From 8d90eeeb6e0195af9facbee506937be638e5024f Mon Sep 17 00:00:00 2001
From: Vecko <36369090+VeckoTheGecko@users.noreply.github.com>
Date: Wed, 17 Jun 2026 15:26:12 +0200
Subject: [PATCH 1/6] Add data cataloging tutorial

---
 intermediate/cataloging.ipynb | 143 ++++++++++++++++++++++++++++++++++
 1 file changed, 143 insertions(+)
 create mode 100644 intermediate/cataloging.ipynb

diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
new file mode 100644
index 00000000..047b9f09
--- /dev/null
+++ b/intermediate/cataloging.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "source": [
+    "# Data cataloguing for Xarray\n",
+    "\n",
+    "**Goals:** At the end of this tutorial, you'll have an overview of what data cataloging is, why it is done, what tools are available. You'll also know how to open and browse some data catalogues.\n",
+    "\n",
+    "> A data catalog is a detailed inventory of data assets within an organization. It helps users easily discover, understand, manage, curate and access data. -[IBM](https://www.ibm.com/think/topics/data-catalog)\n",
+    "\n",
+    "Within an organisation, you may work with a lot of different datasets. As someone managing this data, you might want the following things:\n",
+    "\n",
+    "- **An organized, easily browsable collection**: By easing discovery and loading of your datasets you can reduce the friction for those analysing the data. Users can eaily get an overview of available datasets, discover datasets of interest, and load them for analysis.\n",
+    "- **Access control and logging**: You can limit who is able to access these datasets, and track when datasets are being accessed - keeping the data private, and providing metrics that can inform data management (e.g., by removing little-used datasets).\n",
+    "- **Combine individual data assets**: Combine individual data assets that are similar into a larger object (e.g., combining individual time snapshots into a larger data cube)\n",
+    "\n",
+    "There are a bunch of solutions available in Xarray that meet some of these needs - we'll broadly call these \"data catalogs\". These solutions differ in functionality, and the correct data cataloguing solution depends on your needs as an institute.\n",
+    "\n",
+    "If you feel there are data cataloguing tools missing from this page, please submit a PR.\n",
+    "\n",
+    "## intake v2\n",
+    "\n",
+    "Intake is a lightweight Python package which allows one to specify data catalogues (or a hierarchy of catalogues) via YAML files. These YAML files describe:\n",
+    "\n",
+    "- how to load the dataset. This is done via \"readers\", which support different file formats, and support lazily defining additional transformations to the dataset using Dask\n",
+    "- additional metadata for the dataset\n",
+    "\n",
+    "Uploading the catalogue file to a Git forge like GitHub allows for versioning of this file.\n",
+    "\n",
+    "This catalogue can be browsed in Python, or via third-party tools which parse the datalog, allowing the user to select and open a dataset. This tool has been adopted in the geosciences community for working with Xarray datasets, but is also flexible to work with other dataset types.\n",
+    "\n",
+    "There is no separate authentication layer in Intake, hence access control and logging depends entirely on the way the data is accessed. If the dataset is loaded from a file location on a mounted drive, then you need to have access to that mounted drive. If the dataset is located in an S3 bucket, you need to have the appropriate access credentials for the resource.\n",
+    "\n",
+    "Intake was originally developed within Anaconda by the team behind Dask.\n",
+    "\n",
+    "### Example using intake v2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526792bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6767fe7",
+   "metadata": {},
+   "source": [
+    "## STAC\n",
+    "\n",
+    "> SpatioTemporal Asset Catalogs\n",
+    "> The STAC specification is a common language to describe geospatial information, so it can more easily be worked with, indexed, and discovered.\n",
+    "> -stacspec.org\n",
+    "\n",
+    "Let's set the scene a bit: During sattelite data processing, many image files (predominantly TIFF) get generated. Each of these files can have different spatial and temporal extents, and over time these individual files can number in the millions, containing terrabytes of data in total. When doing analyses on these files, the first thing one needs to know is whether the file is even relevant to the analysis at hand (i.e., at least \"what is the spatial and temporal extent of the file?\"), however downloading the file just to check this metadata within is very wasteful. This is the context from which the STAC project was born.\n",
+    "\n",
+    "The goal of the STAC project is to make geospatial data more accesible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n",
+    "\n",
+    "- The combining of assets into larger collections representing a larger dataset (e.g., a dataset with multiple time slices)\n",
+    "- For more efficient workflows, as the whole asset doesn't need to be downloaded to check the metadata.\n",
+    "\n",
+    "\n",
+    "Within the STAC project is:\n",
+    "\n",
+    "- **The STAC Specification**: A set of specifications describing (from most atomic to least) STAC Items, STAC Catalogs, and STAC collections. There is also a spec for the STAC API.\n",
+    "- **Tooling for working with STAC**: More on this just now\n",
+    "- **The community**\n",
+    "\n",
+    "There are various tools in the STAC ecosystem. [`odc-stac`](https://github.com/opendatacube/odc-stac), [`stacstac`](https://github.com/gjoseph92/stackstac) and [`xpystac`](https://github.com/stac-utils/xpystac) are all proojects that llow the opening of STAC items as Xarray datasets.\n",
+    "\n",
+    "### More resources\n",
+    "\n",
+    "- https://stacspec.org/en\n",
+    "- https://stacindex.org/\n",
+    "\n",
+    "### Example using odc-stac\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6a76a8e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Commercial offering - Arraylake\n",
+    "\n",
+    "There are also commercial offerings available that have data cataloguing functionality.\n",
+    "\n",
+    "Arraylake is Earthmover's managed cloud platform built on top of the open-source Icechunk data format\n",
+    "(where Icechunk is a project offering versioning of Zarr-like datasets).\n",
+    "\n",
+    "See [here](https://icechunk.io/en/latest/arraylake/) for a full comparison between Icechunk and Arraylake.\n",
+    "\n",
+    "A notable feature is that Arraylake integrates with [Earthmover's data marketplace](https://www.earthmover.io/marketplace/),\n",
+    "powering data discovery and allowing for the dissemination of datasets (or subsets of datasets) to other interested users.\n",
+    "\n",
+    "\n",
+    "\n",
+    "## More resources\n",
+    "\n",
+    "https://guide.cloudnativegeo.org/cookbooks/zarr-stac-report/data-consumers/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06af9324",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ada11feb4d2984977158008080c38ae913d3bb5b Mon Sep 17 00:00:00 2001
From: Vecko <36369090+VeckoTheGecko@users.noreply.github.com>
Date: Fri, 3 Jul 2026 15:23:51 +0200
Subject: [PATCH 2/6] Add examples

---
 intermediate/cataloging.ipynb |   93 ++-
 pixi.lock                     | 1472 +++++++++++++++++----------------
 pyproject.toml                |    2 +
 3 files changed, 834 insertions(+), 733 deletions(-)

diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
index 047b9f09..6d24b882 100644
--- a/intermediate/cataloging.ipynb
+++ b/intermediate/cataloging.ipynb
@@ -40,20 +40,86 @@
     "\n",
     "Intake was originally developed within Anaconda by the team behind Dask.\n",
     "\n",
-    "### Example using intake v2\n"
+    "### Resources\n",
+    "\n",
+    "Project Pythia has a [cookbook on Intake](https://projectpythia.org/intake-cookbook/notebooks/creating-catalogs/).\n",
+    "\n",
+    "### Example code\n",
+    "\n",
+    "Here we work based off [an Intake tutorial on easy.gems!](https://easy.gems.dkrz.de/Processing/intake.html) from the DKRZ (German Climate Supercomputing Center). Note that the tutorial is based on v1 of Intake (which also uses `intake-xarray`).\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "526792bd",
+   "id": "1",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Browse an existing catalogue\n",
+    "\n",
+    "import intake\n",
+    "\n",
+    "cat = intake.open_catalog(\"https://data.nextgems-h2020.eu/online.yaml\")\n",
+    "# TIP: Its just a YAML file - open it in your browser and you'll see!\n",
+    "\n",
+    "# view the available subcatalogues or datasets\n",
+    "list(cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Access catalogue elements using `[\"attr\"]` or `.attr` notation\n",
+    "# NOTE: The `.attr` notation only works if the entry is a valid Python attribute name - which the entry might not be\n",
+    "\n",
+    "# Let's see what the `tutorial` item is...\n",
+    "cat[\"tutorial\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it's a subcatalogue!\n",
+    "\n",
+    "print(list(cat[\"tutorial\"]))\n",
+    "\n",
+    "# Let's look at an entry within...\n",
+    "cat['tutorial']['ICON.native.2d_PT6H_inst']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's open it as an xarray dataset\n",
+    "\n",
+    "ds = cat['tutorial']['ICON.native.2d_PT6H_inst'].to_dask()\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "That's it for the example! Feel free to poke around further to see what other datasets are available in this catalogue, or to see which other catalogues are available online."
+   ]
   },
   {
    "cell_type": "markdown",
-   "id": "d6767fe7",
+   "id": "6",
    "metadata": {},
    "source": [
     "## STAC\n",
@@ -62,9 +128,9 @@
     "> The STAC specification is a common language to describe geospatial information, so it can more easily be worked with, indexed, and discovered.\n",
     "> -stacspec.org\n",
     "\n",
-    "Let's set the scene a bit: During sattelite data processing, many image files (predominantly TIFF) get generated. Each of these files can have different spatial and temporal extents, and over time these individual files can number in the millions, containing terrabytes of data in total. When doing analyses on these files, the first thing one needs to know is whether the file is even relevant to the analysis at hand (i.e., at least \"what is the spatial and temporal extent of the file?\"), however downloading the file just to check this metadata within is very wasteful. This is the context from which the STAC project was born.\n",
+    "Let's set the scene a bit: During satellite data processing, many image files (predominantly TIFF) get generated. Each of these files can have different spatial and temporal extents, and over time these individual files can number in the millions, containing terrabytes of data in total. When doing analyses on these files, the first thing one needs to know is whether the file is even relevant to the analysis at hand (i.e., at least \"what is the spatial and temporal extent of the file?\"), however downloading the file just to check this metadata within is very wasteful. This is the context from which the STAC project was born.\n",
     "\n",
-    "The goal of the STAC project is to make geospatial data more accesible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n",
+    "The goal of the STAC project is to make geospatial data more accessible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n",
     "\n",
     "- The combining of assets into larger collections representing a larger dataset (e.g., a dataset with multiple time slices)\n",
     "- For more efficient workflows, as the whole asset doesn't need to be downloaded to check the metadata.\n",
@@ -83,20 +149,13 @@
     "- https://stacspec.org/en\n",
     "- https://stacindex.org/\n",
     "\n",
-    "### Example using odc-stac\n"
+    "### Example using odc-stac\n",
+    "\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6a76a8e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
-   "id": "2",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Commercial offering - Arraylake\n",
@@ -120,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06af9324",
+   "id": "8",
    "metadata": {},
    "source": []
   }
diff --git a/pixi.lock b/pixi.lock
index e1489a74..ce4b217d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,20 +1,8 @@
 version: 7
 platforms:
 - name: linux-64
-  virtual-packages:
-  - __unix=0=0
-  - __linux=4.18
-  - __glibc=2.28
-  - __archspec=0=x86_64
 - name: osx-arm64
-  virtual-packages:
-  - __unix=0=0
-  - __osx=13.0
-  - __archspec=0=m1
 - name: win-64
-  virtual-packages:
-  - __win=10.0
-  - __archspec=0=x86_64
 environments:
   default:
     channels:
@@ -26,19 +14,19 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.833-hf4c7647_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.3-hdc83ae9_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.14-h6e18c7b_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.14.1-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-haaf8e00_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.7.1-h31346e9_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.11.0-h434eaf2_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.26.3-ha0b0e19_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.16.0-h4976820_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.12.6-hd767fb6_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.7-haaf8e00_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.10-haaf8e00_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.40.1-h085388a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.833-h799c9a6_8.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.3-h206d751_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.13.3-h71f81a8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.18.0-h74b55db_1.conda
@@ -81,7 +69,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.16.0-nompi_py314hddf7a69_102.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-14.2.1-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-2.1.0-nompi_h7d5651c_108.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-2.1.0-nompi_hc8ae080_109.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hicolor-icon-theme-0.17-ha770c72_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/json-c-0.18-h6688a6e_0.conda
@@ -138,13 +126,13 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.4.0-h10be129_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.2.0-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.2-h174a0a3_1.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.10.0-nompi_he3e3c8e_205.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.10.1-nompi_he3e3c8e_200.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda
@@ -154,7 +142,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.33.5-h538a264_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpsl-0.22.0-hd9031aa_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpsl-0.22.0-h49b2146_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libraqm-0.10.5-h6406941_1.conda
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-  - aws-c-common >=0.14.0,<0.14.1.0a0
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   license_family: APACHE
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-  - aws-c-cal >=0.9.14,<0.9.15.0a0
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-  - aws-c-http >=0.11.0,<0.11.1.0a0
-  - aws-c-mqtt >=0.16.0,<0.16.1.0a0
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.3-h206d751_0.conda
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@@ -2026,6 +2023,7 @@ packages:
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   - python_abi 3.14.* *_cp314
   license: MIT
+  license_family: MIT
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   size: 306297
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   timestamp: 1695661547874
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-  md5: 3c126667b98ad8ccf390a91b9097f574
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+  md5: 85e9d531a6f215a5002fd2174e2dcb35
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   - aws-c-s3 >=0.12.6,<0.12.7.0a0
-  - aws-c-sdkutils >=0.2.5,<0.2.6.0a0
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+  - libcurl >=8.21.0,<9.0a0
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+    - libjpeg-turbo >=3.2.0,<4.0a0
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.7.4-nompi_py311h498b1eb_108.conda
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@@ -9663,6 +9710,7 @@ packages:
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   - python_abi 3.14.* *_cp314
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+  license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/netcdf4-1.7.4-nompi_py311h8d5b1ca_108.conda
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-  - _python_abi3_support 1.*
-  - cpython >=3.11
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   license_family: BSD
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   - aws-c-io >=0.26.3,<0.26.4.0a0
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 - conda: https://conda.anaconda.org/conda-forge/win-64/azure-core-cpp-1.16.3-h49e36cd_0.conda
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+  license_family: MIT
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   license_family: BSD
   run_exports: {}
-  size: 1101679
-  timestamp: 1775582027560
+  size: 1097161
+  timestamp: 1775581481450
 - conda: https://conda.anaconda.org/conda-forge/win-64/harfbuzz-14.2.1-h57928b3_1.conda
   sha256: c8bf564f2c415b82f056eff98b8967fb75c86821398998f20289397b5acf1ef7
   md5: e706de885f817f9832c56f1c9ad7ce71
@@ -13318,20 +13365,14 @@ packages:
     - hdf4 >=4.2.15,<4.2.16.0a0
   size: 779637
   timestamp: 1695662145568
-- conda: https://conda.anaconda.org/conda-forge/win-64/hdf5-2.1.0-nompi_ha9ae7cf_108.conda
-  sha256: 3c13bd2e6c2b3946cace7b3b09518002fc61109629ec46040c7df14392e35847
-  md5: a5f85ab8aa403db6a0a78075b6bc4ff5
+- conda: https://conda.anaconda.org/conda-forge/win-64/hdf5-1.14.6-nompi_hae35d4c_110.conda
+  sha256: 54f5211e698a468fd74def162e277d834e956c5b24a9ba4f9cf6298209328475
+  md5: 4c94504a694781771108b07bf4a9370f
   depends:
-  - aws-c-auth >=0.10.3,<0.10.4.0a0
-  - aws-c-common >=0.14.0,<0.14.1.0a0
-  - aws-c-http >=0.11.0,<0.11.1.0a0
-  - aws-c-io >=0.26.3,<0.26.4.0a0
-  - aws-c-s3 >=0.12.6,<0.12.7.0a0
-  - aws-c-sdkutils >=0.2.5,<0.2.6.0a0
   - libaec >=1.1.5,<2.0a0
   - libcurl >=8.20.0,<9.0a0
   - libzlib >=1.3.2,<2.0a0
-  - openssl >=3.5.7,<4.0a0
+  - openssl >=3.5.6,<4.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
@@ -13339,9 +13380,9 @@ packages:
   license_family: BSD
   run_exports:
     weak:
-    - hdf5 >=2.1.0,<3.0a0
-  size: 2557385
-  timestamp: 1781836888760
+    - hdf5 >=1.14.6,<1.14.7.0a0
+  size: 2354049
+  timestamp: 1780581446500
 - conda: https://conda.anaconda.org/conda-forge/win-64/icu-78.3-h637d24d_0.conda
   sha256: 1bda728d70a619731b278c859eda364146cb5b4b8c739a64da8128353d81d1c4
   md5: 0097b24800cb696915c3dbd1f5335d3f
@@ -14073,9 +14114,9 @@ packages:
     - libintl >=0.22.5,<1.0a0
   size: 40746
   timestamp: 1723629745649
-- conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.4.1-hfd05255_0.conda
-  sha256: 698d57b5b90120270eaa401298319fcb25ea186ae95b340c2f4813ed9171083d
-  md5: 25a127bad5470852b30b239f030ec95b
+- conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.2.0-hfd05255_0.conda
+  sha256: 3d6635efa9497b9c5ba7957df8067c0a980d5cb10a6ea136931809eb410f8a99
+  md5: cf219146d5bf2fee5907409ff9f5ac89
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
@@ -14085,9 +14126,9 @@ packages:
   license: IJG AND BSD-3-Clause AND Zlib
   run_exports:
     weak:
-    - libjpeg-turbo >=3.1.4.1,<4.0a0
-  size: 842806
-  timestamp: 1775962811457
+    - libjpeg-turbo >=3.2.0,<4.0a0
+  size: 991057
+  timestamp: 1783731990693
 - conda: https://conda.anaconda.org/conda-forge/win-64/libjxl-0.11.2-h932607e_1.conda
   sha256: 4715e22c602526c85da09f73865676add67e0995a944b821fbff84547a9db533
   md5: 327bce3eb1ef1875c7145e915d25bcd3
@@ -14164,32 +14205,32 @@ packages:
   run_exports: {}
   size: 89411
   timestamp: 1769482314283
-- conda: https://conda.anaconda.org/conda-forge/win-64/libnetcdf-4.10.0-nompi_h7e03e6e_205.conda
-  build_number: 105
-  sha256: aff6e21de03c53ebda8d635aa3ca54379e2f6c5a32cdbe8df8512adc33cea896
-  md5: 5fe21b343464407f480f72302a76829e
+- conda: https://conda.anaconda.org/conda-forge/win-64/libnetcdf-4.10.1-nompi_he1c4404_200.conda
+  build_number: 100
+  sha256: 74c18520802925d4623ae5f285ee3bc5b707d48b80fcd32d936df92065ec5701
+  md5: 3ee891d82d25d3780749dee2cbb9b54a
   depends:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
   - zstd >=1.5.7,<1.6.0a0
-  - libzip >=1.11.2,<2.0a0
-  - blosc >=1.21.6,<2.0a0
-  - hdf4 >=4.2.15,<4.2.16.0a0
-  - libzlib >=1.3.2,<2.0a0
   - libcurl >=8.21.0,<9.0a0
   - bzip2 >=1.0.8,<2.0a0
-  - hdf5 >=2.1.0,<3.0a0
+  - libzip >=1.11.2,<2.0a0
   - libxml2
   - libxml2-16 >=2.14.6
   - libaec >=1.1.5,<2.0a0
+  - hdf5 >=1.14.6,<1.14.7.0a0
+  - libzlib >=1.3.2,<2.0a0
+  - hdf4 >=4.2.15,<4.2.16.0a0
+  - blosc >=1.21.6,<2.0a0
   license: MIT
   license_family: MIT
   run_exports:
     weak:
-    - libnetcdf >=4.10.0,<4.10.1.0a0
-  size: 763370
-  timestamp: 1783192221166
+    - libnetcdf >=4.10.1,<4.10.2.0a0
+  size: 760930
+  timestamp: 1783810940603
 - conda: https://conda.anaconda.org/conda-forge/win-64/libopentelemetry-cpp-1.27.0-hc88f397_0.conda
   sha256: 61779880ca16472beb82806497d8806d8ebfb0d2f76b6dfdf8199b3318e172dd
   md5: 23ccf8e4734ffa194b2c3b318c0b3e8f
@@ -14269,9 +14310,9 @@ packages:
     - libprotobuf >=6.33.5,<6.33.6.0a0
   size: 6950375
   timestamp: 1783169980984
-- conda: https://conda.anaconda.org/conda-forge/win-64/libpsl-0.22.0-hc1744f7_0.conda
-  sha256: b08fa3b66fbd9fea91c8d4cfa34568ea0b1e59636172e33349797fe7ef8a5611
-  md5: 6865b9bb1584e633c436c1196bcc4ed0
+- conda: https://conda.anaconda.org/conda-forge/win-64/libpsl-0.22.0-h25e0afd_1.conda
+  sha256: 040d4adabae2634b13183203e383c21f74ed98028b5d50bf9bae4968dd05aaa5
+  md5: ba200e33e10ccf0d730c4ce87badbdf1
   depends:
   - icu >=78.3,<79.0a0
   - ucrt >=10.0.20348.0
@@ -14282,8 +14323,8 @@ packages:
   run_exports:
     weak:
     - libpsl >=0.22.0,<0.23.0a0
-  size: 85552
-  timestamp: 1782394903537
+  size: 72405
+  timestamp: 1783937048984
 - conda: https://conda.anaconda.org/conda-forge/win-64/libraqm-0.10.5-h50d6d30_1.conda
   sha256: 79ca55f7202daa560bffafcf7aef435e48618fe91a5243b8e4a6ed4edfbe63bc
   md5: e5ab537cdd3462f26be2803209142913
@@ -14766,12 +14807,12 @@ packages:
     - minizip >=4.2.2,<5.0a0
   size: 106476
   timestamp: 1782851531134
-- conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2026.0.0-hac47afa_908.conda
-  sha256: f997bfc9bc4d4e14261cdcd1ad195d64a72ee44dca3145d24c1349f8d1311aa5
-  md5: 36ea6e1292e9d5e89374201da79646ef
+- conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2026.1.0-hac47afa_233.conda
+  sha256: ff355522fb0b6e33841167d9ca749147c8734d8be07b63b2ce25b0db043f42ed
+  md5: 5afbf28c4ca3e05b5469dbbd78c8e704
   depends:
-  - llvm-openmp >=22.1.5
-  - onemkl-license 2026.0.0 h57928b3_908
+  - llvm-openmp >=22.1.8
+  - onemkl-license 2026.1.0 h57928b3_233
   - tbb >=2023.0.0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
@@ -14779,8 +14820,8 @@ packages:
   license: LicenseRef-IntelSimplifiedSoftwareOct2022
   license_family: Proprietary
   run_exports: {}
-  size: 114354729
-  timestamp: 1779293121860
+  size: 114592547
+  timestamp: 1783700769546
 - conda: https://conda.anaconda.org/conda-forge/win-64/msgpack-python-1.2.1-py314hf309875_1.conda
   sha256: 548f4b23229abf8503b1e28438682e79a82ed0f2081a0c5ddfadb49e721b5022
   md5: 2a6b2dce5b76d32c67d11fcdd88ef037
@@ -14809,10 +14850,10 @@ packages:
     - muparser >=2.3.5,<2.4.0a0
   size: 148557
   timestamp: 1747117340968
-- conda: https://conda.anaconda.org/conda-forge/win-64/netcdf4-1.7.4-nompi_py311h5c67aab_108.conda
+- conda: https://conda.anaconda.org/conda-forge/win-64/netcdf4-1.7.4-nompi_py311h4277b3b_109.conda
   noarch: python
-  sha256: 2340ef495184dcef644fff9f2e750d2b511b19405de49a000daa1303147f68a2
-  md5: cf3c38df01872f3ec8c253b71ae3d51b
+  sha256: de1bb01a4672b9e7b5e076bdfaee9db97a85592bdc0989c5530b08c1fc98188c
+  md5: 080a972649746b7a72d0808edfbfa0ea
   depends:
   - python
   - certifi
@@ -14824,17 +14865,16 @@ packages:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - libnetcdf >=4.10.0,<4.10.1.0a0
-  - numpy >=1.23,<3
-  - hdf5 >=2.1.0,<3.0a0
+  - hdf5 >=1.14.6,<1.14.7.0a0
   - _python_abi3_support 1.*
   - cpython >=3.11
+  - libnetcdf >=4.10.1,<4.10.2.0a0
+  - numpy >=1.23,<3
   - libzlib >=1.3.2,<2.0a0
   license: MIT
-  license_family: MIT
   run_exports: {}
-  size: 955531
-  timestamp: 1782151666339
+  size: 955064
+  timestamp: 1783865866891
 - conda: https://conda.anaconda.org/conda-forge/win-64/nh3-0.3.6-py310ha413424_0.conda
   noarch: python
   sha256: aa8dffddfc42df86a87020294d4cb632b2e352dd1185cffd8b712afaaf28c531
@@ -14925,14 +14965,14 @@ packages:
     - numpy >=1.23,<3
   size: 7318163
   timestamp: 1779169232086
-- conda: https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2026.0.0-h57928b3_908.conda
-  sha256: 42ad15cbb3bf31830efa04d4b86dd2d5c0dd590c86f98adcd3c8c1f75acf5dd5
-  md5: 9c9303e08b50e09f5c23e1dac99d0936
+- conda: https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2026.1.0-h57928b3_233.conda
+  sha256: 96dec1c878d6e6f835be8ed57152a37be9a5104ac79c2425815d71c64cf13ee4
+  md5: 1566e2ce8c3bc23a2feb866c4d9e91e3
   license: LicenseRef-IntelSimplifiedSoftwareOct2022
   license_family: Proprietary
   run_exports: {}
-  size: 41580
-  timestamp: 1779292867015
+  size: 53362
+  timestamp: 1783700628723
 - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h0e57b4f_0.conda
   sha256: 24342dee891a49a9ba92e2018ec0bde56cc07fdaec95275f7a55b96f03ea4252
   md5: e723ab7cc2794c954e1b22fde51c16e4
@@ -15690,9 +15730,9 @@ packages:
   run_exports: {}
   size: 351309
   timestamp: 1781180337205
-- conda: https://conda.anaconda.org/conda-forge/win-64/websockets-16.0-py314hc5dbbe4_1.conda
-  sha256: d5b444750891ffe6360f0066601012b71c27dd15a02e5753f4a08415377271c7
-  md5: 5341e4691d00db4239bb9a9df481f5ae
+- conda: https://conda.anaconda.org/conda-forge/win-64/websockets-16.1-py314h13f4da2_0.conda
+  sha256: e9185e045e985e8982c943a0dbfd945ec670797cfd214085cc703562af1672c7
+  md5: d3bc29bf51308e52f9b980a0e627ff39
   depends:
   - python
   - vc >=14.3,<15
@@ -15702,8 +15742,8 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   run_exports: {}
-  size: 439954
-  timestamp: 1768087421360
+  size: 445393
+  timestamp: 1783694747700
 - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2
   sha256: 9df10c5b607dd30e05ba08cbd940009305c75db242476f4e845ea06008b0a283
   md5: 1cee351bf20b830d991dbe0bc8cd7dfe
@@ -15711,11 +15751,11 @@ packages:
   license_family: MIT
   run_exports: {}
   size: 1176306
-- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.17.3-py314h5a2d7ad_1.conda
-  sha256: ecbee7584bc5dfcabed36240059a156dab0d6dd87a0246c71b32b82640558a78
-  md5: 0172693b00f64c34667a5bdda0449eb9
+- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.2.2-py314h5a2d7ad_0.conda
+  sha256: 93cbcaf37eff31ccb2cdaca883f8be991e7c5098b925e8372c3ac615fef47197
+  md5: db00af109424ec883fe7d239a4ccee44
   depends:
-  - python >=3.14.0rc2,<3.15.0a0
+  - python >=3.14,<3.15.0a0
   - python_abi 3.14.* *_cp314
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
@@ -15723,8 +15763,8 @@ packages:
   license: BSD-2-Clause
   license_family: BSD
   run_exports: {}
-  size: 63873
-  timestamp: 1756852097390
+  size: 115544
+  timestamp: 1782133735742
 - conda: https://conda.anaconda.org/conda-forge/win-64/xerces-c-3.3.0-hac47afa_1.conda
   sha256: 9583a8fcf01c59b26a4285bc151b6315fd0bd504e1628f004519dc871eb17073
   md5: d1097e01041cfed41c81f1e3d1f52572
diff --git a/pyproject.toml b/pyproject.toml
index 2e0b3d52..63b0b79f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -111,3 +111,5 @@ flox = ">=0.11.2,<0.12"
 numbagg = ">=0.9.4,<0.10"
 rich = ">=15.0.0,<16"
 imageio = ">=2.37.0,<3"
+intake = "<2"
+intake-xarray = "<2"
\ No newline at end of file

From 9e50c9505d9b786e1638d99c75456675f6fc67f8 Mon Sep 17 00:00:00 2001
From: Vecko <36369090+VeckoTheGecko@users.noreply.github.com>
Date: Mon, 13 Jul 2026 07:35:53 -0500
Subject: [PATCH 3/6] Update TOC

---
 _toc.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/_toc.yml b/_toc.yml
index cfe7f887..d837c3a4 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -57,6 +57,7 @@ parts:
         sections:
           - file: intermediate/remote_data/cmip6-cloud.ipynb
           - file: intermediate/remote_data/remote-data.ipynb
+      - file: intermediate/cataloging.ipynb
       - file: intermediate/data_cleaning/05.1_intro.md
         sections:
           - file: intermediate/data_cleaning/05.2_examples.md

From cab80f1f502d66a83c91f71396e0f4d467f88b0d Mon Sep 17 00:00:00 2001
From: Vecko <36369090+VeckoTheGecko@users.noreply.github.com>
Date: Mon, 13 Jul 2026 07:38:30 -0500
Subject: [PATCH 4/6] Copy edits and review feedback

---
 intermediate/cataloging.ipynb | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
index 6d24b882..7547a892 100644
--- a/intermediate/cataloging.ipynb
+++ b/intermediate/cataloging.ipynb
@@ -132,8 +132,8 @@
     "\n",
     "The goal of the STAC project is to make geospatial data more accessible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n",
     "\n",
-    "- The combining of assets into larger collections representing a larger dataset (e.g., a dataset with multiple time slices)\n",
-    "- For more efficient workflows, as the whole asset doesn't need to be downloaded to check the metadata.\n",
+    "- The efficient querying of assets based on properties of interest without downloading the entire asset.\n",
+    "- The combining of these assets into a datacube object (i.e., Xarray dataset) by reprojecting or interpolating the data to a common grid.\n",
     "\n",
     "\n",
     "Within the STAC project is:\n",
@@ -142,7 +142,9 @@
     "- **Tooling for working with STAC**: More on this just now\n",
     "- **The community**\n",
     "\n",
-    "There are various tools in the STAC ecosystem. [`odc-stac`](https://github.com/opendatacube/odc-stac), [`stacstac`](https://github.com/gjoseph92/stackstac) and [`xpystac`](https://github.com/stac-utils/xpystac) are all proojects that llow the opening of STAC items as Xarray datasets.\n",
+    "There are various tools in the STAC ecosystem. There are online STAC browsers such as the [Radiant Earth STAC browser](https://radiantearth.github.io/stac-browser/#/). There are Python packages for the creating of STAC items and searching of STAC catalogues (e.g., [`rustac-py`](https://stac-utils.github.io/rustac/) and [`pystac`](https://github.com/stac-utils/pystac)). There are also Python packages for combining STAC objects into larger datacubes (e.g., [`odc-stac`](https://github.com/opendatacube/odc-stac), [`stacstac`](https://github.com/gjoseph92/stackstac) and [`xpystac`](https://github.com/stac-utils/xpystac)).\n",
+    "\n",
+    "There are many packages with some overlapping functionality. An example workflow you might follow would be using an online STAC browser to get a better understanding of the STAC assets you're working with, and what properties are available to query on, using `rustac-py` to generate and run the query, and then (once you're happy with the result) using `stackstac` or `odc-stac` to open the result as an Xarray Dataset.\n",
     "\n",
     "### More resources\n",
     "\n",

From b3a12b30c9670317d9a1babb749b19022d45228a Mon Sep 17 00:00:00 2001
From: Vecko <36369090+VeckoTheGecko@users.noreply.github.com>
Date: Mon, 13 Jul 2026 10:25:59 -0500
Subject: [PATCH 5/6] Copy edits

---
 intermediate/cataloging.ipynb | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
index 7547a892..57e87a35 100644
--- a/intermediate/cataloging.ipynb
+++ b/intermediate/cataloging.ipynb
@@ -146,12 +146,11 @@
     "\n",
     "There are many packages with some overlapping functionality. An example workflow you might follow would be using an online STAC browser to get a better understanding of the STAC assets you're working with, and what properties are available to query on, using `rustac-py` to generate and run the query, and then (once you're happy with the result) using `stackstac` or `odc-stac` to open the result as an Xarray Dataset.\n",
     "\n",
-    "### More resources\n",
+    "### Resources\n",
     "\n",
     "- https://stacspec.org/en\n",
     "- https://stacindex.org/\n",
-    "\n",
-    "### Example using odc-stac\n",
+    "- Example using odc-stac: See example notebook from `odc-stac`docs   [\"Access Sentinel 2 Data from AWS\"](https://odc-stac.readthedocs.io/en/latest/notebooks/stac-load-e84-aws.html).\n",
     "\n"
    ]
   },

From 5b20e5bc5853bc6782c195fe9c0c827b526143c6 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 13 Jul 2026 15:41:10 +0000
Subject: [PATCH 6/6] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 intermediate/cataloging.ipynb | 4 ++--
 pyproject.toml                | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
index 57e87a35..60c4a3af 100644
--- a/intermediate/cataloging.ipynb
+++ b/intermediate/cataloging.ipynb
@@ -93,7 +93,7 @@
     "print(list(cat[\"tutorial\"]))\n",
     "\n",
     "# Let's look at an entry within...\n",
-    "cat['tutorial']['ICON.native.2d_PT6H_inst']"
+    "cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"]"
    ]
   },
   {
@@ -105,7 +105,7 @@
    "source": [
     "# Let's open it as an xarray dataset\n",
     "\n",
-    "ds = cat['tutorial']['ICON.native.2d_PT6H_inst'].to_dask()\n",
+    "ds = cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"].to_dask()\n",
     "ds"
    ]
   },
diff --git a/pyproject.toml b/pyproject.toml
index 63b0b79f..26eb0a66 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -112,4 +112,4 @@ numbagg = ">=0.9.4,<0.10"
 rich = ">=15.0.0,<16"
 imageio = ">=2.37.0,<3"
 intake = "<2"
-intake-xarray = "<2"
\ No newline at end of file
+intake-xarray = "<2"