update zenodo address

tutorials/data_generation_ppi.ipynb

@@ -29,7 +29,7 @@
    "source": [
     "### Input Data\n",
     "\n",
-    "The example data used in this tutorial are available on Zenodo at [this record address](https://zenodo.org/record/8187806). To download the raw data used in this tutorial, please visit the link and download `data_raw.zip`. Unzip it, and save the `data_raw/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial.\n",
+    "The example data used in this tutorial are available on Zenodo at [this record address](https://zenodo.org/record/8349335). To download the raw data used in this tutorial, please visit the link and download `data_raw.zip`. Unzip it, and save the `data_raw/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial.\n",
     "\n",
     "Note that the dataset contains only 100 data points, which is not enough to develop an impactful predictive model, and the scope of its use is indeed only demonstrative and informative for the users."
    ]

tutorials/data_generation_srv.ipynb

@@ -29,7 +29,7 @@
    "source": [
     "### Input Data\n",
     "\n",
-    "The example data used in this tutorial are available on Zenodo at [this record address](https://zenodo.org/record/8187806). To download the raw data used in this tutorial, please visit the link and download `data_raw.zip`. Unzip it, and save the `data_raw/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial.\n",
+    "The example data used in this tutorial are available on Zenodo at [this record address](https://zenodo.org/record/8349335). To download the raw data used in this tutorial, please visit the link and download `data_raw.zip`. Unzip it, and save the `data_raw/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial.\n",
     "\n",
     "Note that the dataset contains only 96 data points, which is not enough to develop an impactful predictive model, and the scope of its use is indeed only demonstrative and informative for the users."
    ]

tutorials/training.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "This tutorial will demonstrate the use of DeepRank2 for training graph neural networks (GNNs) and convolutional neural networks (CNNs) using protein-protein interface (PPI) or single-residue variant (SRV) data for classification and regression predictive tasks.\n",
     "\n",
-    "This tutorial assumes that the PPI data of interest have already been generated and saved as [HDF5 files](https://en.wikipedia.org/wiki/Hierarchical_Data_Format), with the data structure that DeepRank2 expects. This data can be generated using the [data_generation_ppi.ipynb](https://github.com/DeepRank/deeprank2/blob/main/tutorials/data_generation_ppi.ipynb) tutorial or downloaded from Zenodo at [this record address](https://zenodo.org/record/8034819). For more details on the data structure, please refer to the other tutorial, which also contains a detailed description of how the data is generated from PDB files.\n",
+    "This tutorial assumes that the PPI data of interest have already been generated and saved as [HDF5 files](https://en.wikipedia.org/wiki/Hierarchical_Data_Format), with the data structure that DeepRank2 expects. This data can be generated using the [data_generation_ppi.ipynb](https://github.com/DeepRank/deeprank2/blob/main/tutorials/data_generation_ppi.ipynb) tutorial or downloaded from Zenodo at [this record address](https://zenodo.org/record/8349335). For more details on the data structure, please refer to the other tutorial, which also contains a detailed description of how the data is generated from PDB files.\n",
     "\n",
     "This tutorial assumes also a basic knowledge of the [PyTorch](https://pytorch.org/) framework, on top of which the machine learning pipeline of DeepRank2 has been developed, for which many online tutorials exist."
    ]
@@ -33,7 +33,7 @@
     "\n",
     "If you have previously run `data_generation_ppi.ipynb` or `data_generation_srv.ipynb` notebook, then their output can be directly used as input for this tutorial.\n",
     "\n",
-    "Alternatively, preprocessed HDF5 files can be downloaded directly from Zenodo at [this record address](https://zenodo.org/record/8187806). To download the data used in this tutorial, please visit the link and download `data_processed.zip`. Unzip it, and save the `data_processed/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial. \n",
+    "Alternatively, preprocessed HDF5 files can be downloaded directly from Zenodo at [this record address](https://zenodo.org/record/8349335). To download the data used in this tutorial, please visit the link and download `data_processed.zip`. Unzip it, and save the `data_processed/` folder in the same directory as this notebook. The name and the location of the folder are optional but recommended, as they are the name and the location we will use to refer to the folder throughout the tutorial. \n",
     "\n",
     "Note that the datasets contain only ~100 data points each, which is not enough to develop an impactful predictive model, and the scope of their use is indeed only demonstrative and informative for the users."
    ]