 > gmx mdrun -s topol.tpr -nsteps 5000000 -plumed plumed.dat
 \endverbatim
 
-As we did in the previous exercize, we can use COLVAR to visualize the behavior of the CV during the simulation.
+As we did in the previous exercise, we can use COLVAR to visualize the behavior of the CV during the simulation.
 Here we will plot at the same time the evolution of the metadynamics CV \f$ \psi \f$ and of the other dihedral \f$ \phi \f$.
 
 \verbatim
@@ -287,17 +287,17 @@ gnuplot> p "COLVAR" u 1:2, "" u 1:3
 \anchor trieste-4-metad-psi-phi-fig
 \image html munster-metad-psi-phi.png "Time evolution of the dihedrals phi and psi during a 10-ns long metadynamics simulation using psi as CV."
 
-By inspecting Figure \ref trieste-4-metad-psi-phi-fig, we notice that something different happened compared to the previous exercize.
+By inspecting Figure \ref trieste-4-metad-psi-phi-fig, we notice that something different happened compared to the previous exercise.
 At first the behavior of \f$ \psi \f$ looks diffusive in the entire CV space. However, around t=1 ns, \f$ \psi \f$ 
 seems trapped in a region of the CV space in which it was previously diffusing without problems. 
 The reason is that the non-biased CV \f$ \phi \f$ after a while has jumped into a different local minima.
 Since \f$ \phi \f$ is not directly biased, one has to wait for this (slow) degree of freedom to 
 equilibrate before the free energy along \f$ \psi \f$ can converge.
 
-Try to repeat the analysis done in the previous exercize, i.e.  calculate the estimate of the free energy as a function of time,
+Try to repeat the analysis done in the previous exercise, i.e.  calculate the estimate of the free energy as a function of time,
 first step to assess the convergence of this metadynamics simulation.
 
-\section trieste-4-ex-3 Exercize 3: estimating the error in free-energies using block-analysis
+\section trieste-4-ex-3 Exercise 3: estimating the error in free-energies using block-analysis
 
 In this exercise, we will calculate the error associated to the free-energy reconstructed
 by a well-tempered metadynamics simulation. The free energy and the errors will be calculated
@@ -320,7 +320,7 @@ In this exercise we will use the umbrella-sampling-like reweighting approach (Me
 
 To calculate the weights, we need to use the PLUMED \ref driver utility and read the HILLS
 file along with the GROMACS trajectory file produced during the metadynamics simulation.
-Let's consider the metadynamics simulation carried out in Exercize 1.
+Let's consider the metadynamics simulation carried out in Exercise 1.
 We need to prepare the `plumed.dat` input file to use in combination with \ref driver.
 Here you can find a sample `plumed.dat` file that you can use as a template.
 Whenever you see an highlighted \highlight{FILL} string, this is a string that you should replace.
@@ -432,17 +432,17 @@ of the block that exceeds the correlation between data points (Fig. \ref trieste
 \anchor trieste-4-block-phi
 \image html trieste-4-block-phi.png "Block analysis of a metadynamics simulation using phi as CV"
 
-To complete this exercize, you should do the following:
-- calculate the error associated to the free energy as a function of the collective variable \f$ \psi \f$ from Exercize 1
-- calculate the error associated to the free energy as a function of the collective variable \f$ \psi \f$ from Exercize 2
-- compare the different behaviors in Exercize 1 and 2
+To complete this exercise, you should do the following:
+- calculate the error associated to the free energy as a function of the collective variable \f$ \psi \f$ from Exercise 1
+- calculate the error associated to the free energy as a function of the collective variable \f$ \psi \f$ from Exercise 2
+- compare the different behaviors in Exercise 1 and 2
 
 What can we learn from this analysis about the convergence of the two metadynamics simulations
 and the quality of the collective variables chosen?
 
 At this time, the most important question of this lecture becomes:
 
-- Could we distinguish the different behavior (in terms of convergence) of the simulations in Exercize 1 and 2
+- Could we distinguish the different behavior (in terms of convergence) of the simulations in Exercise 1 and 2
 simply by looking at the time series of the Gaussian height?
 
 
diff --git a/user-doc/tutorials/a-trieste-5.txt b/user-doc/tutorials/a-trieste-5.txt
index be0a2eff082a476bbee6f7c6722eeb120fd7dcfd..d1bec5d24aa969b56d32115d7a46a26984503029 100644
--- a/user-doc/tutorials/a-trieste-5.txt
+++ b/user-doc/tutorials/a-trieste-5.txt
@@ -26,7 +26,7 @@ The \tarball{trieste-5} for this project contains the following files:
 
 This tutorial has been tested on a pre-release version of version 2.4. In particular, it takes
 advantage of a special syntax for setting up multi-replica simulations that is only available
-since version 2.4. Exercizes could be done also with version 2.3 but using a different syntax
+since version 2.4. Exercise could be done also with version 2.3 but using a different syntax
 with respect to the one suggested.
 
 Also notice that in the `.solutions` directory of the tarball you will find correct input files.
@@ -198,7 +198,7 @@ In short, whenever there are keywords that should vary across replicas, you shou
 As mentioned above, you can always use the old syntax with separate input file, and this is recommended when the
 number of keywords that are different is large.
 
-\section trieste-5-ex-1 Exercize 1: Running multi-replica simulations
+\section trieste-5-ex-1 Exercise 1: Running multi-replica simulations
 
 Write a plumed file that allows you to run a multi-replica simulation of alanine dipeptide
 where the following four replicas are simulated:
@@ -295,7 +295,7 @@ The setup above is very close to the one used in bias exchange simulations.
 However, in bias exchange simulations usually one would use at most one neutral (non-biased) replica.
 In addition, the \ref RANDOM_EXCHANGES command is often used.
 
-\section trieste-5-ex-2 Exercize 2: Analyzing a multiple-restraint simulation
+\section trieste-5-ex-2 Exercise 2: Analyzing a multiple-restraint simulation
 
 In the following we will analyze the result of the simulation above.
 To this aim we will have to use the WHAM method. The WHAM procedure described here is
@@ -436,9 +436,9 @@ parallel tempering simulations \cite sugi-okam99cpl
 and simulated tempering simulations \cite wang2011replica (notice that they 
 can be implemented with PLUMED + GROMACS using \cite bussi2013mp).
 
-\section trieste-5-ex-3 Exercize 3: What if a variable is missing?
+\section trieste-5-ex-3 Exercise 3: What if a variable is missing?
 
-Repeat the exercize above (that is: running replica exchange MD simulation and analyze the result)
+Repeat the exercise above (that is: running replica exchange MD simulation and analyze the result)
 but using only three replicas:
 - two unbiased replicas.
 - one biased replica along psi.
@@ -473,7 +473,7 @@ How many transitions between the two free-energy wells can you observe?
 Remember that replica exchange involves coordinate swaps that do not correspond to the real system dynamics. It is very useful to look at
 "demuxed" trajectories.
 
-\section trieste-5-ex-4 Exercize 4: "demuxing" your trajectories
+\section trieste-5-ex-4 Exercise 4: "demuxing" your trajectories
 
 Use the following commands
 
diff --git a/user-doc/tutorials/a-trieste-6.txt b/user-doc/tutorials/a-trieste-6.txt
index 68dea2c3347fdb706526ee296b94a3c5763a3208..b5066a53261905f47f88036e006e5019279f20a8 100644
--- a/user-doc/tutorials/a-trieste-6.txt
+++ b/user-doc/tutorials/a-trieste-6.txt
@@ -17,7 +17,7 @@ Once this tutorial is completed students will be able to:
 
 \section trieste-6-resources Resources
 
-The reference trajectories and input files for the exercizes proposed in this tutorial 
+The reference trajectories and input files for the exercises proposed in this tutorial 
 can be downloaded from `github` using the following command:
 
 \verbatim
@@ -29,14 +29,14 @@ of 2.4-only features, thus should also work with version 2.3.
  
 \section trieste-6-intro Introduction
 
-In this tutorial we propose exercizes on the following biological systems:
+In this tutorial we propose exercises on the following biological systems:
 - the BRCA1-associated RING domain protein 1 (BARD1 complex) 
 - the cmyc peptide in presence of urea at low concentration (cmyc-urea)
 - the protein G B1 domain  
 
 The exercise are of increasing difficulties, inputs are partially provided for the first and second cases while for the last one the user is expected to be autonomous.
 
-\section trieste-6-ex-1 Exercize 1: analysis of the BARD1 complex simulation 
+\section trieste-6-ex-1 Exercise 1: analysis of the BARD1 complex simulation 
 
 The BARD1 complex is a heterodimer composed by two domains of 112 and 117 residues each. 
 The system is represented at coarse-grained level using the MARTINI force field.
@@ -44,7 +44,7 @@ The system is represented at coarse-grained level using the MARTINI force field.
 \anchor trieste-6-bard1
 \image html trieste-6-bard1.png "The BARD1 heterodimer"
 
-In the TARBALL of this exercize, we provide a long MD simulation of the BARD1 complex in which
+In the TARBALL of this exercise, we provide a long MD simulation of the BARD1 complex in which
 the two domains explore multiple different conformations.
 
 \note We encourage the users to get familiar with the system by visualizing the MD trajectory
@@ -110,11 +110,11 @@ chainB_2: CENTER ATOMS=__FILL__
 dih: TORSION ATOMS=__FILL__
 \endplumedfile
 
-\section trieste-6-ex-2 Exercize 2: analysis of the cmyc-urea simulation
+\section trieste-6-ex-2 Exercise 2: analysis of the cmyc-urea simulation
 
 Cmyc is a small disordered peptide made of 11 aminoacid. In solution, cmyc adopts
 a variety of different, but equally populated conformations. 
-In the TARBALL of this exercize, we provide a long MD simulation of cmyc in presence
+In the TARBALL of this exercise, we provide a long MD simulation of cmyc in presence
 of a single molecule of urea. 
 
 \anchor trieste-6-cmycurea
@@ -166,7 +166,7 @@ For the calculation of ensemble averages of experimental CVs, we suggest to use:
 
 and we encourage the users to look at the examples provided in the manual for the exact syntax.
 
-\section trieste-6-ex-3 Exercize 3: Protein G folding simulations
+\section trieste-6-ex-3 Exercise 3: Protein G folding simulations
 GB1 is a small protein domain with a simple beta-alpha-beta fold. It is a well studied protein that folds on the millisecond time scale.
 Here we use a structure based potential and well-tempered metadynamics to study the free energy of folding and unfolding.
 In the TARBALL of this exercise we provide the files needed to run the simulation, the user should write the plumed input file needed