diff --git a/projects/project_adaptation_fit/adaptation_fit.tex b/projects/project_adaptation_fit/adaptation_fit.tex
index 80bca6f..b89f946 100644
--- a/projects/project_adaptation_fit/adaptation_fit.tex
+++ b/projects/project_adaptation_fit/adaptation_fit.tex
@@ -40,10 +40,12 @@ electroreceptors of the weakly electric fish \textit{Apteronotus
 
 \begin{questions}
   \question In the accompanying datasets you find the
-  \textit{spike\_times} of an P-unit electrorecptor to a stimulus of a
-  certain intensity, i.e. the \textit{contrast} which is also stored
-  in the file. The data is sampled with 20\,kHz sampling frequency and
-  spike times are given in milliseconds relative to the stimulus onset.
+  \textit{spike\_times} of an P-unit electroreceptor to a stimulus of
+  a certain intensity, i.e. the \textit{contrast} which is also stored
+  in the file. The contrast of the stimulus is a measure relative to
+  the amplitude of fish's field, it has no unit. The data is sampled
+  with 20\,kHz sampling frequency and spike times are given in
+  milliseconds relative to the stimulus onset.
   \begin{parts}
     \part Estimate for each stimulus intensity the PSTH and plot
     it. You will see that there are three parts.  (i) The first
diff --git a/projects/project_eod/EOD_data.mat b/projects/project_eod/EOD_data.mat
index 406ee21..e53ceb6 100644
Binary files a/projects/project_eod/EOD_data.mat and b/projects/project_eod/EOD_data.mat differ
diff --git a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex
index 7e185b6..1db9aba 100644
--- a/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex
+++ b/projects/project_stimulus_reconstruction/stimulus_reconstruction.tex
@@ -57,7 +57,7 @@ reconstruct the stimulus a neuron has been stimulated with.
     error using the mean-square-error and express it relative to the
     variance of the original stimulus.
     \begin{equation}
-      err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x}),
+      err = \frac{1}{N} \cdot \displaystyle\sum^{N}_{i=1}(x_i - \bar{x})^2,
     \end{equation}
     with $N$ the number of data points, $x_i$ the current value and
     $\bar{x}$, the average of all $x$.