diff --git a/IEEEProc2025_plots/over_time_experiment.py b/IEEEProc2025_plots/over_time_experiment.py
index 3aaca28..d77ee81 100644
--- a/IEEEProc2025_plots/over_time_experiment.py
+++ b/IEEEProc2025_plots/over_time_experiment.py
@@ -14,7 +14,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.linear_model import LogisticRegression as LR, LogisticRegressionCV
 from tqdm import tqdm
 import quapy as qp
-from data import LabelledCollection, Dataset
+from quapy.data import LabelledCollection, Dataset
 import quapy.functional as F
 from method.composable import QUnfoldWrapper
 from quapy.method.aggregative import DistributionMatchingY, EMQ, KDEyML
@@ -165,7 +165,7 @@ def plot_prevalences(results_dict, target_class=1, target_label='positive', save
         dates_smooth, y_smooth = smooth_curve(dates, target_component)
 
         if method=='true-prev':
-            line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=3, linestyle='-')
+            line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=3, linestyle='-', color='black')
         else:
             line,=plt.plot(dates_smooth, y_smooth, label=method, linewidth=2, linestyle='--')
         plt.plot(dates, target_component, 'o', markersize=10, color=line.get_color())
@@ -259,10 +259,10 @@ else:
 def methods():
     yield 'CC', CC(new_classifier(), fit_classifier=to_fit)
     yield 'ACC', ACC(new_classifier(), fit_classifier=to_fit)
+    yield 'SLD', EMQ(new_classifier(), fit_classifier=to_fit)
     yield 'HDy', DistributionMatchingY(new_classifier(), fit_classifier=to_fit)
     yield 'HDx', HDxDensify()
     yield 'KMM', QUnfoldWrapperDensify(KMM())
-    yield 'SLD', EMQ(new_classifier(), fit_classifier=to_fit)
     yield 'KDEy', KDEyML(new_classifier(), fit_classifier=to_fit)
 
 
diff --git a/IEEEProc2025_plots/plotting_diagonal_4methods.py b/IEEEProc2025_plots/plotting_diagonal_4methods.py
index c0991fb..cbc1b9c 100644
--- a/IEEEProc2025_plots/plotting_diagonal_4methods.py
+++ b/IEEEProc2025_plots/plotting_diagonal_4methods.py
@@ -3,7 +3,7 @@ from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
 from sklearn.model_selection import GridSearchCV
 
 import quapy as qp
-from data import LabelledCollection
+from quapy.data import LabelledCollection
 from method.non_aggregative import DMx
 from protocol import APP
 from quapy.method.aggregative import CC, DMy, ACC, EMQ
@@ -20,9 +20,9 @@ def cls():
 def gen_methods():
     yield CC(cls()), r'CC$_{10' + r'\%}$'
     yield ACC(cls()), 'ACC'
+    yield EMQ(cls()), 'SLD'
     yield DMy(cls(), val_split=10, nbins=10, n_jobs=-1), 'HDy'
     yield DMx(nbins=10, n_jobs=-1), 'HDx'
-    yield EMQ(cls()), 'SLD'
     # yield EMQ(cls(), calib='vs'), 'SLD-VS'
 
 def gen_data():