diff --git a/README.md b/README.md
index a7d4140..53cb23d 100755
--- a/README.md
+++ b/README.md
@@ -11,10 +11,8 @@ The experiments have been run using the following packages (older versions might
 * scikit-learn==0.22.2.post1
 * scipy==1.4.1
 
-
-## Disclaimer:
-The dataset is not distributed in this version. We have asked the Editors of each document for permission to publish the corpus.
-We are waiting for some of these responses to arrive. 
+## Dataset:
+The dataset can be downloaded from [http://hlt.isti.cnr.it/medlatin/](http://hlt.isti.cnr.it/medlatin/).
 
 ## Running the Experiments
 The script in __./src/author_identification.py__ executes the experiments. This is the script syntax (--help):
@@ -42,7 +40,7 @@ optional arguments:
 The following command line:
 ```
 cd src
-python author_identification.py ../Corpora/CorpusI Dante --unknown ../Epistle/EpistolaXIII_1.txt
+python author_identification.py ../Corpora/MedLatin1 Dante --unknown ../Epistle/EpistolaXIII_1.txt
 ```
 
 Will use all texts in ../Corpora/CorpusI as training documents to train a verificator for the 
@@ -54,18 +52,18 @@ to the positive class.
 Similarly, the command line:
 ```
 cd src
-python author_identification.py ../Corpora/CorpusI ALL --loo 
+python author_identification.py ../Corpora/MedLatin1 ALL --loo 
 ```
 will perform a cross-validation of the binary classifier for all authors using all training documents in a leave-one-out (LOO) fashion.
 
 The script will report the results both in the standard output (more elaborated) and in a log file. For example, the last command will produce a log file containing:
 ```
-F1 for ClaraAssisiensis = 0.400
+F1 for ClaraAssisiensis = 0.571
 F1 for Dante = 0.957
 F1 for GiovanniBoccaccio = 1.000
-F1 for GuidoFaba = 0.974
+F1 for GuidoFaba = 0.980
 F1 for PierDellaVigna = 0.993
-LOO Macro-F1 = 0.865
-LOO Micro-F1 = 0.981
+LOO Macro-F1 = 0.900
+LOO Micro-F1 = 0.985
 ```
 (Note that small numerical variations with respect to the original papers might occur due to different software versions and as a result from any stochastic underlying process. Those changes should anyway not alter the conclusions derived from the published results.)