diff --git a/.gitignore b/.gitignore
index 435d880..0cf5551 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,3 @@
-/PATSTAT/EU_CH_scope/cpc_defs.csv
 /misc_code/
 /PATSTAT/appln_data.xlsx
 /PATSTAT/person_data.xlsx
diff --git a/PATSTAT/WESTERN_CH_scope/cpc_defs.csv b/PATSTAT/WESTERN_CH_scope/cpc_defs.csv
new file mode 100644
index 0000000..f6dc1fe
--- /dev/null
+++ b/PATSTAT/WESTERN_CH_scope/cpc_defs.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab0f4acc10a622f8a162a9f1f2aaf39b06799f65feab44412aed2dc2d6f27cf8
+size 159305379
diff --git a/PATSTAT/WESTERN_CH_scope/scope_cpc_defs.csv b/PATSTAT/WESTERN_CH_scope/scope_cpc_defs.csv
new file mode 100644
index 0000000..a062eab
--- /dev/null
+++ b/PATSTAT/WESTERN_CH_scope/scope_cpc_defs.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76479394378a76774399904f0aa8104a2fdf0d2ec39d22a928a0c07eb80e6e0c
+size 209293
diff --git a/PATSTAT/patstat_cpc_parse.ipynb b/PATSTAT/patstat_cpc_parse.ipynb
index 7302714..057f87c 100644
--- a/PATSTAT/patstat_cpc_parse.ipynb
+++ b/PATSTAT/patstat_cpc_parse.ipynb
@@ -244,30 +244,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "6c3baa5b",
-   "metadata": {},
+   "execution_count": 49,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'neural network|machine learn|deep learn|remote sensing|convolutional neural|internet of things|feature extraction|genetic algorithm|big data|artificial intelligence|data driven|support vector machine|logistic regression not p=|optimization algorithm|principal component analysis|artificial neural network|swarm optimization|regularization|linear regression not p=|optimization algorithm|random forest|cloud computing|reinforcement learning|computer vision|kalman filter|image processing|data mining|evolutionary algorithm|edge computing|supervised learning|computational modeling|pattern recognition|image classification|long short-term memor|robotics|image segmentation|convex optimization|covariance matri|attention mechanism|markov chain|object detection not brain|clustering algorithm|recurrent neural network|data augmentation|transfer learning|adversarial network|decision tree|multi agent system|fuzzy set|convolutional network|image reconstruction|data analytic|smart grid|autoencoder|fuzzy logic|radial basis function|bayesian network|dimensionality reduction|face recognition not brain|gaussian process|anomaly detection|k-nearest neighbor|natural language processing|monte carlo method|large dataset|gradient descent|support vector regression|extreme learning machine|perceptron|model selection|ensemble learning|representation learning|recommender system|target tracking|singular value decomposition|feature learning|smart city|sentiment analy|markov decision process|k-means clustering|independent component analysis|brain computer interface|human-computer interaction|markov chain monte carlo|hierarchical clustering|semantic web|semi-supervised learning|human-robot interact|knowledge graph|speech recognition not brain|ensemble model|fog computing|mapreduce|evolutionary computation|data science|text mining|generative model|active learning|swarm intelligence|multi-task learning|language model|collaborative filtering|backpropagation|machine vision|computer-aided diagnosis|gated recurrent unit|lagrange multiplier|expert system|learning rate|hadoop|markov process|nonlinear optimization|learning system|self-organizing map|smart manufacturing|smart home|few shot learning|few-shot learning|meta-learning|meta learning|adversarial training|zero-shot learning|word embedding|expectation maximization algorithm|stochastic gradient descent|ridge regression|deep belief network|non-negative matrix factorization|affective computing|latent dirichlet allocation|kernel method|kernel learning|feature engineering|variational inference|image representation|manifold learning|adversarial example|knowledge distillation|time series forecast|variational autoencoder|lasso regression|smart energy|dbscan|multi-label classification|intelligent robot|ubiquitous computing|gaussian mixture models|smart technolog|boltzmann machine|smart buildings|predictive analytic|pervasive computing|smart agriculture|capsule network|human-in-the-loop|intelligent agent|ai applications|word vector|transformer model|facial recognition|unstructured data|restricted boltzmann machine|albert|lifelong learning|autonomous agents|chatbot|cholesky decomposition|nosql|nosql|explainable ai|seq2seq|probabilistic graphical model|qr decomposition|unsupervised deep learning|data warehouse|quantum machine learning|continual learning|smart environment|multimodal learning|smart health|artificial immune system|swarm robotics|kernel machine|latent factor model|eigendecomposition|adversarial machine|adversarial machine learning|smart mobility|sequence-to-sequence model|eigen decomposition|adversarial robustness|smart parking|adversarial neural|roberta|bidirectional encoder representations from transformer|locally linear embedding|hebbian learning|one-shot learning|multimodal representation|smart tourism|entity extraction|adaptive moment estimation|ontology learning|topic modeling|relational database'"
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keywords_oklist_source= r'..\\WOS\\kw_token_ranked_bibliometrics_okset.xlsx'\n",
+    "keyword_df = pd.read_excel(keywords_oklist_source)\n",
+    "keywords = keyword_df[keyword_df[\"u_Priority (done)\"].isin([\"High\",\"Medium\"])][\"kw_token\"].str.replace('\"','').tolist()\n",
+    "keywords = [kw.replace(\"*\",\"\").replace(\"$\",\"\").lower() for kw in keywords if (\"?\" not in kw and len(kw)>3)]\n",
+    "keywords = \"|\".join([kw for kw in keywords if kw not in [\"classifier\",\"clustering\",\"loss function\",'classification']]+[\"relational database\"])\n",
+    "keywords"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "outputs": [
     {
      "data": {
       "text/plain": "               cpc_id                                           cpc_name   \n12725    A61B1/000096                    {using artificial intelligence}  \\\n13764      A61B5/7264  {Classification of physiological signals or da...   \n13897        A61B6/52  {Devices using data or image processing specia...   \n14016        A61B8/52  {Devices using data or image processing specia...   \n15252   A61B2018/0069                                {using fuzzy logic}   \n...               ...                                                ...   \n250685  Y10S707/99946         Object-oriented database structure network   \n250686  Y10S707/99947       Object-oriented database structure reference   \n250687  Y10S707/99948  Application of database or data structure, e.g...   \n250688  Y10S707/99951                       File or database maintenance   \n250703    Y10S715/968      interface for database querying and retrieval   \n\n       section class subclass group main_group  cpc_version   \n12725        A    61        B     1     000096         2023  \\\n13764        A    61        B     5       7264         2023   \n13897        A    61        B     6         52         2023   \n14016        A    61        B     8         52         2023   \n15252        A    61        B  2018       0069         2023   \n...        ...   ...      ...   ...        ...          ...   \n250685       Y    10        S   707      99946         2023   \n250686       Y    10        S   707      99947         2023   \n250687       Y    10        S   707      99948         2023   \n250688       Y    10        S   707      99951         2023   \n250703       Y    10        S   715        968         2023   \n\n       version https://git-lfs.github.com/spec/v1   \n12725                                         NaN  \\\n13764                                         NaN   \n13897                                         NaN   \n14016                                         NaN   \n15252                                         NaN   \n...                                           ...   \n250685                                        NaN   \n250686                                        NaN   \n250687                                        NaN   \n250688                                        NaN   \n250703                                        NaN   \n\n                                             cpc_taxonomy  \n12725   [(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...  \n13764   [(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...  \n13897   [(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...  \n14016   [(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...  \n15252   [(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...  \n...                                                   ...  \n250685  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...  \n250686  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...  \n250687  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...  \n250688  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...  \n250703  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...  \n\n[317 rows x 10 columns]",
       "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cpc_id</th>\n      <th>cpc_name</th>\n      <th>section</th>\n      <th>class</th>\n      <th>subclass</th>\n      <th>group</th>\n      <th>main_group</th>\n      <th>cpc_version</th>\n      <th>version https://git-lfs.github.com/spec/v1</th>\n      <th>cpc_taxonomy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12725</th>\n      <td>A61B1/000096</td>\n      <td>{using artificial intelligence}</td>\n      <td>A</td>\n      <td>61</td>\n      <td>B</td>\n      <td>1</td>\n      <td>000096</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...</td>\n    </tr>\n    <tr>\n      <th>13764</th>\n      <td>A61B5/7264</td>\n      <td>{Classification of physiological signals or da...</td>\n      <td>A</td>\n      <td>61</td>\n      <td>B</td>\n      <td>5</td>\n      <td>7264</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...</td>\n    </tr>\n    <tr>\n      <th>13897</th>\n      <td>A61B6/52</td>\n      <td>{Devices using data or image processing specia...</td>\n      <td>A</td>\n      <td>61</td>\n      <td>B</td>\n      <td>6</td>\n      <td>52</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...</td>\n    </tr>\n    <tr>\n      <th>14016</th>\n      <td>A61B8/52</td>\n      <td>{Devices using data or image processing specia...</td>\n      <td>A</td>\n      <td>61</td>\n      <td>B</td>\n      <td>8</td>\n      <td>52</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...</td>\n    </tr>\n    <tr>\n      <th>15252</th>\n      <td>A61B2018/0069</td>\n      <td>{using fuzzy logic}</td>\n      <td>A</td>\n      <td>61</td>\n      <td>B</td>\n      <td>2018</td>\n      <td>0069</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A61, MEDICAL OR VETE...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>250685</th>\n      <td>Y10S707/99946</td>\n      <td>Object-oriented database structure network</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>S</td>\n      <td>707</td>\n      <td>99946</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n    </tr>\n    <tr>\n      <th>250686</th>\n      <td>Y10S707/99947</td>\n      <td>Object-oriented database structure reference</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>S</td>\n      <td>707</td>\n      <td>99947</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n    </tr>\n    <tr>\n      <th>250687</th>\n      <td>Y10S707/99948</td>\n      <td>Application of database or data structure, e.g...</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>S</td>\n      <td>707</td>\n      <td>99948</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n    </tr>\n    <tr>\n      <th>250688</th>\n      <td>Y10S707/99951</td>\n      <td>File or database maintenance</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>S</td>\n      <td>707</td>\n      <td>99951</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n    </tr>\n    <tr>\n      <th>250703</th>\n      <td>Y10S715/968</td>\n      <td>interface for database querying and retrieval</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>S</td>\n      <td>715</td>\n      <td>968</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n    </tr>\n  </tbody>\n</table>\n<p>317 rows × 10 columns</p>\n</div>"
      },
-     "execution_count": 22,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "cpc_ids[cpc_ids[\"cpc_name\"].str.lower().str.contains(\"machine learn|neural network|deep learn|deep network|artificial intel*| big data|database|recommender system|computer vision|image processing|language model|language processing|fuzzy logic|principal component|image classification|video classification\", regex=True, na=False)]"
-   ]
+    "scope_df = cpc_ids[cpc_ids[\"cpc_name\"].str.lower().str.contains(\"machine learn|neural network|deep learn|deep network|artificial intel*| big data|database|recommender system|computer vision|image processing|language model|language processing|fuzzy logic|principal component|image classification|video classification\", regex=True, na=False)]\n",
+    "scope_df"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 58,
+   "id": "6c3baa5b",
+   "metadata": {},
    "outputs": [],
    "source": [
+    "scope_ids = scope_df[\"cpc_id\"].unique()\n",
+    "cpc_ids[\"data_scope\"] = cpc_ids[\"cpc_id\"].isin(scope_ids)\n",
     "cpc_ids.to_csv(f\"{outdir}/cpc_defs.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'WESTERN_CH_scope'"
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outdir"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "              cpc_id                                           cpc_name   \n0                  A                                  HUMAN NECESSITIES  \\\n1                A01  AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n2               A01B  SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n3           A01B1/00  Hand tools (edge trimmers for lawns A01G3/06  ...   \n4           A01B1/02  Spades; Shovels {(hand-operated dredgers E02F3...   \n...              ...                                                ...   \n260486  Y10T483/1873                                    Indexing matrix   \n260487  Y10T483/1882                                        Rotary disc   \n260488  Y10T483/1891                                      Chain or belt   \n260489    Y10T483/19                                      Miscellaneous   \n260490           NaN                                                NaN   \n\n       section class subclass group main_group  cpc_version   \n0            A  None     None  None       None         2023  \\\n1            A    01     None  None       None         2023   \n2            A    01        B  None       None         2023   \n3            A    01        B     1         00         2023   \n4            A    01        B     1         02         2023   \n...        ...   ...      ...   ...        ...          ...   \n260486       Y    10        T   483       1873         2023   \n260487       Y    10        T   483       1882         2023   \n260488       Y    10        T   483       1891         2023   \n260489       Y    10        T   483         19         2023   \n260490     NaN   NaN      NaN   NaN        NaN         2022   \n\n               version https://git-lfs.github.com/spec/v1   \n0                                                     NaN  \\\n1                                                     NaN   \n2                                                     NaN   \n3                                                     NaN   \n4                                                     NaN   \n...                                                   ...   \n260486                                                NaN   \n260487                                                NaN   \n260488                                                NaN   \n260489                                                NaN   \n260490  oid sha256:f138d6bdf2939ba576b96b633d81366123b...   \n\n                                             cpc_taxonomy  data_scope  \n0                                [(A, HUMAN NECESSITIES)]       False  \n1       [(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...       False  \n2       [(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...       False  \n3       [(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...       False  \n4       [(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...       False  \n...                                                   ...         ...  \n260486  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...       False  \n260487  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...       False  \n260488  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...       False  \n260489  [(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...       False  \n260490                                                 []       False  \n\n[260491 rows x 11 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cpc_id</th>\n      <th>cpc_name</th>\n      <th>section</th>\n      <th>class</th>\n      <th>subclass</th>\n      <th>group</th>\n      <th>main_group</th>\n      <th>cpc_version</th>\n      <th>version https://git-lfs.github.com/spec/v1</th>\n      <th>cpc_taxonomy</th>\n      <th>data_scope</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>A</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES)]</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A01</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>A</td>\n      <td>01</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A01B</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>A</td>\n      <td>01</td>\n      <td>B</td>\n      <td>None</td>\n      <td>None</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A01B1/00</td>\n      <td>Hand tools (edge trimmers for lawns A01G3/06  ...</td>\n      <td>A</td>\n      <td>01</td>\n      <td>B</td>\n      <td>1</td>\n      <td>00</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A01B1/02</td>\n      <td>Spades; Shovels {(hand-operated dredgers E02F3...</td>\n      <td>A</td>\n      <td>01</td>\n      <td>B</td>\n      <td>1</td>\n      <td>02</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(A, HUMAN NECESSITIES), (A01, AGRICULTURE; FO...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>260486</th>\n      <td>Y10T483/1873</td>\n      <td>Indexing matrix</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>T</td>\n      <td>483</td>\n      <td>1873</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260487</th>\n      <td>Y10T483/1882</td>\n      <td>Rotary disc</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>T</td>\n      <td>483</td>\n      <td>1882</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260488</th>\n      <td>Y10T483/1891</td>\n      <td>Chain or belt</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>T</td>\n      <td>483</td>\n      <td>1891</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260489</th>\n      <td>Y10T483/19</td>\n      <td>Miscellaneous</td>\n      <td>Y</td>\n      <td>10</td>\n      <td>T</td>\n      <td>483</td>\n      <td>19</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[(Y, GENERAL TAGGING OF NEW TECHNOLOGICAL DEVE...</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260490</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2022</td>\n      <td>oid sha256:f138d6bdf2939ba576b96b633d81366123b...</td>\n      <td>[]</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>260491 rows × 11 columns</p>\n</div>"
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cpc_ids"
    ],
    "metadata": {
     "collapsed": false