Updated explanatory text for query.

Author: DanielaSchacherer

notebooks/pathomics/lung_cancer_cptac_DataExploration.ipynb

@@ -1,5 +1,15 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/pathomics/lung_cancer_cptac_DataExploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -146,11 +156,11 @@
         "id": "TM1g60Hx_Iij"
       },
       "source": [
-        "IDC relies on the Google Cloud Platform (GCP) for storage and management of DICOM data. The data are contained in so-called [storage buckets](https://cloud.google.com/storage/docs/key-terms#buckets), from which they can be retrieved on a requester pays basis. Currently, all pathology whole-slide images (WSI) are located in the *idc-open* bucket.\n",
+        "IDC relies on the Google Cloud Platform (GCP) for storage and management of DICOM data. The data are contained in so-called [storage buckets](https://cloud.google.com/storage/docs/key-terms#buckets).\n",
         "\n",
-        "Metadata for the DICOM files — including standard DICOM tags, but also non-DICOM metadata — are stored in the BigQuery table *canceridc-data.idc_current.dicom_all*. The IDC Documentation gives further information on [data organization](https://learn.canceridc.dev/data/organization-of-data) and [code examples](https://learn.canceridc.dev/cookbook/bigquery) on how to query the table. The easiest way to access BigQuery tables from a Jupyter notebook is to use [BigQuery cell magic](https://cloud.google.com/bigquery/docs/visualize-jupyter#querying-and-visualizing-bigquery-data) with the `%%bigquery` command.\n",
+        "Metadata for the DICOM files — including standard DICOM tags, but also non-DICOM metadata — are stored in the BigQuery table *bigquery-public-data.idc_v11.dicom_all*. The IDC Documentation gives further information on [data organization](https://learn.canceridc.dev/data/organization-of-data) and [code examples](https://learn.canceridc.dev/cookbook/bigquery) on how to query the table. The easiest way to access BigQuery tables from a Jupyter notebook is to use [BigQuery cell magic](https://cloud.google.com/bigquery/docs/visualize-jupyter#querying-and-visualizing-bigquery-data) with the `%%bigquery` command.\n",
         "\n",
-        "The following statement loads relevant metadata of all slide images from the CPTAC-LUAD and CPTAC-LSCC datasets into a pandas data frame called `slides_df`."
+        "The following statement loads relevant metadata of all slide images from the CPTAC-LUAD and CPTAC-LSCC datasets into a pandas data frame called `slides_df`. The query might look intimidating at first, however accompanyinig comments try to explain every step in the query."
       ]
     },
     {
@@ -378,7 +388,8 @@
       "name": "cptac_use_case.ipynb",
       "private_outputs": true,
       "provenance": [],
-      "toc_visible": true
+      "toc_visible": true,
+      "include_colab_link": true
     },
     "environment": {
       "name": "tf2-gpu.2-6.m79",