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   "source": [
    "# metadata4Ing\n",
    "\n",
    "[Metadata4Ing](https://nfdi4ing.pages.rwth-aachen.de/metadata4ing/metadata4ing/) is an ontology developed within the NFDI Consortium [NFDI4Ing](https://nfdi4ing.de/) \"with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines.\"\n",
    "\n",
    "The *h5RDMtoolbox* allows translating JSON-LD data into the HDF5 structure and wise versa (see [here](../userguide/wrapper/FAIRAttributes.ipynb)). This way, engineering process metadata can be added easily to the HDF5 file. \n",
    "\n",
    "Consider the following example file taken from the *m4i* ontology [\"first steps guide\"](https://git.rwth-aachen.de/nfdi4ing/metadata4ing/metadata4ing/-/blob/1.2.1/training/first-steps-guide.md), which describes an experimental investigation. You can find the full content of the JSON-LD file [here](min_m4i_ex.jsonld), the following shows an excerpt:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "86c0f874-af71-4aeb-bb15-02aac1a598e8",
   "metadata": {},
   "source": [
    "{\n",
    "    \"@context\": \n",
    "        {\n",
    "            \"@import\": \"https://w3id.org/nfdi4ing/metadata4ing/m4i_context.jsonld\",\n",
    "            \"local\": \"https://local-domain.org/\"\n",
    "        },\n",
    "    \"@graph\": [\n",
    "        {\n",
    "            \"@id\": \"local:preparation_0001\",\n",
    "            \"@type\": \"processing step\",\n",
    "            \"label\": \"Sample preparation and parameter definition\",\n",
    "            \"has participant\": \"local:alex\",\n",
    "            \"start time\": \"2022-09-22T10:31:22\"\n",
    "        },\n",
    "...\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11673a2-beae-4f8b-b038-674f92a75f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5rdmtoolbox as h5tbx\n",
    "_ = h5tbx.set_config(auto_create_h5tbx_version=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6310b7bc-085c-4068-9650-824ebe3e097a",
   "metadata": {},
   "source": [
    "With a single call (`h5tbx.jsonld.to_hdf`) it is possible to read in the JSON-LD data and parse it into the HDF5 structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04c5502d-480f-45cd-804f-6afa50aedafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5tbx.File() as h5:\n",
    "    h5.create_group('metadata')\n",
    "    h5tbx.jsonld.to_hdf(h5.metadata, source='min_m4i_ex.jsonld')\n",
    "    h5.dump(collapsed=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0756149-dc6f-468d-808f-5c8ad88ecae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(h5tbx.dump_jsonld(h5.hdf_filename,\n",
    "                        structural=False,\n",
    "                        resolve_keys=True,\n",
    "                        indent=2,\n",
    "                        context={\"@import\": \"https://w3id.org/nfdi4ing/metadata4ing/m4i_context.jsonld\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c16084-0071-4ca8-a9b2-373247df0860",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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