diff --git a/README.md b/README.md
index 955cee1..a4747aa 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ for facilitating the analysis and interpretation of the experimental results.
 
 ### Last updates:
 
-* Version 0.1.9 is released! major changes can be consulted [here](CHANGE_LOG.txt).
+* Version 0.2.0 is released! major changes can be consulted [here](CHANGE_LOG.txt).
 * The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
 
 ### Installation
@@ -46,12 +46,12 @@ of the test set.
 ```python
 import quapy as qp
 
-dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
-training, test = dataset.train_test
+training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
 
 # create an "Adjusted Classify & Count" quantifier
 model = qp.method.aggregative.ACC()
-model.fit(training)
+Xtr, ytr = training.Xy
+model.fit(Xtr, ytr)
 
 estim_prevalence = model.predict(test.X)
 true_prevalence = test.prevalence()
@@ -79,7 +79,8 @@ quantification methods based on structured output learning, HDy, QuaNet, quantif
     * 32 UCI Machine Learning datasets.
     * 11 Twitter quantification-by-sentiment datasets.
     * 3 product reviews quantification-by-sentiment datasets. 
-    * 4 tasks from LeQua competition (_new in v0.1.7!_)
+    * 4 tasks from LeQua 2022 competition and 4 tasks from LeQua 2024 competition
+    * IFCB for Plancton quantification 
 * Native support for binary and single-label multiclass quantification scenarios.
 * Model selection functionality that minimizes quantification-oriented loss functions.
 * Visualization tools for analysing the experimental results.
@@ -116,3 +117,7 @@ are provided:
 ## Acknowledgments:
 
 <img src="docs/source/SoBigData.png" alt="SoBigData++" width="250"/>
+
+This work has been supported by the QuaDaSh project 
+_"Finanziato dall’Unione europea---Next Generation EU, 
+Missione 4 Componente 2 CUP B53D23026250001"_.
diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py
index 838aee5..c08748f 100644
--- a/quapy/data/datasets.py
+++ b/quapy/data/datasets.py
@@ -114,7 +114,8 @@ def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle
     """
     Loads a Reviews dataset as a Dataset instance, as used in
     `Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."
-    Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.
+    Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
+    <https://dl.acm.org/doi/abs/10.1145/3269206.3269287>`_.
     The list of valid dataset names can be accessed in `quapy.data.datasets.REVIEWS_SENTIMENT_DATASETS`
 
     :param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb'