Release 2026.5.1.4: bump Colab URL tag refs

Author: rhayes777

README.rst

@@ -2,7 +2,7 @@ PyAutoFit: Classy Probabilistic Programming
 ===========================================
 
 .. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
-   :target: https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/start_here.ipynb
+   :target: https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/start_here.ipynb
 
 .. |RTD| image:: https://readthedocs.org/projects/pyautofit/badge/?version=latest
     :target: https://pyautofit.readthedocs.io/en/latest/?badge=latest
@@ -33,7 +33,7 @@ PyAutoFit: Classy Probabilistic Programming
 
 `Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |
 `readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |
-`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/notebooks/overview/overview_1_the_basics.ipynb>`_ |
+`Introduction on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/notebooks/overview/overview_1_the_basics.ipynb>`_ |
 `HowToFit <https://github.com/PyAutoLabs/HowToFit>`_
 
 **PyAutoFit** is a Python based probabilistic programming language for model fitting and Bayesian inference
@@ -55,7 +55,7 @@ The following links are useful for new starters:
 
 - `The PyAutoFit readthedocs <https://pyautofit.readthedocs.io/en/latest>`_, which includes an `installation guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ and an overview of **PyAutoFit**'s core features.
 
-- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
 
 - `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts demonstrating **PyAutoFit**'s features.
 

docs/index.rst

@@ -20,7 +20,7 @@ The following links are useful for new starters:
 
 - `The PyAutoFit readthedocs <https://pyautofit.readthedocs.io/en/latest>`_, which includes an `installation guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ and an overview of **PyAutoFit**'s core features.
 
-- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
+- `The introduction Jupyter Notebook on Colab <https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/notebooks/overview/overview_1_the_basics.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
 
 - `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts demonstrating **PyAutoFit**'s features.
 

paper/paper.md

@@ -39,7 +39,7 @@ knowledge of a problem via non-linear search chaining. Accompanying `PyAutoFit`
 which includes example scripts, together with the standalone [HowToFit](https://github.com/PyAutoLabs/HowToFit)
 lecture series which introduces non-experts to model-fitting and provides a guide on how to begin a project
 using `PyAutoFit`. Readers can try `PyAutoFit` right now by 
-going to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/start_here.ipynb)
+going to [the introduction Jupyter notebook on Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/start_here.ipynb)
 or checkout our [readthedocs](https://pyautofit.readthedocs.io/en/latest/) for a complete overview
 of **PyAutoFit**'s features.
 
@@ -134,7 +134,7 @@ statistical inference methods. Complementing the workspace is the standalone
 [HowToFit](https://github.com/PyAutoLabs/HowToFit) repository, a series of Jupyter notebook tutorials aimed at
 non-experts, introducing them to model-fitting and Bayesian inference. They teach users how to write model-components 
 and `Analysis` classes in `PyAutoFit`, use these to fit a dataset and interpret the model-fitting results. The lectures 
-are available on our [Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.1/start_here.ipynb) and may therefore be 
+are available on our [Colab](https://colab.research.google.com/github/PyAutoLabs/autofit_workspace/blob/2026.5.1.4/start_here.ipynb) and may therefore be 
 taken without a local `PyAutoFit` installation.
 
 # Software Citations