Added links to the RSP data acces page

docs/index.md

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 An LSST:UK Tour
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-Many of the astrophysical insights that the LSST will provide will stem directly from the sheer volume of data delivered by the Vera C. Rubin Observatory. In many cases it will be impractical, if not impossible, to download and locally process the large amounts of data that will deliver the most groundbreaking research. With this in mind, the Rubin project will instead employ a “bring-your-code-to-the-data” model, whereby most data remains and is analysed within the Data Access Centers via the Rubin Science Platform (RSP).
+Many of the astrophysical insights that the LSST will provide will stem directly from the sheer volume of data delivered by the Vera C. Rubin Observatory. In many cases it will be impractical, if not impossible, to download and locally process the large amounts of data that will deliver the most groundbreaking research. With this in mind, the Rubin project will instead employ a “bring-your-code-to-the-data” model, whereby most data remains and is analysed within the Data Access Centers via the [Rubin Science Platform](https://data.lsst.cloud/) (RSP).
 
 Since most LSST data analysis will not be performed locally on one’s own filesystem and data formats, users will first need to become familiar with the RSP. While the learning curve is not especially steep, there are a number of concepts that are likely to be unfamiliar to many researchers when they start accessing LSST data products. With this in mind, James Mullaney from the University of Sheffield is running a number of sessions for UK-based astronomers which will introduce the Rubin Science Platform and demonstrate some of its features.
 

docs/material.md

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 Each session consists of four parts. The first part is a slides-based presentation which introduces the Rubin Project and key concepts within the Rubin Science Platform. This is followed by three tutorials that utilise jupyter notebooks to demonstrate how to interrogate the RSP databases, retrieve table and imaging data, and re-analyse some data using a re-configured LSST data processing task.
 
-All the material used during each session is freely available online via the links below. While anyone can *view* the jupyter notebooks on GitHub, they can only be *executed* from within the Rubin Science Platform (RSP) since that is where the required python modules and data repositories reside. As such, anyone who wishes to execute the notebooks will need to have data access rights to log into the RSP. Please also note the the notebooks are also currently set up to use the DP0.2 dataset, although reconfiguring them to use a different dataset would only require a straightforward change of the `repo` an `collection` parameters at the start. 
+All the material used during each session is freely available online via the links below. While anyone can *view* the jupyter notebooks on GitHub, they can only be *executed* from within the [Rubin Science Platform](https://data.lsst.cloud/) (RSP) since that is where the required python modules and data repositories reside. As such, anyone who wishes to execute the notebooks will need to have data access rights to log into the RSP. Please also note the the notebooks are also currently set up to use the DP0.2 dataset, although reconfiguring them to use a different dataset would only require a straightforward change of the `repo` an `collection` parameters at the start. 
 
 * Presentation: [Google Slides](https://docs.google.com/presentation/d/1TU5R4Tk8KbkuEGj4Qqk2ok00PMuIUihu32k874-ca9c/edit?usp=sharing)
 * Tutorial 1: [Jupyter Notebook](https://github.com/lsst-uk/rsp-uk-notebooks/blob/main/Intro_to_RSP/IntroToRSPSession1.ipynb)
 * Tutorial 2: [Jupyter Notebook](https://github.com/lsst-uk/rsp-uk-notebooks/blob/main/Intro_to_RSP/IntroToRSPSession2.ipynb)
 * Tutorial 3: [Jupyter Notebook](https://github.com/lsst-uk/rsp-uk-notebooks/blob/main/Intro_to_RSP/IntroToRSPSession3.ipynb)
 
-Further, while not explicitly used during the sessions, a further notebook that lists what will likely be most popular datasets, and how to retrieve them via the Data Butler, is provided in the following notebook:
+Further, while not explicitly used during the sessions, an additional notebook is provided that lists what will likely be most popular datasets, and how to retrieve them via the Data Butler:
 
 * Key dataset types: [Jupyter Notebook](https://github.com/lsst-uk/rsp-uk-notebooks/blob/main/Intro_to_RSP/KeyDatasets.ipynb)