Reduce LSST Focus in TAPE Intro Docs (#407)

* Update TAPE intros to focus less on LSST

* Address review comment

README.md

@@ -13,13 +13,11 @@
 [![Read the Docs](https://img.shields.io/readthedocs/tape)](https://tape.readthedocs.io/)
 [![benchmarks](https://img.shields.io/github/actions/workflow/status/lincc-frameworks/tape/asv-main.yml?label=benchmarks)](https://lincc-frameworks.github.io/tape/)
 
-Package for working with LSST time series data
-
-Given the duration and cadence of [Vera C. Rubin LSST](https://www.lsst.org/about), the survey will generate a vast amount of time series information capturing the variability of various objects. Scientists will need flexible and highly scalable tools to store and analyze O(Billions) of time series.  The **Time series Analysis and Processing Engine** (TAPE) is a framework for distributed time series analysis which enables the user to scale their algorithm to LSST data sizes. It allows for efficient and scalable evaluation of algorithms on time domain data through built-in fitting and analysis methods as well as support for user-provided algorithms. TAPE supports ingestion of multiple time series formats, enabling easy access to both LSST time series objects and data from other astronomical surveys.
+The **Time series Analysis and Processing Engine** (TAPE) is a framework for distributed time series analysis which enables the user to scale their algorithms to large datasets, created to work towards the goal of making [LSST](https://www.lsst.org/about) time series analysis accessible. It allows for efficient and scalable evaluation of algorithms on time domain data through built-in fitting and analysis methods as well as support for user-provided algorithms. TAPE supports ingestion of multiple time series formats, enabling easy access to both LSST time series objects and data from other astronomical surveys.
 
 In short term we are working on two main goals of the project:
-  - Enable ease of access to TimeSeries objects in LSST
   - Enable efficient and scalable evaluation of algorithms on time-domain data
+  - Enable ease of access to time-domain data in LSST
 
 This is a LINCC Frameworks project - find more information about LINCC Frameworks [here](https://www.lsstcorporation.org/lincc/frameworks).
 
@@ -36,7 +34,7 @@ pip install lf-tape
 Download code and install dependencies in a conda environment. Run unit tests at the end as a verification that the packages are properly installed.
 
 ```
-$ conda create -n seriesenv python=3.10
+$ conda create -n seriesenv python=3.11
 $ conda activate seriesenv
 
 $ git clone https://github.com/lincc-frameworks/tape

docs/gettingstarted/installation.rst

@@ -19,7 +19,7 @@ TAPE source code and installs it and any needed dependencies in a fresh conda en
 
 .. code-block:: bash
 
-    conda create -n tape_env python=3.10
+    conda create -n tape_env python=3.11
     conda activate tape_env
 
     git clone https://github.com/lincc-frameworks/tape

docs/index.rst

@@ -5,13 +5,16 @@
 TAPE (Timeseries Analysis & Processing Engine)
 ==============================================
 
-A Python package for scalable computing with LSST timeseries data.
+A Python package for scalable computing of timeseries data.
 
 TAPE offers a complete ecosystem for loading, filtering, and analyzing
-LSST timeseries data. Over the survey lifetime of the LSST, on order ~billions
-of objects will have multiband lightcurves available for analysis. TAPE
-is built to enable users to run provided and user-defined analysis functions 
-at scale in a parallelized and/or distributed manner.
+timeseries data. TAPE is built to enable users to run provided and user-defined 
+analysis functions at scale in a parallelized and/or distributed manner.
+
+Over the survey lifetime of the [LSST](https://www.lsst.org/about), on order 
+~billionsof objects will have multiband lightcurves available, and TAPE has
+been built as a framework with the goal of making analysis of LSST-scale
+data accessible.
 
 TAPE is built on top of `Dask <https://www.dask.org/>`_, and leverages 
 its "lazy evaluation" to only load data and run computations when needed.
@@ -36,7 +39,7 @@ create and activate a new environment.
 
 .. code-block:: console
 
-   >> conda create env -n <env_name> python=3.10
+   >> conda create env -n <env_name> python=3.11
    >> conda activate <env_name>