diff --git a/HCK21_2024_Janelia_NDRH/README.md b/HCK21_2024_Janelia_NDRH/README.md
index 4b02fa418..8471afd75 100644
--- a/HCK21_2024_Janelia_NDRH/README.md
+++ b/HCK21_2024_Janelia_NDRH/README.md
@@ -89,6 +89,11 @@ Thanks to the generous sponsorship of The Janelia Research Campus, this event wi
 
 Resources will be posted here to help participants prepare for the event.
 
+* Search API tutorials
+    * All slides: [Google Slides](https://drive.google.com/drive/folders/1DAmQr4qWCamhj_2Zyke0kkHGvV8Kyq13?usp=sharing)
+    * [Basic API tutorial](tutorials/simple_dandiset_search.ipynb)
+    * [Advanced API tutorial](tutorials/advanced_asset_search.ipynb)
+
 * A report of the first NeuroDataReHack event: ([PDF](../HCK14_2022_Seattle_RH/report/Report_Neurodata_Rehack_v2.pdf))
 * A report of the second NeuroDataReHack event: ([PDF](../HCK16_2023_Granada_RH/report/Report__NeuroDataReHack_2023.pdf))
 * Recordings of talks will be made available on the [NWB Youtube channel](https://www.youtube.com/channel/UCfD_mU-EFz135a9TpNFJP5A).
diff --git a/HCK21_2024_Janelia_NDRH/tutorials/advanced_asset_search.ipynb b/HCK21_2024_Janelia_NDRH/tutorials/advanced_asset_search.ipynb
new file mode 100644
index 000000000..ad2f18c9f
--- /dev/null
+++ b/HCK21_2024_Janelia_NDRH/tutorials/advanced_asset_search.ipynb
@@ -0,0 +1,538 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62326652-fb78-4838-9bbd-6f29e09793fc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import numpy as np\n",
+    "from dandi.dandiapi import DandiAPIClient\n",
+    "from tqdm.notebook import tqdm\n",
+    "from isodate import parse_duration, Duration\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c949acf-7661-41ec-801b-7f7aec844ee0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "client = DandiAPIClient()\n",
+    "dandisets = list(client.get_dandisets())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a226e74-0f13-40c6-9e8d-9a3eb3fbd3ad",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# More specific identification of NWB dandisets\n",
+    "\n",
+    "The simpler tutorial only tested if the phrase \"NWB\" was in the name of any of the data standards for a dandiset.\n",
+    "\n",
+    "The more official and precise method is to use the specific [RRID of NWB](https://scicrunch.org/resolver/RRID:SCR_015242), which is `\"RRID:SCR_015242\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "01b611f1-6d7f-40d7-8922-26eb1c5b2264",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2988d690aff4294aaf1a13e597bd6df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/465 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are currently 277 NWB datasets on DANDI!\n"
+     ]
+    }
+   ],
+   "source": [
+    "nwb_dandisets = []\n",
+    "\n",
+    "for dandiset in tqdm(dandisets):\n",
+    "    raw_metadata = dandiset.get_raw_metadata()\n",
+    "\n",
+    "    if any(\n",
+    "        data_standard['identifier'] == \"RRID:SCR_015242\"  # this is the RRID for NWB\n",
+    "        for data_standard in raw_metadata['assetsSummary'].get('dataStandard', [])\n",
+    "    ):\n",
+    "        nwb_dandisets.append(dandiset)\n",
+    "print(f\"There are currently {len(nwb_dandisets)} NWB datasets on DANDI!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7962c8a-f93c-4de2-aa9a-bee6516da634",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Average age of subjects used in a dandiset\n",
+    "\n",
+    "Let's consider a more advanced calculation - finding the average age of all the subjects used in a particular dandiset.\n",
+    "\n",
+    "For this we will be directly accessing the asset level fields `wasAttributedTo` as a key of the `asset_metadata`, instead of as an attribute.\n",
+    "\n",
+    "We will also have to do some manual data manipulation to parse the form of the [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f5dbbb89-0b66-4557-8cea-829e92a4f4fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9d262c3538174436832a3ce7ce46d6a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/42 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The average age of the subjects in dandiset #398 is: 170.74276620370375 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "def iso_to_fractional_days(age_iso: str, experiment_date: str) -> float:\n",
+    "    \"\"\"\n",
+    "    Defining a helper function which parses the ISO 8601 age and returns it in float-valued seconds.\n",
+    "    \n",
+    "    This is because a dattetime.timedelta can only return either its `.days` (integer, rounded down) or\n",
+    "    its `total_seconds()`.\n",
+    "    \n",
+    "    This helper also resolves some complications that can arise in other datasets when the age is measured in years,\n",
+    "    or if the the age is a range.\n",
+    "    \"\"\"\n",
+    "    if \"/\" in age_iso:  # Some ages can be have upper and lower ranges due to uncertainty\n",
+    "        return  # Skip\n",
+    "\n",
+    "    age_duration = parse_duration(datestring=age_iso)\n",
+    "\n",
+    "    if isinstance(age_duration, Duration):\n",
+    "        experiment_datetime = datetime.fromisoformat(experiment_date)\n",
+    "        time_delta = age_duration.totimedelta(end=experiment_datetime)\n",
+    "    else:\n",
+    "        time_delta = age_duration\n",
+    "\n",
+    "    return time_delta.total_seconds() / (  # Evaluate using the total number of seconds\n",
+    "        60 *  # 60 seconds per minute\n",
+    "        60 *  # 60 minutes per hour\n",
+    "        24  # 24 hours per day (ignoring daylight savings time)\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "all_subject_ages_in_days = []\n",
+    "\n",
+    "dandiset = client.get_dandiset(\"000398\")\n",
+    "assets = list(dandiset.get_assets())\n",
+    "for asset in tqdm(assets):\n",
+    "    raw_metadata = asset.get_raw_metadata()\n",
+    "    subjects = raw_metadata[\"wasAttributedTo\"]\n",
+    "\n",
+    "    for subject_metadata in subjects:\n",
+    "        if \"age\" in subject_metadata:\n",
+    "            age_in_days = iso_to_fractional_days(\n",
+    "                age_iso=subject_metadata[\"age\"][\"value\"],\n",
+    "                experiment_date=raw_metadata[\"wasGeneratedBy\"][0][\"startDate\"]\n",
+    "            )\n",
+    "\n",
+    "            if age_in_days:  # Skip if the age is null\n",
+    "                all_subject_ages_in_days.append(age_in_days)\n",
+    "print(f\"The average age of the subjects in dandiset #398 is: {np.mean(all_subject_ages_in_days)} days\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb82bd5c-cbf1-4312-81a3-da0120a0d154",
+   "metadata": {},
+   "source": [
+    "# Count the number of spiking units across all sessions in an experiment\n",
+    "\n",
+    "The number of units identified from spike sorting is not something that DANDI extracts automatically during upload...\n",
+    "\n",
+    "But we can calculate it ourselves without downloading an entire dandiset!\n",
+    "\n",
+    "We do this by streaming directly from the archive, which requires us to retrieve the asset path on the S3 backend of the DANDI archive and then set the `driver` argument to `ros3` (Read-Only S3).\n",
+    "\n",
+    "There are several ways to retrieve the S3 path, but the easiest is to use the NWB Inspector helper function `nwbinspector.tools.get_s3_urls_and_dandi_paths`, which will format the path in the way `ros3` expects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2edbd010-1c2c-469c-a492-5e96d6e98a36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting remfile\n",
+      "  Downloading remfile-0.1.13-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.11/site-packages (from remfile) (1.26.3)\n",
+      "Requirement already satisfied: h5py in /opt/conda/lib/python3.11/site-packages (from remfile) (3.10.0)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.11/site-packages (from remfile) (2.31.0)\n",
+      "Requirement already satisfied: mpi4py>=3.1.1 in /opt/conda/lib/python3.11/site-packages (from h5py->remfile) (3.1.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.11/site-packages (from requests->remfile) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.11/site-packages (from requests->remfile) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.11/site-packages (from requests->remfile) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.11/site-packages (from requests->remfile) (2024.2.2)\n",
+      "Downloading remfile-0.1.13-py3-none-any.whl (11 kB)\n",
+      "Installing collected packages: remfile\n",
+      "Successfully installed remfile-0.1.13\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install remfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e23e50af-678f-4517-b0dc-ba10ba85d529",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from warnings import simplefilter\n",
+    "simplefilter(\"ignore\")  # Suppress namespace warnings from reading older NWB files\n",
+    "\n",
+    "from nwbinspector.tools import get_s3_urls_and_dandi_paths\n",
+    "from pynwb import NWBHDF5IO\n",
+    "import remfile\n",
+    "import h5py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "85ac164c-5613-4eaa-8a20-0e2ae870583b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ad4fa87e6fb4bf29cef775cff1b0dd1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s3_urls = get_s3_urls_and_dandi_paths(dandiset_id=\"000059\")\n",
+    "\n",
+    "num_units_per_asset = dict()\n",
+    "for s3_url in tqdm(s3_urls):\n",
+    "\n",
+    "    rem_file = remfile.File(s3_url)\n",
+    "    h5py_file = h5py.File(rem_file, \"r\")\n",
+    "    io = NWBHDF5IO(file=h5py_file, load_namespaces=True)\n",
+    "    nwbfile = io.read()\n",
+    "    \n",
+    "    if nwbfile.units:\n",
+    "        num_units_per_asset.update({s3_url: len(nwbfile.units)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7ba55e2e-12ae-4626-842a-bb42c18e3686",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'https://dandiarchive.s3.amazonaws.com/blobs/093/2c2/0932c245-ac35-4dfd-be76-20ae328f43a4': 395,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/dac/f24/dacf2486-4b1c-4b7f-b3a6-43453670e436': 381,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/fd9/600/fd9600d5-3623-48fb-b84b-796b7e00b9cf': 365,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/f4f/37e/f4f37e2f-5b44-4ab9-a8a0-9fbdc218b357': 344,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/235/5bd/2355bdc4-394a-4a9e-bbd7-fbfcc4d9ec7b': 361,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/a19/13b/a1913b51-ab11-42af-a5ca-7b346d59ef37': 915,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/927/c68/927c68bb-82c2-4cdb-bde8-af836d0d1664': 1114,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/8ea/05c/8ea05cea-0159-4b78-9058-30de1348e7c7': 468,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/824/ef1/824ef138-56b9-4abc-bb3f-233d623c9f3e': 928,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/cd1/1ae/cd11ae52-a90d-48cc-bc16-c90113fd094b': 1024,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/45e/204/45e20453-213b-4dd2-ab89-fcaa92b2f891': 1730,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/fa7/9ee/fa79ee85-df07-403b-8c30-89bd2f2d7684': 854,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/9f8/0d9/9f80d963-413e-4c17-a18b-30aff27e0e56': 1827,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/f2f/f6b/f2ff6b52-63da-46a8-ac45-c70e792835a9': 855,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/503/121/50312181-7b3f-4456-bf12-3aa891b9ab31': 1384,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/4d4/da2/4d4da28c-15c9-4cd7-8aea-f09771f70c98': 1191,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/075/b32/075b32be-1c44-4e2f-8a91-3eeb182af587': 1509,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/85b/a21/85ba21dd-bafb-4523-a697-6b6342346e29': 1183,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/70f/91c/70f91c46-5ed4-4577-9fec-8d616ddd1364': 1664,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/dd2/649/dd264957-d469-4a1c-b303-ebb3a6a06be3': 1344,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/626/ac4/626ac469-e53b-4c83-a5db-8baf977dd62c': 584,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/af4/1b2/af41b25a-510c-4bad-abe3-d3fb6aec76b0': 476,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/4c4/f6f/4c4f6ff5-199b-4f5a-b241-4e5d6cf8a5ad': 409,\n",
+       " 'https://dandiarchive.s3.amazonaws.com/blobs/d6e/dc0/d6edc032-a51e-4890-a621-06e168fa788f': 1014}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_units_per_asset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "46e23b08-4a86-42c2-8ffc-5e3161900cc9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dandiset #59 has a total of 22319 identified spiking units!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Dandiset #59 has a total of {sum(num_units_per_asset.values())} identified spiking units!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56643220-eef1-4dbb-a104-c378897649c4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Going beyond\n",
+    "These examples show a few types of queries, but since the metadata structures are quite rich on both the dandiset and asset levels, they enable many complex queries beyond the examples here.\n",
+    "\n",
+    "These metadata structures are also expanding over time as DANDI becomes more strict about what counts as essential metadata.\n",
+    "\n",
+    "The `.get_raw_metadata` method of both `client.get_dandiset(...)` and `client.get_dandiset(...).get_assets()` provides a nice view into the available fields.\n",
+    "\n",
+    "Note: for any attribute, it is recommended to first check that it is not `None` before checking for its value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ff061749-dd12-4cc8-a70a-ad42b8ed862c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"id\": \"dandiasset:11c25674-6eff-43a8-8dba-7dea2e8c76c4\",\n",
+      "    \"path\": \"sub-San4/sub-San4_ses-20200302T142114_ecephys.nwb\",\n",
+      "    \"access\": [\n",
+      "        {\n",
+      "            \"status\": \"dandi:OpenAccess\",\n",
+      "            \"schemaKey\": \"AccessRequirements\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"digest\": {\n",
+      "        \"dandi:dandi-etag\": \"4c907ae8685aea1bfbe57316942b881f-4\",\n",
+      "        \"dandi:sha2-256\": \"b770e3ac3f75f40618de2ba2a81e996429d5fc01dd530e9d826acc7a1ad0853c\"\n",
+      "    },\n",
+      "    \"@context\": \"https://raw.githubusercontent.com/dandi/schema/master/releases/0.6.3/context.json\",\n",
+      "    \"approach\": [\n",
+      "        {\n",
+      "            \"name\": \"electrophysiological approach\",\n",
+      "            \"schemaKey\": \"ApproachType\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"schemaKey\": \"Asset\",\n",
+      "    \"contentUrl\": [\n",
+      "        \"https://api.dandiarchive.org/api/assets/11c25674-6eff-43a8-8dba-7dea2e8c76c4/download/\",\n",
+      "        \"https://dandiarchive.s3.amazonaws.com/blobs/429/baa/429baaad-a057-411d-8957-8460947aef73\"\n",
+      "    ],\n",
+      "    \"identifier\": \"11c25674-6eff-43a8-8dba-7dea2e8c76c4\",\n",
+      "    \"contentSize\": 237267068,\n",
+      "    \"publishedBy\": {\n",
+      "        \"id\": \"urn:uuid:f9fc60e7-3126-4912-8222-4de0c7d2cd7a\",\n",
+      "        \"name\": \"DANDI publish\",\n",
+      "        \"endDate\": \"2022-12-08T18:03:14.228709+00:00\",\n",
+      "        \"schemaKey\": \"PublishActivity\",\n",
+      "        \"startDate\": \"2022-12-08T18:03:14.228709+00:00\",\n",
+      "        \"wasAssociatedWith\": [\n",
+      "            {\n",
+      "                \"id\": \"urn:uuid:2a080a04-80f8-4d25-835b-77aa87eb4b81\",\n",
+      "                \"name\": \"DANDI API\",\n",
+      "                \"version\": \"0.1.0\",\n",
+      "                \"schemaKey\": \"Software\",\n",
+      "                \"identifier\": \"RRID:SCR_017571\"\n",
+      "            }\n",
+      "        ]\n",
+      "    },\n",
+      "    \"dateModified\": \"2022-12-02T20:15:00.997018-08:00\",\n",
+      "    \"datePublished\": \"2022-12-08T18:03:14.228709+00:00\",\n",
+      "    \"schemaVersion\": \"0.6.3\",\n",
+      "    \"encodingFormat\": \"application/x-nwb\",\n",
+      "    \"wasGeneratedBy\": [\n",
+      "        {\n",
+      "            \"name\": \"Acquisition session\",\n",
+      "            \"schemaKey\": \"Session\",\n",
+      "            \"startDate\": \"2020-03-02T14:21:14-08:00\",\n",
+      "            \"description\": \"Fig 3i, S10\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"id\": \"urn:uuid:a698c09b-ca57-4cf3-a523-dfbb00a8f524\",\n",
+      "            \"name\": \"Metadata generation\",\n",
+      "            \"schemaKey\": \"Activity\",\n",
+      "            \"description\": \"Metadata generated by DANDI cli\",\n",
+      "            \"wasAssociatedWith\": [\n",
+      "                {\n",
+      "                    \"url\": \"https://github.com/dandi/dandi-cli\",\n",
+      "                    \"name\": \"DANDI Command Line Interface\",\n",
+      "                    \"version\": \"0.46.6\",\n",
+      "                    \"schemaKey\": \"Software\",\n",
+      "                    \"identifier\": \"RRID:SCR_019009\"\n",
+      "                }\n",
+      "            ]\n",
+      "        }\n",
+      "    ],\n",
+      "    \"wasAttributedTo\": [\n",
+      "        {\n",
+      "            \"age\": {\n",
+      "                \"value\": \"P209DT55274S\",\n",
+      "                \"unitText\": \"ISO-8601 duration\",\n",
+      "                \"schemaKey\": \"PropertyValue\",\n",
+      "                \"valueReference\": {\n",
+      "                    \"value\": \"dandi:BirthReference\",\n",
+      "                    \"schemaKey\": \"PropertyValue\"\n",
+      "                }\n",
+      "            },\n",
+      "            \"sex\": {\n",
+      "                \"name\": \"Male\",\n",
+      "                \"schemaKey\": \"SexType\",\n",
+      "                \"identifier\": \"http://purl.obolibrary.org/obo/PATO_0000384\"\n",
+      "            },\n",
+      "            \"species\": {\n",
+      "                \"name\": \"Mus musculus - House mouse\",\n",
+      "                \"schemaKey\": \"SpeciesType\",\n",
+      "                \"identifier\": \"http://purl.obolibrary.org/obo/NCBITaxon_10090\"\n",
+      "            },\n",
+      "            \"genotype\": \"Emx1-Cre[tg/wt];Ai32[tg/wt]\",\n",
+      "            \"schemaKey\": \"Participant\",\n",
+      "            \"identifier\": \"San4\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"blobDateModified\": \"2022-12-02T17:21:54.708718-08:00\",\n",
+      "    \"variableMeasured\": [\n",
+      "        {\n",
+      "            \"value\": \"ElectrodeGroup\",\n",
+      "            \"schemaKey\": \"PropertyValue\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"value\": \"ElectricalSeries\",\n",
+      "            \"schemaKey\": \"PropertyValue\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"measurementTechnique\": [\n",
+      "        {\n",
+      "            \"name\": \"multi electrode extracellular electrophysiology recording technique\",\n",
+      "            \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"name\": \"surgical technique\",\n",
+      "            \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "        }\n",
+      "    ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(json.dumps(assets[0].get_raw_metadata(), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21f004d3-1b3d-496c-b71f-2f4061cc95c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/HCK21_2024_Janelia_NDRH/tutorials/simple_dandiset_search.ipynb b/HCK21_2024_Janelia_NDRH/tutorials/simple_dandiset_search.ipynb
new file mode 100644
index 000000000..d7ed7a032
--- /dev/null
+++ b/HCK21_2024_Janelia_NDRH/tutorials/simple_dandiset_search.ipynb
@@ -0,0 +1,508 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62326652-fb78-4838-9bbd-6f29e09793fc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from dandi.dandiapi import DandiAPIClient\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c949acf-7661-41ec-801b-7f7aec844ee0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "client = DandiAPIClient()\n",
+    "dandisets = list(client.get_dandisets())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a226e74-0f13-40c6-9e8d-9a3eb3fbd3ad",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Identify NWB dandisets\n",
+    "Most dandisets hold NWB-formatted data, but DANDI also hold data of other formats.\n",
+    "\n",
+    "Let's start by filtering down to only the dandisets that contain at least one NWB file.\n",
+    "\n",
+    "We can do this by querying the metadata of each dandiset, which tells us the data formats within in `raw_metadata[\"assetsSummary\"][\"dataStandard\"]`.\n",
+    "\n",
+    "If no data has been uploaded to that dandiset, the \"dataStandard\" field is not present.\n",
+    "\n",
+    "We handle this by using the `.get` method to iterate over an empty list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4663d6eb-bf20-4414-85e2-239b19bf7299",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e914609a88654d35af8aa4cd7b3d57ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/465 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are currently 277 NWB datasets on DANDI!\n"
+     ]
+    }
+   ],
+   "source": [
+    "nwb_dandisets = []\n",
+    "\n",
+    "for dandiset in tqdm(dandisets):\n",
+    "    raw_metadata = dandiset.get_raw_metadata()\n",
+    "\n",
+    "    if any(\n",
+    "        \"NWB\" in data_standard[\"name\"]\n",
+    "        for data_standard in raw_metadata[\"assetsSummary\"].get(\"dataStandard\", [])\n",
+    "    ):\n",
+    "        nwb_dandisets.append(dandiset)\n",
+    "print(f\"There are currently {len(nwb_dandisets)} NWB datasets on DANDI!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77b3784a-6428-47df-88ff-d028f32366dd",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Filtering dandisets: species\n",
+    "Let's use the `nwb_dandisets` list from the previous recipe and see which of them used mice in their study.\n",
+    "\n",
+    "You can find this information in `raw_metadata[\"assetsSummary\"][\"species\"]`.\n",
+    "\n",
+    "We'll use the same `.get` trick as above for if no data has been uploaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a665b84-bfa4-49b4-9251-3468c10484d8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c54bc235105459591c5acbff4e15f44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/277 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are currently 118 NWB datasets on DANDI that use mice!\n"
+     ]
+    }
+   ],
+   "source": [
+    "mouse_nwb_dandisets = []\n",
+    "\n",
+    "for dandiset in tqdm(nwb_dandisets):\n",
+    "    raw_metadata = dandiset.get_raw_metadata()\n",
+    "\n",
+    "    if any(\n",
+    "        \"mouse\" in species[\"name\"]\n",
+    "        for species in raw_metadata[\"assetsSummary\"].get(\"species\", [])\n",
+    "    ):\n",
+    "        mouse_nwb_dandisets.append(dandiset)\n",
+    "print(f\"There are currently {len(mouse_nwb_dandisets)} NWB datasets on DANDI that use mice!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7962c8a-f93c-4de2-aa9a-bee6516da634",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Filtering by session: species and sex\n",
+    "Let's say you have identified a dandiset of interest, \"000005\", and you want to identify all of the sessions on female mice.\n",
+    "\n",
+    "You can do this by querying asset-level metadata.\n",
+    "\n",
+    "Assets correspond to individual NWB files, and contain metadata extracted from those files.\n",
+    "\n",
+    "The metadata of each asset contains a `.wasAttributedTo` attribute, which is a list of `Participant` objects corresponding to the subjects for that session.\n",
+    "\n",
+    "We do that by first testing that attribute exists (is not `None` - some older dandisets may not have included it) and then checking the value of its `name` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e0017ebf-e2b6-4157-a37f-6c32f1b8869b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43ff3312140d46d79fa63b1bd10622ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/148 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dandiset #5 has 69 out of 148 files that use female mice!\n"
+     ]
+    }
+   ],
+   "source": [
+    "dandiset = client.get_dandiset(\"000005\")\n",
+    "female_mouse_nwb_sessions = []\n",
+    "\n",
+    "assets = list(dandiset.get_assets())\n",
+    "for asset in tqdm(assets):\n",
+    "    asset_metadata = asset.get_metadata()\n",
+    "    subjects = asset_metadata.wasAttributedTo\n",
+    "\n",
+    "    if any(\n",
+    "        subject.species and \"mouse\" in subject.species.name.lower()\n",
+    "        and subject.sex and subject.sex.name == \"Female\"\n",
+    "        for subject in subjects\n",
+    "    ):\n",
+    "        female_mouse_nwb_sessions.append(asset)\n",
+    "print(f\"Dandiset #5 has {len(female_mouse_nwb_sessions)} out of {len(assets)} files that use female mice!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d73c25b-fda3-44b3-8b2b-ae6aebe7caa5",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Going beyond\n",
+    "These examples show a few types of queries, but since the metadata structures are quite rich on both the dandiset and asset levels, they enable many complex queries beyond the examples here.\n",
+    "\n",
+    "These metadata structures are also expanding over time as DANDI becomes more strict about what counts as essential metadata.\n",
+    "\n",
+    "The `.get_raw_metadata` method of both `client.get_dandiset(...)` and `client.get_dandiset(...).get_assets()` provides a nice view into the available fields.\n",
+    "\n",
+    "Note: for any attribute, it is recommended to first check that it is not `None` before checking for its value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c14ef53c-05fe-4e97-a0ef-20b469782a88",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"id\": \"DANDI:000003/0.230629.1955\",\n",
+      "    \"doi\": \"10.48324/dandi.000003/0.230629.1955\",\n",
+      "    \"url\": \"https://dandiarchive.org/dandiset/000003/0.230629.1955\",\n",
+      "    \"name\": \"Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells\",\n",
+      "    \"about\": [\n",
+      "        {\n",
+      "            \"name\": \"hippocampus\",\n",
+      "            \"schemaKey\": \"Anatomy\",\n",
+      "            \"identifier\": \"UBERON:0002421\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"access\": [\n",
+      "        {\n",
+      "            \"status\": \"dandi:OpenAccess\",\n",
+      "            \"schemaKey\": \"AccessRequirements\",\n",
+      "            \"contactPoint\": {\n",
+      "                \"email\": \"petersen.peter@gmail.com\",\n",
+      "                \"schemaKey\": \"ContactPoint\"\n",
+      "            }\n",
+      "        }\n",
+      "    ],\n",
+      "    \"license\": [\n",
+      "        \"spdx:CC-BY-4.0\"\n",
+      "    ],\n",
+      "    \"version\": \"0.230629.1955\",\n",
+      "    \"@context\": \"https://raw.githubusercontent.com/dandi/schema/master/releases/0.6.0/context.json\",\n",
+      "    \"citation\": \"Senzai, Yuta; Fernandez-Ruiz, Antonio; Buzs\\u00e1ki, Gy\\u00f6rgy (2023) Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells (Version 0.230629.1955) [Data set]. DANDI archive. https://doi.org/10.48324/dandi.000003/0.230629.1955\",\n",
+      "    \"keywords\": [\n",
+      "        \"cell types\",\n",
+      "        \"current source density\",\n",
+      "        \"laminar recordings\",\n",
+      "        \"oscillations\",\n",
+      "        \"mossy cells\",\n",
+      "        \"granule cells\",\n",
+      "        \"optogenetics\"\n",
+      "    ],\n",
+      "    \"protocol\": [],\n",
+      "    \"schemaKey\": \"Dandiset\",\n",
+      "    \"identifier\": \"DANDI:000003\",\n",
+      "    \"repository\": \"https://dandiarchive.org\",\n",
+      "    \"contributor\": [\n",
+      "        {\n",
+      "            \"name\": \"Senzai, Yuta\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Author\",\n",
+      "                \"dcite:ContactPerson\",\n",
+      "                \"dcite:DataCollector\",\n",
+      "                \"dcite:FormalAnalysis\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Person\",\n",
+      "            \"affiliation\": [],\n",
+      "            \"includeInCitation\": true\n",
+      "        },\n",
+      "        {\n",
+      "            \"name\": \"Fernandez-Ruiz, Antonio\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Author\",\n",
+      "                \"dcite:FormalAnalysis\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Person\",\n",
+      "            \"identifier\": \"0000-0001-8481-0796\",\n",
+      "            \"affiliation\": [],\n",
+      "            \"includeInCitation\": true\n",
+      "        },\n",
+      "        {\n",
+      "            \"name\": \"Buzs\\u00e1ki, Gy\\u00f6rgy\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Author\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Person\",\n",
+      "            \"identifier\": \"0000-0002-3100-4800\",\n",
+      "            \"affiliation\": [\n",
+      "                {\n",
+      "                    \"name\": \"New York University Langone Medical Center\",\n",
+      "                    \"schemaKey\": \"Affiliation\",\n",
+      "                    \"identifier\": \"https://ror.org/005dvqh91\"\n",
+      "                }\n",
+      "            ],\n",
+      "            \"includeInCitation\": true\n",
+      "        },\n",
+      "        {\n",
+      "            \"url\": \"https://www.nih.gov/\",\n",
+      "            \"name\": \"National Institutes of Health\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Funder\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Organization\",\n",
+      "            \"identifier\": \"https://ror.org/01cwqze88\",\n",
+      "            \"awardNumber\": \"NIHMH54671, MH107396, NS090583\",\n",
+      "            \"contactPoint\": [],\n",
+      "            \"includeInCitation\": false\n",
+      "        },\n",
+      "        {\n",
+      "            \"url\": \"http://hnf.jp/\",\n",
+      "            \"name\": \"Nakajima Foundation\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Funder\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Organization\",\n",
+      "            \"identifier\": \"https://ror.org/000k40t10\",\n",
+      "            \"contactPoint\": [],\n",
+      "            \"includeInCitation\": false\n",
+      "        },\n",
+      "        {\n",
+      "            \"url\": \"http://www.nsf.gov/\",\n",
+      "            \"name\": \"National Science Foundation\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Funder\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Organization\",\n",
+      "            \"identifier\": \"https://ror.org/021nxhr62\",\n",
+      "            \"awardNumber\": \"PIRE\",\n",
+      "            \"contactPoint\": [],\n",
+      "            \"includeInCitation\": false\n",
+      "        },\n",
+      "        {\n",
+      "            \"url\": \"https://www.simonsfoundation.org\",\n",
+      "            \"name\": \"Simons Foundation\",\n",
+      "            \"roleName\": [\n",
+      "                \"dcite:Funder\"\n",
+      "            ],\n",
+      "            \"schemaKey\": \"Organization\",\n",
+      "            \"identifier\": \"https://ror.org/01cmst727\",\n",
+      "            \"contactPoint\": [],\n",
+      "            \"includeInCitation\": false\n",
+      "        }\n",
+      "    ],\n",
+      "    \"dateCreated\": \"2020-03-15T22:56:55.655000+00:00\",\n",
+      "    \"description\": \"Data from \\\"Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells\\\" Senzai, Buzsaki, Neuron 2017. Electrophysiology recordings of hippocampus during theta maze exploration.\",\n",
+      "    \"publishedBy\": {\n",
+      "        \"id\": \"urn:uuid:1d146605-5d67-4517-91b4-51cd3b9a5cf3\",\n",
+      "        \"name\": \"DANDI publish\",\n",
+      "        \"endDate\": \"2023-06-29T19:55:30.807473+00:00\",\n",
+      "        \"schemaKey\": \"PublishActivity\",\n",
+      "        \"startDate\": \"2023-06-29T19:55:30.807473+00:00\",\n",
+      "        \"wasAssociatedWith\": [\n",
+      "            {\n",
+      "                \"id\": \"urn:uuid:0fca6ff4-84ca-42fe-bbed-85d3fb4291f0\",\n",
+      "                \"name\": \"DANDI API\",\n",
+      "                \"version\": \"0.1.0\",\n",
+      "                \"schemaKey\": \"Software\",\n",
+      "                \"identifier\": \"RRID:SCR_017571\"\n",
+      "            }\n",
+      "        ]\n",
+      "    },\n",
+      "    \"studyTarget\": [],\n",
+      "    \"assetsSummary\": {\n",
+      "        \"species\": [\n",
+      "            {\n",
+      "                \"name\": \"House mouse\",\n",
+      "                \"schemaKey\": \"SpeciesType\",\n",
+      "                \"identifier\": \"http://purl.obolibrary.org/obo/NCBITaxon_10090\"\n",
+      "            }\n",
+      "        ],\n",
+      "        \"approach\": [\n",
+      "            {\n",
+      "                \"name\": \"electrophysiological approach\",\n",
+      "                \"schemaKey\": \"ApproachType\"\n",
+      "            },\n",
+      "            {\n",
+      "                \"name\": \"behavioral approach\",\n",
+      "                \"schemaKey\": \"ApproachType\"\n",
+      "            }\n",
+      "        ],\n",
+      "        \"schemaKey\": \"AssetsSummary\",\n",
+      "        \"dataStandard\": [\n",
+      "            {\n",
+      "                \"name\": \"Neurodata Without Borders (NWB)\",\n",
+      "                \"schemaKey\": \"StandardsType\",\n",
+      "                \"identifier\": \"RRID:SCR_015242\"\n",
+      "            }\n",
+      "        ],\n",
+      "        \"numberOfBytes\": 2559248010229,\n",
+      "        \"numberOfFiles\": 101,\n",
+      "        \"numberOfSubjects\": 16,\n",
+      "        \"variableMeasured\": [\n",
+      "            \"DecompositionSeries\",\n",
+      "            \"LFP\",\n",
+      "            \"Units\",\n",
+      "            \"Position\",\n",
+      "            \"ElectricalSeries\"\n",
+      "        ],\n",
+      "        \"measurementTechnique\": [\n",
+      "            {\n",
+      "                \"name\": \"signal filtering technique\",\n",
+      "                \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "            },\n",
+      "            {\n",
+      "                \"name\": \"fourier analysis technique\",\n",
+      "                \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "            },\n",
+      "            {\n",
+      "                \"name\": \"spike sorting technique\",\n",
+      "                \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "            },\n",
+      "            {\n",
+      "                \"name\": \"behavioral technique\",\n",
+      "                \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "            },\n",
+      "            {\n",
+      "                \"name\": \"multi electrode extracellular electrophysiology recording technique\",\n",
+      "                \"schemaKey\": \"MeasurementTechniqueType\"\n",
+      "            }\n",
+      "        ]\n",
+      "    },\n",
+      "    \"datePublished\": \"2023-06-29T19:55:30.807473+00:00\",\n",
+      "    \"schemaVersion\": \"0.6.0\",\n",
+      "    \"ethicsApproval\": [],\n",
+      "    \"wasGeneratedBy\": [],\n",
+      "    \"relatedResource\": [\n",
+      "        {\n",
+      "            \"url\": \"https://doi.org/10.1016/j.neuron.2016.12.011\",\n",
+      "            \"name\": \"Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells\",\n",
+      "            \"relation\": \"dcite:IsDescribedBy\",\n",
+      "            \"schemaKey\": \"Resource\",\n",
+      "            \"identifier\": \"doi:10.1016/j.neuron.2016.12.011\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"manifestLocation\": [\n",
+      "        \"https://dandiarchive.s3.amazonaws.com/dandisets/000003/0.230629.1955/assets.yaml\"\n",
+      "    ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(json.dumps(dandisets[0].get_raw_metadata(), indent=4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c65543d5-2950-4537-9594-d9141a217b8d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}