diff --git a/datasets/hgnc/config.yml b/datasets/hgnc/config.yml
index 12c5972..b0df4a6 100644
--- a/datasets/hgnc/config.yml
+++ b/datasets/hgnc/config.yml
@@ -1,5 +1,6 @@
 
 dataset_to_process: "hgnc"
+# THIS FILE IS NOT USED BY GITHUB ACTIONS
 
 ## For XML workflows
 # dir_to_process:
diff --git a/datasets/hgnc/download/download.sh b/datasets/hgnc/download/download.sh
index d2b63be..99847a0 100755
--- a/datasets/hgnc/download/download.sh
+++ b/datasets/hgnc/download/download.sh
@@ -3,12 +3,12 @@
 ## Download sample TSV files from GitHub (OMOP CDM mappings, about 15M)
 wget -N ftp://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/tsv/hgnc_complete_set.txt
 
-# Convert TSV to CSV for RMLStreamer
+# Convert TSV to CSV for RML Mapper
 sed -e 's/"//g' -e 's/\t/","/g' -e 's/^/"/' -e 's/$/"/' -e 's/\r//' hgnc_complete_set.txt > hgnc.csv
 
+##Example to run quick python scripts:
 
 # pip install pandas
-
 # python3 <<HEREDOC
 # import pandas as pd
 # csv_table=pd.read_table('hgnc_complete_set.tsv',sep='\t')
diff --git a/datasets/hgnc/process-hgnc.ipynb b/datasets/hgnc/process-hgnc.ipynb
deleted file mode 100644
index 5f680c8..0000000
--- a/datasets/hgnc/process-hgnc.ipynb
+++ /dev/null
@@ -1,133 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Process HUGO Gene Nomenclature Committee Data\n",
-    "\n",
-    "Jupyter Notebook to download and preprocess files to transform to BioLink RDF.\n",
-    "\n",
-    "### Download files\n",
-    "\n",
-    "The download can be defined:\n",
-    "* in this Jupyter Notebook using Python\n",
-    "* as a Bash script in the `download/download.sh` file, and executed using `d2s download hgnc`\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import glob\n",
-    "import requests\n",
-    "import functools\n",
-    "import shutil\n",
-    "import pandas as pd \n",
-    "\n",
-    "# Use Pandas, load file in memory\n",
-    "def convert_tsv_to_csv(tsv_file):\n",
-    "    csv_table=pd.read_table(tsv_file,sep='\\t')\n",
-    "    csv_table.to_csv(tsv_file[:-4] + '.csv',index=False)\n",
-    "\n",
-    "# Variables and path for the dataset\n",
-    "dataset_id = 'hgnc'\n",
-    "dsri_flink_pod_id = 'flink-jobmanager-###'\n",
-    "input_folder = '/notebooks/workspace/input/' + dataset_id\n",
-    "mapping_folder = '/notebooks/datasets/' + dataset_id + '/mapping'\n",
-    "os.makedirs(input_folder, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use input folder as working folder\n",
-    "os.chdir(input_folder)\n",
-    "\n",
-    "files_to_download = [\n",
-    "    'https://raw.githubusercontent.com/MaastrichtU-IDS/d2s-scripts-repository/master/resources/cohd-sample/concepts.tsv'\n",
-    "]\n",
-    "\n",
-    "# Download each file and uncompress them if needed\n",
-    "# Use Bash because faster and more reliable than Python\n",
-    "for download_url in files_to_download:\n",
-    "    os.system('wget -N ' + download_url)\n",
-    "    os.system('find . -name \"*.tar.gz\" -exec tar -xzvf {} \\;')\n",
-    "    os.system('unzip -o \\*.zip')\n",
-    "\n",
-    "# Rename .txt to .tsv\n",
-    "listing = glob.glob('*.txt')\n",
-    "for filename in listing:\n",
-    "    os.rename(filename, filename[:-4] + '.tsv')\n",
-    "\n",
-    "    \n",
-    "## Convert TSV to CSV to be processed with the RMLStreamer\n",
-    "# use Pandas (load in memory)\n",
-    "convert_tsv_to_csv('concepts.tsv')\n",
-    "# Use Bash \n",
-    "# cmd_convert_csv = \"\"\"sed -e 's/\"/\\\\\"/g' -e 's/\\t/\",\"/g' -e 's/^/\"/' -e 's/$/\"/'  -e 's/\\r//' concepts.tsv > concepts.csv\"\"\"\n",
-    "# os.system(cmd_convert_csv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Process and load concepts\n",
-    "\n",
-    "We will use CWL workflows to integrate data with SPARQL queries. The structured data is first converted to a generic RDF based on the data structure, then mapped to BioLink using SPARQL. The SPARQL queries are defined in `.rq` files and can be [accessed on GitHub](https://github.com/MaastrichtU-IDS/d2s-project-template/tree/master/datasets/hgnc/mapping).\n",
-    "\n",
-    "Start the required services (here on our server, defined by the `-d trek` arg):\n",
-    "\n",
-    "```bash\n",
-    "d2s start tmp-virtuoso drill -d trek\n",
-    "```\n",
-    "\n",
-    "Run one of the following d2s command in the d2s-project folder:\n",
-    "\n",
-    "```bash\n",
-    "d2s run csv-virtuoso.cwl hgnc\n",
-    "d2s run xml-virtuoso.cwl hgnc\n",
-    "```\n",
-    "\n",
-    "[HCLS metadata](https://www.w3.org/TR/hcls-dataset/) can be computed for the hgnc graph:\n",
-    "\n",
-    "```bash\n",
-    "d2s run compute-hcls-metadata.cwl hgnc\n",
-    "```\n",
-    "\n",
-    "## Load the BioLink model\n",
-    "\n",
-    "Load the [BioLink model ontology as Turtle](https://github.com/biolink/biolink-model/blob/master/biolink-model.ttl) in the graph `https://w3id.org/biolink/biolink-model` in the triplestore\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file