diff --git a/demo.ipynb b/demo.ipynb
new file mode 100644
index 0000000..38b6537
--- /dev/null
+++ b/demo.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8dbbe089-484a-4539-acac-b4e324494f7c",
+   "metadata": {},
+   "source": [
+    "# Demo run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "844ab0fd-5e73-4629-b2fb-40ce50b755fe",
+   "metadata": {},
+   "source": [
+    "This notebook shows an example way of using FRIdata toolbox."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "697ce575-1fb5-4f58-b881-bfd91220618c",
+   "metadata": {},
+   "source": [
+    "## Prerequisities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91f9b031-94bf-4d42-83ce-d433c9677258",
+   "metadata": {},
+   "source": [
+    "- conda environment is set (accordingly to README.md file)\n",
+    "- FRIdata CLI tool is created\n",
+    "- File with IDs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4328af25-ac41-4061-be92-f4b195902bdc",
+   "metadata": {},
+   "source": [
+    "## 1. Create a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9db9ff88-a90e-4e9a-a2c2-0ad243ad96f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "export PATH_TO_DATA=\"<path to data>\"\n",
+    "export IDS_FILE_PATH=\"<path to idx files>\"\n",
+    "export DASHBOARD_NAME=\"testset_inference_over\"\n",
+    "export AFDB_PATH=\"$PATH_TO_DATA/afdb/structures\"\n",
+    "\n",
+    "eval \"$(conda shell.bash hook)\"\n",
+    "conda activate tbe\n",
+    "\n",
+    "fridata generate_data \\\n",
+    " -t dataset \\\n",
+    " -d AFDB \\\n",
+    " -c subset \\\n",
+    " --overwrite \\\n",
+    " --version ${DASHBOARD_NAME}  \\\n",
+    " -i ${IDS_FILE_PATH} \\\n",
+    " --input-path ${AFDB_PATH} \\\n",
+    " -e \"esm2_t33_650M_UR50D\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f867009b-d9d5-4038-8669-6bc9bf1c40b5",
+   "metadata": {},
+   "source": [
+    "## 2. Create dashboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4203f2e3-3f3d-4a80-8cb3-4ed3b6b9c9ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "# Path to dataset.json generated from 1. task\n",
+    "export PATH_TO_DATA=\"<path to data>\"\n",
+    "export PATH_TO_DATASET_JSON=\"$PATH_TO_DATA/datasets/AFDB-subset--testset_inference_over/dataset.json\"\n",
+    "\n",
+    "eval \"$(conda shell.bash hook)\"\n",
+    "conda activate tbe\n",
+    "\n",
+    "fridata create_dashboard \\\n",
+    " --dataset ${PATH_TO_DATASET_JSON}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b91b83c-3ff5-41cd-8c4d-2971baadd2f0",
+   "metadata": {},
+   "source": [
+    "Generated dashboard is saved to \"./reports/AFDB-subset--testset_inference_over.html\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "50a15ebb-9f64-4846-9587-0b6984727891",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"600\"\n",
+       "            src=\"reports/AFDB-subset--testset_inference_over.html\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x1090e6420>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "\n",
+    "IFrame(src='reports/AFDB-subset--testset_inference_over.html', width=700, height=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76ae7b67-5565-41c2-97c3-91f9a63ba542",
+   "metadata": {},
+   "source": [
+    "## 3. Loading dataset for more details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea69cf70-2c29-47d4-a6d8-5153c7a9b1b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "export PATH_TO_DATA=\"<path to data>\"\n",
+    "\n",
+    "eval \"$(conda shell.bash hook)\"\n",
+    "conda activate tbe\n",
+    "\n",
+    "fridata load --file-path \"$PATH_TO_DATA/datasets/AFDB-subset--testset_inference_over\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15688d7f-e57f-4305-b8ee-5b9db79fefe3",
+   "metadata": {},
+   "source": [
+    "Results:\n",
+    "\n",
+    "```\n",
+    "db_type=<DatabaseType.AFDB: 'AFDB'> collection_type=<CollectionType.subset: 'subset'>\n",
+    "proteome=''\n",
+    "version='testset_inference_over'\n",
+    "ids_file=PosixPath('<Path>') \n",
+    "seqres_file=None \n",
+    "archive_path=None \n",
+    "overwrite=True \n",
+    "batch_size=1000 \n",
+    "binary_data_download=False \n",
+    "is_hpc_cluster=False \n",
+    "input_path=PosixPath('<Path to dataset>') \n",
+    "embedder_type=<EmbedderType.ESM2_T33_650M: 'esm2_t33_650M_UR50D'> \n",
+    "embedding_size=1280 \n",
+    "created_at='1778013094005832' \n",
+    "config=Config(debug_mode='warning', data_path='<Path to data>', disto_type='CA', disto_thr='inf', separator='-', batch_size=1000)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5fe4827-1f06-4a27-874b-b26b158ad784",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "export PATH_TO_DATA=\"<path to data>\"\n",
+    "export PATH_TO_STRUCTURES=\"$PATH_TO_DATA/structures/AFDB/subset_/testset_inference_over/0/pdbs.h5\"\n",
+    "export PATH_TO_EMBEDDINGS=\"$PATH_TO_DATA/embeddings/AFDB-subset--_test_dask/batch_0.h5\"\n",
+    "export PATH_TO_DISTOGRAMS=\"$PATH_TO_DATA/distograms/AFDB-subset--_test_dask/batch_0.h5\"\n",
+    "export PATH_TO_COORDINATES=\"$PATH_TO_DATA/coordinates/AFDB-subset--_test_dask/batch_0_ca.h5\"\n",
+    "\n",
+    "eval \"$(conda shell.bash hook)\"\n",
+    "conda activate tbe\n",
+    "\n",
+    "fridata inspect_h5 --help"
+   ]
+  }
+ ],
+ "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": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/reports/AFDB-subset--testset_inference_over.html b/reports/AFDB-subset--testset_inference_over.html
new file mode 100644
index 0000000..abe5670
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+++ b/reports/AFDB-subset--testset_inference_over.html
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+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>AFDB-subset--testset_inference_over</title>
+  <style>
+    body { font-family: system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, sans-serif; margin: 24px; }
+    .badge { display: inline-block; padding: 2px 8px; border-radius: 10px; font-size: 12px; margin-right: 6px; }
+    .ok { background: #e8f5e9; color: #1b5e20; }
+    .no { background: #ffebee; color: #b71c1c; }
+    table { border-collapse: collapse; width: 100%; margin: 12px 0; }
+    th, td { border: 1px solid #ddd; padding: 6px 8px; text-align: left; }
+    th { background: #fafafa; }
+    tfoot td { border-top: 2px solid #bbb; }
+    .panel { border: 1px solid #eee; padding: 12px; border-radius: 6px; margin: 12px 0; }
+    details > summary { cursor: pointer; }
+    .muted { color: #666; }
+    .pct { float: right; color: #999; font-size: 0.9em; }
+    .controls { margin: 8px 0; }
+  </style>
+  <script type="application/json" id="summary-data">{"dataset": {"identity": {"db": "AFDB", "collection": "subset", "slug": "testset_inference_over", "folder_name": "AFDB-subset--testset_inference_over"}, "path": "/datasets/AFDB-subset--testset_inference_over"}, "index_paths": {"dataset": {"forward": "/datasets/AFDB-subset--testset_inference_over/dataset.idx", "reversed": "/datasets/AFDB-subset--testset_inference_over/dataset_reversed.idx"}, "sequences": {"forward": null, "reversed": null}, "coordinates": {"forward": null, "reversed": null}, "embeddings": {"forward": null, "reversed": null}, "distograms": {"forward": null, "reversed": null}}, "per_index": {"dataset": {"index_type": "dataset", "forward_present": true, "reversed_present": true, "num_proteins_forward": 81, "num_files_referenced": 1, "num_edges_reversed": 81, "by_dataset": {"testset_inference_over": {"files_referenced": 1, "proteins_referencing": 81, "is_self": true, "files_per_batch": {"1": 1}}}}, "sequences": {"index_type": "sequences", "forward_present": false, "reversed_present": false, "num_proteins_forward": 0, "num_files_referenced": 0, "num_edges_reversed": 0, "by_dataset": {}}, "coordinates": {"index_type": "coordinates", "forward_present": false, "reversed_present": false, "num_proteins_forward": 0, "num_files_referenced": 0, "num_edges_reversed": 0, "by_dataset": {}}, "embeddings": {"index_type": "embeddings", "forward_present": false, "reversed_present": false, "num_proteins_forward": 0, "num_files_referenced": 0, "num_edges_reversed": 0, "by_dataset": {}}, "distograms": {"index_type": "distograms", "forward_present": false, "reversed_present": false, "num_proteins_forward": 0, "num_files_referenced": 0, "num_edges_reversed": 0, "by_dataset": {}}}, "global": {"aggregate": {"testset_inference_over": {"files_referenced": 1, "proteins_referencing": 81}}, "top": [{"slug": "testset_inference_over", "files_referenced": 1, "proteins_referencing": 81}]}}</script>
+  <script>
+function $(id) { return document.getElementById(id); }
+
+function render() {
+  const payload = JSON.parse($("summary-data").textContent);
+  const root = $("root");
+  root.innerHTML = '';
+  const header = document.createElement('div');
+  header.innerHTML = `<h2>${payload.dataset.identity.folder_name}</h2><div class="muted">${payload.dataset.path}</div>`;
+  root.appendChild(header);
+
+  // Per-index panels
+  for (const [t, stats] of Object.entries(payload.per_index)) {
+    const panel = document.createElement('div');
+    panel.className = 'panel';
+    panel.innerHTML = `<h3>${t}</h3>
+      <div class="muted">proteins: ${stats.num_proteins_forward}, files referenced: ${stats.num_files_referenced}`;
+
+    const ds = stats.by_dataset || {};
+    const keys = Object.keys(ds);
+    if (keys.length) {
+      const table = document.createElement('table');
+      table.innerHTML = '<thead><tr><th>dataset slug</th><th>files referenced</th><th>proteins referencing</th></tr></thead>';
+      const sorted = keys.sort();
+      let sumFiles = 0, sumProteins = 0;
+      for (const slug of sorted) {
+        sumFiles += ds[slug].files_referenced || 0;
+        sumProteins += ds[slug].proteins_referencing || 0;
+      }
+      const tbody = document.createElement('tbody');
+      for (const slug of sorted) {
+        const f = ds[slug].files_referenced || 0;
+        const p = ds[slug].proteins_referencing || 0;
+        const fPct = sumFiles ? (f / sumFiles * 100).toFixed(1) : '0.0';
+        const pPct = sumProteins ? (p / sumProteins * 100).toFixed(1) : '0.0';
+        const row = document.createElement('tr');
+        row.innerHTML = `<td>${slug}</td><td>${f} <span class="pct">${fPct}%</span></td><td>${p} <span class="pct">${pPct}%</span></td>`;
+        tbody.appendChild(row);
+      }
+      table.appendChild(tbody);
+      const tfoot = document.createElement('tfoot');
+      tfoot.innerHTML = `<tr><td><strong>Total</strong></td><td><strong>${sumFiles}</strong></td><td><strong>${sumProteins}</strong></td></tr>`;
+      table.appendChild(tfoot);
+      panel.appendChild(table);
+
+      const details = document.createElement('details');
+      details.innerHTML = '<summary>Show batches per dataset</summary>';
+      for (const slug of keys.sort()) {
+        const sub = document.createElement('div');
+        const batches = ds[slug].files_per_batch || {};
+        const subt = document.createElement('table');
+        subt.innerHTML = '<thead><tr><th>dataset slug</th><th>batch id</th><th>files</th></tr></thead>';
+        const sb = document.createElement('tbody');
+        for (const [b, n] of Object.entries(batches)) {
+          const r = document.createElement('tr');
+          r.innerHTML = `<td>${slug}</td><td>${b}</td><td>${n}</td>`;
+          sb.appendChild(r);
+        }
+        subt.appendChild(sb);
+        sub.appendChild(subt);
+        details.appendChild(sub);
+      }
+      panel.appendChild(details);
+    }
+    root.appendChild(panel);
+  }
+
+  // Global rollup
+  const roll = document.createElement('div');
+  roll.className = 'panel';
+  roll.innerHTML = '<h3>Global rollup</h3>';
+  const table = document.createElement('table');
+  table.innerHTML = '<thead><tr><th>dataset slug</th><th>files referenced</th><th>proteins referencing</th></tr></thead>';
+  const tb = document.createElement('tbody');
+  let gSumFiles = 0, gSumProteins = 0;
+  for (const row of payload.global.top) {
+    gSumFiles += row.files_referenced;
+    gSumProteins += row.proteins_referencing;
+  }
+  for (const row of payload.global.top) {
+    const fPct = gSumFiles ? (row.files_referenced / gSumFiles * 100).toFixed(1) : '0.0';
+    const pPct = gSumProteins ? (row.proteins_referencing / gSumProteins * 100).toFixed(1) : '0.0';
+    const tr = document.createElement('tr');
+    tr.innerHTML = `<td>${row.slug}</td><td>${row.files_referenced} <span class="pct">${fPct}%</span></td><td>${row.proteins_referencing} <span class="pct">${pPct}%</span></td>`;
+    tb.appendChild(tr);
+  }
+  table.appendChild(tb);
+  const gFoot = document.createElement('tfoot');
+  gFoot.innerHTML = `<tr><td><strong>Total</strong></td><td><strong>${gSumFiles}</strong></td><td><strong>${gSumProteins}</strong></td></tr>`;
+  table.appendChild(gFoot);
+  roll.appendChild(table);
+  root.appendChild(roll);
+}
+
+window.addEventListener('DOMContentLoaded', render);
+
+  </script>
+</head>
+<body>
+  <div id="root"></div>
+</body>
+</html>