NASA ADS Citation/Link in README

README.rst

@@ -7,9 +7,9 @@ Overview
 
 :Name: Fully Bayesian Forecast Example
 :Author: Thomas Gessey-Jones
-:Version: 0.1.2
+:Version: 0.1.3
 :Homepage: https://github.com/ThomasGesseyJones/FullyBayesianForecastsExample
-:Letter: https://arxiv.org/abs/2309.06942
+:Letter: https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G
 
 .. image:: https://img.shields.io/badge/python-3.8-blue.svg
    :target: https://www.python.org/downloads/
@@ -18,13 +18,13 @@ Overview
    :target: https://github.com/ThomasGesseyJones/ErrorAffirmations/blob/main/LICENSE
    :alt: License information
 .. image:: https://img.shields.io/badge/arXiv-2309.06942-b31b1b.svg?style=flat
-    :target: https://arxiv.org/abs/2309.06942
+    :target: https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G
     :alt: arXiv link
 
 
 Example of a fully Bayesian forecast performed using an `Evidence Network <https://ui.adsabs.harvard.edu/abs/2023arXiv230511241J/abstract>`__.
 This code also replicates the analysis of
-`Gessey-Jones et al. (2023) <https://arxiv.org/abs/2309.06942>`__.
+`Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__.
 This repository thus serves the dual purposes of providing an example code base others
 can modify to perform their own fully Bayesian forecasts and also providing a
 reproducible analysis pipeline for the letter.
@@ -111,7 +111,7 @@ scripts can be run from the terminal using the following commands:
     python visualize_forecasts.py
 
 to run with the default noise level of 79 mK and replicate the
-analysis from `Gessey-Jones et al. (2023) <https://arxiv.org/abs/2309.06942>`__.
+analysis from `Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__.
 Alternatively you can pass
 the scripts a command line argument to specify the experiments noise level in K. For example
 to run with a noise level of 100 mK you would run the following commands:
@@ -138,15 +138,15 @@ The various figures produced in the analysis are stored in the
 figures_and_results directory alongside the timing_data to assess the
 performance of the methodology and some summary statistics of the evidence
 networks performance. The figures and data generated in the
-analysis for `Gessey-Jones et al. (2023) <https://arxiv.org/abs/2309.06942>`__. are provided in this
+analysis for `Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__ are provided in this
 repository for reference.
 
 Licence and Citation
 --------------------
 
 The software is free to use on the MIT open source license.
 If you use the software for academic purposes then we request that you cite
-the `letter <https://arxiv.org/abs/2309.06942>`__ ::
+the `letter <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__ ::
 
   Gessey-Jones, T. and W. J. Handley. “Fully Bayesian Forecasts with Evidence
   Networks.” (2023). arXiv:2309.06942
@@ -155,21 +155,24 @@ If you are using Bibtex you can use the following to cite the letter
 
 .. code:: bibtex
 
-  @ARTICLE{GesseyJones2023,
-           author = {{Gessey-Jones}, T. and {Handley}, W.~J.},
-            title = "{Fully Bayesian Forecasts with Evidence Networks}",
-          journal = {arXiv e-prints},
-         keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology},
-             year = 2023,
-            month = sep,
-              eid = {arXiv:2309.06942},
-            pages = {arXiv:2309.06942},
-              doi = {10.48550/arXiv.2309.06942},
-    archivePrefix = {arXiv},
-           eprint = {2309.06942},
-     primaryClass = {astro-ph.IM}
+  @ARTICLE{2023arXiv230906942G,
+         author = {{Gessey-Jones}, T. and {Handley}, W.~J.},
+          title = "{Fully Bayesian Forecasts with Evidence Networks}",
+        journal = {arXiv e-prints},
+       keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology},
+           year = 2023,
+          month = sep,
+            eid = {arXiv:2309.06942},
+          pages = {arXiv:2309.06942},
+            doi = {10.48550/arXiv.2309.06942},
+  archivePrefix = {arXiv},
+         eprint = {2309.06942},
+   primaryClass = {astro-ph.IM},
+         adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G},
+        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
   }
 
+
 Note some of the packages used (see below) in this code have their own licenses that
 require citation when used for academic purposes (e.g. `globalemu <https://github.com/htjb/globalemu>`__ and
 `pypolychord <https://github.com/PolyChord/PolyChordLite>`__). Please check the licenses of these packages for more details.