diff --git a/.github/workflows/documentation-build.yml b/.github/workflows/documentation-build.yml
index d628da20..8d58f6f9 100644
--- a/.github/workflows/documentation-build.yml
+++ b/.github/workflows/documentation-build.yml
@@ -34,15 +34,17 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: 3.11
+        python-version: 3.10.12
     - name: Install Pandoc, repo and dependencies
       run: |
         sudo apt install pandoc
+        pip install sphinx==8.1.3
         pip install . '.[dev,docs]'
     - name: Build and Commit
       uses: sphinx-notes/pages@master
       with:
-        install_requirements: true
+        install_requirements: false
+        sphinx_version: 8.1.3
         documentation_path: docs/src
     - name: Push changes
       uses: ad-m/github-push-action@master
diff --git a/docs/src/api/data.rst b/docs/src/api/data.rst
index e23e9db8..60abd70a 100644
--- a/docs/src/api/data.rst
+++ b/docs/src/api/data.rst
@@ -1,4 +1,4 @@
-.. automodule:: easyreflectometry.data
+.. automodule:: easyreflectometry.data.measurement
     :members:
     :undoc-members:
     :show-inheritance:
\ No newline at end of file
diff --git a/docs/src/api/model.rst b/docs/src/api/model.rst
index 676cd0c9..4157e161 100644
--- a/docs/src/api/model.rst
+++ b/docs/src/api/model.rst
@@ -1,4 +1,4 @@
-.. automodule:: easyreflectometry.model
+.. automodule:: easyreflectometry.model.model
     :members:
     :undoc-members:
     :show-inheritance:
diff --git a/docs/src/api/sample.rst b/docs/src/api/sample.rst
index e3518d9a..d7ed6167 100644
--- a/docs/src/api/sample.rst
+++ b/docs/src/api/sample.rst
@@ -1,5 +1,4 @@
-
-.. automodule:: easyreflectometry.sample
+.. automodule:: easyreflectometry.sample.collections.sample
     :members:
     :undoc-members:
     :show-inheritance:
\ No newline at end of file
diff --git a/docs/src/conf.py b/docs/src/conf.py
index 5ddb27ce..36235949 100644
--- a/docs/src/conf.py
+++ b/docs/src/conf.py
@@ -25,8 +25,7 @@
 
 import easyreflectometry
 
-# sys.path.insert(0, os.path.abspath('../src'))
-sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "src"))
+sys.path.insert(0, os.path.abspath('../src'))
 
 main_root = Path(__file__).parents[2]
 sys.path.append(str(main_root))
diff --git a/docs/src/index.rst b/docs/src/index.rst
index e2c8dc09..5cb980fc 100644
--- a/docs/src/index.rst
+++ b/docs/src/index.rst
@@ -5,12 +5,12 @@
    :hidden:
    :caption: Contents:
    
-   installation
-   usage
-   tutorials/tutorials
-   contributing
-   authors
-   api/api
+   installation.rst
+   usage.rst
+   tutorials/tutorials.rst
+   contributing.rst
+   authors.rst
+   api/api.rst
 
 
 Indices and tables
diff --git a/docs/src/tutorials/advancedfitting/advancedfitting.rst b/docs/src/tutorials/advancedfitting/advancedfitting.rst
new file mode 100644
index 00000000..8c12ba54
--- /dev/null
+++ b/docs/src/tutorials/advancedfitting/advancedfitting.rst
@@ -0,0 +1,9 @@
+Advanced Fitting
+================
+
+These are advanced fitting examples using the :py:mod:`easyreflectometry` library, to get an understanding of the possibilities.
+
+.. toctree:: 
+   :maxdepth: 1
+   
+   multi_contrast.ipynb
\ No newline at end of file
diff --git a/docs/src/tutorials/advancedfitting/multi_contrast.ipynb b/docs/src/tutorials/advancedfitting/multi_contrast.ipynb
index 1dd68e76..39c6fba5 100644
--- a/docs/src/tutorials/advancedfitting/multi_contrast.ipynb
+++ b/docs/src/tutorials/advancedfitting/multi_contrast.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "In the use of neutron reflectometry, it is common to use multiple isotopic contrasts of experimental data to analyse a system in a constrained fashion. \n",
     "That is to say, that we have data from multiple different species (where isotopic substitution has been used to produce data with different scattering length density) that share the same structure. \n",
-    "In this tutorial we will look at how `easyreflectometry` can be used to fit multiple contrasts of data from a [surfactant monolayer](./monolayer.rst) system, if you haven't looked at the tutorial for a single contrast of surfactant monolayer data it is suggested that you as this tutorial will build on it."
+    "In this tutorial we will look at how `easyreflectometry` can be used to fit multiple contrasts of data from a [surfactant monolayer](../fitting/monolayer.ipynb) system, if you haven't looked at the tutorial for a single contrast of surfactant monolayer data it is suggested that you as this tutorial will build on it."
    ]
   },
   {
@@ -22,19 +22,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e7055fbd-2a72-40ce-aca5-6e35f723f8ec",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "easyreflectometry: 1.3.0\n",
-      "refnx: 0.1.51\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -75,22 +66,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "694b4e5e-2d1a-402e-aa3f-a26cc82f7774",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from 'https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/multiple.ort' to file 'C:\\Users\\mads-\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\Local\\pooch\\pooch\\Cache\\14e53c019def90deab6565306d5c3891-multiple.ort'.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "file_path = pooch.retrieve(\n",
     "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/multiple.ort\",\n",
+    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/advancedfitting/multiple.ort\",\n",
     "    known_hash=\"241bcb819cdae47fbbb310a99c2456c7332312719496b936a153dc7dee83e62c\",\n",
     ")\n",
     "data = load(file_path)"
@@ -106,42 +89,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "82c89248-9852-4196-859c-6beb8232599b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "()"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.dims"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "095696d6-cb54-4c6c-b0d8-45b1c7ad103f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot(data)"
    ]
@@ -164,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "7834b80e-1dc9-47b8-923a-dec58e3494be",
    "metadata": {},
    "outputs": [],
@@ -183,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "58855c68-c95b-40de-9fee-a662771c247b",
    "metadata": {},
    "outputs": [],
@@ -202,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "08112c21-2027-47ba-845b-a3135439d862",
    "metadata": {},
    "outputs": [],
@@ -220,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "e7caedc2-8305-4f3b-bad2-c042bacb363b",
    "metadata": {},
    "outputs": [],
@@ -240,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "74e19578-03d4-4295-b6e2-a65fb2508c78",
    "metadata": {},
    "outputs": [],
@@ -263,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "c4aa6c00",
    "metadata": {},
    "outputs": [],
@@ -303,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "16a970d8",
    "metadata": {},
    "outputs": [],
@@ -343,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "021272e0-7abb-4703-a4cd-92daed10ae50",
    "metadata": {},
    "outputs": [],
@@ -386,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "24808923-ba02-4a7d-9be3-8d717b08aa8e",
    "metadata": {},
    "outputs": [],
@@ -411,28 +372,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "f21a92b7-f2b0-4315-a92b-950aa1e80b63",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(12.0, 12.0, 12.0)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "d13d2o.head_layer.thickness.value, d70d2o.head_layer.thickness.value, d83acmw.head_layer.thickness.value"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "e31a4807-c6f4-4bfd-986e-749955aa7e49",
    "metadata": {},
    "outputs": [],
@@ -442,21 +392,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "df2dbb56-6dc1-4388-97b7-1aef9da1769e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.0, 10.0, 10.0)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "d13d2o.head_layer.thickness.value, d70d2o.head_layer.thickness.value, d83acmw.head_layer.thickness.value"
    ]
@@ -473,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "6e92498a-581b-445d-94cd-dda6474f1984",
    "metadata": {},
    "outputs": [],
@@ -517,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "cf35ee97-2d64-4a0f-9212-7cca8bb2dcfc",
    "metadata": {},
    "outputs": [],
@@ -548,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "dc8e7f8f-6b4b-45ce-a38c-274dea683df4",
    "metadata": {},
    "outputs": [],
@@ -568,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "ce890544-f3cf-4a74-b927-d19dc292e12c",
    "metadata": {},
    "outputs": [],
@@ -579,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "0cb9d7b4-aa04-4c73-a700-4ff5af7e6f47",
    "metadata": {},
    "outputs": [],
@@ -597,42 +536,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "28f5d3e2-0e2f-4ef0-bc70-0f508b7fbc52",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(44.43261084507265, 44.43261084507265, 44.43261084507265)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "d13d2o.head_layer.area_per_molecule, d70d2o.head_layer.area_per_molecule, d83acmw.head_layer.area_per_molecule"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "faf00d17-55ea-45c3-be9e-772bf5cea70f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plot(analysed)"
    ]
@@ -640,7 +557,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv2",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -654,7 +571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/basic/assemblies_library.rst b/docs/src/tutorials/basic/assemblies_library.rst
index 1f7d8ab5..7e79844f 100644
--- a/docs/src/tutorials/basic/assemblies_library.rst
+++ b/docs/src/tutorials/basic/assemblies_library.rst
@@ -1,5 +1,5 @@
 Creating multilayers and surfactant layers
-==========
+===========================================
 
 :py:mod:`easyreflectometry` is designed to be used with a broad range of different assemblies.  
 Assemblies are collective layers behaving as a single object, for example, a multilayer or a surfactant layer.
diff --git a/docs/src/tutorials/basic/sample.rst b/docs/src/tutorials/basic/basic.rst
similarity index 94%
rename from docs/src/tutorials/basic/sample.rst
rename to docs/src/tutorials/basic/basic.rst
index 1ba61061..bcb94f58 100644
--- a/docs/src/tutorials/basic/sample.rst
+++ b/docs/src/tutorials/basic/basic.rst
@@ -1,12 +1,13 @@
 Basic
-======
+=====
 
 The :py:mod:`easyreflectometry` package is focused on making easy to use functionality for specific modelling approaches. 
 In order to achieve this, we have a sample library with different functionality. 
 
 .. toctree:: 
    :maxdepth: 1
-
+   
+   model
    material_library
    layer_library
    assemblies_library
\ No newline at end of file
diff --git a/docs/src/tutorials/basic/layer_library.rst b/docs/src/tutorials/basic/layer_library.rst
index 28d5e438..76c75ad4 100644
--- a/docs/src/tutorials/basic/layer_library.rst
+++ b/docs/src/tutorials/basic/layer_library.rst
@@ -1,5 +1,5 @@
 Defining Layers
-======
+===============
 
 Similar to a range of different `materials`_, there are a few different ways that a layer can be defined in :py:mod:`easyreflectometry`.
 
diff --git a/docs/src/tutorials/basic/material_library.rst b/docs/src/tutorials/basic/material_library.rst
index 2224aefc..2aaf0911 100644
--- a/docs/src/tutorials/basic/material_library.rst
+++ b/docs/src/tutorials/basic/material_library.rst
@@ -1,5 +1,5 @@
 Defining materials
-=========
+==================
 
 In order to support a wide range of applications (and to build complex `assemblies`_) there are a few different types of material that can be utilised in :py:mod:`easyreflectometry`. 
 These can include constraints or enable the user to define the material based on chemical or physical properties. 
diff --git a/docs/src/tutorials/basic/model.rst b/docs/src/tutorials/basic/model.rst
index 597ce52f..47134397 100644
--- a/docs/src/tutorials/basic/model.rst
+++ b/docs/src/tutorials/basic/model.rst
@@ -1,5 +1,5 @@
 Creating a model
-=====
+================
 
 The main component of an experiment in :py:mod:`easyreflectometry` is the :py:class:`Model`. 
 This is a description of the :py:class:`Sample` and the environment in which the experiment is performed. 
diff --git a/docs/src/tutorials/extra/extra.rst b/docs/src/tutorials/extra/extra.rst
new file mode 100644
index 00000000..9d68b33a
--- /dev/null
+++ b/docs/src/tutorials/extra/extra.rst
@@ -0,0 +1,7 @@
+Extra
+=====
+
+These are extra examples using the :py:mod:`easyreflectometry` library, to get an understanding of the possibilities.
+
+.. toctree:: 
+   :maxdepth: 1
\ No newline at end of file
diff --git a/docs/src/tutorials/fitting/d70d2o.ort b/docs/src/tutorials/fitting/d70d2o.ort
new file mode 100644
index 00000000..528ff127
--- /dev/null
+++ b/docs/src/tutorials/fitting/d70d2o.ort
@@ -0,0 +1,91 @@
+# # ORSO reflectivity data file | 0.1 standard | YAML encoding | https://www.reflectometry.org/
+# data_source:
+#     owner:
+#         name: Andrew McCluskey
+#         affiliation: European Spallation Source
+#         contact: andrew.mccluskey@ess.eu
+#     experiment:
+#         facility: ISIS
+#         start_date: 2015-05-20
+#         title: A single contrast of DSPC
+#         instrument: surf
+#         probe: neutron
+#     sample:
+#         name: DSPC monolayer
+#         category: air / liquid
+#         composition: air / d70DSPC / D2O
+#     measurement:
+#         instrument_settings:
+#             wavelength:
+#                magnitude: 12
+#                unit: angstrom
+#             incident_angle: 
+#                magnitude: 0.2
+#                unit: deg
+#         data_files:
+#             - Unknown.nxs
+#         scheme: angle-dispersive
+# reduction:
+#     software: mantid
+#     timestamp: 2015-08-27T15:33:59+01:00
+#     corrections:
+#          - footprint
+#          - incident intensity
+#          - detector efficiency
+# columns:
+#     - {name: Qz, unit: 1/angstrom, dimension: WW transfer}
+#     - {name: R, dimension: reflectivity}
+#     - {name: sR, dimension: error-reflectivity}
+#     - {name: sQz, unit: 1/angstrom, dimension: resolution-WW transfer}
+##                  Qz                    RQz                     sR                    sQz        
+5.1792999999999999e-02 1.4254000000000000e-04 2.9187999999999999e-06 2.5896500000000002e-03
+5.4383000000000001e-02 1.1424000000000000e-04 2.3277000000000002e-06 2.7191500000000000e-03
+5.7102000000000000e-02 9.5197000000000004e-05 1.9168000000000000e-06 2.8551000000000002e-03
+5.9957000000000003e-02 7.4925000000000005e-05 1.5375000000000001e-06 2.9978500000000003e-03
+6.2954999999999997e-02 6.1081999999999996e-05 1.2839999999999999e-06 3.1477499999999999e-03
+6.6102999999999995e-02 5.2451000000000003e-05 1.0896999999999999e-06 3.3051499999999998e-03
+6.9407999999999997e-02 4.3195000000000002e-05 9.3939000000000000e-07 3.4704000000000002e-03
+7.2877999999999998e-02 3.5877999999999998e-05 8.0683999999999999e-07 3.6439000000000003e-03
+7.6522000000000007e-02 2.8937000000000001e-05 6.8090000000000002e-07 3.8261000000000007e-03
+8.0348000000000003e-02 2.7223999999999999e-05 6.3740000000000003e-07 4.0174000000000000e-03
+8.4364999999999996e-02 2.3142999999999998e-05 5.5753000000000004e-07 4.2182499999999998e-03
+8.8583999999999996e-02 1.9910000000000001e-05 4.8592000000000000e-07 4.4292000000000003e-03
+9.3012999999999998e-02 1.7467000000000001e-05 4.3365000000000002e-07 4.6506500000000001e-03
+9.7664000000000001e-02 1.5252000000000001e-05 3.8785999999999998e-07 4.8832000000000007e-03
+1.0255000000000000e-01 1.4654999999999999e-05 3.6547000000000000e-07 5.1275000000000001e-03
+1.0767000000000000e-01 1.3203000000000000e-05 3.3522000000000001e-07 5.3835000000000003e-03
+1.1305999999999999e-01 1.2407000000000000e-05 3.1808000000000000e-07 5.6530000000000000e-03
+1.1871000000000000e-01 1.1365999999999999e-05 3.0100000000000001e-07 5.9354999999999998e-03
+1.2465000000000000e-01 1.0529000000000000e-05 2.7911000000000002e-07 6.2325000000000002e-03
+1.3088000000000000e-01 9.5791999999999997e-06 2.6436000000000002e-07 6.5440000000000003e-03
+1.3741999999999999e-01 8.5473000000000003e-06 2.5890999999999998e-07 6.8709999999999995e-03
+1.4429000000000000e-01 8.2166000000000002e-06 2.5923000000000003e-07 7.2145000000000004e-03
+1.5151000000000001e-01 7.1802000000000003e-06 2.5376999999999998e-07 7.5755000000000006e-03
+1.5908000000000000e-01 6.9426000000000002e-06 2.6561000000000001e-07 7.9540000000000010e-03
+1.6703999999999999e-01 5.5822999999999999e-06 7.3669999999999997e-07 8.3520000000000000e-03
+1.7538999999999999e-01 5.1837000000000000e-06 4.6409999999999998e-07 8.7694999999999995e-03
+1.8415999999999999e-01 3.9739000000000002e-06 3.0730000000000001e-07 9.2079999999999992e-03
+1.9336999999999999e-01 4.1088999999999997e-06 9.5290000000000002e-07 9.6685000000000000e-03
+2.0304000000000000e-01 3.0409000000000000e-06 2.3160000000000001e-07 1.0152000000000001e-02
+2.1318999999999999e-01 2.7470999999999999e-06 4.2269999999999999e-07 1.0659500000000001e-02
+2.2384999999999999e-01 2.3638000000000001e-06 1.4970000000000000e-07 1.1192500000000001e-02
+2.3504000000000000e-01 2.3843000000000002e-06 8.1460000000000000e-07 1.1752000000000000e-02
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diff --git a/docs/src/tutorials/fitting/fitting.rst b/docs/src/tutorials/fitting/fitting.rst
new file mode 100644
index 00000000..52b6c14f
--- /dev/null
+++ b/docs/src/tutorials/fitting/fitting.rst
@@ -0,0 +1,12 @@
+Fitting
+=======
+
+These are basic fitting examples using the :py:mod:`easyreflectometry` library, to get an understanding of the possibilities.
+
+.. toctree:: 
+   :maxdepth: 1
+   
+   simple_fitting.ipynb
+   repeating.ipynb
+   monolayer.ipynb
+   material_solvated.ipynb
\ No newline at end of file
diff --git a/docs/src/tutorials/fitting/material_solvated.ipynb b/docs/src/tutorials/fitting/material_solvated.ipynb
index 213c9bab..4665e87b 100644
--- a/docs/src/tutorials/fitting/material_solvated.ipynb
+++ b/docs/src/tutorials/fitting/material_solvated.ipynb
@@ -7,12 +7,12 @@
    "source": [
     "# Using the MaterialSolvated\n",
     "\n",
-    "In the [first tutorial](../fitting/simple_fitting.rst), we looked at a straight forward example of a polymer film at the solid/liquid interface. \n",
+    "In the [first tutorial](../fitting/simple_fitting.ipynb), we looked at a straight forward example of a polymer film at the solid/liquid interface. \n",
     "To analyse this data, we constructed a model with two layers, one SiO<sub>2</sub> and one of the polymer film, and when the analysis was performed, the scattering length density of the polymer film was allowed to vary to find an optimum value. \n",
     "However, it is likely that this scattering length density is in fact a compound value arising from the mixture of the polymer with some D<sub>2</sub>O intercalated. \n",
     "Therefore, if, for example, the surface covereage of the polymer was known, it may be possible to determine the scattering length density alone. \n",
     "Of course, this could be calculated from the optimal scattering length density for the film, but it is more intuitive to include this in our modelling approach. \n",
-    "Here, we will show how to use the `MaterialSolvated` [type](../../sample/material_library.rst#materialsolvated) to perform this analysis. "
+    "Here, we will show how to use the `MaterialSolvated` [type](../basic/material_library.rst#materialsolvated) to perform this analysis. "
    ]
   },
   {
@@ -78,7 +78,7 @@
    "id": "3ac19f8b-099d-4b19-8d37-7dd651a25949",
    "metadata": {},
    "source": [
-    "For information about the data being read in and the details of the model see [the previous tutorial](../fitting/simple_fitting.rst). \n",
+    "For information about the data being read in and the details of the model see [the previous tutorial](../fitting/simple_fitting.ipynb). \n",
     "We will gloss over these details here.\n",
     "We use `pooch` to fetch the file from the repository."
    ]
@@ -152,9 +152,12 @@
    "source": [
     "So for the `solvated_film` object, the scattering length density is calculated as,\n",
     "\n",
-    "$$ \\rho_{\\mathrm{solv film}} = (1-\\phi)\\rho_{\\mathrm{film}} + \\phi\\rho_{\\mathrm{D}_2\\mathrm{O}}, $$\n",
+    "<center>\n",
+    "<img src=\"eq_solvated.svg\" title=\"\\rho_{\\mathrm{solv film}}=(1-\\phi)\\rho_{\\mathrm{film}}+\\phi\\rho_{\\mathrm{D}_2\\mathrm{O}}\" />\n",
+    "</center>\n",
+    "<!-- Using https://editor.codecogs.com/ -->\n",
     "\n",
-    "where the scattering length densities are given with $\\rho$ and the coverage with $\\phi$. \n",
+    "where the scattering length densities are given with <mi>ρ</mi> and the coverage with <mi>ϕ</mi>. \n",
     "This means that when we investigate the `solvated_film` object, the scattering length density will be 3.09e-6 Å<sup>-2</sup>"
    ]
   },
@@ -348,13 +351,13 @@
    "id": "0e2fbca8-3717-4fee-8854-f081dd79c4c3",
    "metadata": {},
    "source": [
-    "The fit reproducing the measured reflectivity curve yields that the scattering length density (SLD) of the layer is 2.36E-6 Å<sup>-2</sup>.  Remember this layer is composed of 75% of the polymer film layer (SLD of 1.026E-6 Å<sup>-2</sup> fitted) and 25% of D2O (SLD of 6.36E-6  Å<sup>-2</sup> known) making (0.75 * 1.026 + 0.25 * 6.36)E-6 = 2.36E-6 Å<sup>-2</sup>.  This is the same as the result from the [previous tutorial](../fitting/simple_fitting.rst)."
+    "The fit reproducing the measured reflectivity curve yields that the scattering length density (SLD) of the layer is 2.36E-6 Å<sup>-2</sup>.  Remember this layer is composed of 75% of the polymer film layer (SLD of 1.026E-6 Å<sup>-2</sup> fitted) and 25% of D2O (SLD of 6.36E-6  Å<sup>-2</sup> known) making (0.75 * 1.026 + 0.25 * 6.36)E-6 = 2.36E-6 Å<sup>-2</sup>.  This is the same as the result from the [previous tutorial](./simple_fitting.ipynb)."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv2",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -368,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/fitting/monolayer.ipynb b/docs/src/tutorials/fitting/monolayer.ipynb
index 6ec2e726..cc521620 100644
--- a/docs/src/tutorials/fitting/monolayer.ipynb
+++ b/docs/src/tutorials/fitting/monolayer.ipynb
@@ -8,7 +8,7 @@
     "# Investigation of a surfactant monolayer\n",
     "\n",
     "A common system that is studied with neutron and X-ray reflectometry are surfactant monolayers. \n",
-    "In this tutorial, we will look at how the `easyreflectometry` assembly `SurfactantLayer` ([detailed here](../../sample/assemblies_library.rst#surfactantlayer)) can be used to model a phospholipid bilayer. \n",
+    "In this tutorial, we will look at how the `easyreflectometry` assembly `SurfactantLayer` ([detailed here](../basic/assemblies_library.rst#surfactantlayer)) can be used to model a phospholipid bilayer. \n",
     "First, we will import the relevant packages and functions."
    ]
   },
@@ -76,9 +76,9 @@
    "source": [
     "## Reading in experimental data\n",
     "\n",
-    "As has been [shown previously](../fitting/simple_fitting.rst), we use the `load` function to read in our experimental data. \n",
+    "As has been [shown previously](../fitting/simple_fitting.ipynb), we use the `load` function to read in our experimental data. \n",
     "For this tutorial we will be looking at [DSPC](https://en.wikipedia.org/wiki/Distearoylphosphatidylcholine), a phospholipid molecule that will self-assemble into a monolayer at the air-water interface. \n",
-    "The data being used has kindly been shared by the authors of [previous work on the system](#hollinshead2009). We use `pooch` to fetch the file from our repository."
+    "The data being used has kindly been shared by the authors of [previous work on the system](https://doi.org/10.1021/la8028319)(Hollinshead et al. 2009). We use `pooch` to fetch the file from our repository."
    ]
   },
   {
@@ -90,8 +90,8 @@
    "source": [
     "file_path = pooch.retrieve(\n",
     "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/d70d2o.ort\",\n",
-    "    known_hash=\"d877793b3c415834791b2c114dceb12f4564c083b2e7bf51b7a9f289a58165a2\",\n",
+    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/fitting/d70d2o.ort\",\n",
+    "    known_hash=\"3e4750536621be8eec493fa21a287e408d384f29cacb113b71d02690d99f0998\",\n",
     ")\n",
     "data = load(file_path)\n",
     "plot(data)"
@@ -104,7 +104,7 @@
    "source": [
     "## Building the model\n",
     "\n",
-    "The study of surfactant monolayers is very popular in the literature, including the models based on [functional descriptions](#hollinshead2009), [slab models](#campbell2018), and [molecular dynamics simulations](#mccluskey2019). \n",
+    "The study of surfactant monolayers is very popular in the literature, including the models based on [functional descriptions](https://doi.org/10.1021/la8028319)(Hollinshead et al. 2009), [slab models](https://doi.org/10.1016/j.jcis.2018.07.022)(Campbell et al. 2018), and [molecular dynamics simulations](https://doi.org/10.1088/2399-6528/ab12a9) (McCluskey et al. 2019). \n",
     "In `easyreflectometry`, we use a slab model description taht has become particularly commonplace. \n",
     "A graphical representation of the slab model for a phosphoplipid monolayer is shown below.\n",
     "\n",
@@ -117,9 +117,12 @@
     "\n",
     "The slab model for a phospholipid monolayer involves describing the system as consisting of two components, that for the hydrophilic head group layer and that for the hydrophobic tail group layer. \n",
     "Each of these layers have some thickness that can be estimated by considering the size of the head and tail groups.\n",
-    "The scattering length density ($\\rho$) for the layers is then defined based on the layer thickness ($d$), the scattering length for the lipid head or tail molecule ($b$), the surface number density of the monolayer (defined by the area per molecule, $\\mathrm{APM}$) and the presence of solvent in the head or tail molecules ($\\phi$ solvation), where the solvent has a known scattering length density ($\\rho_{\\mathrm{solvent}}$), \\n\",\n",
+    "The scattering length density (<mi>ρ</mi>) for the layers is then defined based on the layer thickness (<mi>d</mi>), the scattering length for the lipid head or tail molecule (<mi>b</mi>), the surface number density of the monolayer (defined by the area per molecule, APM) and the presence of solvent in the head or tail molecules (<mi>ϕ</mi> solvation), where the solvent has a known scattering length density (<mi>ρ</mi> solvent),\n",
     "\n",
-    "$$ \\rho = \\frac{b}{d\\mathrm{APM}}(1-\\phi) + \\rho_{\\mathrm{solvent}}\\phi. $$\n",
+    "<center>\n",
+    "    <img src=\"eq_monolayer.svg\" title=\"\\rho=\\frac{b}{d\\mathrm{APM}}(1-\\phi)+\\rho_{\\mathrm{solvent}}\\phi\" />\n",
+    "</center>\n",
+    "<!-- Using https://editor.codecogs.com/ -->\n",
     "\n",
     "This approach has two benefits: \n",
     "1. By constraining the area per molecule of the head and tail groups to be the same, the analysis can ensure that the number density of the two components is equal (i.e. for every head group there is a tail group), as would be expected given the chemical bonding.\n",
@@ -178,7 +181,7 @@
    "id": "7f59e62a-071a-4921-bfd2-0f998e172885",
    "metadata": {},
    "source": [
-    "Next we define estimates for the thickness of each layer, for this we will use values from [a previous work](#mccluskey2020), as we will not be varying these parameters. "
+    "Next we define estimates for the thickness of each layer, for this we will use values from [a previous work](https://doi.org/10.1088/2632-2153/ab94c4)(McCluskey et al. 2020), as we will not be varying these parameters. "
    ]
   },
   {
@@ -366,7 +369,7 @@
    "id": "514afa7a-cfa4-4ef9-a2c9-105c4d56ec6a",
    "metadata": {},
    "source": [
-    "Finally, as with other [tutorials](./tutorials.rst), we create the `CalculatorFactory` (and connect this to our model) and `MultiFitter` objects and perform the fit. "
+    "Finally, as with other [tutorials](../tutorials.rst), we create the `CalculatorFactory` (and connect this to our model) and `MultiFitter` objects and perform the fit. "
    ]
   },
   {
@@ -416,7 +419,7 @@
    "id": "8dd6ead8-5019-44eb-9fc9-d8309443bc46",
    "metadata": {},
    "source": [
-    "We can see above that the solvent surface coverage of the surfactant was found to be around 60 % (solvent_fraction in head_layer) and the area per molecule around 50 Å<sup>2</sup>, in agreement with [previous investigations](#mccluskey2019). "
+    "We can see above that the solvent surface coverage of the surfactant was found to be around 60 % (solvent_fraction in head_layer) and the area per molecule around 50 Å<sup>2</sup>, in agreement with [previous investigations](https://doi.org/10.1088/2399-6528/ab12a9) (McCluskey et al. 2019). "
    ]
   },
   {
@@ -432,10 +435,9 @@
    "id": "208e5f3c-3355-4c68-b8d2-27e883ab8e7f",
    "metadata": {},
    "source": [
-    "<div id='hollinshead2009'></div>\n",
     "Hollinshead, C. M., Harvey, R. D., Barlow, D. J., Webster, J. R. P., Hughes, A. V., Weston, A., Lawrence, M. J., **2009**,\n",
     "*Effects of Surface Pressure on the Structure of Distearoylphosphatidylcholine Monolayers Formed at the Air/Water Interface*,\n",
-    "[Langmuir, 25, 4070-4077](https://doi.org/10.1021/la8028319)"
+    "[Langmuir, 25, 4070-4077](https://doi.org/10.1021/la8028319)\n"
    ]
   },
   {
@@ -443,7 +445,6 @@
    "id": "3a95ae4f-99e3-4b04-837f-1b92fb4c7d19",
    "metadata": {},
    "source": [
-    "<div id='campbell2018'></div>\n",
     "Campbell, R. A., Saaka, Y., Shao, Y., Gerelli, Y., Cubitt, R., Nazaruk, E., Matyszewska, D. Lawrence, M. J., **2018**,\n",
     "*Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data*,\n",
     "[Journal of Colloid and Interface Science, 531, 98-108](https://doi.org/10.1016/j.jcis.2018.07.022)"
@@ -454,7 +455,6 @@
    "id": "d481d2f2-1388-4fa4-852c-741c74998261",
    "metadata": {},
    "source": [
-    "<div id='mccluskey2019'></div>\n",
     "McCluskey, A. R., Grant, J., Smith, A. J., Rawle, J. L., Barlow, D. J., Lawrence, M. J., Parker, S. C., Edler, K. J., **2019**,\n",
     "*Assessing molecular simulation for the analysis of lipid monolayer reflectometry*,\n",
     "[Journal of Physics Communications, 3, 075001](https://doi.org/10.1088/2399-6528/ab12a9)"
@@ -465,7 +465,6 @@
    "id": "30fb4898-c55c-413a-81ce-f8163ed56175",
    "metadata": {},
    "source": [
-    "<div id='mccluskey2020'></div>\n",
     "McCluskey, A. R., Cooper, J. F. K., Arnold, T., Snow, T., **2020**,\n",
     "*A general approach to maximise information density in neutron reflectometry analysis*,\n",
     "[Machine Learning: Science and Technology, 1, 035002](https://doi.org/10.1088/2632-2153/ab94c4)"
@@ -474,7 +473,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv2",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -488,7 +487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/fitting/repeating.ipynb b/docs/src/tutorials/fitting/repeating.ipynb
index 75335c98..dd093c68 100644
--- a/docs/src/tutorials/fitting/repeating.ipynb
+++ b/docs/src/tutorials/fitting/repeating.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# A multilayer fitting model\n",
     "\n",
-    "One of the main tools in `easyreflectometry` is the [assemblies library](../../sample/assemblies_library.rst). \n",
+    "One of the main tools in `easyreflectometry` is the [assemblies library](../basic/assemblies_library.rst). \n",
     "This allows the user to define their model, using specific parameters for their system of interest (if it is included in the assemblies library). \n",
     "These assemblies will impose necessary constraints and computational efficiencies based on the assembly that is used. \n",
     "\n",
-    "In this tutorial, we will look at one of these assemblies, that of a `RepeatingMultilayer` ([documented here](../../sample/assemblies_library.rst#repeatingmultilayer)). \n",
+    "In this tutorial, we will look at one of these assemblies, that of a `RepeatingMultilayer` ([documented here](../basic/assemblies_library.rst#repeatingmultilayer)). \n",
     "This tutorial is based on an example from the [BornAgain](https://www.bornagainproject.org) documentation looking at [specular reflectivity analysis](https://www.bornagainproject.org/m/py/fitting/extended/fit-specular-data/). \n",
     "Before performing analysis, we should import the packages that we need."
    ]
@@ -59,7 +59,7 @@
    "id": "8fd3e8f7-84ac-41c4-a89d-922ed82a001e",
    "metadata": {},
    "source": [
-    "As mentioned in the [previous tutorial](./simple_fitting.rst), we share the version of the software packages we will use. "
+    "As mentioned in the [previous tutorial](./simple_fitting.ipynb), we share the version of the software packages we will use. "
    ]
   },
   {
@@ -171,7 +171,7 @@
    "id": "f63ec440-089f-46cf-8ff5-be5012ad8dc8",
    "metadata": {},
    "source": [
-    "Then, to produce the repeating multilayer, we use the `RepeatingMultilayer` [assembly type](../../sample/assemblies_library.rst#RepeatingMultilayer). \n",
+    "Then, to produce the repeating multilayer, we use the `RepeatingMultilayer` [assembly type](../basic/assemblies_library.rst#RepeatingMultilayer). \n",
     "This can be constructed in a range of different ways, however here we pass a list of `Layer` type objects and a number of repetitions. "
    ]
   },
@@ -237,7 +237,7 @@
    "source": [
     "## Choosing our calculation engine\n",
     "\n",
-    "In the [previous tutorial](./simple_fitting.rst), we used the default [refnx](https://refnx.readthedocs.io/) engine for our analysis. \n",
+    "In the [previous tutorial](./simple_fitting.ipynb), we used the default [refnx](https://refnx.readthedocs.io/) engine for our analysis. \n",
     "Here, we will change our engine to be [*Refl1D*](https://refl1d.readthedocs.io/en/latest/).\n",
     "This is achieved with the `interface.switch('refl1d')` method below. "
    ]
@@ -327,7 +327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv2",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -341,7 +341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/fitting/simple_fitting.ipynb b/docs/src/tutorials/fitting/simple_fitting.ipynb
index 8e61f0f2..5dcd5eb8 100644
--- a/docs/src/tutorials/fitting/simple_fitting.ipynb
+++ b/docs/src/tutorials/fitting/simple_fitting.ipynb
@@ -231,7 +231,7 @@
    "id": "7247fc52-6874-47eb-a234-c7bb7db229fd",
    "metadata": {},
    "source": [
-    "Given that the silicon and silicon dioxide layer both compose the solid subphase, it can be helpful to combine these as a `Multilayer` [assembly type](../../sample/assemblies_library.rst#multilayer) in our code. "
+    "Given that the silicon and silicon dioxide layer both compose the solid subphase, it can be helpful to combine these as a `Multilayer` [assembly type](../basic/assemblies_library.rst#multilayer) in our code. "
    ]
   },
   {
@@ -249,7 +249,7 @@
    "id": "a3a2df9b-8ddb-4b3e-a5c3-55b72280b651",
    "metadata": {},
    "source": [
-    "These objects are then combined as a `Sample`, where the constructor takes a series of layers (or some more complex `easyreflectometry` [assemblies](../../sample/assemblies_library.rst)) and, optionally, some name for the sample."
+    "These objects are then combined as a `Sample`, where the constructor takes a series of layers (or some more complex `easyreflectometry` [assemblies](../basic/assemblies_library.rst)) and, optionally, some name for the sample."
    ]
   },
   {
@@ -524,7 +524,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".test",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -538,7 +538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/project.ipynb b/docs/src/tutorials/project.ipynb
deleted file mode 100644
index 1200f995..00000000
--- a/docs/src/tutorials/project.ipynb
+++ /dev/null
@@ -1,178 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Project\n",
-    "This notebook serves to demonstrate some of the functionality of the Project object.\n",
-    "\n",
-    "## Setup\n",
-    "First configure matplotlib to place figures in notebook and import needed modules."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from easyreflectometry import Project\n",
-    "from easyreflectometry.summary import Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Project object\n",
-    "\n",
-    "First we will create a `Project` object. There should only be one such object. The project is follwoing set to have the current folder at its root and the we give it the name: `MyNewProject`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project = Project()\n",
-    "project.set_path_project_parent('.')\n",
-    "project._info['name'] = 'MyNewProject'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will then populate this `Project` with the default model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.default_model()\n",
-    "print(project.models)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also see which materials that are defined in the `Project`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(project._materials)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is possible to save a `Project` object.  It will be place in the project folder and the state will be save in the file name `project.json`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.save_as_json(overwrite=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also generate a summary of the project.  We are also going to store the PDF and HTML files with the summary in the project folder."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary = Summary(project)\n",
-    "summary.save_pdf_summary(str(project.path / 'summary.pdf'))\n",
-    "summary.save_html_summary(str(project.path / 'summary.html'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Link to PDF summary](MyNewProject/summary.pdf) in `MyNewProject/summary.pdf`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can reset the project to a blank state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.reset()\n",
-    "print(project.models)\n",
-    "print(project._materials)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then let us try to load the state we saved above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.set_path_project_parent('.')\n",
-    "project._info['name'] = 'MyNewProject'\n",
-    "project.load_from_json()\n",
-    "print(project.models)\n",
-    "print(project._materials)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/src/tutorials/simulation/magnetism.ipynb b/docs/src/tutorials/simulation/magnetism.ipynb
index a72fe39f..82834ee2 100644
--- a/docs/src/tutorials/simulation/magnetism.ipynb
+++ b/docs/src/tutorials/simulation/magnetism.ipynb
@@ -383,7 +383,7 @@
    "id": "971baee6",
    "metadata": {},
    "source": [
-    "# Afterthoughts \n",
+    "## Afterthoughts \n",
     "Just for completion we will do a few additional calculations and comparisons of reflectivity."
    ]
   },
diff --git a/docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-0.0.ort b/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-0.0.ort
similarity index 100%
rename from docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-0.0.ort
rename to docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-0.0.ort
diff --git a/docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-1.0.ort b/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-1.0.ort
similarity index 100%
rename from docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-1.0.ort
rename to docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-1.0.ort
diff --git a/docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-10.0.ort b/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-10.0.ort
similarity index 100%
rename from docs/src/tutorials/fitting/mod_pointwise_two_layer_sample_dq-10.0.ort
rename to docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-10.0.ort
diff --git a/docs/src/tutorials/simulation/resolution_functions copy.ipynb b/docs/src/tutorials/simulation/resolution_functions copy.ipynb
deleted file mode 100644
index dabafc8b..00000000
--- a/docs/src/tutorials/simulation/resolution_functions copy.ipynb	
+++ /dev/null
@@ -1,402 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a60117e3-d089-4375-ac7c-12a52ed47271",
-   "metadata": {},
-   "source": [
-    "# Resolution functions\n",
-    "The purpose of the resolution function is to enable the `easyreflectometry` model to quantify the experimental uncertainties in wavelength and incident angle.\n",
-    "When determining reflectivity the resolution function defines the smearing to apply.  For a given Q-point such smearing is applied by determining an average of the neighboring Q-point weigthed by a normal distribution, which has a Q-point dependent Full Width at the Half Maximum (FWHM) that again is defined by the resolution function.\n",
-    "\n",
-    "Often we rely on a resolution function that has a simple functional dependecy of the Q-point.  By this is understood that the applied smearing in an Q point-has a FWHM that is given as a percentage of the value of the Q-point.\n",
-    "\n",
-    "Alternatively the FWHM value might be determined and declared directly for each measured Q-point.\n",
-    "When this is the case the provided Q-points and the corresponding FWHM values can be used to declare a linear spline function and thereby enable a determination of the reflectivity at an arbitrary point within the provided range of discrete Q-points."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5d0bd58",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "First configure matplotlib to place figures in notebook and import needed modules. Note that the plot function needs installation of `plopp` seperately or installation of `easyreflectometry[dev]`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29d5d62d-af4a-416d-bbe2-1338d32b30f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import scipp as sc\n",
-    "import refnx\n",
-    "import pooch\n",
-    "\n",
-    "import easyreflectometry\n",
-    "\n",
-    "from easyreflectometry.calculators import CalculatorFactory\n",
-    "from easyreflectometry.data import load\n",
-    "from easyreflectometry.model import Model\n",
-    "from easyreflectometry.model import LinearSpline\n",
-    "from easyreflectometry.model import PercentageFwhm\n",
-    "from easyreflectometry.sample import Layer\n",
-    "from easyreflectometry.sample import Material\n",
-    "from easyreflectometry.sample import Multilayer\n",
-    "from easyreflectometry.sample import Sample\n",
-    "from easyreflectometry.plot import plot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fd3e8f7-84ac-41c4-a89d-922ed82a001e",
-   "metadata": {},
-   "source": [
-    "For reference we fetch the version of the software packages we are using. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "549734c1-bbd9-41f3-8a20-d7a8ded37802",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'numpy: {np.__version__}')\n",
-    "print(f'scipp: {sc.__version__}')\n",
-    "print(f'easyreflectometry: {easyreflectometry.__version__}')\n",
-    "print(f'refnx: {refnx.__version__}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "687719c7-d5a2-4d60-953f-c854c5ca1c4a",
-   "metadata": {},
-   "source": [
-    "## Reading in measured data\n",
-    "\n",
-    "The data that we will investigate in this tutorial was generated with `Refnx` and are stored in `.ort` [format file](https://github.com/reflectivity/file_format/blob/master/specification.md) files. In this tutorial we are investigation how we can include resolution effects when simulating and reproducing data measured in an experiment.  For an `.ort` file the resoultion data for reflectivity is stored in the fourth column.\n",
-    "\n",
-    "IMPORTANT when using `easyreflectometry` functionality for loading an `.ort` file we store the resolution data as a variance (squared value).  As a consequence one needs to take the squareroot of the loaded data to recover the raw values (fourth column).\n",
-    "We use `pooch` to fetch the file from the repository."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "609174e5-1371-412d-a29f-cb05bfe36df0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_path_0 = pooch.retrieve(\n",
-    "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-0.0.ort\",\n",
-    "    known_hash=\"f8a3e7007b83f0de4e2c761134e7d1c55027f0099528bd56f746b50349369f50\",\n",
-    ")\n",
-    "file_path_1 = pooch.retrieve(\n",
-    "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-1.0.ort\",\n",
-    "    known_hash=\"9d81a512cbe45f923806ad307e476b27535614b2e08a2bf0f4559ab608a34f7a\",\n",
-    ")\n",
-    "file_path_10 = pooch.retrieve(\n",
-    "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-10.0.ort\",\n",
-    "    known_hash=\"991395c0b6a91bf60c12d234c645143dcac1cab929944fc4e452020d44b787ad\",\n",
-    ")\n",
-    "dict_reference = {}\n",
-    "dict_reference['0'] = load(file_path_0)\n",
-    "dict_reference['1'] = load(file_path_1)\n",
-    "dict_reference['10'] = load(file_path_10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ab3a164-62c8-4bd3-b0d8-e6f22c83dc74",
-   "metadata": {},
-   "source": [
-    "As an example we can plot the reference data without any resolution effects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31ab44d3-826a-4270-9046-ad667dcb66ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot(dict_reference['0'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dad78ccc-1e6f-47cd-8557-c4fa6c736f4b",
-   "metadata": {},
-   "source": [
-    "## Building our model\n",
-    "\n",
-    "The system that was used to produce the data shown above is based on a silicon subphase with two layers upon it. \n",
-    "These two layers are charachterized by having a scattering length density (SLD) of respectively 4 and 8.\n",
-    "Both layers have a rougness of 2 but their thicknesses are 100 and 150 angstrom respectively.\n",
-    "We show the model that will be used graphically below. \n",
-    "\n",
-    "<center>\n",
-    "    <img src='two_layers.png' alt='A slab model description of the two_layers system.' width='300px'></img>\n",
-    "</center>\n",
-    "<center>\n",
-    "    A slab model description of the two layer.\n",
-    "</center>\n",
-    "\n",
-    "To construct such a layer structure, first we create each of the materials, the associated layers, and the sub and super phases. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f95d620-35b7-4b47-a3b4-9e33d5525b50",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sld_4 = Material(sld=4.0, isld=0, name='Sld 4')\n",
-    "sld_8 = Material(sld=8.0, isld=0, name='Sld 8')\n",
-    "vacuum = Material(sld=0, isld=0, name='Vacuum')\n",
-    "si = Material(sld=2.047, isld=0, name='Si')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9a0b37ed-8714-4614-b49f-1e86ac232ac1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sld_4_layer = Layer(material=sld_4, thickness=100, roughness=2, name='SLD 4 Layer')\n",
-    "sld_8_layer = Layer(material=sld_8, thickness=150, roughness=2, name='SLD 8 Layer')\n",
-    "superphase = Layer(material=vacuum, thickness=0, roughness=0, name='Vacuum Superphase')\n",
-    "subphase = Layer(material=si, thickness=0, roughness=2, name='Si Subphase')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f63ec440-089f-46cf-8ff5-be5012ad8dc8",
-   "metadata": {},
-   "source": [
-    "Then, to produce the two layered structure, we use the `Multilayer` [assembly type](../../sample/assemblies_library.rst#Multilayer)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "311b25a1-6d5d-4e91-a72e-394ad8dcf464",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "two_layers = Multilayer([sld_4_layer, sld_8_layer], name='SLD 4/8 Layer')\n",
-    "two_layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c32d8ad-9baf-41bf-9fd8-419b92be36c4",
-   "metadata": {},
-   "source": [
-    "From this, we can construct our structure and combine this with a scaling and background."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2af8c30b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = Sample(Multilayer(superphase), two_layers, Multilayer(subphase), name='Two Layer Sample')\n",
-    "model = Model(\n",
-    "    sample=sample,\n",
-    "    scale=1,\n",
-    "    background=0,\n",
-    "    name='Two Layer Model',\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f0581b9-7690-4b17-9a4f-766ed92aaba2",
-   "metadata": {},
-   "source": [
-    "## Set the calculation engine\n",
-    "\n",
-    "We will use the default [Refnx](https://refnx.readthedocs.io/) calculator for our analysis. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1500603-d85d-4e16-b697-e1bf16502991",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "interface = CalculatorFactory()\n",
-    "model.interface = interface\n",
-    "print(interface.current_interface.name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "defd6dd5-c618-4af6-a5c7-17532207f0a0",
-   "metadata": {},
-   "source": [
-    "## Resolution functions\n",
-    "\n",
-    "We now define the different resoultion functions.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "880d10d7-b655-4ef1-b376-21b2e4394160",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resolution_function_dict = {}\n",
-    "resolution_function_dict['0'] = LinearSpline(\n",
-    "    q_data_points=dict_reference['0']['coords']['Qz_0'].values,\n",
-    "    fwhm_values=np.sqrt(dict_reference['0']['coords']['Qz_0'].variances),\n",
-    ")\n",
-    "\n",
-    "resolution_function_dict['1'] = LinearSpline(\n",
-    "    q_data_points=dict_reference['1']['coords']['Qz_0'].values,\n",
-    "    fwhm_values=np.sqrt(dict_reference['1']['coords']['Qz_0'].variances),\n",
-    ")\n",
-    "\n",
-    "resolution_function_dict['10'] = LinearSpline(\n",
-    "    q_data_points=dict_reference['10']['coords']['Qz_0'].values,\n",
-    "    fwhm_values=np.sqrt(dict_reference['10']['coords']['Qz_0'].variances),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0481604e-3973-4e5d-a0ee-2f5915461d71",
-   "metadata": {},
-   "source": [
-    "## Simulations\n",
-    "The next step is to visualise how the resolution functions affect the model.  \n",
-    "Furthermore, we compare the here determined reflectivities (Resolution) and the ones that were determined in `Refnx` (Reference)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e59d3153-f0da-4fce-a4f0-a424010acbec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in resolution_function_dict.keys():\n",
-    "    reference_coords = dict_reference[key]['coords']['Qz_0'].values\n",
-    "    reference_data = dict_reference[key]['data']['R_0'].values\n",
-    "    model_coords = np.linspace(\n",
-    "        start=min(reference_coords),\n",
-    "        stop=max(reference_coords),\n",
-    "        num=1000,\n",
-    "    )\n",
-    "    model.resolution_function = resolution_function_dict[key]\n",
-    "    model_data = model.interface().reflectity_profile(\n",
-    "        model_coords,\n",
-    "        model.unique_name,\n",
-    "    )\n",
-    "    plt.plot(model_coords, model_data, 'k-', label=f'Resolution: {key}%')\n",
-    "    plt.plot(reference_coords, reference_data, 'rx', label=f'Reference')\n",
-    "    ax = plt.gca()\n",
-    "    ax.set_xlim([-0.01, 0.45])\n",
-    "    ax.set_ylim([1e-10, 2.5])\n",
-    "    plt.legend()\n",
-    "    plt.yscale('log')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "60d1896d-06ca-4bd2-b44c-d3788304220c",
-   "metadata": {},
-   "source": [
-    "From the plots it is apparent that an increasing resolution flattens the reflectivity profile."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3c97d1b",
-   "metadata": {},
-   "source": [
-    "## Afterthoughts\n",
-    "As a last task we will compare the reflectivity determined using a percentage resolution function and a point-wise function.\n",
-    "We should recall that the \"experimental\" data was generated using `Refnx`.\n",
-    "By comparing the reflectivities determined using a resolution function with a FWHM of 1.0% and the point-wise FHWN constructed from data in a `.ort` file it is apparent that this reference data also was constructed using a resolution function of 1.0%."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ca0932f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "key = '1'\n",
-    "reference_coords = dict_reference[key]['coords']['Qz_0'].values\n",
-    "reference_data = dict_reference[key]['data']['R_0'].values\n",
-    "model_coords = np.linspace(\n",
-    "    start=min(reference_coords),\n",
-    "    stop=max(reference_coords),\n",
-    "    num=1000,\n",
-    ")\n",
-    "\n",
-    "model.resolution_function = resolution_function_dict[key]\n",
-    "model_data = model.interface().reflectity_profile(\n",
-    "    model_coords,\n",
-    "    model.unique_name,\n",
-    ")\n",
-    "plt.plot(model_coords, model_data, 'k-', label=f'Variable', linewidth=5)\n",
-    "\n",
-    "model.resolution_function = PercentageFwhm(1.0)\n",
-    "model_data = model.interface().reflectity_profile(\n",
-    "    model_coords,\n",
-    "    model.unique_name,\n",
-    ")\n",
-    "plt.plot(model_coords, model_data, 'r-', label=f'Percentage')\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xlim([-0.01, 0.45])\n",
-    "ax.set_ylim([1e-10, 2.5])\n",
-    "plt.legend()\n",
-    "plt.yscale('log')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv2",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/src/tutorials/simulation/resolution_functions.ipynb b/docs/src/tutorials/simulation/resolution_functions.ipynb
index dabafc8b..237963e7 100644
--- a/docs/src/tutorials/simulation/resolution_functions.ipynb
+++ b/docs/src/tutorials/simulation/resolution_functions.ipynb
@@ -96,17 +96,17 @@
    "source": [
     "file_path_0 = pooch.retrieve(\n",
     "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-0.0.ort\",\n",
+    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-0.0.ort\",\n",
     "    known_hash=\"f8a3e7007b83f0de4e2c761134e7d1c55027f0099528bd56f746b50349369f50\",\n",
     ")\n",
     "file_path_1 = pooch.retrieve(\n",
     "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-1.0.ort\",\n",
+    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-1.0.ort\",\n",
     "    known_hash=\"9d81a512cbe45f923806ad307e476b27535614b2e08a2bf0f4559ab608a34f7a\",\n",
     ")\n",
     "file_path_10 = pooch.retrieve(\n",
     "    # URL to one of Pooch's test files\n",
-    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/sample/mod_pointwise_two_layer_sample_dq-10.0.ort\",\n",
+    "    url=\"https://raw.githubusercontent.com/EasyScience/EasyReflectometryLib/master/docs/src/tutorials/simulation/mod_pointwise_two_layer_sample_dq-10.0.ort\",\n",
     "    known_hash=\"991395c0b6a91bf60c12d234c645143dcac1cab929944fc4e452020d44b787ad\",\n",
     ")\n",
     "dict_reference = {}\n",
@@ -186,7 +186,7 @@
    "id": "f63ec440-089f-46cf-8ff5-be5012ad8dc8",
    "metadata": {},
    "source": [
-    "Then, to produce the two layered structure, we use the `Multilayer` [assembly type](../../sample/assemblies_library.rst#Multilayer)."
+    "Then, to produce the two layered structure, we use the `Multilayer` [assembly type](../basic/assemblies_library.rst#Multilayer)."
    ]
   },
   {
@@ -380,7 +380,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv2",
+   "display_name": "easyref",
    "language": "python",
    "name": "python3"
   },
@@ -394,7 +394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/tutorials/simulation/simulation.rst b/docs/src/tutorials/simulation/simulation.rst
new file mode 100644
index 00000000..4f187b05
--- /dev/null
+++ b/docs/src/tutorials/simulation/simulation.rst
@@ -0,0 +1,10 @@
+Simulation
+==========
+
+These are basic simulation examples using the :py:mod:`easyreflectometry` library, to get an understanding of the possibilities.
+
+.. toctree:: 
+   :maxdepth: 1
+   
+   magnetism.ipynb
+   resolution_functions.ipynb
\ No newline at end of file
diff --git a/docs/src/tutorials/tutorials.rst b/docs/src/tutorials/tutorials.rst
index ea6868d7..b73e6666 100644
--- a/docs/src/tutorials/tutorials.rst
+++ b/docs/src/tutorials/tutorials.rst
@@ -1,6 +1,6 @@
-================
-**How to use**
-================
+==========
+How to use
+==========
 
 Dictionary
 ==========
@@ -40,10 +40,10 @@ And we are working to add more, in particular `bornagain`_ and `GenX`_.
 .. _`GenX`: https://aglavic.github.io/genx/doc/
 
 .. toctree:: 
-   :maxdepth: 1
+   :maxdepth: 2
 
-   tutorials/basic
-   tutorials/simulation
-   tutorials/fitting
-   tutorials/advancedfitting
-   tutorials/extra
+   basic/basic
+   simulation/simulation
+   fitting/fitting
+   advancedfitting/advancedfitting
+   extra/extra
diff --git a/docs/src/usage.rst b/docs/src/usage.rst
index c20cc720..fb6c24bf 100644
--- a/docs/src/usage.rst
+++ b/docs/src/usage.rst
@@ -1,6 +1,6 @@
-=====
+===============
 Getting started
-=====
+===============
 
 To use :py:mod:`easyreflectometry` in a project::
 
@@ -39,4 +39,4 @@ To use :py:mod:`easyreflectometry` in a project::
 
 Details of specific usage of :py:mod:`easyreflectometry` can be found in the `tutorials`_.
 
-.. _`tutorials`: ./tutorials/tutorials.html
\ No newline at end of file
+.. _`tutorials`: ./tutorials/tutorials.rst
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index eebf6063..e0da3a2d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,13 +58,13 @@ dev = [
 ]
 
 docs = [
-    "myst_parser==4.0.1",
-    "nbsphinx==0.9.7",
-    "sphinx",
+    "myst_parser",
+    "nbsphinx",
+    "sphinx==8.1.3",
     "sphinx_autodoc_typehints",
-    "sphinx-book-theme==1.1.4",
-    "sphinx-copybutton==0.5.2",
-    "toml==0.10.2",
+    "sphinx_book_theme",
+    "sphinx-copybutton",
+    "toml"
 ]
 
 [project.urls]
diff --git a/src/easyreflectometry/data/measurement.py b/src/easyreflectometry/data/measurement.py
index 3664af4d..e7fe8b9c 100644
--- a/src/easyreflectometry/data/measurement.py
+++ b/src/easyreflectometry/data/measurement.py
@@ -11,17 +11,6 @@
 from easyreflectometry.data import DataSet1D
 
 
-def load_as_dataset(fname: Union[TextIO, str]) -> DataSet1D:
-    """Load data from an ORSO .ort file as a DataSet1D."""
-    data_group = load(fname)
-    return DataSet1D(
-        x=data_group['coords']['Qz_0'].values,
-        y=data_group['data']['R_0'].values,
-        ye=data_group['data']['R_0'].variances,
-        xe=data_group['coords']['Qz_0'].variances,
-    )
-
-
 def load(fname: Union[TextIO, str]) -> sc.DataGroup:
     """Load data from an ORSO .ort file.
 
@@ -33,6 +22,17 @@ def load(fname: Union[TextIO, str]) -> sc.DataGroup:
         return _load_txt(fname)
 
 
+def load_as_dataset(fname: Union[TextIO, str]) -> DataSet1D:
+    """Load data from an ORSO .ort file as a DataSet1D."""
+    data_group = load(fname)
+    return DataSet1D(
+        x=data_group['coords']['Qz_0'].values,
+        y=data_group['data']['R_0'].values,
+        ye=data_group['data']['R_0'].variances,
+        xe=data_group['coords']['Qz_0'].variances,
+    )
+
+
 def _load_orso(fname: Union[TextIO, str]) -> sc.DataGroup:
     """Load from an ORSO compatible file.
 
diff --git a/src/easyreflectometry/sample/collections/sample.py b/src/easyreflectometry/sample/collections/sample.py
index 12394474..9f44cc9c 100644
--- a/src/easyreflectometry/sample/collections/sample.py
+++ b/src/easyreflectometry/sample/collections/sample.py
@@ -15,6 +15,7 @@
 
 # Needs to be a function, elements are added to the global_object.map
 def DEFAULT_ELEMENTS(interface):
+    """:meta private:"""
     return (
         Multilayer(interface=interface),
         Multilayer(interface=interface),