diff --git a/.github/workflows/notebook_check_python.yml b/.github/workflows/notebook_check_python.yml
index c7abb3a7..9e01e9d1 100644
--- a/.github/workflows/notebook_check_python.yml
+++ b/.github/workflows/notebook_check_python.yml
@@ -41,4 +41,9 @@ jobs:
     - id: execute-taxonomic
       name: Execute taxonomic notebook
       run: |
-        jupyter nbconvert --execute --to notebook --inplace taxonomic_dist_by_soil_layer/python/taxonomic_dist_soil_layer.ipynb
\ No newline at end of file
+        jupyter nbconvert --execute --to notebook --inplace taxonomic_dist_by_soil_layer/python/taxonomic_dist_soil_layer.ipynb
+
+    - id: execute-NOM
+      name: Execute NOM notebook
+      run: |
+        jupyter nbconvert --execute --to notebook --inplace NOM_visualizations/python/nom_data.ipynb
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 61a97070..6157959b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,4 +7,6 @@
 .DS_Store
 venv/
 .virtual_documents/
-taxonomic_dist_by_soil_layer/python/contig_notebook_session.pkl
\ No newline at end of file
+taxonomic_dist_by_soil_layer/python/contig_notebook_session.pkl
+__pycache__/
+.pyc
\ No newline at end of file
diff --git a/NOM_visualizations/README.md b/NOM_visualizations/README.md
new file mode 100644
index 00000000..6beb5069
--- /dev/null
+++ b/NOM_visualizations/README.md
@@ -0,0 +1,7 @@
+# Exploring NOM Metadata and Visualization via the NMDC Runtime API
+
+This folder includes two notebooks (in R and Python) that demonstrate how metadata from natural organic matter (NOM) can be gathered via the [NMDC-runtime API](https://api.microbiomedata.org/docs) and analyzed.
+
+## Python
+- [Static rendered Jupyter notebook](https://nbviewer.org/github/microbiomedata/nmdc_notebooks/blob/main/NOM_visualizations/python/nom_data.ipynb). This is the recommended way to interact with the notebook. _Viewing only, not editable_
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/microbiomedata/nmdc_notebooks/blob/main/NOM_visualizations/python/nom_data.ipynb). **Running this notebook in the colab interactive environment is not recommended due to long API calls** _You need a google account to use this option_
\ No newline at end of file
diff --git a/NOM_visualizations/python/nmdc_api.py b/NOM_visualizations/python/nmdc_api.py
new file mode 100644
index 00000000..b114d912
--- /dev/null
+++ b/NOM_visualizations/python/nmdc_api.py
@@ -0,0 +1,159 @@
+#!venv/bin python
+
+#packages used in these functions
+import requests
+import pandas as pd
+
+## Define a general API call function to nmdc-runtime
+    # This function provides a general-purpose way to make an API request to NMDC's runtime API. Note that this 
+    # function will only return the first page of results. The function's input includes the name of the collection to access (e.g. `biosample_set`), 
+    # the filter to be performed, the maximum page size, and a list of the fields to be retrieved. It returns the metadata as a json object.
+
+def get_first_page_results(collection: str, filter: str, max_page_size: int, fields: str):
+    og_url = f'https://api.microbiomedata.org/nmdcschema/{collection}?&filter={filter}&max_page_size={max_page_size}&projection={fields}'
+    resp = requests.get(og_url)
+    data = resp.json()
+    
+    return data
+
+
+## Define an nmdc-runtime API call function to include pagination
+    # The `get_next_results` function uses the `get_first_page_results` function, defined above, 
+    # to retrieve the rest of the results from a call with multiple pages. It takes the same inputs as 
+    # the `get_first_page_results` function above: the name of the collection to be retrieved, the filter string, 
+    # the maximum page size, and a list of the fields to be returned. This function returns the list of the results.
+    # It uses the `next_page_token` key in each page of results to retrieve the following page.
+
+def get_next_results(collection: str, filter: str, max_page_size: int, fields: str):
+
+    # Get initial results (before next_page_token is given in the results)
+    result_list = []
+    initial_data = get_first_page_results(collection, filter, max_page_size, fields)
+    results = initial_data["resources"]
+    
+    # append first page of results to an empty list
+    for result in results:
+        result_list.append(result)
+    
+    # if there are multiple pages of results returned
+    if initial_data.get("next_page_token"):
+        next_page_token = initial_data["next_page_token"]
+
+        while True:
+            url = f'https://api.microbiomedata.org/nmdcschema/{collection}?&filter={filter}&max_page_size={max_page_size}&page_token={next_page_token}&projection={fields}'
+            response = requests.get(url)
+            data_next = response.json()
+            
+            results = data_next.get("resources", [])
+            result_list.extend(results)
+            next_page_token = data_next.get("next_page_token")
+        
+            if not next_page_token:
+                break
+
+    return result_list
+
+# Define a data frame convert function
+    # This function converts a list (for example, the output of the `get_first_page_results` or the `get_next_results` function) into 
+    # a dataframe using Python's Pandas library. It returns a data frame.
+
+def convert_df(results_list: list):
+
+    df = pd.DataFrame(results_list)
+
+    return df
+
+## Define a function to split a list into chunks 
+    # Since we will need to use a list of ids to query a new collection in the API, we need to limit the number of ids we put in a query. 
+    # This function splits a list into chunks of 100. Note that the `chunk_size` has a default of 100, but can be adjusted.
+
+def split_list(input_list, chunk_size=100):
+    result = []
+    
+    for i in range(0, len(input_list), chunk_size):
+        result.append(input_list[i:i + chunk_size])
+        
+    return result
+
+
+## Define a function to use double quotation marks
+    # Since the mongo-like filtering criteria for the API requests require double quotation marks (") instead of 
+    # single quotation marks ('), a function is defined to replace single quotes with double quotes to properly 
+    # structure a mongo filter paramter. The function takes a list (usually of ids) and returns a string with the 
+    # ids listed with double quotation marks. E.g the input is `['A','B','C']` and the output would be `'"A","B",C"'`.
+
+def string_mongo_list(a_list: list):
+    
+    string_with_double_quotes = str(a_list).replace("'", '"')
+
+    return string_with_double_quotes
+
+
+## Define a function to get a list of ids from initial results
+    # In order to use the identifiers retrieved from an initial API request in another API request, this function is defined to 
+    # take the initial request results and use the `id_name` key from the results to create a list of all the ids. The input 
+    # is the initial result list and the name of the id field.
+
+def get_id_list(result_list: list, id_name: str):
+    id_list = []
+    for item in result_list:
+        if type(item[id_name]) == str:
+            id_list.append(item[id_name])
+        elif type(item[id_name]) == list:
+            for another_item in item[id_name]:
+                id_list.append(another_item)
+
+    return id_list
+
+
+## Define an API request function that uses a list of ids to filter a new collection
+    # This function takes the `newest_results` request (e.g. `biosamples`) and 
+    # constructs a list of ids using `get_id_results`.
+    # It then uses the `split_list` function to chunk the list of ids into sets of 100 to query the API. 
+    # `id_field` is a field in `newest_results` containing the list of ids to search for in the query_collection (e.g. `biosample_id`).
+    # `match_id_field` is the field in query_collection that will be searched. query_fields is a list of the fields to be returned.
+
+def get_id_results(newest_results: list, id_field: str, query_collection: str, match_id_field: str, query_fields: str):
+
+    # split old results into list
+    result_ids = get_id_list(newest_results, id_field)
+
+    # chunk up the results into sets of 100 using the split_list function and call the get_first_page_results function and append
+    # results to list
+    chunked_list = split_list(result_ids)
+    next_results = []
+    for chunk in chunked_list:
+        filter_string = string_mongo_list(chunk)
+        # quotes around match_id_field need to look a lot different for the final data object query
+        if "data_object_type" in match_id_field:
+            data = get_first_page_results(query_collection, f'{{{match_id_field}: {{"$in": {filter_string}}}}}', 100, query_fields)
+        else: 
+            data = get_first_page_results(query_collection, f'{{"{match_id_field}": {{"$in": {filter_string}}}}}', 100, query_fields)
+        next_results.extend(data["resources"])
+
+    return next_results
+
+
+## Define a merging function to join results
+    # This function merges new results with the previous results that were used for the new API request. It uses two keys from each result to match on. `df1` 
+    # is the data frame whose matching `key1` value is a STRING. `df2` is the other data frame whose matching `key2` has either a string OR list as a value. 
+    # df1_explode_list and df2_explode_list are optional lists of columns in either dataframe that need to be exploded because they are lists (this is because 
+    # drop_duplicates cant take list input in any column). Note that each if statement includes dropping duplicates after merging as the dataframes are being 
+    # exploded which creates many duplicate rows after merging takes place.
+
+def merge_df(df1, df2, key1: str, key2: str,df1_explode_list=None,df2_explode_list=None):
+    if df1_explode_list is not None:
+        # Explode the lists in the df (necessary for drop duplicates)
+        for list in df1_explode_list:
+            df1 = df1.explode(list)
+    if df2_explode_list is not None:
+        # Explode the lists in the df (necessary for drop duplicates)
+        for list in df2_explode_list:
+            df2 = df2.explode(list)  
+    # Merge dataframes
+    merged_df=pd.merge(df1,df2,left_on=key1, right_on=key2)
+    # Drop any duplicated rows
+    merged_df.drop_duplicates(keep="first", inplace=True)
+    return(merged_df)
+
+
diff --git a/NOM_visualizations/python/nom_data.ipynb b/NOM_visualizations/python/nom_data.ipynb
new file mode 100644
index 00000000..191fef4e
--- /dev/null
+++ b/NOM_visualizations/python/nom_data.ipynb
@@ -0,0 +1,3385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Exploration and Visualization of NOM Data in NMDC (FT ICR-MS)\n",
+    "This notebook identifies natural organic matter (NOM) data sets in the National Microbiome Data Collaborative (NMDC), filters those datasets based on quality control metrics, and analyzes the molecular composition of the chosen datasets via heatmaps and Van Krevelen plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from io import StringIO\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import sys\n",
+    "from matplotlib.patches import Patch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the python script 'nmdc_api', also located in this repo folder, as a module. It contains functions that streamline API calls and help with output formatting. If loop indicates how to access script if using google colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if 'google.colab' in sys.modules:\n",
+    "  !wget https://raw.githubusercontent.com/microbiomedata/nmdc_notebooks/refs/heads/main/NOM_visualizations/python/nmdc_api.py\n",
+    "  import nmdc_api as func\n",
+    "else:\n",
+    "  import nmdc_api as func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gather the IDs for processed NOM results in NMDC by filtering for data objects of type \"FT ICR-MS\"\n",
+    "\n",
+    "Use the python requests library and the [NMDC metadata collection_name endpoint](https://api.microbiomedata.org/docs#/metadata/list_from_collection_nmdcschema__collection_name__get) to gather processed NOM results. More information regarding the API can be found [here](https://github.com/microbiomedata/NMDC_documentation/blob/main/docs/howto_guides/api_gui.md). \n",
+    "\n",
+    "The function `get_next_results` and its documentation can be found in nmdc_api.py within this folder.\n",
+    "\n",
+    "Filter `data_object_set` by `data_object_type` using a keyword search of “FT ICR-MS Analysis Results”. Extract the fields `id` (necessary for traversing the NMDC schema), `url` (necessary for pulling data) and `md5_checksum` (used to check uniqueness of data set). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>processed_nom_id</th>\n",
+       "      <th>processed_nom_md5_checksum</th>\n",
+       "      <th>processed_nom_url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:dobj-11-00dewm52</td>\n",
+       "      <td>2a532dca15798e470103ebd752a0937f</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:dobj-11-00wm3313</td>\n",
+       "      <td>3ce562ac512457ea54bdda05a4f01ede</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:dobj-11-01kye625</td>\n",
+       "      <td>38930c28eae561bc807bd01823f04167</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:dobj-11-02trja88</td>\n",
+       "      <td>e6bafa5fabbebfb0061aa2587e223979</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/grow/result...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:dobj-11-0312n668</td>\n",
+       "      <td>20a5193d5fb54bf2a54c54b6f95a099d</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>nmdc:dobj-13-zrp1qw41</td>\n",
+       "      <td>98b97b78fff542b66e72f4b3f792d80f</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/SBR...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>nmdc:dobj-13-zsqpnm92</td>\n",
+       "      <td>3e9e19910edb209d211d9f915e36b8cb</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>nmdc:dobj-13-zvnmsp76</td>\n",
+       "      <td>aec0521d6a36a440e41052f8eadc0d1d</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Ung...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2545</th>\n",
+       "      <td>nmdc:dobj-13-zvzx2462</td>\n",
+       "      <td>9f0d52cc46d247b8d2ba12d5842b9fb6</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>nmdc:dobj-13-zye5fe51</td>\n",
+       "      <td>8ddaeeffe93db9c4258a03e881d329cf</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           processed_nom_id        processed_nom_md5_checksum  \\\n",
+       "0     nmdc:dobj-11-00dewm52  2a532dca15798e470103ebd752a0937f   \n",
+       "1     nmdc:dobj-11-00wm3313  3ce562ac512457ea54bdda05a4f01ede   \n",
+       "2     nmdc:dobj-11-01kye625  38930c28eae561bc807bd01823f04167   \n",
+       "3     nmdc:dobj-11-02trja88  e6bafa5fabbebfb0061aa2587e223979   \n",
+       "4     nmdc:dobj-11-0312n668  20a5193d5fb54bf2a54c54b6f95a099d   \n",
+       "...                     ...                               ...   \n",
+       "2542  nmdc:dobj-13-zrp1qw41  98b97b78fff542b66e72f4b3f792d80f   \n",
+       "2543  nmdc:dobj-13-zsqpnm92  3e9e19910edb209d211d9f915e36b8cb   \n",
+       "2544  nmdc:dobj-13-zvnmsp76  aec0521d6a36a440e41052f8eadc0d1d   \n",
+       "2545  nmdc:dobj-13-zvzx2462  9f0d52cc46d247b8d2ba12d5842b9fb6   \n",
+       "2546  nmdc:dobj-13-zye5fe51  8ddaeeffe93db9c4258a03e881d329cf   \n",
+       "\n",
+       "                                      processed_nom_url  \n",
+       "0     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "1     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "2     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "3     https://nmdcdemo.emsl.pnnl.gov/nom/grow/result...  \n",
+       "4     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "...                                                 ...  \n",
+       "2542  https://nmdcdemo.emsl.pnnl.gov/nom/results/SBR...  \n",
+       "2543  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...  \n",
+       "2544  https://nmdcdemo.emsl.pnnl.gov/nom/results/Ung...  \n",
+       "2545  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...  \n",
+       "2546  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...  \n",
+       "\n",
+       "[2547 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pull all NOM data objects\n",
+    "processed_nom=func.get_next_results(collection='data_object_set',\\\n",
+    "                       filter='{\"data_object_type\":{\"$regex\": \"FT ICR-MS Analysis Results\"}}',\\\n",
+    "                        max_page_size=100,fields='id,md5_checksum,url')\n",
+    "\n",
+    "# clarify names\n",
+    "for dataobject in processed_nom:\n",
+    "    dataobject[\"processed_nom_id\"] = dataobject.pop(\"id\")\n",
+    "    dataobject[\"processed_nom_md5_checksum\"] = dataobject.pop(\"md5_checksum\")\n",
+    "    dataobject[\"processed_nom_url\"] = dataobject.pop(\"url\")\n",
+    "\n",
+    "# convert to df\n",
+    "processed_nom_df = func.convert_df(processed_nom)\n",
+    "processed_nom_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Continue traversing the NMDC schema by using the list of identifiers from the previous API call to query the next collection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the analysis records that produced these processed nom object IDs by matching object `processed_nom_id` to the `has_output` slot in the collection `nom_analysis_activity_set`. Also extract the `has_input` slot as it will be used for the next traversal, grabbing the raw data objects that are used as input to the nom analysis records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>analysis_id</th>\n",
+       "      <th>analysis_has_input</th>\n",
+       "      <th>analysis_has_output</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:wfnom-11-fwhgp651.1</td>\n",
+       "      <td>[nmdc:dobj-11-hrqfj247]</td>\n",
+       "      <td>[nmdc:dobj-11-0dn7p856]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:wfnom-11-snvsmz18.1</td>\n",
+       "      <td>[nmdc:dobj-11-esgqw196]</td>\n",
+       "      <td>[nmdc:dobj-11-0bmapy68]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:wfnom-11-ph9mfs20.1</td>\n",
+       "      <td>[nmdc:dobj-11-sj597780]</td>\n",
+       "      <td>[nmdc:dobj-11-04v02904]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:wfnom-11-x8j6qc18.1</td>\n",
+       "      <td>[nmdc:dobj-11-jjmn7962]</td>\n",
+       "      <td>[nmdc:dobj-11-1qhx4085]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:wfnom-11-479gvz55.1</td>\n",
+       "      <td>[nmdc:dobj-11-rq95kp39]</td>\n",
+       "      <td>[nmdc:dobj-11-0cf8jk36]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>nmdc:wfnom-13-ycxczw19.1</td>\n",
+       "      <td>[nmdc:dobj-13-w9czqg70]</td>\n",
+       "      <td>[nmdc:dobj-13-yasv1664]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>nmdc:wfnom-13-b2e44434.1</td>\n",
+       "      <td>[nmdc:dobj-13-aef3pw71]</td>\n",
+       "      <td>[nmdc:dobj-13-ykh2yk18]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>nmdc:wfnom-13-eb5f8063.1</td>\n",
+       "      <td>[nmdc:dobj-13-b28x6912]</td>\n",
+       "      <td>[nmdc:dobj-13-xstcrm22]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2545</th>\n",
+       "      <td>nmdc:wfnom-13-k9tdtt72.1</td>\n",
+       "      <td>[nmdc:dobj-13-g1wmqa90]</td>\n",
+       "      <td>[nmdc:dobj-13-zkgr9031]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>nmdc:wfnom-13-65eghz72.1</td>\n",
+       "      <td>[nmdc:dobj-13-ewk8zp52]</td>\n",
+       "      <td>[nmdc:dobj-13-yw3xee60]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   analysis_id       analysis_has_input  \\\n",
+       "0     nmdc:wfnom-11-fwhgp651.1  [nmdc:dobj-11-hrqfj247]   \n",
+       "1     nmdc:wfnom-11-snvsmz18.1  [nmdc:dobj-11-esgqw196]   \n",
+       "2     nmdc:wfnom-11-ph9mfs20.1  [nmdc:dobj-11-sj597780]   \n",
+       "3     nmdc:wfnom-11-x8j6qc18.1  [nmdc:dobj-11-jjmn7962]   \n",
+       "4     nmdc:wfnom-11-479gvz55.1  [nmdc:dobj-11-rq95kp39]   \n",
+       "...                        ...                      ...   \n",
+       "2542  nmdc:wfnom-13-ycxczw19.1  [nmdc:dobj-13-w9czqg70]   \n",
+       "2543  nmdc:wfnom-13-b2e44434.1  [nmdc:dobj-13-aef3pw71]   \n",
+       "2544  nmdc:wfnom-13-eb5f8063.1  [nmdc:dobj-13-b28x6912]   \n",
+       "2545  nmdc:wfnom-13-k9tdtt72.1  [nmdc:dobj-13-g1wmqa90]   \n",
+       "2546  nmdc:wfnom-13-65eghz72.1  [nmdc:dobj-13-ewk8zp52]   \n",
+       "\n",
+       "          analysis_has_output  \n",
+       "0     [nmdc:dobj-11-0dn7p856]  \n",
+       "1     [nmdc:dobj-11-0bmapy68]  \n",
+       "2     [nmdc:dobj-11-04v02904]  \n",
+       "3     [nmdc:dobj-11-1qhx4085]  \n",
+       "4     [nmdc:dobj-11-0cf8jk36]  \n",
+       "...                       ...  \n",
+       "2542  [nmdc:dobj-13-yasv1664]  \n",
+       "2543  [nmdc:dobj-13-ykh2yk18]  \n",
+       "2544  [nmdc:dobj-13-xstcrm22]  \n",
+       "2545  [nmdc:dobj-13-zkgr9031]  \n",
+       "2546  [nmdc:dobj-13-yw3xee60]  \n",
+       "\n",
+       "[2547 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "analysis_dataobj=func.get_id_results(\n",
+    "                                    newest_results=processed_nom,\\\n",
+    "                                    id_field=\"processed_nom_id\",\\\n",
+    "                                    query_collection=\"nom_analysis_activity_set\",\\\n",
+    "                                    match_id_field=\"has_output\",\\\n",
+    "                                    query_fields=\"id,has_input,has_output\")\n",
+    "\n",
+    "# clarify names\n",
+    "for dataobject in analysis_dataobj:\n",
+    "    dataobject[\"analysis_id\"] = dataobject.pop(\"id\")\n",
+    "    dataobject[\"analysis_has_input\"] = dataobject.pop(\"has_input\")\n",
+    "    dataobject[\"analysis_has_output\"] = dataobject.pop(\"has_output\")\n",
+    "\n",
+    "# convert to data frame\n",
+    "analysis_dataobj_df = func.convert_df(analysis_dataobj)\n",
+    "analysis_dataobj_df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the raw data objects used as input for these analysis records by matching the analysis record's `has_input` slot to the `id` slot in the collection `data_object_set`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>raw_id</th>\n",
+       "      <th>raw_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:dobj-11-04embv91</td>\n",
+       "      <td>Lybrand_FT_62_W_23Aug19_Alder_Infuse_p3_1_01_4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:dobj-11-04ny1n21</td>\n",
+       "      <td>Lybrand_FT_36_C_30Aug19_Alder_Infuse_p05_1_01_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:dobj-11-09p17z03</td>\n",
+       "      <td>Lybrand_Permafrost_BOG_14_CHCl3_13Dec19_Alder_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:dobj-11-0cmhqk17</td>\n",
+       "      <td>WHONDRS_S19S_0059_ICR_1_43_Alder_Inf_13Sept19_...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:dobj-11-0rgvyp97</td>\n",
+       "      <td>WHONDRS_S19S_R33_14Sept2020_Alder_Infuse_p15_1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>nmdc:dobj-13-ww59kg97</td>\n",
+       "      <td>output: Unground_SBR_Spring_2014_FC_S2_10-20_M...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>nmdc:dobj-13-ym2bx698</td>\n",
+       "      <td>output: Unground_SBR_Spring_2014_FC_S2_00-10_H...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>nmdc:dobj-13-zazrqk87</td>\n",
+       "      <td>output: Brodie_185_w_r1_01Feb19_HESI_neg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2545</th>\n",
+       "      <td>nmdc:dobj-13-zjrg8w43</td>\n",
+       "      <td>output: Brodie_184_H2O_11Mar19_R1_HESI_Neg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>nmdc:dobj-13-zzzyae97</td>\n",
+       "      <td>output: SBR_FC_N1_00-10_H2Oext_13Oct15_Leopard...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     raw_id                                           raw_name\n",
+       "0     nmdc:dobj-11-04embv91  Lybrand_FT_62_W_23Aug19_Alder_Infuse_p3_1_01_4...\n",
+       "1     nmdc:dobj-11-04ny1n21  Lybrand_FT_36_C_30Aug19_Alder_Infuse_p05_1_01_...\n",
+       "2     nmdc:dobj-11-09p17z03  Lybrand_Permafrost_BOG_14_CHCl3_13Dec19_Alder_...\n",
+       "3     nmdc:dobj-11-0cmhqk17  WHONDRS_S19S_0059_ICR_1_43_Alder_Inf_13Sept19_...\n",
+       "4     nmdc:dobj-11-0rgvyp97  WHONDRS_S19S_R33_14Sept2020_Alder_Infuse_p15_1...\n",
+       "...                     ...                                                ...\n",
+       "2542  nmdc:dobj-13-ww59kg97  output: Unground_SBR_Spring_2014_FC_S2_10-20_M...\n",
+       "2543  nmdc:dobj-13-ym2bx698  output: Unground_SBR_Spring_2014_FC_S2_00-10_H...\n",
+       "2544  nmdc:dobj-13-zazrqk87           output: Brodie_185_w_r1_01Feb19_HESI_neg\n",
+       "2545  nmdc:dobj-13-zjrg8w43         output: Brodie_184_H2O_11Mar19_R1_HESI_Neg\n",
+       "2546  nmdc:dobj-13-zzzyae97  output: SBR_FC_N1_00-10_H2Oext_13Oct15_Leopard...\n",
+       "\n",
+       "[2547 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_dataobj=func.get_id_results(\n",
+    "                                    newest_results=analysis_dataobj,\\\n",
+    "                                    id_field=\"analysis_has_input\",\\\n",
+    "                                    query_collection=\"data_object_set\",\\\n",
+    "                                    match_id_field=\"id\",\\\n",
+    "                                    query_fields=\"id,name\")\n",
+    "# clarify names\n",
+    "for dataobject in raw_dataobj:\n",
+    "    dataobject[\"raw_id\"] = dataobject.pop(\"id\")\n",
+    "    dataobject[\"raw_name\"] = dataobject.pop(\"name\")\n",
+    "\n",
+    "raw_df = func.convert_df(raw_dataobj)\n",
+    "\n",
+    "raw_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the omics processing records that produced these raw data objects by matching the data object's `id` slot to the `has_output` slot in the collection `omics_processing_set`. Once again extract the `has_input` slot as it will be used for the next traversal, grabbing the biosample data objects that are used as input to the omics processing records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>omicsprocess_id</th>\n",
+       "      <th>omicsprocess_has_output</th>\n",
+       "      <th>omicsprocess_has_input</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:omprc-11-adjx8k29</td>\n",
+       "      <td>[nmdc:dobj-11-04embv91]</td>\n",
+       "      <td>[nmdc:bsm-11-ax989290]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:omprc-11-w1kvtj73</td>\n",
+       "      <td>[nmdc:dobj-11-04ny1n21]</td>\n",
+       "      <td>[nmdc:bsm-11-qvtb2v69]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:omprc-11-rj3bqn04</td>\n",
+       "      <td>[nmdc:dobj-11-09p17z03]</td>\n",
+       "      <td>[nmdc:bsm-11-6aqn1d84]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:omprc-11-a1szxs11</td>\n",
+       "      <td>[nmdc:dobj-11-0cmhqk17]</td>\n",
+       "      <td>[nmdc:bsm-11-b7c2dc48]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:omprc-11-sc2gv291</td>\n",
+       "      <td>[nmdc:dobj-11-0rgvyp97]</td>\n",
+       "      <td>[nmdc:bsm-11-m1cbn542]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>nmdc:omprc-13-agdd4h68</td>\n",
+       "      <td>[nmdc:dobj-13-ww59kg97]</td>\n",
+       "      <td>[nmdc:bsm-13-ty597764]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>nmdc:omprc-13-820dfq84</td>\n",
+       "      <td>[nmdc:dobj-13-ym2bx698]</td>\n",
+       "      <td>[nmdc:bsm-13-rrsd4804]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>nmdc:omprc-11-5y5txf92</td>\n",
+       "      <td>[nmdc:dobj-13-zazrqk87]</td>\n",
+       "      <td>[nmdc:bsm-11-4sw8dr23]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2545</th>\n",
+       "      <td>nmdc:omprc-11-077nww93</td>\n",
+       "      <td>[nmdc:dobj-13-zjrg8w43]</td>\n",
+       "      <td>[nmdc:bsm-11-afgbs159]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>nmdc:omprc-13-xemx6b61</td>\n",
+       "      <td>[nmdc:dobj-13-zzzyae97]</td>\n",
+       "      <td>[nmdc:bsm-13-z9wten89]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             omicsprocess_id  omicsprocess_has_output  omicsprocess_has_input\n",
+       "0     nmdc:omprc-11-adjx8k29  [nmdc:dobj-11-04embv91]  [nmdc:bsm-11-ax989290]\n",
+       "1     nmdc:omprc-11-w1kvtj73  [nmdc:dobj-11-04ny1n21]  [nmdc:bsm-11-qvtb2v69]\n",
+       "2     nmdc:omprc-11-rj3bqn04  [nmdc:dobj-11-09p17z03]  [nmdc:bsm-11-6aqn1d84]\n",
+       "3     nmdc:omprc-11-a1szxs11  [nmdc:dobj-11-0cmhqk17]  [nmdc:bsm-11-b7c2dc48]\n",
+       "4     nmdc:omprc-11-sc2gv291  [nmdc:dobj-11-0rgvyp97]  [nmdc:bsm-11-m1cbn542]\n",
+       "...                      ...                      ...                     ...\n",
+       "2542  nmdc:omprc-13-agdd4h68  [nmdc:dobj-13-ww59kg97]  [nmdc:bsm-13-ty597764]\n",
+       "2543  nmdc:omprc-13-820dfq84  [nmdc:dobj-13-ym2bx698]  [nmdc:bsm-13-rrsd4804]\n",
+       "2544  nmdc:omprc-11-5y5txf92  [nmdc:dobj-13-zazrqk87]  [nmdc:bsm-11-4sw8dr23]\n",
+       "2545  nmdc:omprc-11-077nww93  [nmdc:dobj-13-zjrg8w43]  [nmdc:bsm-11-afgbs159]\n",
+       "2546  nmdc:omprc-13-xemx6b61  [nmdc:dobj-13-zzzyae97]  [nmdc:bsm-13-z9wten89]\n",
+       "\n",
+       "[2547 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "omicsprocess_dataobj=func.get_id_results(\n",
+    "                                    newest_results=raw_dataobj,\\\n",
+    "                                    id_field=\"raw_id\",\\\n",
+    "                                    query_collection=\"omics_processing_set\",\\\n",
+    "                                    match_id_field=\"has_output\",\\\n",
+    "                                    query_fields=\"id,has_input,has_output\")\n",
+    "\n",
+    "# clarify names\n",
+    "for dataobject in omicsprocess_dataobj:\n",
+    "    dataobject[\"omicsprocess_id\"] = dataobject.pop(\"id\")\n",
+    "    dataobject[\"omicsprocess_has_output\"] = dataobject.pop(\"has_output\")\n",
+    "    dataobject[\"omicsprocess_has_input\"] = dataobject.pop(\"has_input\")\n",
+    "# convert to data frame\n",
+    "omicsprocess_dataobj_df = func.convert_df(omicsprocess_dataobj)\n",
+    "omicsprocess_dataobj_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the biosample data objects used as input for these omics processing records by matching the processing record's `has_input` slot to the `id` slot in the collection `biosample_set`. Query all fields in this API call by leaving `query_fields` as an empty list and utilize informative columns to group biosamples into a `type`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>part_of</th>\n",
+       "      <th>env_broad_scale</th>\n",
+       "      <th>env_local_scale</th>\n",
+       "      <th>env_medium</th>\n",
+       "      <th>samp_name</th>\n",
+       "      <th>emsl_biosample_identifiers</th>\n",
+       "      <th>carb_nitro_ratio</th>\n",
+       "      <th>collection_date</th>\n",
+       "      <th>cur_vegetation</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>...</th>\n",
+       "      <th>img_identifiers</th>\n",
+       "      <th>insdc_biosample_identifiers</th>\n",
+       "      <th>add_date</th>\n",
+       "      <th>location</th>\n",
+       "      <th>mod_date</th>\n",
+       "      <th>tot_org_carb</th>\n",
+       "      <th>sample_link</th>\n",
+       "      <th>community</th>\n",
+       "      <th>alternative_identifiers</th>\n",
+       "      <th>igsn_biosample_identifiers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[nmdc:sty-11-28tm5d36]</td>\n",
+       "      <td>{'has_raw_value': 'urban biome [ENVO:01000249]...</td>\n",
+       "      <td>{'has_raw_value': 'woodland clearing [ENVO:000...</td>\n",
+       "      <td>{'has_raw_value': 'forest soil [ENVO:00002261]...</td>\n",
+       "      <td>WLUP_CoreB_TOP</td>\n",
+       "      <td>[UUID:WLUP-CB-T-a07898a8-bff9-4bc2-ae4c-345682...</td>\n",
+       "      <td>{'has_raw_value': '15.699'}</td>\n",
+       "      <td>{'has_raw_value': '2022-04-04T00:00:00'}</td>\n",
+       "      <td>{'has_raw_value': 'Deciduous trees, skunk cabb...</td>\n",
+       "      <td>{'has_minimum_numeric_value': 0.0, 'has_maximu...</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[nmdc:sty-11-db67n062]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000446', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000516', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00001998', 'term': {'i...</td>\n",
+       "      <td>Lybrand_Permafrost_BOG_12_H2O</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2018-07-22 00:00:00'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[nmdc:sty-11-8xdqsn54]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000446', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000888', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00001998', 'term': {'i...</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>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[nmdc:sty-11-28tm5d36]</td>\n",
+       "      <td>{'has_raw_value': 'anthropogenic terrestrial b...</td>\n",
+       "      <td>{'has_raw_value': 'agricultural field [ENVO:00...</td>\n",
+       "      <td>{'has_raw_value': 'agricultural soil [ENVO:000...</td>\n",
+       "      <td>PRS2_CoreB_TOP</td>\n",
+       "      <td>[UUID:PSR2-CB-T-177b015d-e006-4aca-977d-c9c118...</td>\n",
+       "      <td>{'has_raw_value': '9.6'}</td>\n",
+       "      <td>{'has_raw_value': '2022-02-04'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_minimum_numeric_value': 0.0, 'has_maximu...</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[nmdc:sty-11-db67n062]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000446', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000861', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00001998', 'term': {'i...</td>\n",
+       "      <td>Lybrand_Permafrost_HE_02_CHCl3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2018-07-18 00:00:00'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2165</th>\n",
+       "      <td>[nmdc:sty-11-aygzgv51]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000873', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000022', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000017', 'term': {'i...</td>\n",
+       "      <td>GW-RW N3_00_10</td>\n",
+       "      <td>[emsl:63ca3868-6647-11eb-ae93-0242ac130002]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2014-04-01'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Columbia River, Washington, USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>microbial communities</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2166</th>\n",
+       "      <td>[nmdc:sty-11-aygzgv51]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000873', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000022', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000017', 'term': {'i...</td>\n",
+       "      <td>GW-RW S2_00_10</td>\n",
+       "      <td>[emsl:63ca47e0-6647-11eb-ae93-0242ac130002]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2014-04-01'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Columbia River, Washington, USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>microbial communities</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2167</th>\n",
+       "      <td>[nmdc:sty-11-aygzgv51]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000873', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000022', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000017', 'term': {'i...</td>\n",
+       "      <td>GW-RW S3_30_40</td>\n",
+       "      <td>[emsl:63ca5014-6647-11eb-ae93-0242ac130002]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2014-04-01'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Columbia River, Washington, USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>microbial communities</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2168</th>\n",
+       "      <td>[nmdc:sty-11-aygzgv51]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000873', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000022', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000017', 'term': {'i...</td>\n",
+       "      <td>GW-RW S2_10_20</td>\n",
+       "      <td>[emsl:63ca489e-6647-11eb-ae93-0242ac130002]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2014-04-01'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Columbia River, Washington, USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>microbial communities</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169</th>\n",
+       "      <td>[nmdc:sty-11-aygzgv51]</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000873', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:00000022', 'term': {'i...</td>\n",
+       "      <td>{'has_raw_value': 'ENVO:01000017', 'term': {'i...</td>\n",
+       "      <td>GW-RW N1_00_10</td>\n",
+       "      <td>[emsl:63ca2c7e-6647-11eb-ae93-0242ac130002]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'has_raw_value': '2014-04-01'}</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Columbia River, Washington, USA</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>microbial communities</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2170 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     part_of  \\\n",
+       "0     [nmdc:sty-11-28tm5d36]   \n",
+       "1     [nmdc:sty-11-db67n062]   \n",
+       "2     [nmdc:sty-11-8xdqsn54]   \n",
+       "3     [nmdc:sty-11-28tm5d36]   \n",
+       "4     [nmdc:sty-11-db67n062]   \n",
+       "...                      ...   \n",
+       "2165  [nmdc:sty-11-aygzgv51]   \n",
+       "2166  [nmdc:sty-11-aygzgv51]   \n",
+       "2167  [nmdc:sty-11-aygzgv51]   \n",
+       "2168  [nmdc:sty-11-aygzgv51]   \n",
+       "2169  [nmdc:sty-11-aygzgv51]   \n",
+       "\n",
+       "                                        env_broad_scale  \\\n",
+       "0     {'has_raw_value': 'urban biome [ENVO:01000249]...   \n",
+       "1     {'has_raw_value': 'ENVO:00000446', 'term': {'i...   \n",
+       "2     {'has_raw_value': 'ENVO:00000446', 'term': {'i...   \n",
+       "3     {'has_raw_value': 'anthropogenic terrestrial b...   \n",
+       "4     {'has_raw_value': 'ENVO:00000446', 'term': {'i...   \n",
+       "...                                                 ...   \n",
+       "2165  {'has_raw_value': 'ENVO:00000873', 'term': {'i...   \n",
+       "2166  {'has_raw_value': 'ENVO:00000873', 'term': {'i...   \n",
+       "2167  {'has_raw_value': 'ENVO:00000873', 'term': {'i...   \n",
+       "2168  {'has_raw_value': 'ENVO:00000873', 'term': {'i...   \n",
+       "2169  {'has_raw_value': 'ENVO:00000873', 'term': {'i...   \n",
+       "\n",
+       "                                        env_local_scale  \\\n",
+       "0     {'has_raw_value': 'woodland clearing [ENVO:000...   \n",
+       "1     {'has_raw_value': 'ENVO:00000516', 'term': {'i...   \n",
+       "2     {'has_raw_value': 'ENVO:01000888', 'term': {'i...   \n",
+       "3     {'has_raw_value': 'agricultural field [ENVO:00...   \n",
+       "4     {'has_raw_value': 'ENVO:01000861', 'term': {'i...   \n",
+       "...                                                 ...   \n",
+       "2165  {'has_raw_value': 'ENVO:00000022', 'term': {'i...   \n",
+       "2166  {'has_raw_value': 'ENVO:00000022', 'term': {'i...   \n",
+       "2167  {'has_raw_value': 'ENVO:00000022', 'term': {'i...   \n",
+       "2168  {'has_raw_value': 'ENVO:00000022', 'term': {'i...   \n",
+       "2169  {'has_raw_value': 'ENVO:00000022', 'term': {'i...   \n",
+       "\n",
+       "                                             env_medium  \\\n",
+       "0     {'has_raw_value': 'forest soil [ENVO:00002261]...   \n",
+       "1     {'has_raw_value': 'ENVO:00001998', 'term': {'i...   \n",
+       "2     {'has_raw_value': 'ENVO:00001998', 'term': {'i...   \n",
+       "3     {'has_raw_value': 'agricultural soil [ENVO:000...   \n",
+       "4     {'has_raw_value': 'ENVO:00001998', 'term': {'i...   \n",
+       "...                                                 ...   \n",
+       "2165  {'has_raw_value': 'ENVO:01000017', 'term': {'i...   \n",
+       "2166  {'has_raw_value': 'ENVO:01000017', 'term': {'i...   \n",
+       "2167  {'has_raw_value': 'ENVO:01000017', 'term': {'i...   \n",
+       "2168  {'has_raw_value': 'ENVO:01000017', 'term': {'i...   \n",
+       "2169  {'has_raw_value': 'ENVO:01000017', 'term': {'i...   \n",
+       "\n",
+       "                           samp_name  \\\n",
+       "0                     WLUP_CoreB_TOP   \n",
+       "1      Lybrand_Permafrost_BOG_12_H2O   \n",
+       "2                                NaN   \n",
+       "3                     PRS2_CoreB_TOP   \n",
+       "4     Lybrand_Permafrost_HE_02_CHCl3   \n",
+       "...                              ...   \n",
+       "2165                  GW-RW N3_00_10   \n",
+       "2166                  GW-RW S2_00_10   \n",
+       "2167                  GW-RW S3_30_40   \n",
+       "2168                  GW-RW S2_10_20   \n",
+       "2169                  GW-RW N1_00_10   \n",
+       "\n",
+       "                             emsl_biosample_identifiers  \\\n",
+       "0     [UUID:WLUP-CB-T-a07898a8-bff9-4bc2-ae4c-345682...   \n",
+       "1                                                   NaN   \n",
+       "2                                                   NaN   \n",
+       "3     [UUID:PSR2-CB-T-177b015d-e006-4aca-977d-c9c118...   \n",
+       "4                                                   NaN   \n",
+       "...                                                 ...   \n",
+       "2165        [emsl:63ca3868-6647-11eb-ae93-0242ac130002]   \n",
+       "2166        [emsl:63ca47e0-6647-11eb-ae93-0242ac130002]   \n",
+       "2167        [emsl:63ca5014-6647-11eb-ae93-0242ac130002]   \n",
+       "2168        [emsl:63ca489e-6647-11eb-ae93-0242ac130002]   \n",
+       "2169        [emsl:63ca2c7e-6647-11eb-ae93-0242ac130002]   \n",
+       "\n",
+       "                 carb_nitro_ratio                           collection_date  \\\n",
+       "0     {'has_raw_value': '15.699'}  {'has_raw_value': '2022-04-04T00:00:00'}   \n",
+       "1                             NaN  {'has_raw_value': '2018-07-22 00:00:00'}   \n",
+       "2                             NaN                                       NaN   \n",
+       "3        {'has_raw_value': '9.6'}           {'has_raw_value': '2022-02-04'}   \n",
+       "4                             NaN  {'has_raw_value': '2018-07-18 00:00:00'}   \n",
+       "...                           ...                                       ...   \n",
+       "2165                          NaN           {'has_raw_value': '2014-04-01'}   \n",
+       "2166                          NaN           {'has_raw_value': '2014-04-01'}   \n",
+       "2167                          NaN           {'has_raw_value': '2014-04-01'}   \n",
+       "2168                          NaN           {'has_raw_value': '2014-04-01'}   \n",
+       "2169                          NaN           {'has_raw_value': '2014-04-01'}   \n",
+       "\n",
+       "                                         cur_vegetation  \\\n",
+       "0     {'has_raw_value': 'Deciduous trees, skunk cabb...   \n",
+       "1                                                   NaN   \n",
+       "2                                                   NaN   \n",
+       "3                                                   NaN   \n",
+       "4                                                   NaN   \n",
+       "...                                                 ...   \n",
+       "2165                                                NaN   \n",
+       "2166                                                NaN   \n",
+       "2167                                                NaN   \n",
+       "2168                                                NaN   \n",
+       "2169                                                NaN   \n",
+       "\n",
+       "                                                  depth  ... img_identifiers  \\\n",
+       "0     {'has_minimum_numeric_value': 0.0, 'has_maximu...  ...             NaN   \n",
+       "1                                                   NaN  ...             NaN   \n",
+       "2                                                   NaN  ...             NaN   \n",
+       "3     {'has_minimum_numeric_value': 0.0, 'has_maximu...  ...             NaN   \n",
+       "4                                                   NaN  ...             NaN   \n",
+       "...                                                 ...  ...             ...   \n",
+       "2165                                                NaN  ...             NaN   \n",
+       "2166                                                NaN  ...             NaN   \n",
+       "2167                                                NaN  ...             NaN   \n",
+       "2168                                                NaN  ...             NaN   \n",
+       "2169                                                NaN  ...             NaN   \n",
+       "\n",
+       "     insdc_biosample_identifiers add_date                         location  \\\n",
+       "0                            NaN      NaN                              NaN   \n",
+       "1                            NaN      NaN                              NaN   \n",
+       "2                            NaN      NaN                              NaN   \n",
+       "3                            NaN      NaN                              NaN   \n",
+       "4                            NaN      NaN                              NaN   \n",
+       "...                          ...      ...                              ...   \n",
+       "2165                         NaN      NaN  Columbia River, Washington, USA   \n",
+       "2166                         NaN      NaN  Columbia River, Washington, USA   \n",
+       "2167                         NaN      NaN  Columbia River, Washington, USA   \n",
+       "2168                         NaN      NaN  Columbia River, Washington, USA   \n",
+       "2169                         NaN      NaN  Columbia River, Washington, USA   \n",
+       "\n",
+       "     mod_date tot_org_carb sample_link              community  \\\n",
+       "0         NaN          NaN         NaN                    NaN   \n",
+       "1         NaN          NaN         NaN                    NaN   \n",
+       "2         NaN          NaN         NaN                    NaN   \n",
+       "3         NaN          NaN         NaN                    NaN   \n",
+       "4         NaN          NaN         NaN                    NaN   \n",
+       "...       ...          ...         ...                    ...   \n",
+       "2165      NaN          NaN         NaN  microbial communities   \n",
+       "2166      NaN          NaN         NaN  microbial communities   \n",
+       "2167      NaN          NaN         NaN  microbial communities   \n",
+       "2168      NaN          NaN         NaN  microbial communities   \n",
+       "2169      NaN          NaN         NaN  microbial communities   \n",
+       "\n",
+       "     alternative_identifiers  igsn_biosample_identifiers  \n",
+       "0                        NaN                         NaN  \n",
+       "1                        NaN                         NaN  \n",
+       "2                        NaN                         NaN  \n",
+       "3                        NaN                         NaN  \n",
+       "4                        NaN                         NaN  \n",
+       "...                      ...                         ...  \n",
+       "2165                     NaN                         NaN  \n",
+       "2166                     NaN                         NaN  \n",
+       "2167                     NaN                         NaN  \n",
+       "2168                     NaN                         NaN  \n",
+       "2169                     NaN                         NaN  \n",
+       "\n",
+       "[2170 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "biosample_dataobj=func.get_id_results(\n",
+    "                                    newest_results=omicsprocess_dataobj,\\\n",
+    "                                    id_field=\"omicsprocess_has_input\",\\\n",
+    "                                    query_collection=\"biosample_set\",\\\n",
+    "                                    match_id_field=\"id\",\\\n",
+    "                                    query_fields=\"\")\n",
+    "\n",
+    "# clarify names\n",
+    "for dataobject in biosample_dataobj:\n",
+    "    dataobject[\"biosample_id\"] = dataobject.pop(\"id\")\n",
+    "\n",
+    "# convert to data frame\n",
+    "biosample_dataobj_df = func.convert_df(biosample_dataobj)\n",
+    "\n",
+    "biosample_dataobj_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assign a general type for each sample by parsing their ENVO IDs. This was done manually by searching ENVO ID's on the [ontology search website](https://www.ebi.ac.uk/ols4/ontologies/envo)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>biosample_id</th>\n",
+       "      <th>geo_loc_name.has_raw_value</th>\n",
+       "      <th>env_medium.term.name</th>\n",
+       "      <th>env_medium.term.id</th>\n",
+       "      <th>sample_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:bsm-11-17ag3b30</td>\n",
+       "      <td>USA: Alaska, Coldfoot</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>ENVO:00001998</td>\n",
+       "      <td>soil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:bsm-11-26bgjj05</td>\n",
+       "      <td>USA: ND, Stutsman</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00001998</td>\n",
+       "      <td>soil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:bsm-11-2d3eam48</td>\n",
+       "      <td>USA: Washington, Prosser Non-irrigated Bare</td>\n",
+       "      <td>agricultural soil</td>\n",
+       "      <td>ENVO:00002259</td>\n",
+       "      <td>soil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:bsm-11-2n9dds87</td>\n",
+       "      <td>USA: Alaska, Healy</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>ENVO:00001998</td>\n",
+       "      <td>soil</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2165</th>\n",
+       "      <td>nmdc:bsm-13-q0qxrf57</td>\n",
+       "      <td>USA: Columbia River, Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:01000017</td>\n",
+       "      <td>sand</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2166</th>\n",
+       "      <td>nmdc:bsm-13-rrsd4804</td>\n",
+       "      <td>USA: Columbia River, Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:01000017</td>\n",
+       "      <td>sand</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2167</th>\n",
+       "      <td>nmdc:bsm-13-tr7n0581</td>\n",
+       "      <td>USA: Columbia River, Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:01000017</td>\n",
+       "      <td>sand</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2168</th>\n",
+       "      <td>nmdc:bsm-13-ty597764</td>\n",
+       "      <td>USA: Columbia River, Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:01000017</td>\n",
+       "      <td>sand</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2169</th>\n",
+       "      <td>nmdc:bsm-13-z9wten89</td>\n",
+       "      <td>USA: Columbia River, Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:01000017</td>\n",
+       "      <td>sand</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2170 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              biosample_id                   geo_loc_name.has_raw_value  \\\n",
+       "0     nmdc:bsm-11-12esnc57   USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "1     nmdc:bsm-11-17ag3b30                        USA: Alaska, Coldfoot   \n",
+       "2     nmdc:bsm-11-26bgjj05                            USA: ND, Stutsman   \n",
+       "3     nmdc:bsm-11-2d3eam48  USA: Washington, Prosser Non-irrigated Bare   \n",
+       "4     nmdc:bsm-11-2n9dds87                           USA: Alaska, Healy   \n",
+       "...                    ...                                          ...   \n",
+       "2165  nmdc:bsm-13-q0qxrf57              USA: Columbia River, Washington   \n",
+       "2166  nmdc:bsm-13-rrsd4804              USA: Columbia River, Washington   \n",
+       "2167  nmdc:bsm-13-tr7n0581              USA: Columbia River, Washington   \n",
+       "2168  nmdc:bsm-13-ty597764              USA: Columbia River, Washington   \n",
+       "2169  nmdc:bsm-13-z9wten89              USA: Columbia River, Washington   \n",
+       "\n",
+       "     env_medium.term.name env_medium.term.id sample_type  \n",
+       "0            forest soil       ENVO:00002261        soil  \n",
+       "1                    soil      ENVO:00001998        soil  \n",
+       "2                     NaN      ENVO:00001998        soil  \n",
+       "3      agricultural soil       ENVO:00002259        soil  \n",
+       "4                    soil      ENVO:00001998        soil  \n",
+       "...                   ...                ...         ...  \n",
+       "2165                  NaN      ENVO:01000017        sand  \n",
+       "2166                  NaN      ENVO:01000017        sand  \n",
+       "2167                  NaN      ENVO:01000017        sand  \n",
+       "2168                  NaN      ENVO:01000017        sand  \n",
+       "2169                  NaN      ENVO:01000017        sand  \n",
+       "\n",
+       "[2170 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "biosample_dataobj_flat=pd.json_normalize(biosample_dataobj)\n",
+    "biosample_dataobj_flat_df=func.convert_df(biosample_dataobj_flat)\n",
+    "\n",
+    "biosample_dataobj_flat_df['sample_type']=\"\"\n",
+    "\n",
+    "biosample_dataobj_flat_df['env_medium.term.id'].drop_duplicates()\n",
+    "\n",
+    "biosample_dataobj_flat_df.loc[biosample_dataobj_flat_df['env_medium.term.id'].isin([\"ENVO:00002261\",\"ENVO:00001998\",\"ENVO:00002259\",\n",
+    "                                                                                    \"ENVO:01001616\",\"ENVO:00005750\",\"ENVO:00005761\",\n",
+    "                                                                                    \"ENVO:00005760\",\"ENVO:00005773\",\"ENVO:00005802\",\n",
+    "                                                                                    \"ENVO:00005774\"]),'sample_type'] = 'soil'\n",
+    "biosample_dataobj_flat_df.loc[biosample_dataobj_flat_df['env_medium.term.id'].isin([\"ENVO:00002042\"]),'sample_type'] = 'water'\n",
+    "biosample_dataobj_flat_df.loc[biosample_dataobj_flat_df['env_medium.term.id'].isin([\"ENVO:00002007\"]),'sample_type'] = 'sediment'\n",
+    "biosample_dataobj_flat_df.loc[biosample_dataobj_flat_df['env_medium.term.id'].isin([\"ENVO:01000017\"]),'sample_type'] = 'sand'\n",
+    "\n",
+    "#filter to desired metadata columns\n",
+    "biosample_dataobj_flat_df=biosample_dataobj_flat_df[['biosample_id','geo_loc_name.has_raw_value','env_medium.term.name','env_medium.term.id','sample_type']]\n",
+    "\n",
+    "biosample_dataobj_flat_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create final data frame of relevant metadata and NMDC schema information for each NOM processed data object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create merged dataframe with results from schema traversal and metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>biosample_id</th>\n",
+       "      <th>geo_loc_name.has_raw_value</th>\n",
+       "      <th>env_medium.term.name</th>\n",
+       "      <th>env_medium.term.id</th>\n",
+       "      <th>sample_type</th>\n",
+       "      <th>raw_id</th>\n",
+       "      <th>raw_name</th>\n",
+       "      <th>processed_nom_id</th>\n",
+       "      <th>processed_nom_md5_checksum</th>\n",
+       "      <th>processed_nom_url</th>\n",
+       "      <th>analysis_id</th>\n",
+       "      <th>analysis_has_input</th>\n",
+       "      <th>analysis_has_output</th>\n",
+       "      <th>omicsprocess_id</th>\n",
+       "      <th>omicsprocess_has_output</th>\n",
+       "      <th>omicsprocess_has_input</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-gdhnkg66</td>\n",
+       "      <td>1000S_WLUP_FTMS_SPE_TOP_3_run2_Fir_28Apr22_300...</td>\n",
+       "      <td>nmdc:dobj-11-0dyc2f79</td>\n",
+       "      <td>9ace043441672422f7991411014ab9cb</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "      <td>nmdc:wfnom-11-xnhh0t17.1</td>\n",
+       "      <td>nmdc:dobj-11-gdhnkg66</td>\n",
+       "      <td>nmdc:dobj-11-0dyc2f79</td>\n",
+       "      <td>nmdc:omprc-11-wj8myx84</td>\n",
+       "      <td>nmdc:dobj-11-gdhnkg66</td>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-vkheqz50</td>\n",
+       "      <td>1000S_WLUP_FTMS_SPE_TOP_1_run2_Fir_22Apr22_300...</td>\n",
+       "      <td>nmdc:dobj-11-07gsmc68</td>\n",
+       "      <td>9363e9de79c39013257ddb4d967006b2</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "      <td>nmdc:wfnom-11-r8x4ew73.1</td>\n",
+       "      <td>nmdc:dobj-11-vkheqz50</td>\n",
+       "      <td>nmdc:dobj-11-07gsmc68</td>\n",
+       "      <td>nmdc:omprc-11-s2590964</td>\n",
+       "      <td>nmdc:dobj-11-vkheqz50</td>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-qt6pmm77</td>\n",
+       "      <td>1000S_WLUP_FTMS_SPE_TOP_3_run1_Fir_25Apr22_300...</td>\n",
+       "      <td>nmdc:dobj-11-jj2r0a49</td>\n",
+       "      <td>a502ec422a6f960972b759b143d8ad9c</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "      <td>nmdc:wfnom-11-edr3rn40.1</td>\n",
+       "      <td>nmdc:dobj-11-qt6pmm77</td>\n",
+       "      <td>nmdc:dobj-11-jj2r0a49</td>\n",
+       "      <td>nmdc:omprc-11-6mbfsz04</td>\n",
+       "      <td>nmdc:dobj-11-qt6pmm77</td>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-z002ga43</td>\n",
+       "      <td>1000S_WLUP_FTMS_SPE_TOP_2_run2_Fir_22Apr22_300...</td>\n",
+       "      <td>nmdc:dobj-11-mmdwds36</td>\n",
+       "      <td>dff9718e9428ec3aa77fed987deb637d</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "      <td>nmdc:wfnom-11-zr24j979.1</td>\n",
+       "      <td>nmdc:dobj-11-z002ga43</td>\n",
+       "      <td>nmdc:dobj-11-mmdwds36</td>\n",
+       "      <td>nmdc:omprc-11-6ejjgv07</td>\n",
+       "      <td>nmdc:dobj-11-z002ga43</td>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>USA: Maryland, Winters Lane Upper Soil Pit</td>\n",
+       "      <td>forest soil</td>\n",
+       "      <td>ENVO:00002261</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-hdd01t09</td>\n",
+       "      <td>1000S_WLUP_FTMS_SPE_TOP_1_run1_Fir_22Apr22_300...</td>\n",
+       "      <td>nmdc:dobj-11-s42fww29</td>\n",
+       "      <td>05cf8e9c0394dbdf80c3c6b89a4f3956</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "      <td>nmdc:wfnom-11-befkpb58.1</td>\n",
+       "      <td>nmdc:dobj-11-hdd01t09</td>\n",
+       "      <td>nmdc:dobj-11-s42fww29</td>\n",
+       "      <td>nmdc:omprc-11-mcataq54</td>\n",
+       "      <td>nmdc:dobj-11-hdd01t09</td>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9621</th>\n",
+       "      <td>nmdc:bsm-13-zxqyyz58</td>\n",
+       "      <td>USA: Minnesota, Marcel Experimental Forest, Sp...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00005774</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-y1we6577</td>\n",
+       "      <td>Rachael_21T_19-87_M_03Mar17_leopard_Infuse.raw</td>\n",
+       "      <td>nmdc:dobj-13-jmv5e702</td>\n",
+       "      <td>03a41ac4ad0381c412b3a55b06633cfa</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "      <td>nmdc:wfnom-13-s9ez8969.1</td>\n",
+       "      <td>nmdc:dobj-13-y1we6577</td>\n",
+       "      <td>nmdc:dobj-13-jmv5e702</td>\n",
+       "      <td>nmdc:omprc-13-pbbt1t23</td>\n",
+       "      <td>nmdc:dobj-13-y1we6577</td>\n",
+       "      <td>nmdc:bsm-13-zxqyyz58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9622</th>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "      <td>USA: Colorado</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00005802</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-9za40068</td>\n",
+       "      <td>output: Brodie_153_w_r2_29Jan19_HESI_neg</td>\n",
+       "      <td>nmdc:dobj-13-qxc45786</td>\n",
+       "      <td>927a408981415d70c2111b54f37299da</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "      <td>nmdc:wfnom-13-meh2bb53.1</td>\n",
+       "      <td>nmdc:dobj-13-9za40068</td>\n",
+       "      <td>nmdc:dobj-13-qxc45786</td>\n",
+       "      <td>nmdc:omprc-11-77zzn373</td>\n",
+       "      <td>nmdc:dobj-13-9za40068</td>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9623</th>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "      <td>USA: Colorado</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00005802</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-qw6d7s16</td>\n",
+       "      <td>output: Brodie_153_w_r3_31Jan19_HESI_neg</td>\n",
+       "      <td>nmdc:dobj-13-sajy7y74</td>\n",
+       "      <td>e8571cad964446d32e686e9e94cb106b</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "      <td>nmdc:wfnom-13-5csd6908.1</td>\n",
+       "      <td>nmdc:dobj-13-qw6d7s16</td>\n",
+       "      <td>nmdc:dobj-13-sajy7y74</td>\n",
+       "      <td>nmdc:omprc-11-bgtzcs75</td>\n",
+       "      <td>nmdc:dobj-13-qw6d7s16</td>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9626</th>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "      <td>USA: Minnesota, Marcel Experimental Forest, Sp...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00005774</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-p6nbwp18</td>\n",
+       "      <td>Rachael_21T_19-15_M_14Mar17_leopard_Infuse.raw</td>\n",
+       "      <td>nmdc:dobj-13-n2g0fg49</td>\n",
+       "      <td>8ace8bbe037be3d09031d60f12d56010</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "      <td>nmdc:wfnom-13-2w27bd07.1</td>\n",
+       "      <td>nmdc:dobj-13-p6nbwp18</td>\n",
+       "      <td>nmdc:dobj-13-n2g0fg49</td>\n",
+       "      <td>nmdc:omprc-13-dsva8j03</td>\n",
+       "      <td>nmdc:dobj-13-p6nbwp18</td>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9627</th>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "      <td>USA: Minnesota, Marcel Experimental Forest, Sp...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ENVO:00005774</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-r4nvvk04</td>\n",
+       "      <td>Rachael_21T_19-15_C_20Mar17_leopard_Infuse.raw</td>\n",
+       "      <td>nmdc:dobj-13-t7bfec16</td>\n",
+       "      <td>2b2e4204fa2cf843dd19af5746f1be13</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "      <td>nmdc:wfnom-13-4pva8736.1</td>\n",
+       "      <td>nmdc:dobj-13-r4nvvk04</td>\n",
+       "      <td>nmdc:dobj-13-t7bfec16</td>\n",
+       "      <td>nmdc:omprc-13-jef2rw89</td>\n",
+       "      <td>nmdc:dobj-13-r4nvvk04</td>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              biosample_id                         geo_loc_name.has_raw_value  \\\n",
+       "0     nmdc:bsm-11-12esnc57         USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "1     nmdc:bsm-11-12esnc57         USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "2     nmdc:bsm-11-12esnc57         USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "3     nmdc:bsm-11-12esnc57         USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "4     nmdc:bsm-11-12esnc57         USA: Maryland, Winters Lane Upper Soil Pit   \n",
+       "...                    ...                                                ...   \n",
+       "9621  nmdc:bsm-13-zxqyyz58  USA: Minnesota, Marcel Experimental Forest, Sp...   \n",
+       "9622  nmdc:bsm-11-hbdmpd66                                      USA: Colorado   \n",
+       "9623  nmdc:bsm-11-hbdmpd66                                      USA: Colorado   \n",
+       "9626  nmdc:bsm-13-tk2ebg43  USA: Minnesota, Marcel Experimental Forest, Sp...   \n",
+       "9627  nmdc:bsm-13-tk2ebg43  USA: Minnesota, Marcel Experimental Forest, Sp...   \n",
+       "\n",
+       "     env_medium.term.name env_medium.term.id sample_type  \\\n",
+       "0            forest soil       ENVO:00002261        soil   \n",
+       "1            forest soil       ENVO:00002261        soil   \n",
+       "2            forest soil       ENVO:00002261        soil   \n",
+       "3            forest soil       ENVO:00002261        soil   \n",
+       "4            forest soil       ENVO:00002261        soil   \n",
+       "...                   ...                ...         ...   \n",
+       "9621                  NaN      ENVO:00005774        soil   \n",
+       "9622                  NaN      ENVO:00005802        soil   \n",
+       "9623                  NaN      ENVO:00005802        soil   \n",
+       "9626                  NaN      ENVO:00005774        soil   \n",
+       "9627                  NaN      ENVO:00005774        soil   \n",
+       "\n",
+       "                     raw_id  \\\n",
+       "0     nmdc:dobj-11-gdhnkg66   \n",
+       "1     nmdc:dobj-11-vkheqz50   \n",
+       "2     nmdc:dobj-11-qt6pmm77   \n",
+       "3     nmdc:dobj-11-z002ga43   \n",
+       "4     nmdc:dobj-11-hdd01t09   \n",
+       "...                     ...   \n",
+       "9621  nmdc:dobj-13-y1we6577   \n",
+       "9622  nmdc:dobj-13-9za40068   \n",
+       "9623  nmdc:dobj-13-qw6d7s16   \n",
+       "9626  nmdc:dobj-13-p6nbwp18   \n",
+       "9627  nmdc:dobj-13-r4nvvk04   \n",
+       "\n",
+       "                                               raw_name  \\\n",
+       "0     1000S_WLUP_FTMS_SPE_TOP_3_run2_Fir_28Apr22_300...   \n",
+       "1     1000S_WLUP_FTMS_SPE_TOP_1_run2_Fir_22Apr22_300...   \n",
+       "2     1000S_WLUP_FTMS_SPE_TOP_3_run1_Fir_25Apr22_300...   \n",
+       "3     1000S_WLUP_FTMS_SPE_TOP_2_run2_Fir_22Apr22_300...   \n",
+       "4     1000S_WLUP_FTMS_SPE_TOP_1_run1_Fir_22Apr22_300...   \n",
+       "...                                                 ...   \n",
+       "9621     Rachael_21T_19-87_M_03Mar17_leopard_Infuse.raw   \n",
+       "9622           output: Brodie_153_w_r2_29Jan19_HESI_neg   \n",
+       "9623           output: Brodie_153_w_r3_31Jan19_HESI_neg   \n",
+       "9626     Rachael_21T_19-15_M_14Mar17_leopard_Infuse.raw   \n",
+       "9627     Rachael_21T_19-15_C_20Mar17_leopard_Infuse.raw   \n",
+       "\n",
+       "           processed_nom_id        processed_nom_md5_checksum  \\\n",
+       "0     nmdc:dobj-11-0dyc2f79  9ace043441672422f7991411014ab9cb   \n",
+       "1     nmdc:dobj-11-07gsmc68  9363e9de79c39013257ddb4d967006b2   \n",
+       "2     nmdc:dobj-11-jj2r0a49  a502ec422a6f960972b759b143d8ad9c   \n",
+       "3     nmdc:dobj-11-mmdwds36  dff9718e9428ec3aa77fed987deb637d   \n",
+       "4     nmdc:dobj-11-s42fww29  05cf8e9c0394dbdf80c3c6b89a4f3956   \n",
+       "...                     ...                               ...   \n",
+       "9621  nmdc:dobj-13-jmv5e702  03a41ac4ad0381c412b3a55b06633cfa   \n",
+       "9622  nmdc:dobj-13-qxc45786  927a408981415d70c2111b54f37299da   \n",
+       "9623  nmdc:dobj-13-sajy7y74  e8571cad964446d32e686e9e94cb106b   \n",
+       "9626  nmdc:dobj-13-n2g0fg49  8ace8bbe037be3d09031d60f12d56010   \n",
+       "9627  nmdc:dobj-13-t7bfec16  2b2e4204fa2cf843dd19af5746f1be13   \n",
+       "\n",
+       "                                      processed_nom_url  \\\n",
+       "0     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...   \n",
+       "1     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...   \n",
+       "2     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...   \n",
+       "3     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...   \n",
+       "4     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...   \n",
+       "...                                                 ...   \n",
+       "9621  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...   \n",
+       "9622  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...   \n",
+       "9623  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...   \n",
+       "9626  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...   \n",
+       "9627  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...   \n",
+       "\n",
+       "                   analysis_id     analysis_has_input    analysis_has_output  \\\n",
+       "0     nmdc:wfnom-11-xnhh0t17.1  nmdc:dobj-11-gdhnkg66  nmdc:dobj-11-0dyc2f79   \n",
+       "1     nmdc:wfnom-11-r8x4ew73.1  nmdc:dobj-11-vkheqz50  nmdc:dobj-11-07gsmc68   \n",
+       "2     nmdc:wfnom-11-edr3rn40.1  nmdc:dobj-11-qt6pmm77  nmdc:dobj-11-jj2r0a49   \n",
+       "3     nmdc:wfnom-11-zr24j979.1  nmdc:dobj-11-z002ga43  nmdc:dobj-11-mmdwds36   \n",
+       "4     nmdc:wfnom-11-befkpb58.1  nmdc:dobj-11-hdd01t09  nmdc:dobj-11-s42fww29   \n",
+       "...                        ...                    ...                    ...   \n",
+       "9621  nmdc:wfnom-13-s9ez8969.1  nmdc:dobj-13-y1we6577  nmdc:dobj-13-jmv5e702   \n",
+       "9622  nmdc:wfnom-13-meh2bb53.1  nmdc:dobj-13-9za40068  nmdc:dobj-13-qxc45786   \n",
+       "9623  nmdc:wfnom-13-5csd6908.1  nmdc:dobj-13-qw6d7s16  nmdc:dobj-13-sajy7y74   \n",
+       "9626  nmdc:wfnom-13-2w27bd07.1  nmdc:dobj-13-p6nbwp18  nmdc:dobj-13-n2g0fg49   \n",
+       "9627  nmdc:wfnom-13-4pva8736.1  nmdc:dobj-13-r4nvvk04  nmdc:dobj-13-t7bfec16   \n",
+       "\n",
+       "             omicsprocess_id omicsprocess_has_output omicsprocess_has_input  \n",
+       "0     nmdc:omprc-11-wj8myx84   nmdc:dobj-11-gdhnkg66   nmdc:bsm-11-12esnc57  \n",
+       "1     nmdc:omprc-11-s2590964   nmdc:dobj-11-vkheqz50   nmdc:bsm-11-12esnc57  \n",
+       "2     nmdc:omprc-11-6mbfsz04   nmdc:dobj-11-qt6pmm77   nmdc:bsm-11-12esnc57  \n",
+       "3     nmdc:omprc-11-6ejjgv07   nmdc:dobj-11-z002ga43   nmdc:bsm-11-12esnc57  \n",
+       "4     nmdc:omprc-11-mcataq54   nmdc:dobj-11-hdd01t09   nmdc:bsm-11-12esnc57  \n",
+       "...                      ...                     ...                    ...  \n",
+       "9621  nmdc:omprc-13-pbbt1t23   nmdc:dobj-13-y1we6577   nmdc:bsm-13-zxqyyz58  \n",
+       "9622  nmdc:omprc-11-77zzn373   nmdc:dobj-13-9za40068   nmdc:bsm-11-hbdmpd66  \n",
+       "9623  nmdc:omprc-11-bgtzcs75   nmdc:dobj-13-qw6d7s16   nmdc:bsm-11-hbdmpd66  \n",
+       "9626  nmdc:omprc-13-dsva8j03   nmdc:dobj-13-p6nbwp18   nmdc:bsm-13-tk2ebg43  \n",
+       "9627  nmdc:omprc-13-jef2rw89   nmdc:dobj-13-r4nvvk04   nmdc:bsm-13-tk2ebg43  \n",
+       "\n",
+       "[2547 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#match all processed nom objects (via processed_nom_id) to analysis objects (via analysis_has_output) and expand lists has_input and has_output\n",
+    "processed_obj_to_analysis_df=func.merge_df(processed_nom_df,analysis_dataobj_df,\"processed_nom_id\",\"analysis_has_output\",[],[\"analysis_has_input\",\"analysis_has_output\"])\n",
+    "\n",
+    "#match raw data objects (via raw_id) to all_analysis_df (via analysis_has_input)\n",
+    "processed_obj_to_raw_df=func.merge_df(raw_df,processed_obj_to_analysis_df,\"raw_id\",\"analysis_has_input\",[],[])\n",
+    "\n",
+    "#match processed_obj_to_raw_df (via raw_id) to omics processing objects (via omicsprocess_has_output) and expand lists has_input and has_output\n",
+    "processed_obj_to_omicsprocess_df=func.merge_df(processed_obj_to_raw_df,omicsprocess_dataobj_df,\"raw_id\",\"omicsprocess_has_output\",[],[\"omicsprocess_has_input\",\"omicsprocess_has_output\"])\n",
+    "\n",
+    "#match biosample objects (via biosample_id) to processed_obj_to_omicsprocess_df (via omicsprocess_has_input)\n",
+    "merged_df=func.merge_df(biosample_dataobj_flat_df,processed_obj_to_omicsprocess_df,\"biosample_id\",\"omicsprocess_has_input\",[],[])\n",
+    "\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the md5_checksum to check that each row/processed NOM object has an associated url that is unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#are there any md5_checksum values that occur more than once (i.e. are associated with more than one processed nom id)\n",
+    "len(merged_df[merged_df.duplicated('processed_nom_md5_checksum')]['processed_nom_md5_checksum'].unique())==0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check that all processed nom results from first query are present in merged dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "processed_nom_df['processed_nom_id'].sort_values().tolist()==merged_df['processed_nom_id'].sort_values().tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clean up final dataframe, removing unneeded/intermediate identifier columns from schema traversal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>biosample_id</th>\n",
+       "      <th>sample_type</th>\n",
+       "      <th>processed_nom_id</th>\n",
+       "      <th>processed_nom_url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-0dyc2f79</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-07gsmc68</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-jj2r0a49</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-mmdwds36</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:bsm-11-12esnc57</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-11-s42fww29</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9621</th>\n",
+       "      <td>nmdc:bsm-13-zxqyyz58</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-jmv5e702</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9622</th>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-qxc45786</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9623</th>\n",
+       "      <td>nmdc:bsm-11-hbdmpd66</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-sajy7y74</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9626</th>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-n2g0fg49</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9627</th>\n",
+       "      <td>nmdc:bsm-13-tk2ebg43</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>nmdc:dobj-13-t7bfec16</td>\n",
+       "      <td>https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              biosample_id sample_type       processed_nom_id  \\\n",
+       "0     nmdc:bsm-11-12esnc57        soil  nmdc:dobj-11-0dyc2f79   \n",
+       "1     nmdc:bsm-11-12esnc57        soil  nmdc:dobj-11-07gsmc68   \n",
+       "2     nmdc:bsm-11-12esnc57        soil  nmdc:dobj-11-jj2r0a49   \n",
+       "3     nmdc:bsm-11-12esnc57        soil  nmdc:dobj-11-mmdwds36   \n",
+       "4     nmdc:bsm-11-12esnc57        soil  nmdc:dobj-11-s42fww29   \n",
+       "...                    ...         ...                    ...   \n",
+       "9621  nmdc:bsm-13-zxqyyz58        soil  nmdc:dobj-13-jmv5e702   \n",
+       "9622  nmdc:bsm-11-hbdmpd66        soil  nmdc:dobj-13-qxc45786   \n",
+       "9623  nmdc:bsm-11-hbdmpd66        soil  nmdc:dobj-13-sajy7y74   \n",
+       "9626  nmdc:bsm-13-tk2ebg43        soil  nmdc:dobj-13-n2g0fg49   \n",
+       "9627  nmdc:bsm-13-tk2ebg43        soil  nmdc:dobj-13-t7bfec16   \n",
+       "\n",
+       "                                      processed_nom_url  \n",
+       "0     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "1     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "2     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "3     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "4     https://nmdcdemo.emsl.pnnl.gov/nom/1000soils/r...  \n",
+       "...                                                 ...  \n",
+       "9621  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...  \n",
+       "9622  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...  \n",
+       "9623  https://nmdcdemo.emsl.pnnl.gov/nom/results/Bro...  \n",
+       "9626  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...  \n",
+       "9627  https://nmdcdemo.emsl.pnnl.gov/nom/results/Rac...  \n",
+       "\n",
+       "[2547 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_list = merged_df.columns.tolist()\n",
+    "columns_to_keep = [\"biosample_id\",\"processed_nom_id\",\"sample_type\",\"processed_nom_url\"]\n",
+    "columns_to_remove = list(set(column_list).difference(columns_to_keep))\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "merged_df_cleaned = merged_df.drop(columns=columns_to_remove)\n",
+    "\n",
+    "# remove duplicate rows when keeping only necessary columns (should remove none)\n",
+    "final_df=merged_df_cleaned.drop_duplicates(keep=\"first\")\n",
+    "\n",
+    "final_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate quality control statistics on processed nom results (this takes a while to run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Explore what the files associated with these NOM data objects look like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>m/z</th>\n",
+       "      <th>Calibrated m/z</th>\n",
+       "      <th>Calculated m/z</th>\n",
+       "      <th>Peak Height</th>\n",
+       "      <th>Peak Area</th>\n",
+       "      <th>Resolving Power</th>\n",
+       "      <th>S/N</th>\n",
+       "      <th>Ion Charge</th>\n",
+       "      <th>m/z Error (ppm)</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Mono Isotopic Index</th>\n",
+       "      <th>Molecular Formula</th>\n",
+       "      <th>C</th>\n",
+       "      <th>H</th>\n",
+       "      <th>O</th>\n",
+       "      <th>N</th>\n",
+       "      <th>S</th>\n",
+       "      <th>13C</th>\n",
+       "      <th>18O</th>\n",
+       "      <th>34S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>121.029630</td>\n",
+       "      <td>121.029532</td>\n",
+       "      <td>121.029503</td>\n",
+       "      <td>2.053749e+07</td>\n",
+       "      <td>1733.686860</td>\n",
+       "      <td>1.862520e+06</td>\n",
+       "      <td>94.864999</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.236952</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C7 H6 O2</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>125.024551</td>\n",
+       "      <td>125.024447</td>\n",
+       "      <td>125.024418</td>\n",
+       "      <td>6.872170e+06</td>\n",
+       "      <td>460.820768</td>\n",
+       "      <td>1.803007e+06</td>\n",
+       "      <td>31.743342</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.235671</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C6 H6 O3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19</td>\n",
+       "      <td>127.040191</td>\n",
+       "      <td>127.040084</td>\n",
+       "      <td>127.040068</td>\n",
+       "      <td>6.932442e+06</td>\n",
+       "      <td>558.934590</td>\n",
+       "      <td>1.774400e+06</td>\n",
+       "      <td>32.021746</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.125863</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C6 H8 O3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21</td>\n",
+       "      <td>127.076580</td>\n",
+       "      <td>127.076473</td>\n",
+       "      <td>127.076453</td>\n",
+       "      <td>7.184993e+06</td>\n",
+       "      <td>547.351481</td>\n",
+       "      <td>1.774748e+06</td>\n",
+       "      <td>33.188306</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.157776</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C7 H12 O2</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>129.055855</td>\n",
+       "      <td>129.055746</td>\n",
+       "      <td>129.055718</td>\n",
+       "      <td>3.702771e+08</td>\n",
+       "      <td>30885.460582</td>\n",
+       "      <td>1.746686e+06</td>\n",
+       "      <td>1710.352267</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.215493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C6 H10 O3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8184</th>\n",
+       "      <td>8176</td>\n",
+       "      <td>854.742128</td>\n",
+       "      <td>854.740814</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.781581e+06</td>\n",
+       "      <td>29298.928644</td>\n",
+       "      <td>2.637286e+05</td>\n",
+       "      <td>40.563131</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8185</th>\n",
+       "      <td>8179</td>\n",
+       "      <td>857.253655</td>\n",
+       "      <td>857.252336</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.486738e+06</td>\n",
+       "      <td>37580.480456</td>\n",
+       "      <td>2.103243e+05</td>\n",
+       "      <td>39.201218</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8186</th>\n",
+       "      <td>8180</td>\n",
+       "      <td>914.166591</td>\n",
+       "      <td>914.165162</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.141723e+06</td>\n",
+       "      <td>25761.652892</td>\n",
+       "      <td>2.465855e+05</td>\n",
+       "      <td>28.369324</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8187</th>\n",
+       "      <td>8182</td>\n",
+       "      <td>935.768503</td>\n",
+       "      <td>935.767031</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.808959e+06</td>\n",
+       "      <td>30718.602669</td>\n",
+       "      <td>2.408929e+05</td>\n",
+       "      <td>36.070475</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8188</th>\n",
+       "      <td>8183</td>\n",
+       "      <td>1170.432246</td>\n",
+       "      <td>1170.430293</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.369216e+06</td>\n",
+       "      <td>71730.684620</td>\n",
+       "      <td>1.540467e+05</td>\n",
+       "      <td>43.277483</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8189 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Index          m/z  Calibrated m/z  Calculated m/z   Peak Height  \\\n",
+       "0        10   121.029630      121.029532      121.029503  2.053749e+07   \n",
+       "1        14   125.024551      125.024447      125.024418  6.872170e+06   \n",
+       "2        19   127.040191      127.040084      127.040068  6.932442e+06   \n",
+       "3        21   127.076580      127.076473      127.076453  7.184993e+06   \n",
+       "4        25   129.055855      129.055746      129.055718  3.702771e+08   \n",
+       "...     ...          ...             ...             ...           ...   \n",
+       "8184   8176   854.742128      854.740814             NaN  8.781581e+06   \n",
+       "8185   8179   857.253655      857.252336             NaN  8.486738e+06   \n",
+       "8186   8180   914.166591      914.165162             NaN  6.141723e+06   \n",
+       "8187   8182   935.768503      935.767031             NaN  7.808959e+06   \n",
+       "8188   8183  1170.432246     1170.430293             NaN  9.369216e+06   \n",
+       "\n",
+       "         Peak Area  Resolving Power          S/N  Ion Charge  m/z Error (ppm)  \\\n",
+       "0      1733.686860     1.862520e+06    94.864999          -1         0.236952   \n",
+       "1       460.820768     1.803007e+06    31.743342          -1         0.235671   \n",
+       "2       558.934590     1.774400e+06    32.021746          -1         0.125863   \n",
+       "3       547.351481     1.774748e+06    33.188306          -1         0.157776   \n",
+       "4     30885.460582     1.746686e+06  1710.352267          -1         0.215493   \n",
+       "...            ...              ...          ...         ...              ...   \n",
+       "8184  29298.928644     2.637286e+05    40.563131          -1              NaN   \n",
+       "8185  37580.480456     2.103243e+05    39.201218          -1              NaN   \n",
+       "8186  25761.652892     2.465855e+05    28.369324          -1              NaN   \n",
+       "8187  30718.602669     2.408929e+05    36.070475          -1              NaN   \n",
+       "8188  71730.684620     1.540467e+05    43.277483          -1              NaN   \n",
+       "\n",
+       "      ...  Mono Isotopic Index  Molecular Formula    C     H    O   N   S 13C  \\\n",
+       "0     ...                  NaN           C7 H6 O2  7.0   6.0  2.0 NaN NaN NaN   \n",
+       "1     ...                  NaN           C6 H6 O3  6.0   6.0  3.0 NaN NaN NaN   \n",
+       "2     ...                  NaN           C6 H8 O3  6.0   8.0  3.0 NaN NaN NaN   \n",
+       "3     ...                  NaN          C7 H12 O2  7.0  12.0  2.0 NaN NaN NaN   \n",
+       "4     ...                  NaN          C6 H10 O3  6.0  10.0  3.0 NaN NaN NaN   \n",
+       "...   ...                  ...                ...  ...   ...  ...  ..  ..  ..   \n",
+       "8184  ...                  NaN                NaN  NaN   NaN  NaN NaN NaN NaN   \n",
+       "8185  ...                  NaN                NaN  NaN   NaN  NaN NaN NaN NaN   \n",
+       "8186  ...                  NaN                NaN  NaN   NaN  NaN NaN NaN NaN   \n",
+       "8187  ...                  NaN                NaN  NaN   NaN  NaN NaN NaN NaN   \n",
+       "8188  ...                  NaN                NaN  NaN   NaN  NaN NaN NaN NaN   \n",
+       "\n",
+       "      18O  34S  \n",
+       "0     NaN  NaN  \n",
+       "1     NaN  NaN  \n",
+       "2     NaN  NaN  \n",
+       "3     NaN  NaN  \n",
+       "4     NaN  NaN  \n",
+       "...   ...  ...  \n",
+       "8184  NaN  NaN  \n",
+       "8185  NaN  NaN  \n",
+       "8186  NaN  NaN  \n",
+       "8187  NaN  NaN  \n",
+       "8188  NaN  NaN  \n",
+       "\n",
+       "[8189 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#example file\n",
+    "url=final_df.iloc[0][\"processed_nom_url\"]\n",
+    "\n",
+    "#pull data as csv using url\n",
+    "response = requests.get(url)\n",
+    "csv_data = StringIO(response.text)\n",
+    "csv_df = pd.read_csv(csv_data)\n",
+    "csv_data.close()\n",
+    "csv_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate % of peaks assigned and extract molecular formulas, H/C and O/C values from the processed NOM data sets. If there are multiple matches for a m/z peak, filter to the match with the highest confidence score. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed 50 rows\n",
+      "Processed 100 rows\n",
+      "Processed 150 rows\n",
+      "Processed 200 rows\n",
+      "Processed 250 rows\n",
+      "Processed 300 rows\n",
+      "Processed 350 rows\n",
+      "Processed 400 rows\n",
+      "Processed 450 rows\n",
+      "Processed 500 rows\n",
+      "Processed 550 rows\n",
+      "Processed 600 rows\n",
+      "Processed 650 rows\n",
+      "Processed 700 rows\n",
+      "Processed 750 rows\n",
+      "Processed 800 rows\n",
+      "Processed 850 rows\n",
+      "Processed 900 rows\n",
+      "Processed 950 rows\n",
+      "Processed 1000 rows\n",
+      "Processed 1050 rows\n",
+      "Processed 1100 rows\n",
+      "Processed 1150 rows\n",
+      "Processed 1200 rows\n",
+      "Processed 1250 rows\n",
+      "Processed 1300 rows\n",
+      "Processed 1350 rows\n",
+      "Processed 1400 rows\n",
+      "Processed 1450 rows\n",
+      "Processed 1500 rows\n",
+      "Processed 1550 rows\n",
+      "Processed 1600 rows\n",
+      "Processed 1650 rows\n",
+      "Processed 1700 rows\n",
+      "Processed 1750 rows\n",
+      "Processed 1800 rows\n",
+      "Processed 1850 rows\n",
+      "Processed 1900 rows\n",
+      "Processed 1950 rows\n",
+      "Processed 2000 rows\n",
+      "Processed 2050 rows\n",
+      "Processed 2100 rows\n",
+      "Processed 2150 rows\n",
+      "Processed 2200 rows\n",
+      "Processed 2250 rows\n",
+      "Processed 2300 rows\n",
+      "Processed 2350 rows\n",
+      "Processed 2400 rows\n",
+      "Processed 2450 rows\n",
+      "Processed 2500 rows\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>processed</th>\n",
+       "      <th>sample_type</th>\n",
+       "      <th>assigned_peak_count</th>\n",
+       "      <th>assigned_perc</th>\n",
+       "      <th>mol_form</th>\n",
+       "      <th>H/C</th>\n",
+       "      <th>O/C</th>\n",
+       "      <th>Confidence Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:dobj-11-0dyc2f79</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>6447</td>\n",
+       "      <td>0.787757</td>\n",
+       "      <td>[C7 H6 O2, C6 H6 O3, C6 H8 O3, C7 H12 O2, C6 H...</td>\n",
+       "      <td>[0.8571428571428571, 1.0, 1.3333333333333333, ...</td>\n",
+       "      <td>[0.2857142857142857, 0.5, 0.5, 0.2857142857142...</td>\n",
+       "      <td>[0.5939184313430103, 0.5939837062548324, 0.598...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:dobj-11-07gsmc68</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>159</td>\n",
+       "      <td>0.670886</td>\n",
+       "      <td>[C6 H10 O3, C7 H6 O4, C8 H6 O4, C7 H7 O4 N1, C...</td>\n",
+       "      <td>[1.6666666666666667, 0.8571428571428571, 0.75,...</td>\n",
+       "      <td>[0.5, 0.5714285714285714, 0.5, 0.5714285714285...</td>\n",
+       "      <td>[0.5999995374122304, 0.5999873231359499, 0.599...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:dobj-11-jj2r0a49</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>6476</td>\n",
+       "      <td>0.791397</td>\n",
+       "      <td>[C7 H6 O2, C7 H8 O2, C6 H6 O3, C6 H8 O3, C7 H1...</td>\n",
+       "      <td>[0.8571428571428571, 1.1428571428571428, 1.0, ...</td>\n",
+       "      <td>[0.2857142857142857, 0.2857142857142857, 0.5, ...</td>\n",
+       "      <td>[0.594972809057153, 0.5968223855870005, 0.5955...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:dobj-11-mmdwds36</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>978</td>\n",
+       "      <td>0.858648</td>\n",
+       "      <td>[C6 H10 O3, C7 H6 O3, C9 H7 O1 N1, C7 H6 O4, C...</td>\n",
+       "      <td>[1.6666666666666667, 0.8571428571428571, 0.777...</td>\n",
+       "      <td>[0.5, 0.4285714285714285, 0.1111111111111111, ...</td>\n",
+       "      <td>[0.5981229110745099, 0.5987881165418503, 0.599...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:dobj-11-s42fww29</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>72</td>\n",
+       "      <td>0.541353</td>\n",
+       "      <td>[C6 H10 O3, C7 H6 O4, C8 H6 O4, C9 H16 O4, C9 ...</td>\n",
+       "      <td>[1.6666666666666667, 0.8571428571428571, 0.75,...</td>\n",
+       "      <td>[0.5, 0.5714285714285714, 0.5, 0.4444444444444...</td>\n",
+       "      <td>[0.599256952488123, 0.5995466840776257, 0.5994...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>nmdc:dobj-13-jmv5e702</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>109</td>\n",
+       "      <td>0.893443</td>\n",
+       "      <td>[C14 H14 O7, C15 H20 O6, C14 H18 O7, C15 H18 O...</td>\n",
+       "      <td>[1.0, 1.3333333333333333, 1.2857142857142858, ...</td>\n",
+       "      <td>[0.5, 0.4, 0.5, 0.4666666666666667, 0.46666666...</td>\n",
+       "      <td>[0.5998871527848394, 0.5998129533736624, 0.599...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>nmdc:dobj-13-qxc45786</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.873278</td>\n",
+       "      <td>[C13 H18 O3, C14 H22 O2, C13 H20 O3, C12 H18 O...</td>\n",
+       "      <td>[1.3846153846153846, 1.5714285714285714, 1.538...</td>\n",
+       "      <td>[0.2307692307692307, 0.1428571428571428, 0.230...</td>\n",
+       "      <td>[0.5998976808023823, 0.5993917838085816, 0.595...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>nmdc:dobj-13-sajy7y74</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>219</td>\n",
+       "      <td>0.883065</td>\n",
+       "      <td>[C13 H20 O3, C13 H22 O3, C6 H13 O6 S1 N1, C10 ...</td>\n",
+       "      <td>[1.5384615384615383, 1.6923076923076923, 2.166...</td>\n",
+       "      <td>[0.2307692307692307, 0.2307692307692307, 1.0, ...</td>\n",
+       "      <td>[0.5933488191108491, 0.596763102334653, 0.5945...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2545</th>\n",
+       "      <td>nmdc:dobj-13-n2g0fg49</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>132</td>\n",
+       "      <td>0.729282</td>\n",
+       "      <td>[C12 H12 O5, C16 H24 O2, C16 H32 O2, C9 H16 O9...</td>\n",
+       "      <td>[1.0, 1.5, 2.0, 1.7777777777777777, 0.92307692...</td>\n",
+       "      <td>[0.4166666666666667, 0.125, 0.125, 1.0, 0.5384...</td>\n",
+       "      <td>[0.5990281374750781, 0.5993434525256006, 0.599...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>nmdc:dobj-13-t7bfec16</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>210</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>[C12 H12 O5, C15 H30 O2, C16 H32 O2, C15 H32 O...</td>\n",
+       "      <td>[1.0, 2.0, 2.0, 2.1333333333333333, 2.0, 0.923...</td>\n",
+       "      <td>[0.4166666666666667, 0.1333333333333333, 0.125...</td>\n",
+       "      <td>[0.5999343810182991, 0.5996873146404225, 0.994...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  processed sample_type  assigned_peak_count  assigned_perc  \\\n",
+       "0     nmdc:dobj-11-0dyc2f79        soil                 6447       0.787757   \n",
+       "1     nmdc:dobj-11-07gsmc68        soil                  159       0.670886   \n",
+       "2     nmdc:dobj-11-jj2r0a49        soil                 6476       0.791397   \n",
+       "3     nmdc:dobj-11-mmdwds36        soil                  978       0.858648   \n",
+       "4     nmdc:dobj-11-s42fww29        soil                   72       0.541353   \n",
+       "...                     ...         ...                  ...            ...   \n",
+       "2542  nmdc:dobj-13-jmv5e702        soil                  109       0.893443   \n",
+       "2543  nmdc:dobj-13-qxc45786        soil                  317       0.873278   \n",
+       "2544  nmdc:dobj-13-sajy7y74        soil                  219       0.883065   \n",
+       "2545  nmdc:dobj-13-n2g0fg49        soil                  132       0.729282   \n",
+       "2546  nmdc:dobj-13-t7bfec16        soil                  210       0.560000   \n",
+       "\n",
+       "                                               mol_form  \\\n",
+       "0     [C7 H6 O2, C6 H6 O3, C6 H8 O3, C7 H12 O2, C6 H...   \n",
+       "1     [C6 H10 O3, C7 H6 O4, C8 H6 O4, C7 H7 O4 N1, C...   \n",
+       "2     [C7 H6 O2, C7 H8 O2, C6 H6 O3, C6 H8 O3, C7 H1...   \n",
+       "3     [C6 H10 O3, C7 H6 O3, C9 H7 O1 N1, C7 H6 O4, C...   \n",
+       "4     [C6 H10 O3, C7 H6 O4, C8 H6 O4, C9 H16 O4, C9 ...   \n",
+       "...                                                 ...   \n",
+       "2542  [C14 H14 O7, C15 H20 O6, C14 H18 O7, C15 H18 O...   \n",
+       "2543  [C13 H18 O3, C14 H22 O2, C13 H20 O3, C12 H18 O...   \n",
+       "2544  [C13 H20 O3, C13 H22 O3, C6 H13 O6 S1 N1, C10 ...   \n",
+       "2545  [C12 H12 O5, C16 H24 O2, C16 H32 O2, C9 H16 O9...   \n",
+       "2546  [C12 H12 O5, C15 H30 O2, C16 H32 O2, C15 H32 O...   \n",
+       "\n",
+       "                                                    H/C  \\\n",
+       "0     [0.8571428571428571, 1.0, 1.3333333333333333, ...   \n",
+       "1     [1.6666666666666667, 0.8571428571428571, 0.75,...   \n",
+       "2     [0.8571428571428571, 1.1428571428571428, 1.0, ...   \n",
+       "3     [1.6666666666666667, 0.8571428571428571, 0.777...   \n",
+       "4     [1.6666666666666667, 0.8571428571428571, 0.75,...   \n",
+       "...                                                 ...   \n",
+       "2542  [1.0, 1.3333333333333333, 1.2857142857142858, ...   \n",
+       "2543  [1.3846153846153846, 1.5714285714285714, 1.538...   \n",
+       "2544  [1.5384615384615383, 1.6923076923076923, 2.166...   \n",
+       "2545  [1.0, 1.5, 2.0, 1.7777777777777777, 0.92307692...   \n",
+       "2546  [1.0, 2.0, 2.0, 2.1333333333333333, 2.0, 0.923...   \n",
+       "\n",
+       "                                                    O/C  \\\n",
+       "0     [0.2857142857142857, 0.5, 0.5, 0.2857142857142...   \n",
+       "1     [0.5, 0.5714285714285714, 0.5, 0.5714285714285...   \n",
+       "2     [0.2857142857142857, 0.2857142857142857, 0.5, ...   \n",
+       "3     [0.5, 0.4285714285714285, 0.1111111111111111, ...   \n",
+       "4     [0.5, 0.5714285714285714, 0.5, 0.4444444444444...   \n",
+       "...                                                 ...   \n",
+       "2542  [0.5, 0.4, 0.5, 0.4666666666666667, 0.46666666...   \n",
+       "2543  [0.2307692307692307, 0.1428571428571428, 0.230...   \n",
+       "2544  [0.2307692307692307, 0.2307692307692307, 1.0, ...   \n",
+       "2545  [0.4166666666666667, 0.125, 0.125, 1.0, 0.5384...   \n",
+       "2546  [0.4166666666666667, 0.1333333333333333, 0.125...   \n",
+       "\n",
+       "                                       Confidence Score  \n",
+       "0     [0.5939184313430103, 0.5939837062548324, 0.598...  \n",
+       "1     [0.5999995374122304, 0.5999873231359499, 0.599...  \n",
+       "2     [0.594972809057153, 0.5968223855870005, 0.5955...  \n",
+       "3     [0.5981229110745099, 0.5987881165418503, 0.599...  \n",
+       "4     [0.599256952488123, 0.5995466840776257, 0.5994...  \n",
+       "...                                                 ...  \n",
+       "2542  [0.5998871527848394, 0.5998129533736624, 0.599...  \n",
+       "2543  [0.5998976808023823, 0.5993917838085816, 0.595...  \n",
+       "2544  [0.5933488191108491, 0.596763102334653, 0.5945...  \n",
+       "2545  [0.5990281374750781, 0.5993434525256006, 0.599...  \n",
+       "2546  [0.5999343810182991, 0.5996873146404225, 0.994...  \n",
+       "\n",
+       "[2547 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "errors = {}\n",
+    "iteration_counter = 0\n",
+    "mol_dict=[]\n",
+    "multi_peakmatch_file_counter=0\n",
+    "multi_peakmatch_peak_counter=0\n",
+    "\n",
+    "for index, row in final_df.iterrows():\n",
+    "\n",
+    "    iteration_counter += 1\n",
+    "\n",
+    "    # print an update for every 50 iterations\n",
+    "    if iteration_counter % 50 == 0:\n",
+    "        print(f\"Processed {iteration_counter} rows\")\n",
+    "    \n",
+    "    #save data set level information\n",
+    "    url = row[\"processed_nom_url\"]\n",
+    "    processed=row['processed_nom_id']\n",
+    "    sample_type=row['sample_type']\n",
+    "\n",
+    "    try:\n",
+    "        \n",
+    "        # get CSV data using URL\n",
+    "        response = requests.get(url)\n",
+    "        csv_data = StringIO(response.text)\n",
+    "        csv_df = pd.read_csv(csv_data)\n",
+    "        csv_data.close()\n",
+    "\n",
+    "        #check if any peaks (m/z) have multiple matches. if so take highest scoring match\n",
+    "\n",
+    "        #list of peaks\n",
+    "        peak_matches=csv_df[\"m/z\"]\n",
+    "        #list of duplicated peaks in csv_df\n",
+    "        dup_peaks=peak_matches[peak_matches.duplicated()]\n",
+    "        multi_peakmatch_peak_counter+=len(dup_peaks)\n",
+    "        #if there are duplicate peaks:\n",
+    "        if len(dup_peaks) > 0:\n",
+    "            multi_peakmatch_file_counter+=1\n",
+    "            #group cv_df by m/z and filter to max confidence score\n",
+    "            idx = csv_df.groupby('m/z')['Confidence Score'].max()\n",
+    "            #merge back with original df (that has all columns) to filter duplicate peak entries\n",
+    "            csv_df=csv_df.merge(idx,on=['m/z','Confidence Score'])\n",
+    "\n",
+    "        #calculate assigned peak percent\n",
+    "        unassigned=csv_df['Molecular Formula'].isnull().sum()\n",
+    "        assigned=csv_df['Molecular Formula'].count()\n",
+    "        assigned_perc=assigned/(unassigned+assigned)\n",
+    "\n",
+    "        #make dictionary\n",
+    "        #columns to be made into lists\n",
+    "        mol_df=csv_df[['Molecular Formula','H/C','O/C','Confidence Score']]\n",
+    "\n",
+    "        #drop unassigned peaks\n",
+    "        mol_df=mol_df.dropna(subset='Molecular Formula')\n",
+    "\n",
+    "        #append dictionary\n",
+    "        mol_dict.append({'processed':processed,\n",
+    "                         'sample_type':sample_type,\n",
+    "                         'assigned_peak_count':assigned,\n",
+    "                         'assigned_perc':assigned_perc,\n",
+    "                         'mol_form':mol_df['Molecular Formula'].to_list(),\n",
+    "                         'H/C':mol_df['H/C'].to_list(),\n",
+    "                         'O/C':mol_df['O/C'].to_list(),\n",
+    "                         'Confidence Score':mol_df['Confidence Score'].to_list()\n",
+    "                        })\n",
+    "    #if error print info\n",
+    "    except Exception as e:\n",
+    "        print(f\"An error occurred: {e}\")\n",
+    "        errors[\"processed_id\"] = processed\n",
+    "        errors[\"url\"] = url\n",
+    "        continue\n",
+    "\n",
+    "#turn dictionaries into dataframes\n",
+    "nom_summary_df=pd.DataFrame(mol_dict)\n",
+    "\n",
+    "nom_summary_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform quality control filtering of data sets using the chosen metadata information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assess the number and percentage of peaks identified across files in each sample type. This will help set thresholds for data set filtering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x35551a950>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "peak_count_plot=sns.FacetGrid(nom_summary_df,col=\"sample_type\",hue=\"sample_type\",sharex=False,sharey=False)\n",
+    "peak_count_plot.map(sns.histplot,'assigned_peak_count')\n",
+    "peak_count_plot.set_xlabels(\"number of identified peaks\")\n",
+    "peak_count_plot.set_ylabels(\"data set count\")\n",
+    "peak_count_plot.set_titles(col_template=\"{col_name}\")\n",
+    "\n",
+    "peak_perc_plot=sns.FacetGrid(nom_summary_df,col=\"sample_type\",hue=\"sample_type\",sharex=False,sharey=False)\n",
+    "peak_perc_plot.map(sns.histplot,'assigned_perc')\n",
+    "peak_perc_plot.set_xlabels(\"percent of peaks identified\")\n",
+    "peak_perc_plot.set_ylabels(\"data set count\")\n",
+    "peak_perc_plot.set_titles(col_template=\"{col_name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply filters to obtain high quality processed NOM data sets without removing all the files from any one sample type. Based on the figures above, requiring files to have at least 250 identified peaks that account for at least 30% of their total peak count will maintain a healthy number of data sets across sample types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_type\n",
+       "soil        1058\n",
+       "sediment      41\n",
+       "water         31\n",
+       "sand          16\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#filter data sets according to stats on peak assignment\n",
+    "nom_filt=nom_summary_df[nom_summary_df['assigned_peak_count']>=250]\n",
+    "nom_filt=nom_filt[nom_filt['assigned_perc']>=0.3]\n",
+    "\n",
+    "#expand listed columns in molecular formula df\n",
+    "nom_filt_expanded=nom_filt.explode(['mol_form','H/C','O/C','Confidence Score'])\n",
+    "\n",
+    "#resave expanded columns as numeric\n",
+    "nom_filt_expanded['O/C']=pd.to_numeric(nom_filt_expanded['O/C'])\n",
+    "nom_filt_expanded['H/C']=pd.to_numeric(nom_filt_expanded['H/C'])\n",
+    "nom_filt_expanded['Confidence Score']=pd.to_numeric(nom_filt_expanded['Confidence Score'])\n",
+    "\n",
+    "#metadata group column\n",
+    "grouping_column='sample_type'\n",
+    "\n",
+    "#count number of datasets in each type\n",
+    "count_type=nom_filt_expanded[['processed',grouping_column]].drop_duplicates().value_counts(grouping_column)\n",
+    "\n",
+    "count_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Randomly sample 15 data sets to visualize from each sample type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>processed</th>\n",
+       "      <th>sample_type</th>\n",
+       "      <th>assigned_peak_count</th>\n",
+       "      <th>assigned_perc</th>\n",
+       "      <th>mol_form</th>\n",
+       "      <th>H/C</th>\n",
+       "      <th>O/C</th>\n",
+       "      <th>Confidence Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nmdc:dobj-11-nfgc3817</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>830</td>\n",
+       "      <td>0.620329</td>\n",
+       "      <td>C15 H30 O2</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.597574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nmdc:dobj-11-nfgc3817</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>830</td>\n",
+       "      <td>0.620329</td>\n",
+       "      <td>C14 H28 O3</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.214286</td>\n",
+       "      <td>0.593197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nmdc:dobj-11-nfgc3817</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>830</td>\n",
+       "      <td>0.620329</td>\n",
+       "      <td>C16 H32 O2</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.599769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nmdc:dobj-11-nfgc3817</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>830</td>\n",
+       "      <td>0.620329</td>\n",
+       "      <td>C15 H30 O3</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.597039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nmdc:dobj-11-nfgc3817</td>\n",
+       "      <td>soil</td>\n",
+       "      <td>830</td>\n",
+       "      <td>0.620329</td>\n",
+       "      <td>C17 H34 O2</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.595529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115683</th>\n",
+       "      <td>nmdc:dobj-13-bdyz9y36</td>\n",
+       "      <td>sand</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.849866</td>\n",
+       "      <td>C15 H30 O2 13C1</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.938602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115684</th>\n",
+       "      <td>nmdc:dobj-13-bdyz9y36</td>\n",
+       "      <td>sand</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.849866</td>\n",
+       "      <td>C15 H30 O2</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.593121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115685</th>\n",
+       "      <td>nmdc:dobj-13-bdyz9y36</td>\n",
+       "      <td>sand</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.849866</td>\n",
+       "      <td>C15 H28 O2</td>\n",
+       "      <td>1.866667</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.556436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115686</th>\n",
+       "      <td>nmdc:dobj-13-bdyz9y36</td>\n",
+       "      <td>sand</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.849866</td>\n",
+       "      <td>C8 H16 O6 S1</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.066961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115687</th>\n",
+       "      <td>nmdc:dobj-13-bdyz9y36</td>\n",
+       "      <td>sand</td>\n",
+       "      <td>317</td>\n",
+       "      <td>0.849866</td>\n",
+       "      <td>C7 H12 O6 S1</td>\n",
+       "      <td>1.714286</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.013037</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115688 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    processed sample_type  assigned_peak_count  assigned_perc  \\\n",
+       "0       nmdc:dobj-11-nfgc3817        soil                  830       0.620329   \n",
+       "1       nmdc:dobj-11-nfgc3817        soil                  830       0.620329   \n",
+       "2       nmdc:dobj-11-nfgc3817        soil                  830       0.620329   \n",
+       "3       nmdc:dobj-11-nfgc3817        soil                  830       0.620329   \n",
+       "4       nmdc:dobj-11-nfgc3817        soil                  830       0.620329   \n",
+       "...                       ...         ...                  ...            ...   \n",
+       "115683  nmdc:dobj-13-bdyz9y36        sand                  317       0.849866   \n",
+       "115684  nmdc:dobj-13-bdyz9y36        sand                  317       0.849866   \n",
+       "115685  nmdc:dobj-13-bdyz9y36        sand                  317       0.849866   \n",
+       "115686  nmdc:dobj-13-bdyz9y36        sand                  317       0.849866   \n",
+       "115687  nmdc:dobj-13-bdyz9y36        sand                  317       0.849866   \n",
+       "\n",
+       "               mol_form       H/C       O/C  Confidence Score  \n",
+       "0            C15 H30 O2  2.000000  0.133333          0.597574  \n",
+       "1            C14 H28 O3  2.000000  0.214286          0.593197  \n",
+       "2            C16 H32 O2  2.000000  0.125000          0.599769  \n",
+       "3            C15 H30 O3  2.000000  0.200000          0.597039  \n",
+       "4            C17 H34 O2  2.000000  0.117647          0.595529  \n",
+       "...                 ...       ...       ...               ...  \n",
+       "115683  C15 H30 O2 13C1  2.000000  0.133333          0.938602  \n",
+       "115684       C15 H30 O2  2.000000  0.133333          0.593121  \n",
+       "115685       C15 H28 O2  1.866667  0.133333          0.556436  \n",
+       "115686     C8 H16 O6 S1  2.000000  0.750000          0.066961  \n",
+       "115687     C7 H12 O6 S1  1.714286  0.857143          0.013037  \n",
+       "\n",
+       "[115688 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#determine sampling size based on counts above\n",
+    "n=15\n",
+    "\n",
+    "#list the different types\n",
+    "list_type=count_type.index.tolist()\n",
+    "\n",
+    "#for each type, randomly sample n data sets and save them into list\n",
+    "nom_sampled=[]\n",
+    "for type in list_type:\n",
+    "    #each processed ID and sample type\n",
+    "    nom_type=nom_filt_expanded[['processed',grouping_column]].drop_duplicates()\n",
+    "    #filter to current sample type\n",
+    "    nom_type=nom_type[nom_type[grouping_column]==type]\n",
+    "    #randomly sample n processed IDs in current sample type\n",
+    "    nom_type=nom_type.sample(n=n, random_state=2)\n",
+    "    #save\n",
+    "    nom_sampled.append(nom_type)\n",
+    "\n",
+    "#resave list as dataframe\n",
+    "nom_sampled=pd.concat(nom_sampled)\n",
+    "\n",
+    "#remerge rest of the data for the sampled data sets\n",
+    "nom_sampled=nom_sampled.merge(nom_filt_expanded,on=['processed',grouping_column],how=\"left\")\n",
+    "\n",
+    "nom_sampled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Filter to high confidence peaks (score greater than 0.3) and high frequency molecular formulas (present in more than 5 data sets). This will leave us with the most informative data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D+/btUbduXSQlJUnjYmJiYGdnh65duxrsQ19fX1SuXBn79u17Zp329vZIS0vDxYsXZfVFz8YLwalM2NraAoDsW36vXr0KMzMzeHl5GSx3dnaGvb09rl69arD8yYABAA4ODgbv9culn7tu3boGy6tXr24QeIBHn/v066+/onr16oXO9eSFnE/WqZ9PX6f+f1KNGzcusr6bN2/izp07WLlyZZF3Gj7rAlKVSoXp06dj+vTpuH79Og4cOIDIyEhs2bIFFhYW2LBhA27evIns7Oyn1gI82l/6/8k+rmHDhtL6x+fw9PQ0GKf/Bzw4OLjIbdy9excODg5YsmQJgoOD4ebmBl9fX/To0QNDhw5F7dq1n1rj01SqVAk1a9Ys0JOrq2uBkP94T4978rja2dkBANzc3ApdLud12bJlS2zduhV5eXk4deoUfvjhB0RERKB///5ITU2Ft7c3AOCXX37BrFmzkJycjPv37xvMcffuXWmbpVGnmZlZgX1dr149ACjyWq3ivF6vXr0KLy8vgz9WAKB+/fqFPq8kcnJyDK4VunXrFubMmYNNmzYV+L15VuDRK84xKExgYCACAwNx//59pKSkYPPmzYiOjkbPnj1x/vx5ODo64uLFi7h7926B65z05Fw0PnfuXPTu3Rv16tVD48aN0a1bNwwZMqREdxPSIwxNVCZsbW3h6uqKM2fOFOt5T/7jWZSi7n4TMi5QfR46nQ5du3bFlClTCl2v/x+KXmnUqT/r8tZbbxUZNIrzj6CLiwsGDRqEfv36oVGjRtiyZUuZfsbLk3ct6fv5+OOP4ePjU+hzKleuDAB4/fXX0b59e/zwww+Ij4/Hxx9/jMWLF2Pr1q3o3r17iepRqVTPffdeUce1NI63UqlEy5Yt0bJlS9SrVw/Dhw9HTEwMZs2ahfT0dHTp0gUNGjRAeHg43NzcoFQqsXPnTkRERBjcjFDWdRaltF+vz+PPP//E3bt3Df4Ye/3115GUlITJkyfDx8cHlStXhk6nQ7du3Qrsv8IU9xg8jZWVFdq3b4/27dujWrVqmDNnDnbt2oXg4GDodDo4Ojrim2++KfS5Rf3h9rhXXnkF6enp+PHHHxEfH48vv/wSERERiI6OfiE+isMUMTRRmenZsydWrlyJ5ORkg7cICuPu7g6dToeLFy9Kf90DQFZWFu7cuVOmH0yon/vixYsGf1XfvHmzwF/ederUQU5ODvz9/Utl2/rtPS1cVq9eHTY2NsjPzy+17QKAhYUFmjZtiosXL+Lvv/+Go6MjbG1tnxl03d3dceHChQLL9R+S+axjVadOHQCPgrWcflxcXPDuu+/i3XffxY0bN9C8eXMsWLCgxKGpMO7u7tizZw/u3btncLZJbk9lRf826fXr1wEAP/30EzQaDbZv325wFknOWzUlodPpcOnSJYM/Bn777TcAMHh78nHFeb26u7vjzJkzEEIY/MFU2OurJL7++msAj87sAI/OpCUmJmLOnDmYOXOmNK6wt6+K+gOurI7Bk8e6Tp062LNnD9q2bfvMj0t42h+bVapUwfDhwzF8+HDk5OTglVdewezZsxmaSojXNFGZmTJlCqytrTFq1ChkZWUVWJ+eno7IyEgAQI8ePQAAy5YtMxgTHh4OAAgKCiqzOv39/WFhYYHPPvvM4C/tJ2sBHv2VmpycjN27dxdYd+fOHTx8+LBY265evTpeeeUVrFmzBhkZGQbr9LWYm5ujX79++P777wsNNPrPoyrKxYsXC8ytrzc5ORkODg6oXr06zMzM0KdPH/z00084fvx4gfH6enr06IGjR48iOTlZWpebm4uVK1fCw8NDehupKL6+vqhTpw4++eQT5OTkFNlPfn5+gbdLHB0d4erqWiq3pD+uR48eyM/Px/Llyw2WR0REQKFQlGpAK8y+ffsKPcujv9ZP/3aV/gzR42Pv3r2LtWvXllltj+8TIQSWL18OCwsLdOnSpdDxxXm99ujRA9euXcN3330nLbt//36pfODt3r17MW/ePHh6ekofK1HY/gMK/13Xf3bXk59j9rzHIDExsdDlTx7r119/Hfn5+Zg3b16BsQ8fPjSoy9rautDPW3v8YxSAR2dwvby8Sv3357+EZ5qozNSpUwcbN27EwIED0bBhQ4NPBE9KSkJMTIz0CbbNmjVDcHAwVq5ciTt37qBDhw44evQo1q9fjz59+qBTp05lVmf16tXx/vvvY+HChejZsyd69OiBkydPYteuXahWrZrB2MmTJ2P79u3o2bMnhg0bBl9fX+Tm5uL06dP47rvvcOXKlQLPeZZPP/0U7dq1Q/PmzTFmzBh4enriypUriI2Nlb4vbdGiRdi3bx9at26N0aNHw9vbG7du3cKJEyewZ88e3Lp1q8j5T506hTfffBPdu3dH+/btUaVKFfz1119Yv349rl27hmXLlkn/I/joo48QHx+PDh06YMyYMWjYsCGuX7+OmJgYHDp0CPb29vjggw/w7bffonv37njvvfdQpUoVrF+/HpcvX8b333//zLe+zMzM8OWXX6J79+5o1KgRhg8fjho1auCvv/7Cvn37YGtri59++gn37t1DzZo10b9/fzRr1gyVK1fGnj17cOzYMSxdurRY+/hZevXqhU6dOmH69Om4cuUKmjVrhvj4ePz444+YMGGCdHasrIwbNw7379/Ha6+9hgYNGki/I5s3b4aHhweGDx8OAAgICIBSqUSvXr3w9ttvIycnB6tWrYKjo6N0hqI0qdVqxMXFITg4GK1bt8auXbsQGxuLadOmPfXtIbmv19GjR2P58uUYOnQoUlJS4OLigq+//hpWVlbFqnPXrl04f/48Hj58iKysLOzduxcJCQlwd3fH9u3boVarATw6u/nKK69gyZIl0Gq1qFGjBuLj43H58uUCc/r6+gIApk+fjkGDBsHCwgK9evV67mPQu3dveHp6olevXqhTpw5yc3OxZ88e/PTTT2jZsiV69eoFAOjQoQPefvttLFy4EKmpqQgICICFhQUuXryImJgYREZGShfQ+/r64vPPP8f8+fPh5eUFR0dHdO7cGd7e3ujYsSN8fX1RpUoVHD9+HN99953Bxf1UTOV9ux799/z2229i9OjRwsPDQyiVSmFjYyPatm0rPvvsM/HgwQNpnFarFXPmzBGenp7CwsJCuLm5ialTpxqMEeLRrdSF3UbboUMHg9tu5X7kgBBC5Ofnizlz5ggXFxdhaWkpOnbsKM6cOSPc3d0NPnJAiEe310+dOlV4eXkJpVIpqlWrJl5++WXxySefSLeIF7VtIQreni2EEGfOnBGvvfaasLe3F2q1WtSvX7/AZ+FkZWWJkJAQ4ebmJiwsLISzs7Po0qWLWLlyZYFtPPm8RYsWiQ4dOggXFxdRqVIl4eDgIDp37iy+++67AuOvXr0qhg4dKqpXry5UKpWoXbu2CAkJERqNRhqTnp4u+vfvL9XbqlUrsWPHDoN5irrtX+/kyZOib9++omrVqkKlUgl3d3fx+uuvi8TERCGEEBqNRkyePFk0a9ZM2NjYCGtra9GsWTOxYsWKp/b7tG0HBwcLa2vrQp9z7949MXHiROHq6iosLCxE3bp1xccff2zwsQhCPDp+T34MQlHH+1n7QG/Xrl1ixIgRokGDBqJy5cpCqVQKLy8vMW7cOJGVlWUwdvv27aJp06ZCrVYLDw8PsXjxYrFmzZoCr+mifk/k1q/fV+np6SIgIEBYWVkJJycnMWvWrAK3xxf2mpb7er169ap49dVXhZWVlahWrZoYP368dFu93I8c0P8olUrh7OwsunbtKiIjIw0+/kHvzz//lH7X7OzsxIABA8S1a9cK7WHevHmiRo0awszMzGD/yj0Ghfn222/FoEGDRJ06dYSlpaVQq9XC29tbTJ8+vdB6V65cKXx9fYWlpaWwsbERTZo0EVOmTBHXrl2TxmRmZoqgoCBhY2MjAEj/Ds6fP1+0atVK2NvbC0tLS9GgQQOxYMGCQj/KguRRCFHGV84SEdELZ9iwYfjuu+8KfQuV6L+K1zQRERERycDQRERERCQDQxMRERGRDLymiYiIiEgGnmkiIiIikoGhiYiIiEgGfrhlKdHpdLh27RpsbGxkf38aERERGZcQAvfu3YOrq+szP5yXoamUXLt2rcA3hxMREdGL4Y8//kDNmjWfOoahqZTov+Tzjz/+gK2tbanOrdVqER8fL32M/ouKfZgW9mFa2IdpqSh9ABWnl7LqIzs7G25ubgZf1l0UhqZSon9LztbWtkxCk5WVFWxtbV/4Fzz7MB3sw7SwD9NSUfoAKk4vZd2HnEtreCE4ERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERyVDJ2AUQEdH/13j2bmjyFcYuo8RU5gJLWhm7CqKywTNNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIYNTR5eHhAoVAU+AkJCQEAPHjwACEhIahatSoqV66Mfv36ISsry2COjIwMBAUFwcrKCo6Ojpg8eTIePnxoMGb//v1o3rw5VCoVvLy8sG7dugK1REVFwcPDA2q1Gq1bt8bRo0fLrG8iIiJ68Rg1NB07dgzXr1+XfhISEgAAAwYMAABMnDgRP/30E2JiYnDgwAFcu3YNffv2lZ6fn5+PoKAg5OXlISkpCevXr8e6deswc+ZMaczly5cRFBSETp06ITU1FRMmTMCoUaOwe/duaczmzZsRFhaGWbNm4cSJE2jWrBkCAwNx48aNctoTREREZOqMGpqqV68OZ2dn6WfHjh2oU6cOOnTogLt372L16tUIDw9H586d4evri7Vr1yIpKQmHDx8GAMTHx+Ps2bPYsGEDfHx80L17d8ybNw9RUVHIy8sDAERHR8PT0xNLly5Fw4YNERoaiv79+yMiIkKqIzw8HKNHj8bw4cPh7e2N6OhoWFlZYc2aNUbZL0RERGR6Khm7AL28vDxs2LABYWFhUCgUSElJgVarhb+/vzSmQYMGqFWrFpKTk9GmTRskJyejSZMmcHJyksYEBgZi7NixSEtLw0svvYTk5GSDOfRjJkyYIG03JSUFU6dOldabmZnB398fycnJRdar0Wig0Wikx9nZ2QAArVYLrVb7XPviSfr5Snve8sY+TAv7MC36+lVmwsiVPB99/RXleLzofQAVp5ey6qM485lMaNq2bRvu3LmDYcOGAQAyMzOhVCphb29vMM7JyQmZmZnSmMcDk369ft3TxmRnZ+Pff//F7du3kZ+fX+iY8+fPF1nvwoULMWfOnALL4+PjYWVl9eyGS0D/9uWLjn2YFvZhWua10Bm7hFJRUY5HRekDqDi9lHYf9+/flz3WZELT6tWr0b17d7i6uhq7FFmmTp2KsLAw6XF2djbc3NwQEBAAW1vbUt2WVqtFQkICunbtCgsLi1KduzyxD9PCPkyLvo8Pj5tBo1MYu5wSU5kJzGuhqzDH40XvA6g4vZRVH/p3iuQwidB09epV7NmzB1u3bpWWOTs7Iy8vD3fu3DE425SVlQVnZ2dpzJN3uenvrnt8zJN33GVlZcHW1haWlpYwNzeHubl5oWP0cxRGpVJBpVIVWG5hYVFmL8qynLs8sQ/Twj5Mi0angCb/xQ1NehXleFSUPoCK00tp91GcuUzic5rWrl0LR0dHBAUFSct8fX1hYWGBxMREadmFCxeQkZEBPz8/AICfnx9Onz5tcJdbQkICbG1t4e3tLY15fA79GP0cSqUSvr6+BmN0Oh0SExOlMURERERGP9Ok0+mwdu1aBAcHo1Kl/1+OnZ0dRo4cibCwMFSpUgW2trYYN24c/Pz80KZNGwBAQEAAvL29MWTIECxZsgSZmZmYMWMGQkJCpLNA77zzDpYvX44pU6ZgxIgR2Lt3L7Zs2YLY2FhpW2FhYQgODkaLFi3QqlUrLFu2DLm5uRg+fHj57gwiIiIyWUYPTXv27EFGRgZGjBhRYF1ERATMzMzQr18/aDQaBAYGYsWKFdJ6c3Nz7NixA2PHjoWfnx+sra0RHByMuXPnSmM8PT0RGxuLiRMnIjIyEjVr1sSXX36JwMBAaczAgQNx8+ZNzJw5E5mZmfDx8UFcXFyBi8OJiIjov8vooSkgIABCFH6LrVqtRlRUFKKioop8vru7O3bu3PnUbXTs2BEnT5586pjQ0FCEhoY+u2AiIiL6TzKJa5qIiIiITB1DExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJIPRQ9Nff/2Ft956C1WrVoWlpSWaNGmC48ePS+uFEJg5cyZcXFxgaWkJf39/XLx40WCOW7duYfDgwbC1tYW9vT1GjhyJnJwcgzG//vor2rdvD7VaDTc3NyxZsqRALTExMWjQoAHUajWaNGmCnTt3lk3TRERE9MIxami6ffs22rZtCwsLC+zatQtnz57F0qVL4eDgII1ZsmQJPv30U0RHR+PIkSOwtrZGYGAgHjx4II0ZPHgw0tLSkJCQgB07duDgwYMYM2aMtD47OxsBAQFwd3dHSkoKPv74Y8yePRsrV66UxiQlJeGNN97AyJEjcfLkSfTp0wd9+vTBmTNnymdnEBERkUmrZMyNL168GG5ubli7dq20zNPTU/pvIQSWLVuGGTNmoHfv3gCAr776Ck5OTti2bRsGDRqEc+fOIS4uDseOHUOLFi0AAJ999hl69OiBTz75BK6urvjmm2+Ql5eHNWvWQKlUolGjRkhNTUV4eLgUriIjI9GtWzdMnjwZADBv3jwkJCRg+fLliI6OLq9dQkRERCbKqKFp+/btCAwMxIABA3DgwAHUqFED7777LkaPHg0AuHz5MjIzM+Hv7y89x87ODq1bt0ZycjIGDRqE5ORk2NvbS4EJAPz9/WFmZoYjR47gtddeQ3JyMl555RUolUppTGBgIBYvXozbt2/DwcEBycnJCAsLM6gvMDAQ27ZtK7R2jUYDjUYjPc7OzgYAaLVaaLXa5943j9PPV9rzljf2YVrYh2nR168yE0au5Pno668ox+NF7wOoOL2UVR/Fmc+ooenSpUv4/PPPERYWhmnTpuHYsWN47733oFQqERwcjMzMTACAk5OTwfOcnJykdZmZmXB0dDRYX6lSJVSpUsVgzONnsB6fMzMzEw4ODsjMzHzqdp60cOFCzJkzp8Dy+Ph4WFlZyd0FxZKQkFAm85Y39mFa2IdpmddCZ+wSSkVFOR4VpQ+g4vRS2n3cv39f9lijhiadTocWLVrgo48+AgC89NJLOHPmDKKjoxEcHGzM0p5p6tSpBmemsrOz4ebmhoCAANja2pbqtrRaLRISEtC1a1dYWFiU6tzliX2YFvZhWvR9fHjcDBqdwtjllJjKTGBeC12FOR4veh9AxemlrPrQv1Mkh1FDk4uLC7y9vQ2WNWzYEN9//z0AwNnZGQCQlZUFFxcXaUxWVhZ8fHykMTdu3DCY4+HDh7h165b0fGdnZ2RlZRmM0T9+1hj9+iepVCqoVKoCyy0sLMrsRVmWc5cn9mFa2Idp0egU0OS/uKFJr6Icj4rSB1BxeintPoozl1Hvnmvbti0uXLhgsOy3336Du7s7gEcXhTs7OyMxMVFan52djSNHjsDPzw8A4Ofnhzt37iAlJUUas3fvXuh0OrRu3Voac/DgQYP3LRMSElC/fn3pTj0/Pz+D7ejH6LdDRERE/21GDU0TJ07E4cOH8dFHH+H333/Hxo0bsXLlSoSEhAAAFAoFJkyYgPnz52P79u04ffo0hg4dCldXV/Tp0wfAozNT3bp1w+jRo3H06FH88ssvCA0NxaBBg+Dq6goAePPNN6FUKjFy5EikpaVh8+bNiIyMNHh7bfz48YiLi8PSpUtx/vx5zJ49G8ePH0doaGi57xciIiIyPUZ9e65ly5b44YcfMHXqVMydOxeenp5YtmwZBg8eLI2ZMmUKcnNzMWbMGNy5cwft2rVDXFwc1Gq1NOabb75BaGgounTpAjMzM/Tr1w+ffvqptN7Ozg7x8fEICQmBr68vqlWrhpkzZxp8ltPLL7+MjRs3YsaMGZg2bRrq1q2Lbdu2oXHjxuWzM4iIiMikGTU0AUDPnj3Rs2fPItcrFArMnTsXc+fOLXJMlSpVsHHjxqdup2nTpvj555+fOmbAgAEYMGDA0wsmIiKi/ySjf40KERER0YuAoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZDBqaJo9ezYUCoXBT4MGDaT1Dx48QEhICKpWrYrKlSujX79+yMrKMpgjIyMDQUFBsLKygqOjIyZPnoyHDx8ajNm/fz+aN28OlUoFLy8vrFu3rkAtUVFR8PDwgFqtRuvWrXH06NEy6ZmIiIheTEY/09SoUSNcv35d+jl06JC0buLEifjpp58QExODAwcO4Nq1a+jbt6+0Pj8/H0FBQcjLy0NSUhLWr1+PdevWYebMmdKYy5cvIygoCJ06dUJqaiomTJiAUaNGYffu3dKYzZs3IywsDLNmzcKJEyfQrFkzBAYG4saNG+WzE4iIiMjkGT00VapUCc7OztJPtWrVAAB3797F6tWrER4ejs6dO8PX1xdr165FUlISDh8+DACIj4/H2bNnsWHDBvj4+KB79+6YN28eoqKikJeXBwCIjo6Gp6cnli5dioYNGyI0NBT9+/dHRESEVEN4eDhGjx6N4cOHw9vbG9HR0bCyssKaNWvKf4cQERGRSTJ6aLp48SJcXV1Ru3ZtDB48GBkZGQCAlJQUaLVa+Pv7S2MbNGiAWrVqITk5GQCQnJyMJk2awMnJSRoTGBiI7OxspKWlSWMen0M/Rj9HXl4eUlJSDMaYmZnB399fGkNERERUyZgbb926NdatW4f69evj+vXrmDNnDtq3b48zZ84gMzMTSqUS9vb2Bs9xcnJCZmYmACAzM9MgMOnX69c9bUx2djb+/fdf3L59G/n5+YWOOX/+fJG1azQaaDQa6XF2djYAQKvVQqvVFmMvPJt+vtKet7yxD9PCPkyLvn6VmTByJc9HX39FOR4veh9AxemlrPooznxGDU3du3eX/rtp06Zo3bo13N3dsWXLFlhaWhqxsmdbuHAh5syZU2B5fHw8rKysymSbCQkJZTJveWMfpoV9mJZ5LXTGLqFUVJTjUVH6ACpOL6Xdx/3792WPNWpoepK9vT3q1auH33//HV27dkVeXh7u3LljcLYpKysLzs7OAABnZ+cCd7np7657fMyTd9xlZWXB1tYWlpaWMDc3h7m5eaFj9HMUZurUqQgLC5MeZ2dnw83NDQEBAbC1tS1+80+h1WqRkJCArl27wsLColTnLk/sw7SwD9Oi7+PD42bQ6BTGLqfEVGYC81roKszxeNH7ACpOL2XVh/6dIjlMKjTl5OQgPT0dQ4YMga+vLywsLJCYmIh+/foBAC5cuICMjAz4+fkBAPz8/LBgwQLcuHEDjo6OAB4lUFtbW3h7e0tjdu7cabCdhIQEaQ6lUglfX18kJiaiT58+AACdTofExESEhoYWWatKpYJKpSqw3MLCosxelGU5d3liH6aFfZgWjU4BTf6LG5r0KsrxqCh9ABWnl9LuozhzGfVC8Pfffx8HDhzAlStXkJSUhNdeew3m5uZ44403YGdnh5EjRyIsLAz79u1DSkoKhg8fDj8/P7Rp0wYAEBAQAG9vbwwZMgSnTp3C7t27MWPGDISEhEiB5p133sGlS5cwZcoUnD9/HitWrMCWLVswceJEqY6wsDCsWrUK69evx7lz5zB27Fjk5uZi+PDhRtkvREREZHqMeqbpzz//xBtvvIF//vkH1atXR7t27XD48GFUr14dABAREQEzMzP069cPGo0GgYGBWLFihfR8c3Nz7NixA2PHjoWfnx+sra0RHByMuXPnSmM8PT0RGxuLiRMnIjIyEjVr1sSXX36JwMBAaczAgQNx8+ZNzJw5E5mZmfDx8UFcXFyBi8OJiIjov8uooWnTpk1PXa9WqxEVFYWoqKgix7i7uxd4++1JHTt2xMmTJ586JjQ09KlvxxEREdF/m9E/p4mIiIjoRcDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCRDiULTpUuXSrsOIiIiIpNWotDk5eWFTp06YcOGDXjw4EFp10RERERkckoUmk6cOIGmTZsiLCwMzs7OePvtt3H06NHSro2IiIjIZJQoNPn4+CAyMhLXrl3DmjVrcP36dbRr1w6NGzdGeHg4bt68Wdp1EhERERnVc10IXqlSJfTt2xcxMTFYvHgxfv/9d7z//vtwc3PD0KFDcf369dKqk4iIiMionis0HT9+HO+++y5cXFwQHh6O999/H+np6UhISMC1a9fQu3fv0qqTiIiIyKgqleRJ4eHhWLt2LS5cuIAePXrgq6++Qo8ePWBm9iiDeXp6Yt26dfDw8CjNWomIiIiMpkSh6fPPP8eIESMwbNgwuLi4FDrG0dERq1evfq7iiIiIiExFiULTxYsXnzlGqVQiODi4JNMTERERmZwSXdO0du1axMTEFFgeExOD9evXP3dRRERERKamRKFp4cKFqFatWoHljo6O+Oijj567KCIiIiJTU6LQlJGRAU9PzwLL3d3dkZGR8dxFEREREZmaEoUmR0dH/PrrrwWWnzp1ClWrVn3uooiIiIhMTYlC0xtvvIH33nsP+/btQ35+PvLz87F3716MHz8egwYNKu0aiYiIiIyuRHfPzZs3D1euXEGXLl1QqdKjKXQ6HYYOHcprmoiIiKhCKlFoUiqV2Lx5M+bNm4dTp07B0tISTZo0gbu7e2nXR0RERGQSShSa9OrVq4d69eqVVi1EREREJqtEoSk/Px/r1q1DYmIibty4AZ1OZ7B+7969pVIcERERkakoUWgaP3481q1bh6CgIDRu3BgKhaK06yIiIiIyKSUKTZs2bcKWLVvQo0eP0q6HiIiIyCSV6CMHlEolvLy8SrsWIiIiIpNVotA0adIkREZGQghR2vUQERERmaQSvT136NAh7Nu3D7t27UKjRo1gYWFhsH7r1q2lUhwRERGRqSjRmSZ7e3u89tpr6NChA6pVqwY7OzuDn5JYtGgRFAoFJkyYIC178OABQkJCULVqVVSuXBn9+vVDVlaWwfMyMjIQFBQEKysrODo6YvLkyXj48KHBmP3796N58+ZQqVTw8vLCunXrCmw/KioKHh4eUKvVaN26NY4ePVqiPoiIiKhiKtGZprVr15ZqEceOHcMXX3yBpk2bGiyfOHEiYmNjERMTAzs7O4SGhqJv37745ZdfADz66IOgoCA4OzsjKSkJ169fx9ChQ2FhYSF9Mvnly5cRFBSEd955B9988w0SExMxatQouLi4IDAwEACwefNmhIWFITo6Gq1bt8ayZcsQGBiICxcuwNHRsVR7JSIiohdTic40AcDDhw+xZ88efPHFF7h37x4A4Nq1a8jJySnWPDk5ORg8eDBWrVoFBwcHafndu3exevVqhIeHo3PnzvD19cXatWuRlJSEw4cPAwDi4+Nx9uxZbNiwAT4+PujevTvmzZuHqKgo5OXlAQCio6Ph6emJpUuXomHDhggNDUX//v0REREhbSs8PByjR4/G8OHD4e3tjejoaFhZWWHNmjUl3T1ERERUwZToTNPVq1fRrVs3ZGRkQKPRoGvXrrCxscHixYuh0WgQHR0te66QkBAEBQXB398f8+fPl5anpKRAq9XC399fWtagQQPUqlULycnJaNOmDZKTk9GkSRM4OTlJYwIDAzF27FikpaXhpZdeQnJyssEc+jH6twHz8vKQkpKCqVOnSuvNzMzg7++P5OTkIuvWaDTQaDTS4+zsbACAVquFVquV3b8c+vlKe97yxj5MC/swLfr6VWYv9g02+voryvF40fsAKk4vZdVHceYr8YdbtmjRAqdOnULVqlWl5a+99hpGjx4te55NmzbhxIkTOHbsWIF1mZmZUCqVsLe3N1ju5OSEzMxMaczjgUm/Xr/uaWOys7Px77//4vbt28jPzy90zPnz54usfeHChZgzZ06B5fHx8bCysiryec8jISGhTOYtb+zDtLAP0zKvhe7Zg14AFeV4VJQ+gIrTS2n3cf/+fdljSxSafv75ZyQlJUGpVBos9/DwwF9//SVrjj/++APjx49HQkIC1Gp1ScowqqlTpyIsLEx6nJ2dDTc3NwQEBMDW1rZUt6XVapGQkICuXbsWuFPxRcI+TAv7MC36Pj48bgaN7sX9lgWVmcC8FroKczxe9D6AitNLWfWhf6dIjhKFJp1Oh/z8/ALL//zzT9jY2MiaIyUlBTdu3EDz5s2lZfn5+Th48CCWL1+O3bt3Iy8vD3fu3DE425SVlQVnZ2cAgLOzc4G73PR31z0+5sk77rKysmBrawtLS0uYm5vD3Ny80DH6OQqjUqmgUqkKLLewsCizF2VZzl2e2IdpYR+mRaNTQJP/4oYmvYpyPCpKH0DF6aW0+yjOXCW6EDwgIADLli2THisUCuTk5GDWrFmyv1qlS5cuOH36NFJTU6WfFi1aYPDgwdJ/W1hYIDExUXrOhQsXkJGRAT8/PwCAn58fTp8+jRs3bkhjEhISYGtrC29vb2nM43Pox+jnUCqV8PX1NRij0+mQmJgojSEiIiIq0ZmmpUuXIjAwEN7e3njw4AHefPNNXLx4EdWqVcO3334raw4bGxs0btzYYJm1tTWqVq0qLR85ciTCwsJQpUoV2NraYty4cfDz80ObNm0APApv3t7eGDJkCJYsWYLMzEzMmDEDISEh0lmgd955B8uXL8eUKVMwYsQI7N27F1u2bEFsbKy03bCwMAQHB6NFixZo1aoVli1bhtzcXAwfPrwku4eIiIgqoBKFppo1a+LUqVPYtGkTfv31V+Tk5GDkyJEYPHgwLC0tS624iIgImJmZoV+/ftBoNAgMDMSKFSuk9ebm5tixYwfGjh0LPz8/WFtbIzg4GHPnzpXGeHp6IjY2FhMnTkRkZCRq1qyJL7/8UvqMJgAYOHAgbt68iZkzZyIzMxM+Pj6Ii4srcHE4ERER/XeVKDQBQKVKlfDWW2+VZi3Yv3+/wWO1Wo2oqChERUUV+Rx3d3fs3LnzqfN27NgRJ0+efOqY0NBQhIaGyq6ViIiI/ltKFJq++uqrp64fOnRoiYohIiIiMlUl/pymx2m1Wty/fx9KpRJWVlYMTURERFThlOjuudu3bxv85OTk4MKFC2jXrp3sC8GJiIiIXiQl/u65J9WtWxeLFi0qcBaKiIiIqCIotdAEPLo4/Nq1a6U5JREREZFJKNE1Tdu3bzd4LITA9evXsXz5crRt27ZUCiMiIiIyJSUKTX369DF4rFAoUL16dXTu3BlLly4tjbqIiIiITEqJv3uOiIiI6L+kVK9pIiIiIqqoSnSmKSwsTPbY8PDwkmyCiIiIyKSUKDSdPHkSJ0+ehFarRf369QEAv/32G8zNzdG8eXNpnEKhKJ0qiYiIiIysRKGpV69esLGxwfr16+Hg4ADg0QdeDh8+HO3bt8ekSZNKtUgiIiIiYyvRNU1Lly7FwoULpcAEAA4ODpg/fz7vniMiIqIKqUShKTs7Gzdv3iyw/ObNm7h3795zF0VERERkakoUml577TUMHz4cW7duxZ9//ok///wT33//PUaOHIm+ffuWdo1ERERERleia5qio6Px/vvv480334RWq300UaVKGDlyJD7++ONSLZCIiIjIFJQoNFlZWWHFihX4+OOPkZ6eDgCoU6cOrK2tS7U4IiIiIlPxXB9uef36dVy/fh1169aFtbU1hBClVRcRERGRSSlRaPrnn3/QpUsX1KtXDz169MD169cBACNHjuTHDRAREVGFVKLQNHHiRFhYWCAjIwNWVlbS8oEDByIuLq7UiiMiIiIyFSW6pik+Ph67d+9GzZo1DZbXrVsXV69eLZXCiIiIiExJic405ebmGpxh0rt16xZUKtVzF0VERERkakoUmtq3b4+vvvpKeqxQKKDT6bBkyRJ06tSp1IojIiIiMhUlentuyZIl6NKlC44fP468vDxMmTIFaWlpuHXrFn755ZfSrpGIiIjI6Ep0pqlx48b47bff0K5dO/Tu3Ru5ubno27cvTp48iTp16pR2jURERERGV+wzTVqtFt26dUN0dDSmT59eFjURERERmZxin2mysLDAr7/+Wha1EBEREZmsEr0999Zbb2H16tWlXQsRERGRySrRheAPHz7EmjVrsGfPHvj6+hb4zrnw8PBSKY6IiIjIVBQrNF26dAkeHh44c+YMmjdvDgD47bffDMYoFIrSq46IiIjIRBQrNNWtWxfXr1/Hvn37ADz62pRPP/0UTk5OZVIcERERkako1jVNQgiDx7t27UJubm6pFkRERERkikp0IbjekyGKiIiIqKIqVmhSKBQFrlniNUxERET0X1Csa5qEEBg2bJj0pbwPHjzAO++8U+Duua1bt5ZehUREREQmoFihKTg42ODxW2+9VarFEBEREZmqYoWmtWvXllUdRERERCbtuS4Ef16ff/45mjZtCltbW9ja2sLPzw+7du2S1j948AAhISGoWrUqKleujH79+iErK8tgjoyMDAQFBcHKygqOjo6YPHkyHj58aDBm//79aN68OVQqFby8vLBu3boCtURFRcHDwwNqtRqtW7fG0aNHy6RnIiIiejEZNTTVrFkTixYtQkpKCo4fP47OnTujd+/eSEtLAwBMnDgRP/30E2JiYnDgwAFcu3YNffv2lZ6fn5+PoKAg5OXlISkpCevXr8e6deswc+ZMaczly5cRFBSETp06ITU1FRMmTMCoUaOwe/duaczmzZsRFhaGWbNm4cSJE2jWrBkCAwNx48aN8tsZREREZNKMGpp69eqFHj16oG7duqhXrx4WLFiAypUr4/Dhw7h79y5Wr16N8PBwdO7cGb6+vli7di2SkpJw+PBhAEB8fDzOnj2LDRs2wMfHB927d8e8efMQFRWFvLw8AEB0dDQ8PT2xdOlSNGzYEKGhoejfvz8iIiKkOsLDwzF69GgMHz4c3t7eiI6OhpWVFdasWWOU/UJERESmp0TfPVcW8vPzERMTg9zcXPj5+SElJQVarRb+/v7SmAYNGqBWrVpITk5GmzZtkJycjCZNmhh8InlgYCDGjh2LtLQ0vPTSS0hOTjaYQz9mwoQJAIC8vDykpKRg6tSp0nozMzP4+/sjOTm5yHo1Gg00Go30ODs7GwCg1Wqh1Wqfa188ST9fac9b3tiHaWEfpkVfv8rsxf78O339FeV4vOh9ABWnl7LqozjzGT00nT59Gn5+fnjw4AEqV66MH374Ad7e3khNTYVSqYS9vb3BeCcnJ2RmZgIAMjMzC3yFi/7xs8ZkZ2fj33//xe3bt5Gfn1/omPPnzxdZ98KFCzFnzpwCy+Pj42FlZSWv+WJKSEgok3nLG/swLezDtMxroTN2CaWiohyPitIHUHF6Ke0+7t+/L3us0UNT/fr1kZqairt37+K7775DcHAwDhw4YOyynmnq1KkICwuTHmdnZ8PNzQ0BAQGwtbUt1W1ptVokJCSga9eusLCwKNW5yxP7MC3sw7To+/jwuBk0uhf3Q4NVZgLzWugqzPF40fsAKk4vZdWH/p0iOYwempRKJby8vAAAvr6+OHbsGCIjIzFw4EDk5eXhzp07BmebsrKy4OzsDABwdnYucJeb/u66x8c8ecddVlYWbG1tYWlpCXNzc5ibmxc6Rj9HYVQqlfQhn4+zsLAosxdlWc5dntiHaWEfpkWjU0CT/+KGJr2KcjwqSh9AxemltPsozlxGvRC8MDqdDhqNBr6+vrCwsEBiYqK07sKFC8jIyICfnx8AwM/PD6dPnza4yy0hIQG2trbw9vaWxjw+h36Mfg6lUglfX1+DMTqdDomJidIYIiIiIqOeaZo6dSq6d++OWrVq4d69e9i4cSP279+P3bt3w87ODiNHjkRYWBiqVKkCW1tbjBs3Dn5+fmjTpg0AICAgAN7e3hgyZAiWLFmCzMxMzJgxAyEhIdJZoHfeeQfLly/HlClTMGLECOzduxdbtmxBbGysVEdYWBiCg4PRokULtGrVCsuWLUNubi6GDx9ulP1CREREpseooenGjRsYOnQorl+/Djs7OzRt2hS7d+9G165dAQAREREwMzNDv379oNFoEBgYiBUrVkjPNzc3x44dOzB27Fj4+fnB2toawcHBmDt3rjTG09MTsbGxmDhxIiIjI1GzZk18+eWXCAwMlMYMHDgQN2/exMyZM5GZmQkfHx/ExcUVuDiciIiI/ruMGppWr1791PVqtRpRUVGIiooqcoy7uzt27tz51Hk6duyIkydPPnVMaGgoQkNDnzqGiIiI/rtM7pomIiIiIlPE0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMhg1NC1cuBAtW7aEjY0NHB0d0adPH1y4cMFgzIMHDxASEoKqVauicuXK6NevH7KysgzGZGRkICgoCFZWVnB0dMTkyZPx8OFDgzH79+9H8+bNoVKp4OXlhXXr1hWoJyoqCh4eHlCr1WjdujWOHj1a6j0TERHRi8mooenAgQMICQnB4cOHkZCQAK1Wi4CAAOTm5kpjJk6ciJ9++gkxMTE4cOAArl27hr59+0rr8/PzERQUhLy8PCQlJWH9+vVYt24dZs6cKY25fPkygoKC0KlTJ6SmpmLChAkYNWoUdu/eLY3ZvHkzwsLCMGvWLJw4cQLNmjVDYGAgbty4UT47g4iIiExaJWNuPC4uzuDxunXr4OjoiJSUFLzyyiu4e/cuVq9ejY0bN6Jz584AgLVr16Jhw4Y4fPgw2rRpg/j4eJw9exZ79uyBk5MTfHx8MG/ePPzvf//D7NmzoVQqER0dDU9PTyxduhQA0LBhQxw6dAgREREIDAwEAISHh2P06NEYPnw4ACA6OhqxsbFYs2YNPvjgg3LcK0RERGSKTOqaprt37wIAqlSpAgBISUmBVquFv7+/NKZBgwaoVasWkpOTAQDJyclo0qQJnJycpDGBgYHIzs5GWlqaNObxOfRj9HPk5eUhJSXFYIyZmRn8/f2lMURERPTfZtQzTY/T6XSYMGEC2rZti8aNGwMAMjMzoVQqYW9vbzDWyckJmZmZ0pjHA5N+vX7d08ZkZ2fj33//xe3bt5Gfn1/omPPnzxdar0ajgUajkR5nZ2cDALRaLbRabXFafyb9fKU9b3ljH6aFfZgWff0qM2HkSp6Pvv6Kcjxe9D6AitNLWfVRnPlMJjSFhITgzJkzOHTokLFLkWXhwoWYM2dOgeXx8fGwsrIqk20mJCSUybzljX2YFvZhWua10Bm7hFJRUY5HRekDqDi9lHYf9+/flz3WJEJTaGgoduzYgYMHD6JmzZrScmdnZ+Tl5eHOnTsGZ5uysrLg7OwsjXnyLjf93XWPj3nyjrusrCzY2trC0tIS5ubmMDc3L3SMfo4nTZ06FWFhYdLj7OxsuLm5ISAgALa2tsXcA0+n1WqRkJCArl27wsLColTnLk/sw7SwD9Oi7+PD42bQ6BTGLqfEVGYC81roKszxeNH7ACpOL2XVh/6dIjmMGpqEEBg3bhx++OEH7N+/H56engbrfX19YWFhgcTERPTr1w8AcOHCBWRkZMDPzw8A4OfnhwULFuDGjRtwdHQE8CiF2trawtvbWxqzc+dOg7kTEhKkOZRKJXx9fZGYmIg+ffoAePR2YWJiIkJDQwutXaVSQaVSFVhuYWFRZi/Kspy7PLEP08I+TItGp4Am/8UNTXoV5XhUlD6AitNLafdRnLmMGppCQkKwceNG/Pjjj7CxsZGuQbKzs4OlpSXs7OwwcuRIhIWFoUqVKrC1tcW4cePg5+eHNm3aAAACAgLg7e2NIUOGYMmSJcjMzMSMGTMQEhIihZp33nkHy5cvx5QpUzBixAjs3bsXW7ZsQWxsrFRLWFgYgoOD0aJFC7Rq1QrLli1Dbm6udDcdERER/bcZNTR9/vnnAICOHTsaLF+7di2GDRsGAIiIiICZmRn69esHjUaDwMBArFixQhprbm6OHTt2YOzYsfDz84O1tTWCg4Mxd+5caYynpydiY2MxceJEREZGombNmvjyyy+ljxsAgIEDB+LmzZuYOXMmMjMz4ePjg7i4uAIXhxMREdF/k9HfnnsWtVqNqKgoREVFFTnG3d29wNtvT+rYsSNOnjz51DGhoaFFvh1HRERE/20m9TlNRERERKaKoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpLBqKHp4MGD6NWrF1xdXaFQKLBt2zaD9UIIzJw5Ey4uLrC0tIS/vz8uXrxoMObWrVsYPHgwbG1tYW9vj5EjRyInJ8dgzK+//or27dtDrVbDzc0NS5YsKVBLTEwMGjRoALVajSZNmmDnzp2l3i8RERG9uIwamnJzc9GsWTNERUUVun7JkiX49NNPER0djSNHjsDa2hqBgYF48OCBNGbw4MFIS0tDQkICduzYgYMHD2LMmDHS+uzsbAQEBMDd3R0pKSn4+OOPMXv2bKxcuVIak5SUhDfeeAMjR47EyZMn0adPH/Tp0wdnzpwpu+aJiIjohVLJmBvv3r07unfvXug6IQSWLVuGGTNmoHfv3gCAr776Ck5OTti2bRsGDRqEc+fOIS4uDseOHUOLFi0AAJ999hl69OiBTz75BK6urvjmm2+Ql5eHNWvWQKlUolGjRkhNTUV4eLgUriIjI9GtWzdMnjwZADBv3jwkJCRg+fLliI6OLoc9QURERKbOqKHpaS5fvozMzEz4+/tLy+zs7NC6dWskJydj0KBBSE5Ohr29vRSYAMDf3x9mZmY4cuQIXnvtNSQnJ+OVV16BUqmUxgQGBmLx4sW4ffs2HBwckJycjLCwMIPtBwYGFni78HEajQYajUZ6nJ2dDQDQarXQarXP274B/XylPW95Yx+mhX2YFn39KjNh5Eqej77+inI8XvQ+gIrTS1n1UZz5TDY0ZWZmAgCcnJwMljs5OUnrMjMz4ejoaLC+UqVKqFKlisEYT0/PAnPo1zk4OCAzM/Op2ynMwoULMWfOnALL4+PjYWVlJafFYktISCiTecsb+zAt7MO0zGuhM3YJpaKiHI+K0gdQcXop7T7u378ve6zJhiZTN3XqVIOzU9nZ2XBzc0NAQABsbW1LdVtarRYJCQno2rUrLCwsSnXu8sQ+TAv7MC36Pj48bgaNTmHsckpMZSYwr4WuwhyPF70PoOL0UlZ96N8pksNkQ5OzszMAICsrCy4uLtLyrKws+Pj4SGNu3Lhh8LyHDx/i1q1b0vOdnZ2RlZVlMEb/+Flj9OsLo1KpoFKpCiy3sLAosxdlWc5dntiHaWEfpkWjU0CT/+KGJr2KcjwqSh9AxemltPsozlwm+zlNnp6ecHZ2RmJiorQsOzsbR44cgZ+fHwDAz88Pd+7cQUpKijRm79690Ol0aN26tTTm4MGDBu9ZJiQkoH79+nBwcJDGPL4d/Rj9doiIiIiMGppycnKQmpqK1NRUAI8u/k5NTUVGRgYUCgUmTJiA+fPnY/v27Th9+jSGDh0KV1dX9OnTBwDQsGFDdOvWDaNHj8bRo0fxyy+/IDQ0FIMGDYKrqysA4M0334RSqcTIkSORlpaGzZs3IzIy0uCttfHjxyMuLg5Lly7F+fPnMXv2bBw/fhyhoaHlvUuIiIjIRBn17bnjx4+jU6dO0mN9kAkODsa6deswZcoU5ObmYsyYMbhz5w7atWuHuLg4qNVq6TnffPMNQkND0aVLF5iZmaFfv3749NNPpfV2dnaIj49HSEgIfH19Ua1aNcycOdPgs5xefvllbNy4ETNmzMC0adNQt25dbNu2DY0bNy6HvUBEREQvAqOGpo4dO0KIom+vVSgUmDt3LubOnVvkmCpVqmDjxo1P3U7Tpk3x888/P3XMgAEDMGDAgKcXTERERP9ZJntNExEREZEpYWgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZKhk7AKIiKjiaTx7NzT5CmOXUWIqc4ElrYxdBZkanmkiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZ+DlNRERE/wH87KznxzNNRERERDLwTBMREVERXvSzM4BpnKGpKHimiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaHpCVFRUfDw8IBarUbr1q1x9OhRY5dEREREJoCh6TGbN29GWFgYZs2ahRMnTqBZs2YIDAzEjRs3jF0aERERGRk/cuAx4eHhGD16NIYPHw4AiI6ORmxsLNasWYMPPvjAyNUR0dO86LeG87ZwItPH0PR/8vLykJKSgqlTp0rLzMzM4O/vj+TkZCNWRlS2GDaIiORhaPo/f//9N/Lz8+Hk5GSw3MnJCefPny8wXqPRQKPRSI/v3r0LALh16xa0Wm2p1qbVanH//n34TN8Kje4F/p+bmcCMl3Tsw0To+6ikNUP+C9xHJZ3A/fvsw1SwD9NTUXrR9/HPP//AwsKi1Oa9d+8eAEAI8ewaSm2r/zELFy7EnDlzCiz39PQ0QjUvjjeNXUApYR+mhX2YFvZheipKL2XZx71792BnZ/fUMQxN/6datWowNzdHVlaWwfKsrCw4OzsXGD916lSEhYVJj3U6HW7duoWqVatCoSjdJJ+dnQ03Nzf88ccfsLW1LdW5yxP7MC3sw7SwD9NSUfoAKk4vZdWHEAL37t2Dq6vrM8cyNP0fpVIJX19fJCYmok+fPgAeBaHExESEhoYWGK9SqaBSqQyW2dvbl2mNtra2L/QLXo99mBb2YVrYh2mpKH0AFaeXsujjWWeY9BiaHhMWFobg4GC0aNECrVq1wrJly5CbmyvdTUdERET/XQxNjxk4cCBu3ryJmTNnIjMzEz4+PoiLiytwcTgRERH99zA0PSE0NLTQt+OMSaVSYdasWQXeDnzRsA/Twj5MC/swLRWlD6Di9GIKfSiEnHvsiIiIiP7j+DUqRERERDIwNBERERHJwNBEREREJANDExEREZEMDE0mIioqCh4eHlCr1WjdujWOHj361PExMTFo0KAB1Go1mjRpgp07d5ZTpU9XnD7S0tLQr18/eHh4QKFQYNmyZeVX6DMUp49Vq1ahffv2cHBwgIODA/z9/Z95/MpLcfrYunUrWrRoAXt7e1hbW8PHxwdff/11OVZbtOL+fuht2rQJCoVC+sBaYytOH+vWrYNCoTD4UavV5Vht0Yp7PO7cuYOQkBC4uLhApVKhXr16JvFvVnH66NixY4HjoVAoEBQUVI4VF664x2PZsmWoX78+LC0t4ebmhokTJ+LBgwflVG3RitOHVqvF3LlzUadOHajVajRr1gxxcXFlX6Qgo9u0aZNQKpVizZo1Ii0tTYwePVrY29uLrKysQsf/8ssvwtzcXCxZskScPXtWzJgxQ1hYWIjTp0+Xc+WGitvH0aNHxfvvvy++/fZb4ezsLCIiIsq34CIUt48333xTREVFiZMnT4pz586JYcOGCTs7O/Hnn3+Wc+WGitvHvn37xNatW8XZs2fF77//LpYtWybMzc1FXFxcOVduqLh96F2+fFnUqFFDtG/fXvTu3bt8in2K4vaxdu1aYWtrK65fvy79ZGZmlnPVBRW3D41GI1q0aCF69OghDh06JC5fviz2798vUlNTy7lyQ8Xt459//jE4FmfOnBHm5uZi7dq15Vv4E4rbxzfffCNUKpX45ptvxOXLl8Xu3buFi4uLmDhxYjlXbqi4fUyZMkW4urqK2NhYkZ6eLlasWCHUarU4ceJEmdbJ0GQCWrVqJUJCQqTH+fn5wtXVVSxcuLDQ8a+//roICgoyWNa6dWvx9ttvl2mdz1LcPh7n7u5uMqHpefoQQoiHDx8KGxsbsX79+rIqUZbn7UMIIV566SUxY8aMsihPtpL08fDhQ/Hyyy+LL7/8UgQHB5tEaCpuH2vXrhV2dnblVJ18xe3j888/F7Vr1xZ5eXnlVaIsz/v7ERERIWxsbEROTk5ZlShLcfsICQkRnTt3NlgWFhYm2rZtW6Z1Pktx+3BxcRHLly83WNa3b18xePDgMq2Tb88ZWV5eHlJSUuDv7y8tMzMzg7+/P5KTkwt9TnJyssF4AAgMDCxyfHkoSR+mqDT6uH//PrRaLapUqVJWZT7T8/YhhEBiYiIuXLiAV155pSxLfaqS9jF37lw4Ojpi5MiR5VHmM5W0j5ycHLi7u8PNzQ29e/dGWlpaeZRbpJL0sX37dvj5+SEkJAROTk5o3LgxPvroI+Tn55dX2QWUxu/56tWrMWjQIFhbW5dVmc9Ukj5efvllpKSkSG99Xbp0CTt37kSPHj3KpebClKQPjUZT4O1qS0tLHDp0qExrZWgysr///hv5+fkFvqrFyckJmZmZhT4nMzOzWOPLQ0n6MEWl0cf//vc/uLq6Fgi25amkfdy9exeVK1eGUqlEUFAQPvvsM3Tt2rWsyy1SSfo4dOgQVq9ejVWrVpVHibKUpI/69etjzZo1+PHHH7FhwwbodDq8/PLL+PPPP8uj5EKVpI9Lly7hu+++Q35+Pnbu3IkPP/wQS5cuxfz588uj5EI97+/50aNHcebMGYwaNaqsSpSlJH28+eabmDt3Ltq1awcLCwvUqVMHHTt2xLRp08qj5EKVpI/AwECEh4fj4sWL0Ol0SEhIwNatW3H9+vUyrZWhiagULVq0CJs2bcIPP/xgMhftFoeNjQ1SU1Nx7NgxLFiwAGFhYdi/f7+xy5Lt3r17GDJkCFatWoVq1aoZu5zn4ufnh6FDh8LHxwcdOnTA1q1bUb16dXzxxRfGLq1YdDodHB0dsXLlSvj6+mLgwIGYPn06oqOjjV1aia1evRpNmjRBq1atjF1Kse3fvx8fffQRVqxYgRMnTmDr1q2IjY3FvHnzjF1asURGRqJu3bpo0KABlEolQkNDMXz4cJiZlW2s4XfPGVm1atVgbm6OrKwsg+VZWVlwdnYu9DnOzs7FGl8eStKHKXqePj755BMsWrQIe/bsQdOmTcuyzGcqaR9mZmbw8vICAPj4+ODcuXNYuHAhOnbsWJblFqm4faSnp+PKlSvo1auXtEyn0wEAKlWqhAsXLqBOnTplW3QhSuP3w8LCAi+99BJ+//33sihRlpL04eLiAgsLC5ibm0vLGjZsiMzMTOTl5UGpVJZpzYV5nuORm5uLTZs2Ye7cuWVZoiwl6ePDDz/EkCFDpLNkTZo0QW5uLsaMGYPp06eXeegoTEn6qF69OrZt24YHDx7gn3/+gaurKz744APUrl27TGvlmSYjUyqV8PX1RWJiorRMp9MhMTERfn5+hT7Hz8/PYDwAJCQkFDm+PJSkD1NU0j6WLFmCefPmIS4uDi1atCiPUp+qtI6HTqeDRqMpixJlKW4fDRo0wOnTp5Gamir9vPrqq+jUqRNSU1Ph5uZWnuVLSuN45Ofn4/Tp03BxcSmrMp+pJH20bdsWv//+uxReAeC3336Di4uLUQIT8HzHIyYmBhqNBm+99VZZl/lMJenj/v37BYKRPtAKI30V7fMcD7VajRo1auDhw4f4/vvv0bt377IttkwvMydZNm3aJFQqlVi3bp04e/asGDNmjLC3t5duLx4yZIj44IMPpPG//PKLqFSpkvjkk0/EuXPnxKxZs0zmIweK04dGoxEnT54UJ0+eFC4uLuL9998XJ0+eFBcvXjRWC0KI4vexaNEioVQqxXfffWdwS/K9e/eM1YIQovh9fPTRRyI+Pl6kp6eLs2fPik8++URUqlRJrFq1ylgtCCGK38eTTOXuueL2MWfOHLF7926Rnp4uUlJSxKBBg4RarRZpaWnGakEIUfw+MjIyhI2NjQgNDRUXLlwQO3bsEI6OjmL+/PnGakEIUfLXVbt27cTAgQPLu9wiFbePWbNmCRsbG/Htt9+KS5cuifj4eFGnTh3x+uuvG6sFIUTx+zh8+LD4/vvvRXp6ujh48KDo3Lmz8PT0FLdv3y7TOhmaTMRnn30matWqJZRKpWjVqpU4fPiwtK5Dhw4iODjYYPyWLVtEvXr1hFKpFI0aNRKxsbHlXHHhitPH5cuXBYACPx06dCj/wp9QnD7c3d0L7WPWrFnlX/gTitPH9OnThZeXl1Cr1cLBwUH4+fmJTZs2GaHqgor7+/E4UwlNQhSvjwkTJkhjnZycRI8ePcr8M2jkKu7xSEpKEq1btxYqlUrUrl1bLFiwQDx8+LCcqy6ouH2cP39eABDx8fHlXOnTFacPrVYrZs+eLerUqSPUarVwc3MT7777bpmHDTmK08f+/ftFw4YNhUqlElWrVhVDhgwRf/31V5nXqBDCSOfjiIiIiF4gvKaJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIpMnhMCYMWNQpUoVKBQKpKamomPHjpgwYcJTn+fh4YFly5aVS41EVPExNBFRiWVmZmLcuHGoXbs2VCoV3Nzc0KtXrwLfjfi84uLisG7dOuzYsQPXr19H48aNsXXr1hfum9mL8sMPP6BNmzaws7ODjY0NGjVq9MxASETlr5KxCyCiF9OVK1fQtm1b2Nvb4+OPP0aTJk2g1Wqxe/duhISE4Pz586W2rfT0dLi4uODll1+WllWpUqXU5jemxMREDBw4EAsWLMCrr74KhUKBs2fPIiEhocy2mZ+fD4VCYZRvtCd6oZX5F7UQUYXUvXt3UaNGDZGTk1Ng3ePfY3X16lXx6quvCmtra2FjYyMGDBggfQmnEI++QLRZs2biq6++Eu7u7sLW1lYMHDhQZGdnCyEefXccHvtOP3d3dyHEo++iGj9+vDRPVlaW6Nmzp1Cr1cLDw0Ns2LBBuLu7i4iICIO6Ro4cKapVqyZsbGxEp06dRGpqquxahBAiPz9fLF68WNSpU0colUrh5uZm8OWzGRkZYsCAAcLOzk44ODiIV199VVy+fLnI/Th+/HjRsWPHZ+1usX37dtGiRQvpu7b69Okjrbt165YYMmSIsLe3F5aWlqJbt27it99+k9avXbtW2NnZiR9//FE0bNhQmJubi8uXL4sHDx6ISZMmCVdXV2FlZSVatWol9u3b98xaiP6r+GcGERXbrVu3EBcXh5CQEFhbWxdYb29vDwDQ6XTo3bs3bt26hQMHDiAhIQGXLl3CwIEDDcanp6dj27Zt2LFjB3bs2IEDBw5g0aJFAIDIyEjMnTsXNWvWxPXr13Hs2LFCaxo2bBj++OMP7Nu3D9999x1WrFiBGzduGIwZMGAAbty4gV27diElJQXNmzdHly5dcOvWLVm1AMDUqVOxaNEifPjhhzh79iw2btwIJycnAIBWq0VgYCBsbGzw888/45dffkHlypXRrVs35OXlFVq3s7Mz0tLScObMmSL3d2xsLF577TX06NEDJ0+eRGJiIlq1amXQ+/Hjx7F9+3YkJydDCIEePXpAq9VKY+7fv4/Fixfjyy+/RFpaGhwdHREaGork5GRs2rQJv/76KwYMGIBu3brh4sWLRdZC9J9m7NRGRC+eI0eOCABi69atTx0XHx8vzM3NRUZGhrQsLS1NABBHjx4VQjw6u2NlZWVwNmfy5MmidevW0uOIiAjpDJPe42eaLly4YDCnEEKcO3dOAJDONP3888/C1tZWPHjwwGCeOnXqiC+++EJWLdnZ2UKlUolVq1YV2u/XX38t6tevL3Q6nbRMo9EIS0tLsXv37kKfk5OTI3r06CGdRRs4cKBYvXq1QZ1+fn5i8ODBhT7/t99+EwDEL7/8Ii37+++/haWlpdiyZYsQ4tGZJgAGZ9WuXr0qzM3NC3wzfJcuXcTUqVML3RbRfx2vaSKiYhNCyBp37tw5uLm5wc3NTVrm7e0Ne3t7nDt3Di1btgTw6C43GxsbaYyLi0uBs0TP2k6lSpXg6+srLWvQoIF0xgsATp06hZycHFStWtXguf/++y/S09Olx0+r5dy5c9BoNOjSpUuhdZw6dQq///67wfMB4MGDBwbbeJy1tTViY2ORnp6Offv24fDhw5g0aRIiIyORnJwMKysrpKamYvTo0U/tvXXr1tKyqlWron79+jh37py0TKlUomnTptLj06dPIz8/H/Xq1TOYT6PRFNhHRPQIQxMRFVvdunWhUChK7WJvCwsLg8cKhQI6na5U5tbLycmBi4sL9u/fX2Dd4+HqabVYWlo+cxu+vr745ptvCqyrXr36U59bp04d1KlTB6NGjcL06dNRr149bN68GcOHD3/mduWwtLSEQqEwqNXc3BwpKSkwNzc3GFu5cuXn3h5RRcRrmoio2KpUqYLAwEBERUUhNze3wPo7d+4AABo2bIg//vgDf/zxh7Tu7NmzuHPnDry9vUutngYNGuDhw4dISUmRll24cEGqAwCaN2+OzMxMVKpUCV5eXgY/1apVk7WdunXrwtLSssiPVGjevDkuXrwIR0fHAtuws7OT3Y+HhwesrKykfdu0adMit9mwYUM8fPgQR44ckZb9888/uHDhwlP38UsvvYT8/HzcuHGjQK3Ozs6yayX6L2FoIqISiYqKQn5+Plq1aoXvv/8eFy9exLlz5/Dpp5/Cz88PAODv748mTZpg8ODBOHHiBI4ePYqhQ4eiQ4cOaNGiRanVUr9+fXTr1g1vv/02jhw5gpSUFIwaNcrgDI2/vz/8/PzQp08fxMfH48qVK0hKSsL06dNx/PhxWdtRq9X43//+hylTpuCrr75Ceno6Dh8+jNWrVwMABg8ejGrVqqF37974+eefcfnyZezfvx/vvfce/vzzz0LnnD17NqZMmYL9+/fj8uXLOHnyJEaMGAGtVouuXbsCAGbNmoVvv/0Ws2bNwrlz53D69GksXrwYwKMg17t3b4wePRqHDh3CqVOn8NZbb6FGjRro3bt3kb3Uq1cPgwcPxtChQ7F161ZcvnwZR48excKFCxEbGytrfxD91zA0EVGJ1K5dGydOnECnTp0wadIkNG7cGF27dkViYiI+//xzAI/e2vrxxx/h4OCAV155Bf7+/qhduzY2b95c6vWsXbsWrq6u6NChA/r27YsxY8bA0dFRWq9QKLBz50688sorGD58OOrVq4dBgwbh6tWr0t1vcnz44YeYNGkSZs6ciYYNG2LgwIHSNU9WVlY4ePAgatWqhb59+6Jhw4YYOXIkHjx4AFtb20Ln69ChAy5duoShQ4eiQYMG6N69OzIzMxEfH4/69esDADp27IiYmBhs374dPj4+6Ny5M44ePWrQu6+vL3r27Ak/Pz8IIbBz584CbzUWts+GDh2KSZMmoX79+ujTpw+OHTuGWrVqyd4fRP8lCiH3ik4iIiKi/zCeaSIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmoiIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGT4f1/cmxI81t+yAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#histogram of confidence scores for randomly selected samples\n",
+    "nom_sampled.hist(\"Confidence Score\",bins=np.arange(0,1,0.1))\n",
+    "plt.title('Confidence Scores from Sampled Data Sets')\n",
+    "plt.xlabel(\"Confidence Score\")\n",
+    "plt.xticks(np.arange(0,1,0.1))\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()\n",
+    "\n",
+    "#peak filtering by confidence score\n",
+    "conf_filt=nom_sampled[nom_sampled['Confidence Score']>=0.3]\n",
+    "\n",
+    "#count for each molecular formula\n",
+    "mol_counts=conf_filt.value_counts('mol_form').to_frame().reset_index()\n",
+    "\n",
+    "#histogram of molecular formula counts\n",
+    "mol_counts.hist(\"count\",bins=np.arange(0,50,5))\n",
+    "plt.locator_params(axis='x')\n",
+    "plt.title('Molecular Formula Counts Across Sampled Data Sets')\n",
+    "plt.xlabel(\"Number of Occurences Across Data Sets\")\n",
+    "plt.xticks(np.arange(0,50,5))\n",
+    "plt.ylabel(\"Number of Molecular Formula\")\n",
+    "plt.show()\n",
+    "\n",
+    "#based on this histogram, filter to formulas in more than 5 data sets\n",
+    "mol_counts=mol_counts[mol_counts['count']>=5]\n",
+    "mol_filter=mol_counts.merge(conf_filt,on=['mol_form'],how=\"left\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Assess patterns in the molecular formulas from different sample types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a clustermap of the processed NOM data sets (x axis), indicating the presence (black) or absence (white) of molecular formulas (y axis). The color bar will indicate sample type and help visualize the molecular similarity of data sets both within and between sample types.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x321205250>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#sample type colors\n",
+    "\n",
+    "#set colors for each sample type\n",
+    "type_col=pd.DataFrame({grouping_column:mol_filter[grouping_column].unique(),'color':['#8B4513','olivedrab','blue','#B8860B']})\n",
+    "\n",
+    "#setup color legend for sample type\n",
+    "type_col_dict = dict(zip(type_col[grouping_column].unique(), ['#8B4513','olivedrab','blue','#B8860B']))\n",
+    "handles = [Patch(facecolor=type_col_dict[name]) for name in type_col_dict]\n",
+    "\n",
+    "#map graph colors based on sample type to processed IDs\n",
+    "sample_type=mol_filter[[grouping_column,'processed']].drop_duplicates()\n",
+    "sample_type_col=sample_type.merge(type_col,how='left',on=grouping_column).set_index('processed').drop(grouping_column,axis=1).rename(columns={'color':grouping_column})\n",
+    "\n",
+    "#presence/absence\n",
+    "\n",
+    "#set colors for presence/absence as cmap\n",
+    "colors=['white','black']\n",
+    "custom_palette = sns.color_palette(colors)\n",
+    "custom_cmap = sns.color_palette(custom_palette, as_cmap=True)\n",
+    "\n",
+    "#add column indicating presence in that processed nom id (1 is present)\n",
+    "mol_filter['presence']=1\n",
+    "\n",
+    "#create presence/absence matrix. replace NA with 0 (0 is absent)\n",
+    "formula_matrix=mol_filter[['mol_form','processed','presence']].pivot_table('presence', index='mol_form', columns='processed').fillna(0).astype(int)\n",
+    "\n",
+    "#heatmap (1 is present, zero is absent)\n",
+    "g=sns.clustermap(data=formula_matrix,col_colors=sample_type_col,tree_kws={\"linewidths\": 0.},xticklabels=False,yticklabels=False,cmap=custom_cmap)\n",
+    "g.figure.suptitle(\"Presence of Molecular Formulas Across NOM Data Sets\")\n",
+    "g.ax_heatmap.set_xlabel(\"Processed NOM Data Sets\")\n",
+    "g.ax_heatmap.set_ylabel(\"Molecular Formulas\")\n",
+    "\n",
+    "#adjust plot and legend locations\n",
+    "g.figure.subplots_adjust(top=1.15,right=0.8)\n",
+    "g.ax_cbar.set_position((0.85, 0.45, .05, .05)) #x axis,y axis, width, height\n",
+    "\n",
+    "#adjust cbar legend to indicate presence/absence\n",
+    "g.ax_cbar.set_yticks([0.25,0.75])\n",
+    "g.ax_cbar.set_yticklabels([\"absent\",\"present\"])\n",
+    "\n",
+    "#add sample type legend\n",
+    "plt.legend(handles, type_col_dict, title=None,\n",
+    "           bbox_to_anchor=(0.95, 1.05), bbox_transform=plt.gcf().transFigure)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create Van Krevelen diagrams to visually assess the atomic composition of each sample type. Van Krevelen diagrams plot the hydrogen to carbon ratio against the oxygen to carbon ratio, historically to assess petroleum samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the same molecular formula will have the same H/C and O/C value in every data set, so dots are only unique per sample type\n",
+    "vankrev_data=mol_filter[[grouping_column,'mol_form','H/C','O/C']].drop_duplicates()\n",
+    "\n",
+    "#make van krevlen plot\n",
+    "g=sns.FacetGrid(vankrev_data,col=grouping_column,hue=grouping_column,palette=type_col_dict)\n",
+    "g.map(sns.scatterplot,'O/C','H/C',s=10)\n",
+    "g.set_titles(col_template=\"{col_name}\")\n",
+    "g.figure.suptitle(\"Van Krevlen Plots By General Sample Type\")\n",
+    "g.figure.subplots_adjust(top=0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create marginal density plot to assess how molecular formulas unique to each sample type compare to those shared by all four sample types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#count the number of sample types in which each molecular formula is found\n",
+    "density_counts=vankrev_data['mol_form'].value_counts()\n",
+    "\n",
+    "#map counts back to H/C and O/C data\n",
+    "vankrev_data['common_to'] = vankrev_data['mol_form'].map(density_counts)\n",
+    "\n",
+    "#filter molecular formual present in either all four sample types or in only one\n",
+    "vankrev_data=vankrev_data[vankrev_data['common_to'].isin([1,4])]\n",
+    "\n",
+    "#reformat so sample type is 'all' when molecular formula was in all\n",
+    "vankrev_data.loc[vankrev_data['common_to']==4,'sample_type']='all types'\n",
+    "\n",
+    "#add 'all' to sample type color dictionary\n",
+    "type_col_dict.update({'all types':'black'})\n",
+    "\n",
+    "#make marginal density plot\n",
+    "sns.jointplot(data=vankrev_data, x=\"O/C\", y=\"H/C\", kind=\"scatter\", hue=grouping_column,palette=type_col_dict,s=12,alpha=0.5)\n",
+    "plt.legend(markerscale=3,title=\"unique to\")\n",
+    "plt.suptitle(\"Marginal Density Plot of Molecular Formulas by Sample Type\",y=1.02)\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nmdc_notebooks_kernel",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index 59961697..36cb8f9b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,4 @@ altair==5.1.2
 plotly==5.18.0
 dill==0.3.7
 statsmodels
+seaborn==0.13.2
\ No newline at end of file
diff --git a/requirements_dev.txt b/requirements_dev.txt
index da998edb..41090dfa 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -94,6 +94,7 @@ requests==2.31.0
 rfc3339-validator==0.1.4
 rfc3986-validator==0.1.1
 rpds-py==0.10.6
+seaborn==0.13.2
 Send2Trash==1.8.2
 six==1.16.0
 sniffio==1.3.0