diff --git a/.gitignore b/.gitignore
index aed4ae2..77ce7f5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -133,3 +133,4 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+cmr_cw_searches/*.json
diff --git a/access_metrics/athena-s3-access-metrics.ipynb b/access_metrics/athena-s3-access-metrics.ipynb
new file mode 100644
index 0000000..c8211c3
--- /dev/null
+++ b/access_metrics/athena-s3-access-metrics.ipynb
@@ -0,0 +1,1062 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "da0e6f40",
+   "metadata": {},
+   "source": [
+    "# AWS Athena Queries for S3 Data Access Metrics\n",
+    "\n",
+    "This notebook runs queries on S3 access logs to deliver metrics on data usage from s3://nasa-maap-data-store, the primary MAAP data store bucket.\n",
+    "\n",
+    "Pre-requisites:\n",
+    "* S3 Access Logs have been enabled and are being delivered to a bucket \n",
+    "* A database already exists in Athena which parses logs delivered to that bucket\n",
+    "* Queries have been developed and saved in Athena"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "29e627d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6c787e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Must run this with MAAP MCP Ops AWS credentials set in your environment\n",
+    "client = boto3.client('athena')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6a8db386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def start_query(query_id):\n",
+    "    response = client.batch_get_named_query(\n",
+    "        NamedQueryIds=[\n",
+    "            # NASA MAAP Requests for BytesByCollection since October 2022\n",
+    "            query_id,\n",
+    "        ]\n",
+    "    )\n",
+    "    query_string = response['NamedQueries'][0]['QueryString']\n",
+    "    print(f\"Starting query {query_string}\")\n",
+    "    response = client.start_query_execution(QueryString=query_string)\n",
+    "    query_execution_id = response['QueryExecutionId']\n",
+    "    return query_execution_id\n",
+    "\n",
+    "def wait_for_finished(query_execution_id):\n",
+    "    state = 'RUNNING'\n",
+    "    status = None\n",
+    "    while state == 'RUNNING':\n",
+    "        status = client.get_query_execution(\n",
+    "            QueryExecutionId=query_execution_id\n",
+    "        )\n",
+    "        state = status['QueryExecution']['Status']['State']\n",
+    "        if state == 'RUNNING':\n",
+    "            time.sleep(5)\n",
+    "    return status\n",
+    "\n",
+    "def get_execution_results(query_execution_id):\n",
+    "    # Could use https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/athena/paginator/GetQueryResults.html\n",
+    "    # to paginate, however we only want top results.\n",
+    "    results = client.get_query_results(\n",
+    "        QueryExecutionId=query_execution_id,\n",
+    "        MaxResults=500\n",
+    "    )\n",
+    "    return results\n",
+    "\n",
+    "top_results_cap = 25\n",
+    "def create_results_dataframe(results_response, no_results=top_results_cap):\n",
+    "    results_list = []\n",
+    "    header_row = results_response['ResultSet']['Rows'][0]['Data']\n",
+    "    columns = [column['VarCharValue'] for column in header_row]\n",
+    "    # skip the first row which is the header\n",
+    "    for result in results_response['ResultSet']['Rows'][1:]:\n",
+    "        if len(results_list) == no_results:\n",
+    "            break\n",
+    "        collection_result = {}\n",
+    "        for idx, column in enumerate(columns):\n",
+    "            if 'bytessent' in column:\n",
+    "                collection_result['gb_sent'] = int(result['Data'][idx]['VarCharValue'])/1.074e+9\n",
+    "#             elif 'month' in column:\n",
+    "#                 collection_result['month'] = int(result['Data'][idx]['VarCharValue'])\n",
+    "            else:\n",
+    "                collection_result[column] = result['Data'][idx].get('VarCharValue', '')\n",
+    "        # Boreal TIF files were not stored in sub-directory\n",
+    "        if collection_result['collection'] != '' and not 'boreal_agb' in collection_result['collection']:\n",
+    "            results_list.append(collection_result)\n",
+    "    df = pd.DataFrame.from_dict(results_list)\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ae3bf83",
+   "metadata": {},
+   "source": [
+    "Reference: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/athena.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0907724d",
+   "metadata": {},
+   "source": [
+    "## Get NASA MAAP total data requested by collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "73ab1e77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting query SELECT\n",
+      "  collection, \"sum\"(bytessent) bytessent_sum\n",
+      "FROM\n",
+      "    (\n",
+      "    SELECT split_part(key, '/', 3) as collection, bytessent\n",
+      "    FROM \"nasa_maap_data_store_access_logs_db\".\"logs\"\n",
+      "    WHERE (operation = 'REST.GET.OBJECT' OR operation = 'REST.COPY.PART')\n",
+      "    AND date_parse(requestdatetime, '%d/%b/%Y:%H:%i:%s +0000') >= date_parse('01/Oct/2022:00:00:00 +0000', '%d/%b/%Y:%H:%i:%s +0000')\n",
+      "    AND remoteip NOT LIKE '90.84.45%'\n",
+      "    )\n",
+      "GROUP BY collection\n",
+      "ORDER BY bytessent_sum DESC;    \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2e1fae58-b819-4eef-9e6a-f56c0e6ca5cc'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We could use list_named_queries if we want to find the query ID by name.\n",
+    "# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/athena/client/list_named_queries.html\n",
+    "nasa_query_uuid = 'dae9ead8-0fa2-4ee0-ad0f-88f2b7a77f7d'\n",
+    "query_execution_id = start_query(nasa_query_uuid)\n",
+    "query_execution_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8c24ce31",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/jh/_03qbqf130l8hjh8rpc6f4_c0000gn/T/ipykernel_14403/4098655594.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwait_for_finished\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_execution_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/var/folders/jh/_03qbqf130l8hjh8rpc6f4_c0000gn/T/ipykernel_14403/1774503723.py\u001b[0m in \u001b[0;36mwait_for_finished\u001b[0;34m(query_execution_id)\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'QueryExecution'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'State'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'RUNNING'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m             \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "wait_for_finished(query_execution_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3d32d481",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['collection', 'bytessent_sum']\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = get_execution_results(query_execution_id)\n",
+    "df = create_results_dataframe(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a5558ebf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "193306.30530605032"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['gb_sent'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edeb733d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[0:4].plot.bar(x='collection', y='gb_sent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80a4fb03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[4:].plot.bar(x='collection')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d7dce75",
+   "metadata": {},
+   "source": [
+    "## Get ESA MAAP total data requested by collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e60ce46c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting query SELECT\n",
+      "  collection, \"sum\"(bytessent) bytessent_sum\n",
+      "FROM\n",
+      "    (\n",
+      "    SELECT split_part(key, '/', 3) as collection, bytessent\n",
+      "    FROM \"nasa_maap_data_store_access_logs_db\".\"logs\"\n",
+      "    WHERE (operation = 'REST.GET.OBJECT' OR operation = 'REST.COPY.PART')\n",
+      "    AND date_parse(requestdatetime, '%d/%b/%Y:%H:%i:%s +0000') >= date_parse('01/Oct/2022:00:00:00 +0000', '%d/%b/%Y:%H:%i:%s +0000')\n",
+      "    AND remoteip LIKE '90.84.45%'\n",
+      "    )\n",
+      "GROUP BY collection\n",
+      "ORDER BY bytessent_sum DESC;    \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'712ba35b-5a96-4082-a89e-3de92055d8b5'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "esa_query_uuid = '907c98dc-7457-45af-9a8d-e57b49197d97'\n",
+    "query_execution_id = start_query(esa_query_uuid)\n",
+    "query_execution_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "aa6cff0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'QueryExecution': {'QueryExecutionId': '712ba35b-5a96-4082-a89e-3de92055d8b5',\n",
+       "  'Query': 'SELECT\\n  collection, \"sum\"(bytessent) bytessent_sum\\nFROM\\n    (\\n    SELECT split_part(key, \\'/\\', 3) as collection, bytessent\\n    FROM \"nasa_maap_data_store_access_logs_db\".\"logs\"\\n    WHERE (operation = \\'REST.GET.OBJECT\\' OR operation = \\'REST.COPY.PART\\')\\n    AND date_parse(requestdatetime, \\'%d/%b/%Y:%H:%i:%s +0000\\') >= date_parse(\\'01/Oct/2022:00:00:00 +0000\\', \\'%d/%b/%Y:%H:%i:%s +0000\\')\\n    AND remoteip LIKE \\'90.84.45%\\'\\n    )\\nGROUP BY collection\\nORDER BY bytessent_sum DESC',\n",
+       "  'StatementType': 'DML',\n",
+       "  'ResultConfiguration': {'OutputLocation': 's3://maap-logging/athena-output/712ba35b-5a96-4082-a89e-3de92055d8b5.csv'},\n",
+       "  'QueryExecutionContext': {},\n",
+       "  'Status': {'State': 'SUCCEEDED',\n",
+       "   'SubmissionDateTime': datetime.datetime(2023, 4, 10, 15, 51, 47, 423000, tzinfo=tzlocal()),\n",
+       "   'CompletionDateTime': datetime.datetime(2023, 4, 10, 15, 53, 10, 217000, tzinfo=tzlocal())},\n",
+       "  'Statistics': {'EngineExecutionTimeInMillis': 82619,\n",
+       "   'DataScannedInBytes': 20935317004,\n",
+       "   'TotalExecutionTimeInMillis': 82794,\n",
+       "   'QueryQueueTimeInMillis': 152,\n",
+       "   'QueryPlanningTimeInMillis': 1458,\n",
+       "   'ServiceProcessingTimeInMillis': 23},\n",
+       "  'WorkGroup': 'primary',\n",
+       "  'EngineVersion': {'SelectedEngineVersion': 'AUTO',\n",
+       "   'EffectiveEngineVersion': 'Athena engine version 2'}},\n",
+       " 'ResponseMetadata': {'RequestId': '9d7df9e6-13fe-4f70-9883-e1e324f567bb',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'date': 'Mon, 10 Apr 2023 22:53:10 GMT',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '2518',\n",
+       "   'connection': 'keep-alive',\n",
+       "   'x-amzn-requestid': '9d7df9e6-13fe-4f70-9883-e1e324f567bb'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wait_for_finished(query_execution_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99b02108",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = get_execution_results(query_execution_id)\n",
+    "df = create_results_dataframe(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b278afd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['gb_sent'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13725343",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot.bar(x='collection', y='gb_sent')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eb4da05",
+   "metadata": {},
+   "source": [
+    "## Get collection volume requested by month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "97cf286f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting query WITH bytes_and_dates_collection_requests AS (\n",
+      "    SELECT \n",
+      "        split_part(key, '/', 3) as collection, \n",
+      "        bytessent, \n",
+      "        operation,\n",
+      "        date_parse(requestdatetime, '%d/%b/%Y:%H:%i:%s +0000') as parsed_requestdatetime,\n",
+      "        date_parse('01/Oct/2022:00:00:00 +0000', '%d/%b/%Y:%H:%i:%s +0000') as october_first_datetime\n",
+      "    FROM \n",
+      "        \"nasa_maap_data_store_access_logs_db\".\"logs\"\n",
+      ")\n",
+      "SELECT\n",
+      "    collection, \n",
+      "    SUM(bytessent) as bytessent_sum,\n",
+      "    EXTRACT(month FROM DATE_TRUNC('month', parsed_requestdatetime)) as month,\n",
+      "    EXTRACT(year FROM DATE_TRUNC('month', parsed_requestdatetime)) as year\n",
+      "FROM\n",
+      "    bytes_and_dates_collection_requests\n",
+      "WHERE \n",
+      "    (operation = 'REST.GET.OBJECT' OR operation = 'REST.COPY.PART')\n",
+      "    AND parsed_requestdatetime >= october_first_datetime\n",
+      "GROUP BY \n",
+      "    collection, DATE_TRUNC('month', parsed_requestdatetime)\n",
+      "ORDER BY \n",
+      "    bytessent_sum DESC;\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'afd4c78d-8c45-46fe-966a-52424f7e9716'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthlies_query_uuid = '443d7bad-e3ff-476f-99f3-9d50db08a3d4'\n",
+    "query_execution_id = start_query(monthlies_query_uuid)\n",
+    "query_execution_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "40612189",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'QueryExecution': {'QueryExecutionId': 'afd4c78d-8c45-46fe-966a-52424f7e9716',\n",
+       "  'Query': 'WITH bytes_and_dates_collection_requests AS (\\n    SELECT \\n        split_part(key, \\'/\\', 3) as collection, \\n        bytessent, \\n        operation,\\n        date_parse(requestdatetime, \\'%d/%b/%Y:%H:%i:%s +0000\\') as parsed_requestdatetime,\\n        date_parse(\\'01/Oct/2022:00:00:00 +0000\\', \\'%d/%b/%Y:%H:%i:%s +0000\\') as october_first_datetime\\n    FROM \\n        \"nasa_maap_data_store_access_logs_db\".\"logs\"\\n)\\nSELECT\\n    collection, \\n    SUM(bytessent) as bytessent_sum,\\n    EXTRACT(month FROM DATE_TRUNC(\\'month\\', parsed_requestdatetime)) as month,\\n    EXTRACT(year FROM DATE_TRUNC(\\'month\\', parsed_requestdatetime)) as year\\nFROM\\n    bytes_and_dates_collection_requests\\nWHERE \\n    (operation = \\'REST.GET.OBJECT\\' OR operation = \\'REST.COPY.PART\\')\\n    AND parsed_requestdatetime >= october_first_datetime\\nGROUP BY \\n    collection, DATE_TRUNC(\\'month\\', parsed_requestdatetime)\\nORDER BY \\n    bytessent_sum DESC',\n",
+       "  'StatementType': 'DML',\n",
+       "  'ResultConfiguration': {'OutputLocation': 's3://maap-logging/athena-output/afd4c78d-8c45-46fe-966a-52424f7e9716.csv'},\n",
+       "  'QueryExecutionContext': {},\n",
+       "  'Status': {'State': 'SUCCEEDED',\n",
+       "   'SubmissionDateTime': datetime.datetime(2023, 4, 12, 9, 48, 57, 149000, tzinfo=tzlocal()),\n",
+       "   'CompletionDateTime': datetime.datetime(2023, 4, 12, 9, 50, 11, 868000, tzinfo=tzlocal())},\n",
+       "  'Statistics': {'EngineExecutionTimeInMillis': 74536,\n",
+       "   'DataScannedInBytes': 20936665488,\n",
+       "   'TotalExecutionTimeInMillis': 74719,\n",
+       "   'QueryQueueTimeInMillis': 137,\n",
+       "   'QueryPlanningTimeInMillis': 1410,\n",
+       "   'ServiceProcessingTimeInMillis': 46},\n",
+       "  'WorkGroup': 'primary',\n",
+       "  'EngineVersion': {'SelectedEngineVersion': 'AUTO',\n",
+       "   'EffectiveEngineVersion': 'Athena engine version 2'}},\n",
+       " 'ResponseMetadata': {'RequestId': '20bbc4df-f643-4b58-a48b-cdf9a8fdd0f7',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'date': 'Wed, 12 Apr 2023 16:50:13 GMT',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '3384',\n",
+       "   'connection': 'keep-alive',\n",
+       "   'x-amzn-requestid': '20bbc4df-f643-4b58-a48b-cdf9a8fdd0f7'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wait_for_finished(query_execution_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "776fddd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = get_execution_results(query_execution_id)\n",
+    "df = create_results_dataframe(results, no_results=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5bbfdff2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "        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>collection</th>\n",
+       "      <th>gb_sent</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ATL03___004</td>\n",
+       "      <td>77758.916119</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>30729.569997</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>29977.977281</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>29027.594545</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>7813.718059</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       collection       gb_sent month  year\n",
+       "0     ATL03___004  77758.916119    10  2022\n",
+       "1  GEDI02_A___002  30729.569997     3  2023\n",
+       "2  GEDI02_A___002  29977.977281     2  2023\n",
+       "3  GEDI02_A___002  29027.594545     1  2023\n",
+       "4  GEDI02_A___002   7813.718059    10  2022"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "05cadc50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>collection</th>\n",
+       "      <th>gb_sent</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>month_padded</th>\n",
+       "      <th>year_month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ATL03___004</td>\n",
+       "      <td>77758.916119</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022-10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>30729.569997</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>03</td>\n",
+       "      <td>2023-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>29977.977281</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>02</td>\n",
+       "      <td>2023-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>29027.594545</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>01</td>\n",
+       "      <td>2023-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GEDI02_A___002</td>\n",
+       "      <td>7813.718059</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2022-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       collection       gb_sent month  year month_padded year_month\n",
+       "0     ATL03___004  77758.916119    10  2022           10    2022-10\n",
+       "1  GEDI02_A___002  30729.569997     3  2023           03    2023-03\n",
+       "2  GEDI02_A___002  29977.977281     2  2023           02    2023-02\n",
+       "3  GEDI02_A___002  29027.594545     1  2023           01    2023-01\n",
+       "4  GEDI02_A___002   7813.718059    10  2022           10    2022-10"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['month_padded'] = df['month'].astype(str).str.zfill(2)\n",
+    "df['year_month'] = df['year'] + '-' + df['month_padded']\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "27c67ff3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collection_usage_order = df.groupby(['collection'])['gb_sent'].sum().sort_values(ascending=False)\n",
+    "sorter = list(collection_usage_order.to_dict().keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "cc1bc64a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>collection</th>\n",
+       "      <th>ABLVIS1B___001</th>\n",
+       "      <th>ABLVIS2___001</th>\n",
+       "      <th>ABoVE_UAVSAR_PALSAR___1</th>\n",
+       "      <th>AFLVIS2___001</th>\n",
+       "      <th>ALOS_PSR_L1.5___1</th>\n",
+       "      <th>ALOS_PSR_RTC_HIGH___1</th>\n",
+       "      <th>ATL03___004</th>\n",
+       "      <th>ATL08___005</th>\n",
+       "      <th>AfriSAR_UAVSAR_Coreg_SLC___1</th>\n",
+       "      <th>GEDI01_B___002</th>\n",
+       "      <th>GEDI02_A___002</th>\n",
+       "      <th>GEDI02_B___002</th>\n",
+       "      <th>LVISF1B___001</th>\n",
+       "      <th>Landsat8_SurfaceReflectance___1</th>\n",
+       "      <th>SENTINEL-1B_DP_GRD_HIGH___1</th>\n",
+       "      <th>SRTMGL1_COD___001</th>\n",
+       "      <th>nceo-africa-2017</th>\n",
+       "      <th>polarimetric_ct___1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2022-10</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.922268</td>\n",
+       "      <td>2.794068</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77758.916119</td>\n",
+       "      <td>4851.762071</td>\n",
+       "      <td>0.580534</td>\n",
+       "      <td>7.330544</td>\n",
+       "      <td>7813.718059</td>\n",
+       "      <td>48.436226</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.267864</td>\n",
+       "      <td>1.935536</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-11</th>\n",
+       "      <td>11.777475</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.351730</td>\n",
+       "      <td>1.016025</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>470.775064</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>932.085596</td>\n",
+       "      <td>1829.296405</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.310327</td>\n",
+       "      <td>0.960981</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-12</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.012097</td>\n",
+       "      <td>1.020668</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.851324</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.252880</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6955.143644</td>\n",
+       "      <td>4.037604</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.413963</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.335608</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.658342</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>NaN</td>\n",
+       "      <td>29027.594545</td>\n",
+       "      <td>962.370020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.464365</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.230594</td>\n",
+       "      <td>0.965784</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-02</th>\n",
+       "      <td>0.926718</td>\n",
+       "      <td>2.687962</td>\n",
+       "      <td>0.927868</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.612287</td>\n",
+       "      <td>6.322079</td>\n",
+       "      <td>2.461199</td>\n",
+       "      <td>0.297239</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29977.977281</td>\n",
+       "      <td>493.173030</td>\n",
+       "      <td>4.497289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.278087</td>\n",
+       "      <td>0.814166</td>\n",
+       "      <td>0.396319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-03</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.497771</td>\n",
+       "      <td>0.238699</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.901026</td>\n",
+       "      <td>0.988253</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30729.569997</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.497289</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.952453</td>\n",
+       "      <td>0.265160</td>\n",
+       "      <td>43.552265</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-04</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.389229</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.315981</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1338.400901</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.952050</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "collection  ABLVIS1B___001  ABLVIS2___001  ABoVE_UAVSAR_PALSAR___1  \\\n",
+       "year_month                                                           \n",
+       "2022-10                NaN            NaN                 7.922268   \n",
+       "2022-11          11.777475            NaN                 2.351730   \n",
+       "2022-12                NaN            NaN                 2.012097   \n",
+       "2023-01                NaN            NaN                 1.658342   \n",
+       "2023-02           0.926718       2.687962                 0.927868   \n",
+       "2023-03                NaN       0.497771                 0.238699   \n",
+       "2023-04                NaN            NaN                 0.389229   \n",
+       "\n",
+       "collection  AFLVIS2___001  ALOS_PSR_L1.5___1  ALOS_PSR_RTC_HIGH___1  \\\n",
+       "year_month                                                            \n",
+       "2022-10          2.794068                NaN                    NaN   \n",
+       "2022-11          1.016025                NaN                    NaN   \n",
+       "2022-12          1.020668                NaN                    NaN   \n",
+       "2023-01               NaN                NaN                    NaN   \n",
+       "2023-02               NaN           4.612287               6.322079   \n",
+       "2023-03               NaN           0.901026               0.988253   \n",
+       "2023-04               NaN                NaN                    NaN   \n",
+       "\n",
+       "collection   ATL03___004  ATL08___005  AfriSAR_UAVSAR_Coreg_SLC___1  \\\n",
+       "year_month                                                            \n",
+       "2022-10     77758.916119  4851.762071                      0.580534   \n",
+       "2022-11              NaN   470.775064                           NaN   \n",
+       "2022-12         2.851324          NaN                      2.252880   \n",
+       "2023-01              NaN          NaN                           NaN   \n",
+       "2023-02         2.461199     0.297239                           NaN   \n",
+       "2023-03              NaN          NaN                           NaN   \n",
+       "2023-04              NaN          NaN                      0.315981   \n",
+       "\n",
+       "collection  GEDI01_B___002  GEDI02_A___002  GEDI02_B___002  LVISF1B___001  \\\n",
+       "year_month                                                                  \n",
+       "2022-10           7.330544     7813.718059       48.436226            NaN   \n",
+       "2022-11                NaN      932.085596     1829.296405            NaN   \n",
+       "2022-12                NaN     6955.143644        4.037604            NaN   \n",
+       "2023-01                NaN    29027.594545      962.370020            NaN   \n",
+       "2023-02                NaN    29977.977281      493.173030       4.497289   \n",
+       "2023-03                NaN    30729.569997             NaN       4.497289   \n",
+       "2023-04                NaN     1338.400901             NaN            NaN   \n",
+       "\n",
+       "collection  Landsat8_SurfaceReflectance___1  SENTINEL-1B_DP_GRD_HIGH___1  \\\n",
+       "year_month                                                                 \n",
+       "2022-10                                 NaN                          NaN   \n",
+       "2022-11                                 NaN                          NaN   \n",
+       "2022-12                            0.413963                          NaN   \n",
+       "2023-01                            0.464365                          NaN   \n",
+       "2023-02                                 NaN                          NaN   \n",
+       "2023-03                                 NaN                     0.952453   \n",
+       "2023-04                                 NaN                          NaN   \n",
+       "\n",
+       "collection  SRTMGL1_COD___001  nceo-africa-2017  polarimetric_ct___1  \n",
+       "year_month                                                            \n",
+       "2022-10              0.267864          1.935536                  NaN  \n",
+       "2022-11              0.310327          0.960981                  NaN  \n",
+       "2022-12                   NaN          0.335608                  NaN  \n",
+       "2023-01              0.230594          0.965784                  NaN  \n",
+       "2023-02              0.278087          0.814166             0.396319  \n",
+       "2023-03              0.265160         43.552265                  NaN  \n",
+       "2023-04                   NaN          2.952050                  NaN  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collection_pivot = pd.pivot_table(df,\n",
+    "              index='year_month', \n",
+    "              columns='collection',\n",
+    "              values='gb_sent')\n",
+    "collection_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "0f6f5e3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2880x1440 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sort the collections by most used first and plot\n",
+    "axes = collection_pivot.reindex(sorter, axis=1).plot.bar(\n",
+    "    subplots=True,\n",
+    "    layout=(5,6), \n",
+    "    figsize=(40, 20),\n",
+    "    legend=False,\n",
+    "    ylabel=\"GB\",\n",
+    "    fontsize=14\n",
+    ")\n",
+    "\n",
+    "# Rotate the xtick labels by 30 degrees\n",
+    "for row in axes:\n",
+    "    for ax in row:\n",
+    "        ax.tick_params(axis='x', rotation=30)\n",
+    "\n",
+    "# https://stackoverflow.com/a/66288048/237354\n",
+    "for i, row in enumerate(axes):\n",
+    "    for j, ax in enumerate(row):\n",
+    "        if ((i*6)+j) < len(sorter):\n",
+    "            ax.set_title(f'{sorter[(i*6)+j]}', fontsize=20)            \n",
+    "        \n",
+    "# magic formattting https://stackoverflow.com/a/66288048/237354\n",
+    "fig = axes[0,0].get_figure()\n",
+    "_ = fig.suptitle(f'MAAP Bucket Usage by Month per Collection', fontsize=32, y=0.94)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - Collections search by concept_id.ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - Collections search by concept_id.ipynb
new file mode 100644
index 0000000..a0bbabd
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - Collections search by concept_id.ipynb	
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ad04272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bc7a35fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment\n",
+    "# !env"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "da44182f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 238 ms, sys: 60.4 ms, total: 298 ms\n",
+      "Wall time: 9min 11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \"Searching for collections\"\n",
+    "| filter @message like \"concept_id\"\n",
+    "| parse @message /concept_id \"(?<concept_id>\\S+)\"/\n",
+    "| stats count(*) as number_queries by concept_id\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(60)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "39638c46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"concept_id_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "51bb5e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = json.loads(open(filename).read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "32f84cd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'number_queries': '1092'},\n",
+       " {'concept_id': 'C1201460047-NASA_MAAP', 'number_queries': '27'},\n",
+       " {'concept_id': 'C1202028193-NASA_MAAP', 'number_queries': '22'},\n",
+       " {'concept_id': 'C1200110748-NASA_MAAP', 'number_queries': '17'},\n",
+       " {'concept_id': 'C1201746153-NASA_MAAP', 'number_queries': '15'},\n",
+       " {'concept_id': 'C1202077968-NASA_MAAP', 'number_queries': '15'},\n",
+       " {'concept_id': 'C1201702030-NASA_MAAP', 'number_queries': '12'},\n",
+       " {'concept_id': 'C1200110769-NASA_MAAP', 'number_queries': '12'},\n",
+       " {'concept_id': 'C1201746156-NASA_MAAP', 'number_queries': '12'},\n",
+       " {'concept_id': 'C1200110729-NASA_MAAP', 'number_queries': '10'},\n",
+       " {'concept_id': 'C1201300747-NASA_MAAP', 'number_queries': '9'},\n",
+       " {'concept_id': 'C1200000522-NASA_MAAP', 'number_queries': '9'},\n",
+       " {'concept_id': 'C1201796172-NASA_MAAP', 'number_queries': '9'},\n",
+       " {'concept_id': 'C1200115748-ESA_MAAP', 'number_queries': '6'},\n",
+       " {'concept_id': 'C1200231029-NASA_MAAP', 'number_queries': '6'},\n",
+       " {'concept_id': 'C1201309827-NASA_MAAP', 'number_queries': '6'},\n",
+       " {'concept_id': 'C1200115768-NASA_MAAP', 'number_queries': '5'},\n",
+       " {'concept_id': 'C1200116827-NASA_MAAP', 'number_queries': '5'},\n",
+       " {'concept_id': 'C2237824918-ORNL_CLOUD', 'number_queries': '4'},\n",
+       " {'concept_id': 'C1200015188-NASA_MAAP', 'number_queries': '4'},\n",
+       " {'concept_id': 'C1200109552-ESA_MAAP', 'number_queries': '4'},\n",
+       " {'concept_id': 'C1201702032-NASA_MAAP', 'number_queries': '4'},\n",
+       " {'concept_id': 'C1200116818-NASA_MAAP', 'number_queries': '3'},\n",
+       " {'concept_id': 'C1202077988-NASA_MAAP', 'number_queries': '3'},\n",
+       " {'concept_id': 'C1200097475-NASA_MAAP', 'number_queries': '3'}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "limit = 25\n",
+    "for result in data['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data[0:limit]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "12bc939d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No entries found for C2237824918-ORNL_CLOUD\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmr_url = 'https://cmr.maap-project.org'\n",
+    "collections_search_url = f\"{cmr_url}/search/collections.json\"\n",
+    "granules_search_url = f\"{cmr_url}/search/granules.json\"\n",
+    "results_dict = {}\n",
+    "for entry in restructured_data[0:limit]:\n",
+    "    # There's 1 blank concept_id entry\n",
+    "    if 'concept_id' in entry:\n",
+    "        concept_id = entry['concept_id']\n",
+    "        if concept_id.startswith('C'):\n",
+    "            cmr_response = requests.get(f\"{collections_search_url}?concept_id={concept_id}\")\n",
+    "        else:\n",
+    "            # Are we interested in granules searches? very limited results besides\n",
+    "            next\n",
+    "            #cmr_response = requests.get(f\"{granules_search_url}?concept_id={concept_id}\")\n",
+    "        number_queries = entry['number_queries']\n",
+    "        cmr_data = json.loads(cmr_response.text)['feed']['entry']\n",
+    "        if len(cmr_data) > 0:\n",
+    "            short_name = json.loads(cmr_response.text)['feed']['entry'][0]['short_name']\n",
+    "            results_dict[short_name] = int(number_queries)\n",
+    "        else:\n",
+    "            print(f\"No entries found for {entry['concept_id']}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8bcca790",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'GEDI02_B': 27,\n",
+       " 'GEDI_L4A_AGB_Density_V2_1_2056': 22,\n",
+       " 'ABLVIS1B': 17,\n",
+       " 'GEDI_L4B_Gridded_Biomass_2017': 15,\n",
+       " 'GEDI_L3_LandSurface_Metrics_V2_1952': 12,\n",
+       " 'Landsat8_SurfaceReflectance': 12,\n",
+       " 'AFLVIS2': 10,\n",
+       " 'ATL03': 9,\n",
+       " 'SRTMGL1': 9,\n",
+       " 'GEDI02_A': 6,\n",
+       " 'L2_USER_DATA': 6,\n",
+       " 'AfriSAR_AGB_Maps_1681': 5,\n",
+       " 'GEDI_CalVal_Field_Data': 5,\n",
+       " 'GEDI01_B': 4,\n",
+       " 'AFRISAR_DLR': 4,\n",
+       " 'GEDI_L4A_AGB_Density_V2_1986': 4,\n",
+       " 'ATL08': 3,\n",
+       " 'test_collection_feb23': 3,\n",
+       " 'Global_PALSAR2_PALSAR_Mosiac': 3}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dict = {k: v for k, v in sorted(results_dict.items(), key=lambda item: item[1], reverse=True)}\n",
+    "results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7e68605",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data set\n",
+    "collections = list(results_dict.keys())\n",
+    "number_queries = list(results_dict.values())\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(collections, number_queries, color='y')\n",
+    "addlabels(collections, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - Granules search by collection_concept_id .ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - Granules search by collection_concept_id .ipynb
new file mode 100644
index 0000000..de1436c
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - Granules search by collection_concept_id .ipynb	
@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ad04272",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bc7a35fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment\n",
+    "# !env"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "da44182f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 235 ms, sys: 45.5 ms, total: 281 ms\n",
+      "Wall time: 9min 11s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \"Searching for granules\"\n",
+    "| filter @message like \"collection_concept_id\"\n",
+    "| parse @message /collection_concept_id \"(?<collection_concept_id>\\S+)\"/\n",
+    "| stats count(*) as number_queries by collection_concept_id\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(60)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "39638c46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"granules_collection_concept_id_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "51bb5e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = json.loads(open(filename).read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "32f84cd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'collection_concept_id': 'C1202028193-NASA_MAAP', 'number_queries': '23445'},\n",
+       " {'collection_concept_id': 'C1201746156-NASA_MAAP', 'number_queries': '6140'},\n",
+       " {'collection_concept_id': 'C1201746153-NASA_MAAP', 'number_queries': '792'},\n",
+       " {'collection_concept_id': 'C1201460047-NASA_MAAP', 'number_queries': '703'},\n",
+       " {'collection_concept_id': 'C1200231010-NASA_MAAP', 'number_queries': '260'},\n",
+       " {'collection_concept_id': 'C1200110748-NASA_MAAP', 'number_queries': '173'},\n",
+       " {'number_queries': '114'},\n",
+       " {'collection_concept_id': 'C1201702030-NASA_MAAP', 'number_queries': '88'},\n",
+       " {'collection_concept_id': 'C1200231029-NASA_MAAP', 'number_queries': '57'},\n",
+       " {'collection_concept_id': 'C1201702032-NASA_MAAP', 'number_queries': '20'},\n",
+       " {'collection_concept_id': 'C1200109552-ESA_MAAP', 'number_queries': '16'},\n",
+       " {'collection_concept_id': 'C1200015188-NASA_MAAP', 'number_queries': '4'},\n",
+       " {'collection_concept_id': 'C1200271393-NASA_MAAP', 'number_queries': '3'},\n",
+       " {'collection_concept_id': 'C2067521974-ORNL_CLOUD', 'number_queries': '3'},\n",
+       " {'collection_concept_id': 'C1200015149-NASA_MAAP', 'number_queries': '3'},\n",
+       " {'collection_concept_id': 'C1201796172-NASA_MAAP', 'number_queries': '2'},\n",
+       " {'collection_concept_id': 'C1908348134-LPDAAC_ECS', 'number_queries': '2'},\n",
+       " {'collection_concept_id': 'C1201460047', 'number_queries': '1'},\n",
+       " {'collection_concept_id': 'GlobCover_09', 'number_queries': '1'},\n",
+       " {'collection_concept_id': 'C1234567-PROV1', 'number_queries': '1'},\n",
+       " {'collection_concept_id': '=GlobCover_09', 'number_queries': '1'},\n",
+       " {'collection_concept_id': 'C1200109552', 'number_queries': '1'}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "limit = 25\n",
+    "for result in data['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data[0:limit]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "12bc939d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No entries found for C2067521974-ORNL_CLOUD\n",
+      "No entries found for C1908348134-LPDAAC_ECS\n",
+      "No entries found for C1201460047\n",
+      "No entries found for GlobCover_09\n",
+      "No entries found for C1234567-PROV1\n",
+      "No entries found for =GlobCover_09\n",
+      "No entries found for C1200109552\n"
+     ]
+    }
+   ],
+   "source": [
+    "cmr_url = 'https://cmr.maap-project.org'\n",
+    "collections_search_url = f\"{cmr_url}/search/collections.json\"\n",
+    "results_dict = {}\n",
+    "for entry in restructured_data[0:limit]:\n",
+    "    # There's 1 blank concept_id entry\n",
+    "    if 'collection_concept_id' in entry:\n",
+    "        concept_id = entry['collection_concept_id']\n",
+    "        if concept_id.startswith('C'):\n",
+    "            cmr_response = requests.get(f\"{collections_search_url}?concept_id={concept_id}\")\n",
+    "        else:\n",
+    "            # Are we interested in granules searches? very limited results besides\n",
+    "            next\n",
+    "            #cmr_response = requests.get(f\"{granules_search_url}?concept_id={concept_id}\")\n",
+    "        number_queries = entry['number_queries']\n",
+    "        cmr_data = json.loads(cmr_response.text).get('feed')\n",
+    "        if cmr_data and len(cmr_data) > 0 and cmr_data.get('entry'):\n",
+    "            short_name = json.loads(cmr_response.text)['feed']['entry'][0]['short_name']\n",
+    "            results_dict[short_name] = int(number_queries)\n",
+    "        else:\n",
+    "            print(f\"No entries found for {concept_id}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8bcca790",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'GEDI_L4A_AGB_Density_V2_1_2056': 23445,\n",
+       " 'ATL08': 792,\n",
+       " 'GEDI02_B': 703,\n",
+       " 'SENTINEL-1A_DP_GRD_HIGH': 260,\n",
+       " 'ABLVIS1B': 173,\n",
+       " 'GEDI_L3_LandSurface_Metrics_V2_1952': 88,\n",
+       " 'GEDI02_A': 57,\n",
+       " 'GEDI_L4A_AGB_Density_V2_1986': 20,\n",
+       " 'AFRISAR_DLR': 16,\n",
+       " 'GEDI01_B': 4,\n",
+       " 'SRTMGL1_COD': 3,\n",
+       " 'GlobCover_09': 3,\n",
+       " 'ATL03': 2}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dict = {k: v for k, v in sorted(results_dict.items(), key=lambda item: item[1], reverse=True)}\n",
+    "results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d7e68605",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data set\n",
+    "collections = list(results_dict.keys())\n",
+    "number_queries = list(results_dict.values())\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(collections, number_queries, color='y')\n",
+    "addlabels(collections, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - attributes search.ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - attributes search.ipynb
new file mode 100644
index 0000000..bb86949
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - attributes search.ipynb	
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97458536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2566ce7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eda695ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "fields @timestamp, @message\n",
+      "| filter @message like \"cmr.search.api\"\n",
+      "| filter @message like \"attribute\"\n",
+      "| parse @message /:attribute \\[\"(?<attribute>.+)\"\\]/\n",
+      "| stats count(*) as number_queries by attribute\n",
+      "| sort by number_queries desc\n",
+      "\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 860 ms, sys: 96.2 ms, total: 956 ms\n",
+      "Wall time: 8min 18s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \"attribute\"\n",
+    "| parse @message /:attribute \\[\"(?<attribute>.+)\"\\]/\n",
+    "| stats count(*) as number_queries by attribute\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "print(query)\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(10)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b7e90700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"attribute_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "441d1d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'results': [[{'field': 'attribute',\n",
+       "    'value': 'string,Site Name,Lope National Park Gabon'},\n",
+       "   {'field': 'number_queries', 'value': '80'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'string,Site Name,Mondah Forest Gabon\" \"string,Data Format,ASCII'},\n",
+       "   {'field': 'number_queries', 'value': '78'}],\n",
+       "  [{'field': 'attribute', 'value': 'string,Site Name,Mondah Forest Gabon'},\n",
+       "   {'field': 'number_queries', 'value': '76'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'float,Track Number,002\" \"string,Polarization,HH VV'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'float,Track Number,002\" \"string,Polarization,VV VV'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'float,Track Number,002\" \"string,Polarization,HH HH'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'float,Track Number,002\" \"string,Polarization,HV HV'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'attribute', 'value': 'string,Acquisition Type,Satellite Lidar'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'float,Track Number,001\" \"string,Polarization,HH'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'attribute', 'value': 'string,Site Name,lope'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'attribute', 'value': 'float,Track Number,001'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'attribute', 'value': 'string,Data Format,rdr'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'attribute', 'value': 'Geolocated'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'attribute', 'value': 'Geolocated,BOOLEAN,True'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'attribute', 'value': 'Acquisition Type,string,Satellite Lidar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'attribute', 'value': 'geolocated,boolean,True'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'attribute', 'value': 'Geolocated,boolean,True'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'string,Acquisition Type,string,Satellite Lidar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'attribute',\n",
+       "    'value': 'str,Acquisition Type,string,Satellite Lidar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}]],\n",
+       " 'statistics': {'recordsMatched': 330.0,\n",
+       "  'recordsScanned': 37612816.0,\n",
+       "  'bytesScanned': 13313571410.0},\n",
+       " 'status': 'Complete',\n",
+       " 'ResponseMetadata': {'RequestId': 'e6a108bb-057c-45ab-af54-cb0a7ebdbc8e',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': 'e6a108bb-057c-45ab-af54-cb0a7ebdbc8e',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '2294',\n",
+       "   'date': 'Wed, 12 Apr 2023 17:38:01 GMT'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = json.loads(open(filename).read())\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd811168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'attribute': 'string,Site Name,Lope National Park Gabon',\n",
+       "  'number_queries': '80'},\n",
+       " {'attribute': 'string,Site Name,Mondah Forest Gabon\" \"string,Data Format,ASCII',\n",
+       "  'number_queries': '78'},\n",
+       " {'attribute': 'string,Site Name,Mondah Forest Gabon', 'number_queries': '76'},\n",
+       " {'attribute': 'float,Track Number,002\" \"string,Polarization,HH VV',\n",
+       "  'number_queries': '18'},\n",
+       " {'attribute': 'float,Track Number,002\" \"string,Polarization,VV VV',\n",
+       "  'number_queries': '18'},\n",
+       " {'attribute': 'float,Track Number,002\" \"string,Polarization,HH HH',\n",
+       "  'number_queries': '18'},\n",
+       " {'attribute': 'float,Track Number,002\" \"string,Polarization,HV HV',\n",
+       "  'number_queries': '18'},\n",
+       " {'attribute': 'string,Acquisition Type,Satellite Lidar',\n",
+       "  'number_queries': '4'},\n",
+       " {'attribute': 'float,Track Number,001\" \"string,Polarization,HH',\n",
+       "  'number_queries': '3'},\n",
+       " {'attribute': 'string,Site Name,lope', 'number_queries': '3'},\n",
+       " {'attribute': 'float,Track Number,001', 'number_queries': '3'},\n",
+       " {'attribute': 'string,Data Format,rdr', 'number_queries': '2'},\n",
+       " {'attribute': 'Geolocated', 'number_queries': '2'},\n",
+       " {'attribute': 'Geolocated,BOOLEAN,True', 'number_queries': '1'},\n",
+       " {'number_queries': '1'},\n",
+       " {'attribute': 'Acquisition Type,string,Satellite Lidar',\n",
+       "  'number_queries': '1'},\n",
+       " {'attribute': 'geolocated,boolean,True', 'number_queries': '1'},\n",
+       " {'attribute': 'Geolocated,boolean,True', 'number_queries': '1'},\n",
+       " {'attribute': 'string,Acquisition Type,string,Satellite Lidar',\n",
+       "  'number_queries': '1'},\n",
+       " {'attribute': 'str,Acquisition Type,string,Satellite Lidar',\n",
+       "  'number_queries': '1'}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "for result in logs_response['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ebdb3529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_data = {}\n",
+    "\n",
+    "for result in restructured_data:\n",
+    "    if 'attribute' in result:\n",
+    "        attribute_keyname = ','.join(result['attribute'].split(\",\")[-2:])\n",
+    "        count_data[attribute_keyname] = int(result['number_queries'])\n",
+    "\n",
+    "count_data = {k: v for k, v in sorted(count_data.items(), key=lambda item: item[1], reverse=True)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "69e11b0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "limit = 8\n",
+    "attributes = list(count_data.keys())[0:limit]\n",
+    "number_queries = list(count_data.values())[0:limit]\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(attributes, number_queries, color='y')\n",
+    "addlabels(attributes, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - collections short_name.ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - collections short_name.ipynb
new file mode 100644
index 0000000..2ba2f0c
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - collections short_name.ipynb	
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97458536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2566ce7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eda695ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "fields @timestamp, @message\n",
+      "| filter @message like \"cmr.search.api\"\n",
+      "| filter @message like \"Searching for collections\"\n",
+      "| filter @message like \"short_name\"\n",
+      "| parse @message /:short_name \"(?<short_name>(\\w|-)+)\"/\n",
+      "| stats count(*) as number_queries by short_name\n",
+      "| sort by number_queries desc\n",
+      "\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 909 ms, sys: 99.4 ms, total: 1.01 s\n",
+      "Wall time: 8min 45s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "concept_type = \"collections\"\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \"Searching for \"\"\" + concept_type + '\"' + \"\"\"\n",
+    "| filter @message like \"short_name\"\n",
+    "| parse @message /:short_name \"(?<short_name>(\\w|-)+)\"/\n",
+    "| stats count(*) as number_queries by short_name\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "print(query)\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(10)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b7e90700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"{concept_type}_short_name_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "441d1d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'results': [[{'field': 'short_name', 'value': 'SRTMGL1_COD'},\n",
+       "   {'field': 'number_queries', 'value': '110'}],\n",
+       "  [{'field': 'number_queries', 'value': '43'}],\n",
+       "  [{'field': 'short_name', 'value': 'L2_USER_DATA'},\n",
+       "   {'field': 'number_queries', 'value': '27'}],\n",
+       "  [{'field': 'short_name', 'value': 'AFRISAR_DLR'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'short_name', 'value': 'BIOSAR1'},\n",
+       "   {'field': 'number_queries', 'value': '17'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI02_A'},\n",
+       "   {'field': 'number_queries', 'value': '15'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2010'},\n",
+       "   {'field': 'number_queries', 'value': '10'}],\n",
+       "  [{'field': 'short_name', 'value': 'ABLVIS1B'},\n",
+       "   {'field': 'number_queries', 'value': '10'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI02_B'},\n",
+       "   {'field': 'number_queries', 'value': '9'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI02B'},\n",
+       "   {'field': 'number_queries', 'value': '9'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_L4B_Gridded_Biomass_2017'},\n",
+       "   {'field': 'number_queries', 'value': '9'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_L4A_AGB_Density_V2_1_2056'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Data'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_AGB_Maps_1681'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'short_name', 'value': 'ATL03'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'short_name', 'value': 'nceo_africa_2017'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'short_name', 'value': 'Landsat8_SurfaceReflectance'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover_09'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDIL4B'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'short_name', 'value': 'SENTINEL-1A_DP_GRD_HIGH'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'icesat2-boreal'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'AFLVIS2'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Field_Data'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'ABLVIS'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Geocoded_SLC'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2017'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover_90'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'Polarimetric_CT_1601'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_AGB_Maps_1681Version'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'C1201702030-NASA_MAAP'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Coreg_SLC'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDIL4_B'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover_05_06'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Data_Compressed'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2018'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Data_v2'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Datav2'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_AGB_Maps_1681___1'},\n",
+       "   {'field': 'number_queries', 'value': '1'}]],\n",
+       " 'statistics': {'recordsMatched': 340.0,\n",
+       "  'recordsScanned': 37612808.0,\n",
+       "  'bytesScanned': 13313570327.0},\n",
+       " 'status': 'Complete',\n",
+       " 'ResponseMetadata': {'RequestId': '9c8c2c5d-d49a-4061-b7bc-fb80cc311f90',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': '9c8c2c5d-d49a-4061-b7bc-fb80cc311f90',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '3796',\n",
+       "   'date': 'Wed, 12 Apr 2023 17:38:04 GMT'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = json.loads(open(filename).read())\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd811168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'short_name': 'SRTMGL1_COD', 'number_queries': '110'},\n",
+       " {'number_queries': '43'},\n",
+       " {'short_name': 'L2_USER_DATA', 'number_queries': '27'},\n",
+       " {'short_name': 'AFRISAR_DLR', 'number_queries': '18'},\n",
+       " {'short_name': 'BIOSAR1', 'number_queries': '17'},\n",
+       " {'short_name': 'GEDI02_A', 'number_queries': '15'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2010', 'number_queries': '10'},\n",
+       " {'short_name': 'ABLVIS1B', 'number_queries': '10'},\n",
+       " {'short_name': 'GEDI02_B', 'number_queries': '9'},\n",
+       " {'short_name': 'GEDI02B', 'number_queries': '9'},\n",
+       " {'short_name': 'GEDI_L4B_Gridded_Biomass_2017', 'number_queries': '9'},\n",
+       " {'short_name': 'GEDI_L4A_AGB_Density_V2_1_2056', 'number_queries': '7'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Data', 'number_queries': '7'},\n",
+       " {'short_name': 'AfriSAR_AGB_Maps_1681', 'number_queries': '5'},\n",
+       " {'short_name': 'ATL03', 'number_queries': '5'},\n",
+       " {'short_name': 'nceo_africa_2017', 'number_queries': '4'},\n",
+       " {'short_name': 'Landsat8_SurfaceReflectance', 'number_queries': '3'},\n",
+       " {'short_name': 'GlobCover_09', 'number_queries': '3'},\n",
+       " {'short_name': 'GEDIL4B', 'number_queries': '3'},\n",
+       " {'short_name': 'SENTINEL-1A_DP_GRD_HIGH', 'number_queries': '2'},\n",
+       " {'short_name': 'icesat2-boreal', 'number_queries': '2'},\n",
+       " {'short_name': 'AFLVIS2', 'number_queries': '2'},\n",
+       " {'short_name': 'GEDI_CalVal_Field_Data', 'number_queries': '2'},\n",
+       " {'short_name': 'ABLVIS', 'number_queries': '2'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Geocoded_SLC', 'number_queries': '1'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2017', 'number_queries': '1'},\n",
+       " {'short_name': 'GlobCover', 'number_queries': '1'},\n",
+       " {'short_name': 'GlobCover_90', 'number_queries': '1'},\n",
+       " {'short_name': 'Polarimetric_CT_1601', 'number_queries': '1'},\n",
+       " {'short_name': 'AfriSAR_AGB_Maps_1681Version', 'number_queries': '1'},\n",
+       " {'short_name': 'GEDI', 'number_queries': '1'},\n",
+       " {'short_name': 'C1201702030-NASA_MAAP', 'number_queries': '1'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Coreg_SLC', 'number_queries': '1'},\n",
+       " {'short_name': 'GEDIL4_B', 'number_queries': '1'},\n",
+       " {'short_name': 'GlobCover_05_06', 'number_queries': '1'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Data_Compressed', 'number_queries': '1'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2018', 'number_queries': '1'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Data_v2', 'number_queries': '1'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Datav2', 'number_queries': '1'},\n",
+       " {'short_name': 'AfriSAR_AGB_Maps_1681___1', 'number_queries': '1'}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "for result in logs_response['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ebdb3529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_data = {}\n",
+    "\n",
+    "for result in restructured_data:\n",
+    "    if 'short_name' in result:\n",
+    "        count_data[result['short_name']] = int(result['number_queries'])\n",
+    "\n",
+    "count_data = {k: v for k, v in sorted(count_data.items(), key=lambda item: item[1], reverse=True)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "69e11b0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data set\n",
+    "limit = 20\n",
+    "short_names = list(count_data.keys())[0:limit]\n",
+    "number_queries = list(count_data.values())[0:limit]\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(short_names, number_queries, color='y')\n",
+    "addlabels(short_names, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - granules short_name.ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - granules short_name.ipynb
new file mode 100644
index 0000000..dbaf734
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - granules short_name.ipynb	
@@ -0,0 +1,434 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "97458536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2566ce7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eda695ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "fields @timestamp, @message\n",
+      "| filter @message like \"cmr.search.api\"\n",
+      "| filter @message like \"Searching for granules\"\n",
+      "| filter @message like \"short_name\"\n",
+      "| parse @message /:short_name \"(?<short_name>(\\w|-)+)\"/\n",
+      "| stats count(*) as number_queries by short_name\n",
+      "| sort by number_queries desc\n",
+      "\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 1.01 s, sys: 124 ms, total: 1.13 s\n",
+      "Wall time: 9min 56s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "concept_type = \"granules\"\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \"Searching for \"\"\" + concept_type + '\"' + \"\"\"\n",
+    "| filter @message like \"short_name\"\n",
+    "| parse @message /:short_name \"(?<short_name>(\\w|-)+)\"/\n",
+    "| stats count(*) as number_queries by short_name\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "print(query)\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(10)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b7e90700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"{concept_type}_short_name_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "441d1d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'results': [[{'field': 'short_name', 'value': 'ATL08'},\n",
+       "   {'field': 'number_queries', 'value': '347553'}],\n",
+       "  [{'field': 'short_name', 'value': 'AFRISAR_DLR'},\n",
+       "   {'field': 'number_queries', 'value': '2852'}],\n",
+       "  [{'field': 'short_name', 'value': 'SENTINEL-1A_DP_GRD_HIGH'},\n",
+       "   {'field': 'number_queries', 'value': '2747'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI02_B'},\n",
+       "   {'field': 'number_queries', 'value': '2092'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Data'},\n",
+       "   {'field': 'number_queries', 'value': '1947'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI02_A'},\n",
+       "   {'field': 'number_queries', 'value': '1104'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Coreg_SLC'},\n",
+       "   {'field': 'number_queries', 'value': '204'}],\n",
+       "  [{'field': 'short_name', 'value': 'Global_PALSAR2_PALSAR_Mosiac'},\n",
+       "   {'field': 'number_queries', 'value': '160'}],\n",
+       "  [{'field': 'short_name', 'value': 'Global_PALSAR2_PALSAR_FNF'},\n",
+       "   {'field': 'number_queries', 'value': '160'}],\n",
+       "  [{'field': 'short_name', 'value': 'ATL03'},\n",
+       "   {'field': 'number_queries', 'value': '159'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2010'},\n",
+       "   {'field': 'number_queries', 'value': '139'}],\n",
+       "  [{'field': 'short_name', 'value': 'ABLVIS1B'},\n",
+       "   {'field': 'number_queries', 'value': '133'}],\n",
+       "  [{'field': 'short_name', 'value': 'Polarimetric_CT_1601'},\n",
+       "   {'field': 'number_queries', 'value': '116'}],\n",
+       "  [{'field': 'number_queries', 'value': '105'}],\n",
+       "  [{'field': 'short_name', 'value': 'SRTMGL1_COD'},\n",
+       "   {'field': 'number_queries', 'value': '105'}],\n",
+       "  [{'field': 'short_name', 'value': 'AFLVIS2'},\n",
+       "   {'field': 'number_queries', 'value': '91'}],\n",
+       "  [{'field': 'short_name', 'value': 'ABLVIS2'},\n",
+       "   {'field': 'number_queries', 'value': '89'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Ungeocoded_Covariance'},\n",
+       "   {'field': 'number_queries', 'value': '73'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Geocoded_Covariance'},\n",
+       "   {'field': 'number_queries', 'value': '73'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_AGB_Maps_1681'},\n",
+       "   {'field': 'number_queries', 'value': '67'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Field_Data'},\n",
+       "   {'field': 'number_queries', 'value': '66'}],\n",
+       "  [{'field': 'short_name', 'value': 'ALOS_PSR_RTC_HIGH'},\n",
+       "   {'field': 'number_queries', 'value': '65'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_L4A_AGB_Density_V2_1_2056'},\n",
+       "   {'field': 'number_queries', 'value': '47'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_L4B_Gridded_Biomass_2017'},\n",
+       "   {'field': 'number_queries', 'value': '46'}],\n",
+       "  [{'field': 'short_name', 'value': 'ATL08_ARD-beta'},\n",
+       "   {'field': 'number_queries', 'value': '38'}],\n",
+       "  [{'field': 'short_name', 'value': 'BIOSAR1'},\n",
+       "   {'field': 'number_queries', 'value': '32'}],\n",
+       "  [{'field': 'short_name', 'value': 'Landsat8_SurfaceReflectance'},\n",
+       "   {'field': 'number_queries', 'value': '32'}],\n",
+       "  [{'field': 'short_name', 'value': 'Global_Forest_Change_2000-2017'},\n",
+       "   {'field': 'number_queries', 'value': '25'}],\n",
+       "  [{'field': 'short_name', 'value': 'LVISF1B'},\n",
+       "   {'field': 'number_queries', 'value': '23'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_KZ'},\n",
+       "   {'field': 'number_queries', 'value': '20'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2017'},\n",
+       "   {'field': 'number_queries', 'value': '13'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover_09'},\n",
+       "   {'field': 'number_queries', 'value': '12'}],\n",
+       "  [{'field': 'short_name', 'value': 'ABoVE_UAVSAR_PALSAR'},\n",
+       "   {'field': 'number_queries', 'value': '12'}],\n",
+       "  [{'field': 'short_name', 'value': 'nceo_africa_2017'},\n",
+       "   {'field': 'number_queries', 'value': '11'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Geocoded_SLC'},\n",
+       "   {'field': 'number_queries', 'value': '11'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_CalVal_Lidar_Data_Compressed'},\n",
+       "   {'field': 'number_queries', 'value': '8'}],\n",
+       "  [{'field': 'short_name', 'value': 'SRTMGL1'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'GlobCover_05_06'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'ESACCI_Biomass_L4_AGB_V3_100m_2018'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'L2_USER_DATA'},\n",
+       "   {'field': 'number_queries', 'value': '7'}],\n",
+       "  [{'field': 'short_name', 'value': 'AFRISAR_DLR2'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI_L3_LandSurface_Metrics_V2_1952'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_SLC'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'short_name', 'value': 'AfriSAR_UAVSAR_Normalization_Area'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'short_name', 'value': 'GEDI01_B'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'short_name', 'value': 'HLSL30'},\n",
+       "   {'field': 'number_queries', 'value': '2'}]],\n",
+       " 'statistics': {'recordsMatched': 360480.0,\n",
+       "  'recordsScanned': 37613210.0,\n",
+       "  'bytesScanned': 13313633028.0},\n",
+       " 'status': 'Complete',\n",
+       " 'ResponseMetadata': {'RequestId': '1ad52291-ab73-45b6-a3a9-553b43161e21',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': '1ad52291-ab73-45b6-a3a9-553b43161e21',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '4465',\n",
+       "   'date': 'Wed, 12 Apr 2023 17:51:44 GMT'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = json.loads(open(filename).read())\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dd811168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'short_name': 'ATL08', 'number_queries': '347553'},\n",
+       " {'short_name': 'AFRISAR_DLR', 'number_queries': '2852'},\n",
+       " {'short_name': 'SENTINEL-1A_DP_GRD_HIGH', 'number_queries': '2747'},\n",
+       " {'short_name': 'GEDI02_B', 'number_queries': '2092'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Data', 'number_queries': '1947'},\n",
+       " {'short_name': 'GEDI02_A', 'number_queries': '1104'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Coreg_SLC', 'number_queries': '204'},\n",
+       " {'short_name': 'Global_PALSAR2_PALSAR_Mosiac', 'number_queries': '160'},\n",
+       " {'short_name': 'Global_PALSAR2_PALSAR_FNF', 'number_queries': '160'},\n",
+       " {'short_name': 'ATL03', 'number_queries': '159'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2010', 'number_queries': '139'},\n",
+       " {'short_name': 'ABLVIS1B', 'number_queries': '133'},\n",
+       " {'short_name': 'Polarimetric_CT_1601', 'number_queries': '116'},\n",
+       " {'number_queries': '105'},\n",
+       " {'short_name': 'SRTMGL1_COD', 'number_queries': '105'},\n",
+       " {'short_name': 'AFLVIS2', 'number_queries': '91'},\n",
+       " {'short_name': 'ABLVIS2', 'number_queries': '89'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Ungeocoded_Covariance',\n",
+       "  'number_queries': '73'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Geocoded_Covariance', 'number_queries': '73'},\n",
+       " {'short_name': 'AfriSAR_AGB_Maps_1681', 'number_queries': '67'},\n",
+       " {'short_name': 'GEDI_CalVal_Field_Data', 'number_queries': '66'},\n",
+       " {'short_name': 'ALOS_PSR_RTC_HIGH', 'number_queries': '65'},\n",
+       " {'short_name': 'GEDI_L4A_AGB_Density_V2_1_2056', 'number_queries': '47'},\n",
+       " {'short_name': 'GEDI_L4B_Gridded_Biomass_2017', 'number_queries': '46'},\n",
+       " {'short_name': 'ATL08_ARD-beta', 'number_queries': '38'},\n",
+       " {'short_name': 'BIOSAR1', 'number_queries': '32'},\n",
+       " {'short_name': 'Landsat8_SurfaceReflectance', 'number_queries': '32'},\n",
+       " {'short_name': 'Global_Forest_Change_2000-2017', 'number_queries': '25'},\n",
+       " {'short_name': 'LVISF1B', 'number_queries': '23'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_KZ', 'number_queries': '20'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2017', 'number_queries': '13'},\n",
+       " {'short_name': 'GlobCover_09', 'number_queries': '12'},\n",
+       " {'short_name': 'ABoVE_UAVSAR_PALSAR', 'number_queries': '12'},\n",
+       " {'short_name': 'nceo_africa_2017', 'number_queries': '11'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Geocoded_SLC', 'number_queries': '11'},\n",
+       " {'short_name': 'GEDI_CalVal_Lidar_Data_Compressed', 'number_queries': '8'},\n",
+       " {'short_name': 'SRTMGL1', 'number_queries': '7'},\n",
+       " {'short_name': 'GlobCover_05_06', 'number_queries': '7'},\n",
+       " {'short_name': 'ESACCI_Biomass_L4_AGB_V3_100m_2018', 'number_queries': '7'},\n",
+       " {'short_name': 'L2_USER_DATA', 'number_queries': '7'},\n",
+       " {'short_name': 'AFRISAR_DLR2', 'number_queries': '6'},\n",
+       " {'short_name': 'GEDI_L3_LandSurface_Metrics_V2_1952', 'number_queries': '4'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_SLC', 'number_queries': '4'},\n",
+       " {'short_name': 'AfriSAR_UAVSAR_Normalization_Area', 'number_queries': '4'},\n",
+       " {'short_name': 'GEDI01_B', 'number_queries': '2'},\n",
+       " {'short_name': 'HLSL30', 'number_queries': '2'}]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "for result in logs_response['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ebdb3529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_data = {}\n",
+    "for result in restructured_data:\n",
+    "    if 'short_name' in result:\n",
+    "        count_data[result['short_name']] = int(result['number_queries'])\n",
+    "\n",
+    "count_data = {k: v for k, v in sorted(count_data.items(), key=lambda item: item[1], reverse=True)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "69e11b0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data set\n",
+    "limit = 20\n",
+    "short_names = list(count_data.keys())[0:limit]\n",
+    "number_queries = list(count_data.values())[0:limit]\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(short_names, number_queries, color='y')\n",
+    "addlabels(short_names, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66a42366",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cmr_cw_searches/Plotting CMR Search API CW Logs  - keyword search.ipynb b/cmr_cw_searches/Plotting CMR Search API CW Logs  - keyword search.ipynb
new file mode 100644
index 0000000..3847eb7
--- /dev/null
+++ b/cmr_cw_searches/Plotting CMR Search API CW Logs  - keyword search.ipynb	
@@ -0,0 +1,892 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97458536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2566ce7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: You will need to set AWS access keys for the MCP environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eda695ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "fields @timestamp, @message\n",
+      "| filter @message like \"cmr.search.api\"\n",
+      "| filter @message like \":keyword\"\n",
+      "| parse @message /:keyword \"(?<keyword>.+)\\*\"/\n",
+      "| stats count(*) as number_queries by tolower(keyword)\n",
+      "| sort by number_queries desc\n",
+      "\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "Waiting for query to complete ...\n",
+      "CPU times: user 891 ms, sys: 96.6 ms, total: 987 ms\n",
+      "Wall time: 8min 12s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "client = boto3.client('logs', region_name='us-west-2')\n",
+    "\n",
+    "query = \"\"\"\n",
+    "fields @timestamp, @message\n",
+    "| filter @message like \"cmr.search.api\"\n",
+    "| filter @message like \":keyword\"\n",
+    "| parse @message /:keyword \"(?<keyword>.+)\\*\"/\n",
+    "| stats count(*) as number_queries by tolower(keyword)\n",
+    "| sort by number_queries desc\n",
+    "\"\"\"\n",
+    "\n",
+    "print(query)\n",
+    "log_group = 'cmr-search-ops'\n",
+    "datetime_str = '10/01/22 00:00:00'\n",
+    "starting_datetime_object = datetime.strptime(datetime_str, '%m/%d/%y %H:%M:%S')\n",
+    "\n",
+    "start_query_response = client.start_query(\n",
+    "    logGroupName=log_group,\n",
+    "    startTime=int(starting_datetime_object.timestamp()),\n",
+    "    endTime=int(datetime.now().timestamp()),\n",
+    "    queryString=query,\n",
+    ")\n",
+    "\n",
+    "query_id = start_query_response['queryId']\n",
+    "\n",
+    "logs_response = None\n",
+    "\n",
+    "while logs_response == None or logs_response['status'] == 'Running':\n",
+    "    print('Waiting for query to complete ...')\n",
+    "    time.sleep(10)\n",
+    "    logs_response = client.get_query_results(\n",
+    "        queryId=query_id\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b7e90700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the results so we don't have to re-run the query\n",
+    "filename = f\"keyword_search-{query_id}.json\"\n",
+    "with open(filename, \"w+\") as f:\n",
+    "    f.write(json.dumps(logs_response))\n",
+    "    f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "441d1d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'results': [[{'field': 'tolower(keyword)', 'value': 'biomass'},\n",
+       "   {'field': 'number_queries', 'value': '74'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi'},\n",
+       "   {'field': 'number_queries', 'value': '55'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l1b'},\n",
+       "   {'field': 'number_queries', 'value': '36'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l2b'},\n",
+       "   {'field': 'number_queries', 'value': '24'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation'},\n",
+       "   {'field': 'number_queries', 'value': '23'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* 25m* resolution* palsar-2/palsar* forest/non-forest* map'},\n",
+       "   {'field': 'number_queries', 'value': '22'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset'},\n",
+       "   {'field': 'number_queries', 'value': '19'}],\n",
+       "  [{'field': 'number_queries', 'value': '19'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'globcover* global* land* cover* product'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'nisar'},\n",
+       "   {'field': 'number_queries', 'value': '18'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l2a'},\n",
+       "   {'field': 'number_queries', 'value': '17'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi02'},\n",
+       "   {'field': 'number_queries', 'value': '16'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l3'},\n",
+       "   {'field': 'number_queries', 'value': '14'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'agb'},\n",
+       "   {'field': 'number_queries', 'value': '14'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'atl03'},\n",
+       "   {'field': 'number_queries', 'value': '14'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l4a'},\n",
+       "   {'field': 'number_queries', 'value': '13'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l2b'},\n",
+       "   {'field': 'number_queries', 'value': '10'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '10'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'sentinel-1a* dual-pol* ground* projected* high* and* full* resolution* images'},\n",
+       "   {'field': 'number_queries', 'value': '9'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sentinel'},\n",
+       "   {'field': 'number_queries', 'value': '9'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global* forest* change* 2000-2017'},\n",
+       "   {'field': 'number_queries', 'value': '8'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l4b'},\n",
+       "   {'field': 'number_queries', 'value': '8'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l2a* version* 2'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'atl08'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global* ecosystem'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'lvis'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_dlr'},\n",
+       "   {'field': 'number_queries', 'value': '6'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'globcover'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'lvis* facility* l1b* geolocated* return* energy* waveforms* v001'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* 02'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'icesat-2'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l4b'},\n",
+       "   {'field': 'number_queries', 'value': '5'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'globcover* global* land* cover* product* (2009)'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'arctic-boreal* vulnerability* experiment* uninhabited* aerial* vehicle* synthetic* aperture* radar* polarimetric* sar'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'esa'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'alos'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* ecosystem* dynamics* investigatio'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'hls'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global* biomass* dataset'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biomass* global'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'icesat'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'boreal'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar:* aboveground* biomass* for* lope,* mabounie,* mondah,* and* rabi* sites,* gabon'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biosar1'},\n",
+       "   {'field': 'number_queries', 'value': '4'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* lvis* l1b* geolocated* return* energy* waveforms* v001'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* geocoded* covariance* matrix* product* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'cci'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_uavsar_kz'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'ablvis1b'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'jpl'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sentinel-1a* slant-range* product'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* 25m* resolution* palsar-2/palsar* mosaic'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* vertical* wavenumber* (kz)* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'dlr'},\n",
+       "   {'field': 'number_queries', 'value': '3'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l3'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_uavsar_coreg_slc'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sea* level'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_dlr2'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* airborne'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'advance* land* observing* satellite* phased* array* type* l-band* synthetic* aperture* radar* radiometric* terrain-corrected* high* resolution* products,* equatorial* western* africa,* may* 2006-march* 2011'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'scansar'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_uavsar_geocoded_slc'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biosar'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_uavsar_normalization_area'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* geocoded* slcs* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* field* survey* dataset* v2'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi_l4a_agb_density_v2_1_2056'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'atl03* v005'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi02a'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'above* ground* biomass'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global_forest_change_2000-2017'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_dlr_t2-0_rg'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biomass* globa'},\n",
+       "   {'field': 'number_queries', 'value': '2'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'above* lvis* l2* geolocated* surface* elevation* product* v001'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'tropisar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'esacci_biomass_l4_agb_v3_100m_2017'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* normalization* area* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'ceos'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'shared_bucket/alanxuliang/agb_2020'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'trop'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'agb_2020'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global_palsar2_palsar_fnf'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'global_palsar2_palsar_mosiac'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'population'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'lobal* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biomass* africa'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'doi* 10.5067/atlas/atl03.005'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'boundaries'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': '* global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'cog'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biomass* boreal'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l3* gedi'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'collections'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'age'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'mcd43a4'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar* agb* maps'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sentinal'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'global ecosystem dynamics investigation (gedi) calibration/validation'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'lobal* ecosystem* dynamics'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sentinel-2'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'above* lvis* l1b* geolocated* return* energy* waveforms* v001'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'thailand'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'cloud* optomized* geotiff'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'ocean* colour'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'boreal* biomass'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'frisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'jpl* '},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l3* gedi\\\\\\\\n'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'l4a'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'esacci_biomass_l4_agb_v3_100m_2010'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'lvis* 2019* f'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'boreal* forest* biomass'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'ocean* color'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_dlr_t2-0_rg.tiff'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'above:* lvis* l3* gridded* vegetation* structure* across* north* america'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'boreal* forest* biomass* '},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'biomass* eurasia'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* toolson_area'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'afrisar_uavsar_ungeocoded_covariance'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'afrisar_agb_maps_1681'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'jpl* 2020'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'above_uavsar_palsar'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'mcd43'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'jpl* 2020* '},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l2* '},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi02b'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'cci* biomass'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'sentinel-1'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': '71'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'atl03.005:256319265'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': \"gedi* l2a'\"},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)', 'value': 'gedi* l1a'},\n",
+       "   {'field': 'number_queries', 'value': '1'}],\n",
+       "  [{'field': 'tolower(keyword)',\n",
+       "    'value': 'atlas/icesat-2* l2a* global* geolocated* photon* data* v005'},\n",
+       "   {'field': 'number_queries', 'value': '1'}]],\n",
+       " 'statistics': {'recordsMatched': 699.0,\n",
+       "  'recordsScanned': 37612808.0,\n",
+       "  'bytesScanned': 13313570327.0},\n",
+       " 'status': 'Complete',\n",
+       " 'ResponseMetadata': {'RequestId': '4e3215fc-043f-4616-9fdc-8a5ef33a6247',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'x-amzn-requestid': '4e3215fc-043f-4616-9fdc-8a5ef33a6247',\n",
+       "   'content-type': 'application/x-amz-json-1.1',\n",
+       "   'content-length': '14520',\n",
+       "   'date': 'Wed, 12 Apr 2023 17:37:50 GMT'},\n",
+       "  'RetryAttempts': 0}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = json.loads(open(filename).read())\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd811168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'tolower(keyword)': 'biomass', 'number_queries': '74'},\n",
+       " {'tolower(keyword)': 'gedi', 'number_queries': '55'},\n",
+       " {'tolower(keyword)': 'gedi* l1b', 'number_queries': '36'},\n",
+       " {'tolower(keyword)': 'gedi* l2b', 'number_queries': '24'},\n",
+       " {'tolower(keyword)': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation',\n",
+       "  'number_queries': '23'},\n",
+       " {'tolower(keyword)': 'global* 25m* resolution* palsar-2/palsar* forest/non-forest* map',\n",
+       "  'number_queries': '22'},\n",
+       " {'tolower(keyword)': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset',\n",
+       "  'number_queries': '19'},\n",
+       " {'number_queries': '19'},\n",
+       " {'tolower(keyword)': 'afrisar', 'number_queries': '18'},\n",
+       " {'tolower(keyword)': 'globcover* global* land* cover* product',\n",
+       "  'number_queries': '18'},\n",
+       " {'tolower(keyword)': 'nisar', 'number_queries': '18'},\n",
+       " {'tolower(keyword)': 'gedi* l2a', 'number_queries': '17'},\n",
+       " {'tolower(keyword)': 'gedi02', 'number_queries': '16'},\n",
+       " {'tolower(keyword)': 'gedi* l3', 'number_queries': '14'},\n",
+       " {'tolower(keyword)': 'agb', 'number_queries': '14'},\n",
+       " {'tolower(keyword)': 'atl03', 'number_queries': '14'},\n",
+       " {'tolower(keyword)': 'gedi* l4a', 'number_queries': '13'},\n",
+       " {'tolower(keyword)': 'l2b', 'number_queries': '10'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools',\n",
+       "  'number_queries': '10'},\n",
+       " {'tolower(keyword)': 'sentinel-1a* dual-pol* ground* projected* high* and* full* resolution* images',\n",
+       "  'number_queries': '9'},\n",
+       " {'tolower(keyword)': 'sentinel', 'number_queries': '9'},\n",
+       " {'tolower(keyword)': 'global* forest* change* 2000-2017',\n",
+       "  'number_queries': '8'},\n",
+       " {'tolower(keyword)': 'l4b', 'number_queries': '8'},\n",
+       " {'tolower(keyword)': 'gedi* l2a* version* 2', 'number_queries': '6'},\n",
+       " {'tolower(keyword)': 'atl08', 'number_queries': '6'},\n",
+       " {'tolower(keyword)': 'global* ecosystem', 'number_queries': '6'},\n",
+       " {'tolower(keyword)': 'lvis', 'number_queries': '6'},\n",
+       " {'tolower(keyword)': 'afrisar_dlr', 'number_queries': '6'},\n",
+       " {'tolower(keyword)': 'globcover', 'number_queries': '5'},\n",
+       " {'tolower(keyword)': 'lvis* facility* l1b* geolocated* return* energy* waveforms* v001',\n",
+       "  'number_queries': '5'},\n",
+       " {'tolower(keyword)': 'gedi* 02', 'number_queries': '5'},\n",
+       " {'tolower(keyword)': 'icesat-2', 'number_queries': '5'},\n",
+       " {'tolower(keyword)': 'gedi* l4b', 'number_queries': '5'},\n",
+       " {'tolower(keyword)': 'globcover* global* land* cover* product* (2009)',\n",
+       "  'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'arctic-boreal* vulnerability* experiment* uninhabited* aerial* vehicle* synthetic* aperture* radar* polarimetric* sar',\n",
+       "  'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'esa', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'alos', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'global* ecosystem* dynamics* investigatio',\n",
+       "  'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'hls', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'global* biomass* dataset', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'biomass* global', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'icesat', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'boreal', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'afrisar:* aboveground* biomass* for* lope,* mabounie,* mondah,* and* rabi* sites,* gabon',\n",
+       "  'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'biosar1', 'number_queries': '4'},\n",
+       " {'tolower(keyword)': 'afrisar* lvis* l1b* geolocated* return* energy* waveforms* v001',\n",
+       "  'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* geocoded* covariance* matrix* product* generated* using* nisar* tools',\n",
+       "  'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'cci', 'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'afrisar_uavsar_kz', 'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'ablvis1b', 'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'jpl', 'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'sentinel-1a* slant-range* product',\n",
+       "  'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'global* 25m* resolution* palsar-2/palsar* mosaic',\n",
+       "  'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* vertical* wavenumber* (kz)* generated* using* nisar* tools',\n",
+       "  'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'dlr', 'number_queries': '3'},\n",
+       " {'tolower(keyword)': 'l3', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar_uavsar_coreg_slc', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'sea* level', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar_dlr2', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'gedi* airborne', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'advance* land* observing* satellite* phased* array* type* l-band* synthetic* aperture* radar* radiometric* terrain-corrected* high* resolution* products,* equatorial* western* africa,* may* 2006-march* 2011',\n",
+       "  'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'scansar', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar_uavsar_geocoded_slc', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'biosar', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar_uavsar_normalization_area',\n",
+       "  'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* geocoded* slcs* generated* using* nisar* tools',\n",
+       "  'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* field* survey* dataset* v2',\n",
+       "  'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'gedi_l4a_agb_density_v2_1_2056', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'atl03* v005', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'gedi02a', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'above* ground* biomass', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'global_forest_change_2000-2017', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'afrisar_dlr_t2-0_rg', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'biomass* globa', 'number_queries': '2'},\n",
+       " {'tolower(keyword)': 'above* lvis* l2* geolocated* surface* elevation* product* v001',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'tropisar', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'esacci_biomass_l4_agb_v3_100m_2017',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* normalization* area* generated* using* nisar* tools',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'ceos', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'shared_bucket/alanxuliang/agb_2020',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'trop', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'agb_2020', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'global_palsar2_palsar_fnf', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'global_palsar2_palsar_mosiac', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'population', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'lobal* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'biomass* africa', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'doi* 10.5067/atlas/atl03.005', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'boundaries', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': '* global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'cog', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'biomass* boreal', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'l3* gedi', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'collections', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'age', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'mcd43a4', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar* agb* maps', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'sentinal', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'global ecosystem dynamics investigation (gedi) calibration/validation',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'lobal* ecosystem* dynamics', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'sentinel-2', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'above* lvis* l1b* geolocated* return* energy* waveforms* v001',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'thailand', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'cloud* optomized* geotiff', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'ocean* colour', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'boreal* biomass', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'frisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'jpl* ', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'l3* gedi\\\\\\\\n', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'l4a', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'esacci_biomass_l4_agb_v3_100m_2010',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'lvis* 2019* f', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'boreal* forest* biomass', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'ocean* color', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar_dlr_t2-0_rg.tiff', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'above:* lvis* l3* gridded* vegetation* structure* across* north* america',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'boreal* forest* biomass* ', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'biomass* eurasia', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* toolson_area',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar_uavsar_ungeocoded_covariance',\n",
+       "  'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'afrisar_agb_maps_1681', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'jpl* 2020', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'above_uavsar_palsar', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'mcd43', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'jpl* 2020* ', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'gedi* l2* ', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'gedi02b', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'cci* biomass', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'sentinel-1', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': '71', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'atl03.005:256319265', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': \"gedi* l2a'\", 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'gedi* l1a', 'number_queries': '1'},\n",
+       " {'tolower(keyword)': 'atlas/icesat-2* l2a* global* geolocated* photon* data* v005',\n",
+       "  'number_queries': '1'}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "restructured_data = []\n",
+    "for result in logs_response['results']:\n",
+    "    entry_data = {}\n",
+    "    for entry in result:\n",
+    "        entry_data[entry['field']] = entry['value']\n",
+    "    restructured_data.append(entry_data)\n",
+    "\n",
+    "restructured_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ebdb3529",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'biomass': 74,\n",
+       " 'gedi': 55,\n",
+       " 'gedi* l1b': 36,\n",
+       " 'gedi* l2b': 24,\n",
+       " 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation': 23,\n",
+       " 'global* 25m* resolution* palsar-2/palsar* forest/non-forest* map': 22,\n",
+       " 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset': 19,\n",
+       " 'afrisar': 18,\n",
+       " 'globcover* global* land* cover* product': 18,\n",
+       " 'nisar': 18,\n",
+       " 'gedi* l2a': 17,\n",
+       " 'gedi02': 16,\n",
+       " 'gedi* l3': 14,\n",
+       " 'agb': 14,\n",
+       " 'atl03': 14,\n",
+       " 'gedi* l4a': 13,\n",
+       " 'l2b': 10,\n",
+       " 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools': 10,\n",
+       " 'sentinel-1a* dual-pol* ground* projected* high* and* full* resolution* images': 9,\n",
+       " 'sentinel': 9,\n",
+       " 'global* forest* change* 2000-2017': 8,\n",
+       " 'l4b': 8,\n",
+       " 'gedi* l2a* version* 2': 6,\n",
+       " 'atl08': 6,\n",
+       " 'global* ecosystem': 6,\n",
+       " 'lvis': 6,\n",
+       " 'afrisar_dlr': 6,\n",
+       " 'globcover': 5,\n",
+       " 'lvis* facility* l1b* geolocated* return* energy* waveforms* v001': 5,\n",
+       " 'gedi* 02': 5,\n",
+       " 'icesat-2': 5,\n",
+       " 'gedi* l4b': 5,\n",
+       " 'globcover* global* land* cover* product* (2009)': 4,\n",
+       " 'arctic-boreal* vulnerability* experiment* uninhabited* aerial* vehicle* synthetic* aperture* radar* polarimetric* sar': 4,\n",
+       " 'esa': 4,\n",
+       " 'alos': 4,\n",
+       " 'global* ecosystem* dynamics* investigatio': 4,\n",
+       " 'hls': 4,\n",
+       " 'global* biomass* dataset': 4,\n",
+       " 'biomass* global': 4,\n",
+       " 'icesat': 4,\n",
+       " 'boreal': 4,\n",
+       " 'afrisar:* aboveground* biomass* for* lope,* mabounie,* mondah,* and* rabi* sites,* gabon': 4,\n",
+       " 'biosar1': 4,\n",
+       " 'afrisar* lvis* l1b* geolocated* return* energy* waveforms* v001': 3,\n",
+       " 'afrisar* uavsar* geocoded* covariance* matrix* product* generated* using* nisar* tools': 3,\n",
+       " 'cci': 3,\n",
+       " 'afrisar_uavsar_kz': 3,\n",
+       " 'ablvis1b': 3,\n",
+       " 'jpl': 3,\n",
+       " 'sentinel-1a* slant-range* product': 3,\n",
+       " 'global* 25m* resolution* palsar-2/palsar* mosaic': 3,\n",
+       " 'afrisar* uavsar* vertical* wavenumber* (kz)* generated* using* nisar* tools': 3,\n",
+       " 'dlr': 3,\n",
+       " 'l3': 2,\n",
+       " 'afrisar_uavsar_coreg_slc': 2,\n",
+       " 'sea* level': 2,\n",
+       " 'afrisar_dlr2': 2,\n",
+       " 'gedi* airborne': 2,\n",
+       " 'advance* land* observing* satellite* phased* array* type* l-band* synthetic* aperture* radar* radiometric* terrain-corrected* high* resolution* products,* equatorial* western* africa,* may* 2006-march* 2011': 2,\n",
+       " 'scansar': 2,\n",
+       " 'afrisar_uavsar_geocoded_slc': 2,\n",
+       " 'biosar': 2,\n",
+       " 'afrisar_uavsar_normalization_area': 2,\n",
+       " 'afrisar* uavsar* geocoded* slcs* generated* using* nisar* tools': 2,\n",
+       " 'global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* field* survey* dataset* v2': 2,\n",
+       " 'gedi_l4a_agb_density_v2_1_2056': 2,\n",
+       " 'atl03* v005': 2,\n",
+       " 'gedi02a': 2,\n",
+       " 'above* ground* biomass': 2,\n",
+       " 'global_forest_change_2000-2017': 2,\n",
+       " 'afrisar_dlr_t2-0_rg': 2,\n",
+       " 'biomass* globa': 2,\n",
+       " 'above* lvis* l2* geolocated* surface* elevation* product* v001': 1,\n",
+       " 'tropisar': 1,\n",
+       " 'esacci_biomass_l4_agb_v3_100m_2017': 1,\n",
+       " 'afrisar* uavsar* normalization* area* generated* using* nisar* tools': 1,\n",
+       " 'ceos': 1,\n",
+       " 'shared_bucket/alanxuliang/agb_2020': 1,\n",
+       " 'trop': 1,\n",
+       " 'agb_2020': 1,\n",
+       " 'global_palsar2_palsar_fnf': 1,\n",
+       " 'global_palsar2_palsar_mosiac': 1,\n",
+       " 'population': 1,\n",
+       " 'lobal* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar* dataset': 1,\n",
+       " 'biomass* africa': 1,\n",
+       " 'doi* 10.5067/atlas/atl03.005': 1,\n",
+       " 'boundaries': 1,\n",
+       " '* global* ecosystem* dynamics* investigation* (gedi)* calibration/validation* airborne* lidar': 1,\n",
+       " 'cog': 1,\n",
+       " 'biomass* boreal': 1,\n",
+       " 'l3* gedi': 1,\n",
+       " 'collections': 1,\n",
+       " 'age': 1,\n",
+       " 'mcd43a4': 1,\n",
+       " 'afrisar* agb* maps': 1,\n",
+       " 'sentinal': 1,\n",
+       " 'global ecosystem dynamics investigation (gedi) calibration/validation': 1,\n",
+       " 'lobal* ecosystem* dynamics': 1,\n",
+       " 'sentinel-2': 1,\n",
+       " 'above* lvis* l1b* geolocated* return* energy* waveforms* v001': 1,\n",
+       " 'thailand': 1,\n",
+       " 'cloud* optomized* geotiff': 1,\n",
+       " 'ocean* colour': 1,\n",
+       " 'boreal* biomass': 1,\n",
+       " 'frisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* tools': 1,\n",
+       " 'jpl* ': 1,\n",
+       " 'l3* gedi\\\\\\\\n': 1,\n",
+       " 'l4a': 1,\n",
+       " 'esacci_biomass_l4_agb_v3_100m_2010': 1,\n",
+       " 'lvis* 2019* f': 1,\n",
+       " 'boreal* forest* biomass': 1,\n",
+       " 'ocean* color': 1,\n",
+       " 'afrisar_dlr_t2-0_rg.tiff': 1,\n",
+       " 'above:* lvis* l3* gridded* vegetation* structure* across* north* america': 1,\n",
+       " 'boreal* forest* biomass* ': 1,\n",
+       " 'biomass* eurasia': 1,\n",
+       " 'afrisar* uavsar* ungeocoded* covariance* matrix* product* generated* using* nisar* toolson_area': 1,\n",
+       " 'afrisar_uavsar_ungeocoded_covariance': 1,\n",
+       " 'afrisar_agb_maps_1681': 1,\n",
+       " 'jpl* 2020': 1,\n",
+       " 'above_uavsar_palsar': 1,\n",
+       " 'mcd43': 1,\n",
+       " 'jpl* 2020* ': 1,\n",
+       " 'gedi* l2* ': 1,\n",
+       " 'gedi02b': 1,\n",
+       " 'cci* biomass': 1,\n",
+       " 'sentinel-1': 1,\n",
+       " '71': 1,\n",
+       " 'atl03.005:256319265': 1,\n",
+       " \"gedi* l2a'\": 1,\n",
+       " 'gedi* l1a': 1,\n",
+       " 'atlas/icesat-2* l2a* global* geolocated* photon* data* v005': 1}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count_data = {}\n",
+    "\n",
+    "for result in restructured_data:\n",
+    "    if 'tolower(keyword)' in result:\n",
+    "        count_data[result['tolower(keyword)']] = int(result['number_queries'])\n",
+    "\n",
+    "count_data = {k: v for k, v in sorted(count_data.items(), key=lambda item: item[1], reverse=True)}\n",
+    "count_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "69e11b0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x384 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data set\n",
+    "limit = 10\n",
+    "keywords = list(count_data.keys())[0:limit]\n",
+    "number_queries = list(count_data.values())[0:limit]\n",
+    "\n",
+    "plt.figure(figsize=(9, 4), dpi=96)\n",
+    "\n",
+    "def addlabels(x,y):\n",
+    "    for i in range(len(x)):\n",
+    "        plt.text(i, y[i], y[i], ha = 'center', va= 'bottom')\n",
+    "\n",
+    "plt.bar(keywords, number_queries, color='y')\n",
+    "addlabels(keywords, number_queries)\n",
+    "plt.xticks(rotation=30, ha='right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}