Update tutorial names which were affecting linking

Fix broken links

Author: tennlee

docs/newproject.md

@@ -20,10 +20,10 @@ You will train the model using historical model data and historical point data.
 
 From there, configure the data accessors. The PyEarthTools accessors are currently being extended so that there are a range of accessors for fetching cloud data and working with standard datasets, but for now look at the examples in the tutorial for how to create one for your own data.
 
-Then, make a pipeline. Work out which variables you want, subset the grid points you want, and normlise the data. Take a look at the tutorial on [Working with Multiple Data Sources](notebooks/tutorial/MultipleSources.md) and [MLX Demo](notebooks/tutorial/ML_Demo_CustomArch.md) to see how to approach constructing the pipeline, and refer to [api/pipeline/howto.md](api/pipeline/howto.md) for a more in-depth how-to guide on this process.
+Then, make a pipeline. Work out which variables you want, subset the grid points you want, and normlise the data. Take a look at the tutorial on [Working with Multiple Data Sources](./notebooks/tutorial/MultipleSources) and [MLX Demo](./notebooks/tutorial/MLX-Demo-Custom-Arch) to see how to approach constructing the pipeline, and refer to [api/pipeline/howto.md](api/pipeline/pipeline_how_to.md) for a more in-depth how-to guide on this process.
 
 Visualise some of the samples from the pipeline, and make sure the data looks right. Maybe do a plot of the historical difference between the gridded value and the point value, to see how the two things are different. 
 
-There are a number of ways to train the baseline model. One of the easiest is to use the XGBoost framework, because it's robust and computationally lightweight. There is no tutorial example of this yet, but you might like to look at the [MLX Demo](notebooks/tutorial/ML_Demo_CustomArch.md) and the [CNN Demo](notebooks/tutorial/CNN_Model_Training.md) for inspiration. There are a lot of nuances here for how to manage a large project where you might be running dozens or hundreds of experiments, but the easiest place to start is a single model trained in a Jupyter Notebook. Experiment management information will be added later.
+There are a number of ways to train the baseline model. One of the easiest is to use the XGBoost framework, because it's robust and computationally lightweight. There is no tutorial example of this yet, but you might like to look at the [MLX Demo](./notebooks/tutorial/MLX-Demo-Custom-Arch) and the [CNN Demo](./notebooks/tutorial/CNN-Model-Training) for inspiration. There are a lot of nuances here for how to manage a large project where you might be running dozens or hundreds of experiments, but the easiest place to start is a single model trained in a Jupyter Notebook. Experiment management information will be added later.
 
 Evaluating the model is up to you at this point. They PyEarthTools roadmap includes the development of standard scorecards for an out-of-the-box experience, but for now check out the [scores](https://scores.readthedocs.io/) framework for verification.

notebooks/Gallery.ipynb

@@ -20,7 +20,7 @@
     "These tutorials can be run on a 4GB GPU using relatively low volumes of data (3-10GB). They will also work in HPC environments.\n",
     "\n",
     "- [Train and run a simplified global weather model (low hardware and data requirements)](./tutorial/FourCastMini_Demo.ipynb)  (working on 1 June 2025)\n",
-    "- [MLX Framework Example](./tutorial/MLX_Demo_CustomArch.ipynb) (working as at 8 Jun 2025)\n"
+    "- [MLX Framework Example](./tutorial/MLX-Demo-Custom-Arch.ipynb) (working as at 8 Jun 2025)\n"
    ]
   },
   {
@@ -42,7 +42,7 @@
     "- [Downloading ERA5 Data](./tutorial/Downloading_ERA5.ipynb)   (working as at 1 June 2025)\n",
     "- [Accessing ERA5 Data](./tutorial/Accessing_ERA5_Data.ipynb)   (working as at 1 June 2025)\n",
     "- [Introduction to Pipelines](./tutorial/Data_Pipelines.ipynb)   (working as at 1 June 2025)\n",
-    "- [End-to-end CNN Training Example](./tutorial/CNN_model_training.ipynb)   (working as at 1 June 2025)\n",
+    "- [End-to-end CNN Training Example](./tutorial/CNN-Model-Training.ipynb)   (working as at 1 June 2025)\n",
     "- [Training FourCastNeXt](./tutorial/FourCastNeXt_Training.ipynb)  (not working on 1 June 2025, requires fixes to the configuration files to work for all users, will be restored in future)\n",
     "- [Make a weather prediction with FourCastNeXt](./demo/FourCastNeXt_Inference.ipynb)  (not working on 1 June 2025, requires fixes to the configuration files to work for all users, will be restored in future)\n",
     "- [Working with Multiple Data Sources](./tutorial/MultipleSources.ipynb)  (working as at 1 June 2025)\n",

…ebooks/tutorial/CNN_model_training.ipynb …ebooks/tutorial/CNN-Model-Training.ipynbnotebooks/tutorial/CNN_model_training.ipynb renamed to notebooks/tutorial/CNN-Model-Training.ipynb notebooks/tutorial/CNN_model_training.ipynb renamed to notebooks/tutorial/CNN-Model-Training.ipynb

@@ -8364,7 +8364,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,

…books/tutorial/MLX_Demo_CustomArch.ipynb …ooks/tutorial/MLX-Demo-Custom-Arch.ipynbnotebooks/tutorial/MLX_Demo_CustomArch.ipynb renamed to notebooks/tutorial/MLX-Demo-Custom-Arch.ipynb notebooks/tutorial/MLX_Demo_CustomArch.ipynb renamed to notebooks/tutorial/MLX-Demo-Custom-Arch.ipynb

@@ -31,9 +31,14 @@
 @pyearthtools.zoo.register("Development/FourCastNextRM", exists="ignore")
 class FourCastNextRM(pyearthtools.zoo.BaseForecastModel):
     """
-    FourCastNeXt
+    FourCastNeXt was originally developed by FourCastNeXt ([Guo et al. 2024](https://doi.org/10.48550/arXiv.2401.05584)) 
 
-    Developed by NCI
+    This class provides the underlying architecture as a registered model within the framework,
+    so that it can be trained according to whatever data and resolution may be of interest.
+
+    Users need to train their own model weights.
+
+    
 
     Arguments:
         lead_time (int | str | pyearthtools.data.TimeDelta):