Revisions

docs/paper.md

@@ -94,16 +94,18 @@ In order to meet the needs of researchers, `scores` provides the following key b
 
 **Compatability**
 
-- Provides its own implementations where relevant, avoiding extensive dependencies and allowing greater API consistency.
-- Easy to integrate and use in a wide variety of environments. It has been tested and used on workstations, servers and in high performance computing (supercomputing) environments.
+- Highly modular - provides its own implementations, avoids extensive dependencies and allows greater API consistency.
+- Easy to integrate and use in a wide variety of environments. It has been used on workstations, servers and in high performance computing (supercomputing) environments. 
+- Maintains 100% automated test coverage.
 - Uses Dask [@Dask:2016] for scaling and performance.
 - Expanding support for `pandas` [@pandas:2024; @McKinney:2010].
 
+
 ## Metrics, Statistical Techniques and Data Processing Tools Included in `scores` 
 
 At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. For an up to date list, please see the `scores` documentation.
 
-The ongoing development roadmap includes additional metrics, tools, and statistical tests.
+The ongoing development roadmap includes the addition of more metrics, tools, and statistical tests.
 
 Table: A **Curated Selection** of the Metrics, Tools and Statistical Tests Currently Included in `scores`
 
@@ -121,23 +123,23 @@ Table: A **Curated Selection** of the Metrics, Tools and Statistical Tests Curre
 
 ## Use in Academic Work
 
-In 2015, the Australian Bureau of Meteorology began developing a new verification system called Jive. For a description of Jive see @loveday2024jive. The Jive verification metrics have been used to support several publications [@Griffiths:2017; @Foley:2020; @Taggart:2022d; @Taggart:2022b; @Taggart:2022c]. `scores` has arisen from the Jive verification system and was created to modularise the Jive verification functions and make them available as an open source package. 
+In 2015, the Australian Bureau of Meteorology began developing a new verification system called Jive, which became operational in 2022. For a description of Jive see @loveday2024jive. The Jive verification metrics have been used to support several publications [@Griffiths:2017; @Foley:2020; @Taggart:2022d; @Taggart:2022b; @Taggart:2022c]. `scores` has arisen from the Jive verification system and was created to modularise the Jive verification functions and make them available as an open source package.
 
 `scores` has been used to explore user-focused approaches to evaluating probabilistic and categorical forecasts [@Loveday2024ts].
 
 ## Related Software Packages
 
-There are multiple open source verification packages in a range of languages. Below is a comparison of `scores` to other open source Python verification packages. None of these include all of the functions and key benefits of `scores`(and vice versa). None of these include specific support for emerging metrics.
-
+There are multiple open source verification packages in a range of languages. Below is a comparison of `scores` to other open source Python verification packages. None of these include all of the metrics implemented in `scores` (and vice versa). None of these include specific support for emerging metrics.
+ 
 `xskillscore` [@xskillscore] provides many but not all of the same functions as `scores` and does not have direct support for pandas. The Jupyter Notebook tutorials in `scores` cover a wider array of metrics. 
 
 `climpred` [@Brady:2021] utilises `xskillscore` combined with data handling functionality, and is focused on ensemble forecasts for climate and weather. `climpred` makes some design choices related to data structure (specifically associated with climate modelling) which may not generalise effectively to broader use cases. Releasing `scores` separately allows the differing design philosophies to be considered by the community.
 
-`METplus` [@Brown:2021] is widely used by weather and climate model developers. `METplus` includes a database and a visualisation system, with Python and shell script wrappers to utilise the complex `MET` package. Verification scores in `MET` are implemented in C++ rather than Python. `METplus` is used as a system rather than providing a modular Python API.
+`METplus` [@Brown:2021] is a verification system used by weather and climate model developers. `METplus` includes a database and a visualisation system, with Python and shell script wrappers to utilise the `MET` package for the calculation of scores. `MET` is implemented in C++ rather than Python. `METplus` is used as a system rather than providing a modular Python API.
 
 `Verif` [@nipen2023verif] is a command line tool for generating verification plots whereas `scores` provides a Python API for generating numerical scores. 
 
-`Pysteps` [@gmd-12-4185-2019; @Imhoff:2023] is a package for short-term ensemble prediction systems, and includes a significant verification submodule with many useful verification scores. `PySteps` does not provide a modular (standalone) verification API. 
+`Pysteps` [@gmd-12-4185-2019; @Imhoff:2023] is a package for short-term ensemble prediction systems, and includes a significant verification submodule with many useful verification scores. `PySteps` does not provide a standalone verification API. 
 
 `PyForecastTools` [@Morley:2020] is a Python package for model and forecast validation which supports `dmarray` rather than `xarray` data structures and does not include Jupyter Notebook tutorials.