diff --git a/GetPhenopackets.ipynb b/GetPhenopackets.ipynb
index c438eaf8..2a4bc784 100644
--- a/GetPhenopackets.ipynb
+++ b/GetPhenopackets.ipynb
@@ -628,7 +628,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "We found 390 cohorts\n",
+      "We found 391 cohorts\n",
       "Wrote phenopacket collection MarkDown file to docs/collections.md\n"
      ]
     }
diff --git a/docs/collections.md b/docs/collections.md
index 873f6e87..a267695b 100644
--- a/docs/collections.md
+++ b/docs/collections.md
@@ -251,6 +251,7 @@ Phenopacket store is a repository of [GA4GH phenopackets](https://pubmed.ncbi.nl
 |[WFS1](notebooks/WFS1/WFS1_Wolfram_syndrome_1_individuals.ipynb){:target="_blank"}|10 Phenopackets;[Wolfram syndrome 1](https://omim.org/entry/222300){:target="_blank"}|
 |[COL11A1](notebooks/COL11A1/COL11A1_STL2_individuals.ipynb){:target="_blank"}|2 Phenopackets;[Stickler syndrome, type II](https://omim.org/entry/604841){:target="_blank"}|
 |[SP7](notebooks/SP7/SP7_OI12_individuals.ipynb){:target="_blank"}|1 Phenopackets;[Osteogenesis imperfecta, type XII](https://omim.org/entry/613849){:target="_blank"}|
+|[DOCK11](notebooks/DOCK11/DOCK11_ADMIDX_individuals.ipynb){:target="_blank"}|12 Phenopackets;[Autoinflammatory disease, multisystem, with immune dysregulation, X-linked](https://omim.org/entry/301109){:target="_blank"}|
 |[TBX1](notebooks/TBX1/TBX1_Yagi_2003.ipynb){:target="_blank"}|5 Phenopackets;[DiGeorge syndrome](https://omim.org/entry/188400){:target="_blank"}|
 |[KCNT1](notebooks/KCNT1/KCNT1_DEE14_individuals.ipynb){:target="_blank"}|8 Phenopackets;[Developmental and epileptic encephalopathy 14](https://omim.org/entry/614959){:target="_blank"}|
 |[SKIC3](notebooks/SKIC3/SKIC3_THES1_individuals.ipynb){:target="_blank"}|2 Phenopackets;[Trichohepatoenteric syndrome 1](https://omim.org/entry/222470){:target="_blank"}|
diff --git a/ppktstore_stats.ipynb b/ppktstore_stats.ipynb
index c7a3bdc2..d6c55587 100644
--- a/ppktstore_stats.ipynb
+++ b/ppktstore_stats.ipynb
@@ -30,15 +30,10 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "Not a file: all_phenopackets.zip",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m stats \u001b[38;5;241m=\u001b[39m \u001b[43mPPKtStoreStats\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_zipfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall_phenopackets.zip\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/GIT/phenopacket-store/src/ppktstore/stats/_ppkt_store_stats.py:31\u001b[0m, in \u001b[0;36mPPKtStoreStats.__init__\u001b[0;34m(self, input_zipfile)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"input a dataframe with the data about the release\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03mIn a typical use case this class would be used to check the release prior to uploading the file to GitHub.\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;124;03m    ValueError: if the file does not end with the suffix \".zip\"\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(input_zipfile):\n\u001b[0;32m---> 31\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot a file: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minput_zipfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m input_zipfile\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.zip\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`input_zipfile` must point to a ZIP archive with suffix .zip, but was \u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00minput_zipfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: Not a file: all_phenopackets.zip"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracted 5522 phenopackets in 391 cohorts.\n"
      ]
     }
    ],
@@ -48,9 +43,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "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>disease</th>\n",
+       "      <th>disease_id</th>\n",
+       "      <th>patient_id</th>\n",
+       "      <th>gene</th>\n",
+       "      <th>allele_1</th>\n",
+       "      <th>allele_2</th>\n",
+       "      <th>PMID</th>\n",
+       "      <th>cohort</th>\n",
+       "      <th>filename</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Neurodevelopmental disorder with intracranial hemorrhage, seizures, and spasticity</td>\n",
+       "      <td>OMIM:620371</td>\n",
+       "      <td>Individual KCHYD24-1</td>\n",
+       "      <td>ESAM</td>\n",
+       "      <td>NM_138961.3:c.605T&gt;G</td>\n",
+       "      <td>NM_138961.3:c.605T&gt;G</td>\n",
+       "      <td>PMID:36996813</td>\n",
+       "      <td>ESAM</td>\n",
+       "      <td>PMID_36996813_IndividualKCHYD24-1.json</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Neurodevelopmental disorder with intracranial hemorrhage, seizures, and spasticity</td>\n",
+       "      <td>OMIM:620371</td>\n",
+       "      <td>Individual 1</td>\n",
+       "      <td>ESAM</td>\n",
+       "      <td>NM_138961.3:c.115del</td>\n",
+       "      <td>NM_138961.3:c.115del</td>\n",
+       "      <td>PMID:36996813</td>\n",
+       "      <td>ESAM</td>\n",
+       "      <td>PMID_36996813_Individual1.json</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                              disease  \\\n",
+       "0  Neurodevelopmental disorder with intracranial hemorrhage, seizures, and spasticity   \n",
+       "1  Neurodevelopmental disorder with intracranial hemorrhage, seizures, and spasticity   \n",
+       "\n",
+       "    disease_id            patient_id  gene              allele_1  \\\n",
+       "0  OMIM:620371  Individual KCHYD24-1  ESAM  NM_138961.3:c.605T>G   \n",
+       "1  OMIM:620371          Individual 1  ESAM  NM_138961.3:c.115del   \n",
+       "\n",
+       "               allele_2           PMID cohort  \\\n",
+       "0  NM_138961.3:c.605T>G  PMID:36996813   ESAM   \n",
+       "1  NM_138961.3:c.115del  PMID:36996813   ESAM   \n",
+       "\n",
+       "                                 filename  \n",
+       "0  PMID_36996813_IndividualKCHYD24-1.json  \n",
+       "1          PMID_36996813_Individual1.json  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = stats.get_df()\n",
     "df.head(2)"
@@ -58,9 +137,145 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Got 3109 unique HPOs\n"
+     ]
+    },
+    {
+     "data": {
+      "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>item</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>version</td>\n",
+       "      <td>0.1.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>phenopackets</td>\n",
+       "      <td>5522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>diseases</td>\n",
+       "      <td>438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>genes</td>\n",
+       "      <td>392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>alleles</td>\n",
+       "      <td>3321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PMIDs</td>\n",
+       "      <td>798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>individuals per disease (max)</td>\n",
+       "      <td>463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>individuals per disease (min)</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>individuals per disease (mean)</td>\n",
+       "      <td>12.607306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>individuals per disease (median)</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>genes associated with a single disease</td>\n",
+       "      <td>363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>genes associated with two diseases</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>genes associated with multiple diseases</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>gene with maximum number of diseases</td>\n",
+       "      <td>SCO2 with 7 associated diseases</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>total HPO terms used</td>\n",
+       "      <td>3109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       item                            value\n",
+       "0                                   version                           0.1.15\n",
+       "1                              phenopackets                             5522\n",
+       "2                                  diseases                              438\n",
+       "3                                     genes                              392\n",
+       "4                                   alleles                             3321\n",
+       "5                                     PMIDs                              798\n",
+       "6             individuals per disease (max)                              463\n",
+       "7             individuals per disease (min)                                1\n",
+       "8            individuals per disease (mean)                        12.607306\n",
+       "9          individuals per disease (median)                              4.0\n",
+       "10   genes associated with a single disease                              363\n",
+       "11       genes associated with two diseases                               21\n",
+       "12  genes associated with multiple diseases                                8\n",
+       "13     gene with maximum number of diseases  SCO2 with 7 associated diseases\n",
+       "14                     total HPO terms used                             3109"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats_d = stats.get_descriptive_stats()\n",
     "items = list()\n",
@@ -79,9 +294,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "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>disease</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>OMIM:620371</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OMIM:607259</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       disease  count\n",
+       "0  OMIM:620371     14\n",
+       "1  OMIM:607259      3"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "counts_per_disease = stats.get_counts_per_disease_df()\n",
     "counts_per_disease.head(2)"
@@ -89,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,9 +393,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2100x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_with_max(\n",
     "    disease_counts=counts_per_disease[\"count\"],\n",
@@ -148,9 +425,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No problems found.\n"
+     ]
+    }
+   ],
    "source": [
     "df = stats.check_disease_id()"
    ]
@@ -165,9 +450,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No candidate duplicates found for ERI1\n"
+     ]
+    }
+   ],
    "source": [
     "input_zipfile=\"all_phenopackets.zip\"\n",
     "df = stats.show_possible_duplicates_by_variant(\"ERI1\")"
@@ -183,27 +476,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All phenopackets had at least one variant\n"
+     ]
+    }
+   ],
    "source": [
     "df = stats.find_phenopackets_with_no_variants()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No candidate duplicates found for ARPC5\n"
+     ]
+    },
+    {
+     "data": {
+      "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.show_possible_duplicates_by_variant( \"ARPC5\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All phenopackets had a disease diagnosis\n"
+     ]
+    },
+    {
+     "data": {
+      "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stats.find_phenopackets_with_no_disease()"
    ]
diff --git a/src/ppktstore/stats/_ppkt_store_stats.py b/src/ppktstore/stats/_ppkt_store_stats.py
index 72d1abec..aba21464 100644
--- a/src/ppktstore/stats/_ppkt_store_stats.py
+++ b/src/ppktstore/stats/_ppkt_store_stats.py
@@ -41,6 +41,7 @@ def get_cohort_to_phenopacket_d(self):
   
     def _extract_all_phenopackets_df(self) -> pd.DataFrame:
         all_ppkt = None
+        column_names = ['disease', 'disease_id', 'patient_id', 'gene', 'allele_1', 'allele_2', 'PMID', 'cohort', 'filename']
         for info in self._archive.infolist():
             if info.is_dir():
                 continue
@@ -58,11 +59,11 @@ def _extract_all_phenopackets_df(self) -> pd.DataFrame:
                     continue
                 L = line.decode("utf-8")
                 fields = L.strip().split("\t")
-                if len(fields) != 8:
+                if len(fields) != len(column_names):
                     raise ValueError(f"Malformed line with {len(fields)} fields: {L}")
                 list_of_lists.append(fields)
-        columns = ['disease', 'disease_id', 'patient_id', 'gene', 'allele_1', 'allele_2', 'PMID','filename']
-        return pd.DataFrame(data=list_of_lists, columns=columns)
+        
+        return pd.DataFrame(data=list_of_lists, columns=column_names)
     
     def _extract_all_cohort_phenopackets(self) -> typing.Dict:
         cohort_to_ppkt_d = defaultdict(list)