[JOSS] Editorial suggestions

paper/paper.md

@@ -31,9 +31,9 @@ bibliography: paper.bib
 
 # Summary
 
-PVDeg is an open-source Python package for modeling photovoltaic (PV) degradation, developed at the National Laboratory of the Rockies (NLR), previously known as National Renewable Energy Laboratory (NREL), and supported by the Durable Module Materials (DuraMAT) consortium. It provides modular functions, materials databases, and calculation workflows for simulating degradation mechanisms (e.g., LeTID, hydrolysis, UV exposure) using weather data from the National Solar Radiation Database (NSRDB) and the Photovoltaic Geographical Information System (PVGIS).  By integrating Monte Carlo uncertainty propagation and geospatial processing, PVDeg enables field-relevant predictions and uncertainty quantification of module reliability and lifetime.
+PVDeg is an open-source Python package for modeling degradation of photovoltaic (PV) modules. It provides modular functions, materials databases, and calculation workflows for simulating degradation mechanisms (e.g., LeTID, hydrolysis, UV exposure) using weather data from the National Solar Radiation Database (NSRDB) and the Photovoltaic Geographical Information System (PVGIS). By integrating Monte Carlo uncertainty propagation and geospatial processing, PVDeg enables field-relevant predictions and uncertainty quantification of module reliability and lifetime.
 
-PVDeg is developed openly on GitHub and releases are distributed via the Python Package Index (PyPi). The source code is freely available under the BSD 3-Clause license, and copyrighted by the Alliance for Sustainable Energy allowing permissive use with attribution. PVDeg follows best practices for open-source python software, with a robust testing framework across Python 3.x environments, semantic versioning, and supporting documentation available at pvdegradationtools.readthedocs.io.
+PVDeg is developed openly on GitHub and releases are distributed via the Python Package Index (PyPi). The source code is freely available under the BSD 3-Clause license, and copyrighted by the Alliance for Sustainable Energy allowing permissive use with attribution. PVDeg follows best practices for open-source python software, with a robust testing framework across Python 3.x environments, semantic versioning, and supporting documentation available at pvdegradationtools.readthedocs.io. The package is developed at the National Laboratory of the Rockies (NLR), previously known as the National Renewable Energy Laboratory (NREL), and supported by the Durable Module Materials (DuraMAT) consortium.
 
 As an open-source project, PVDeg welcomes community contributions through GitHub issues and pull requests that support improvements to the codebase, documentation, and material-property databases.
 
@@ -52,22 +52,23 @@ Existing PV modeling tools such as pvlib-python [@pvlib] [@anderson2023pvlib] an
 The core API provides dedicated functions for calculating physical degradation mechanisms, accessing material properties and environmental stressors.  Examples include `pvdeg.humidity.module()` for moisture ingress modeling [@pickett2013hydrolysis], and `pvdeg.letid.calc_letid_outdoors()` for modeling light and elevated temperature induced degradation (LeTID) [@karas2022letidstudy; @repins2023longterm]. These functions rely on standardized environmental stressors such as temperature, irradiance, and humidity, and can be chained to produce lifetime predictions under realistic field conditions.
 
 ## Scenario Class
-To simplify complex workflows, PVDeg wraps its core functions into a ``Scenario`` class that defines locations, module configurations, and degradation mechanisms. This enables user-friendly workflows, simplifying the setup and execution of complex multi-parameter degradation studies. This layer provides an intuitive interface for multiple analyses of different degradation modes climates, and configurations. Tutorials in Jupyter notebooks and hosted examples on *Read the Docs* demonstrate full end-to-end analyses.
+To simplify complex workflows, PVDeg wraps its core functions into a ``Scenario`` class that defines locations, module configurations, and degradation mechanisms. This enables user-friendly workflows, simplifying the setup and execution of complex multi-parameter degradation studies. This layer provides an intuitive interface for multiple analyses of different degradation modes climates, and configurations.
 
 ## Geospatial Analysis
-The geospatial analysis layer enables large-scale spatial analyses by automatically distributing degradation calculations across geographic regions using parallel processing and advanced data structures. It integrates environmental data from NSRDB and PVGIS and automates sampling across latitude-longitude grids to produce maps, such as standoff distance distribution used in IEC TS 63126 compliance studies [@IEC63126]. The geospatial layer includes specialized visualization functions for mapping results and supports both uniform and stochastic spatial sampling strategies to balance computational efficiency with geographic coverage. Parallelization routines are compatible with NREL's open-source *GeoGridFusion* framework [@ford2025geogridfusion; @Tobin2025geogridfusion], allowing users to down-select meteorological datasets efficiently and strategically, and execute computations without high-performance computing access. This capability supports national- and global-scale analyses of degradation phenomena.
+The geospatial analysis layer enables large-scale spatial analyses by automatically distributing degradation calculations across geographic regions using parallel processing and advanced data structures. It integrates environmental data from NSRDB and PVGIS and automates sampling across latitude-longitude grids to produce maps, such as standoff distance distribution used in IEC TS 63126 compliance studies [@IEC63126]. The geospatial layer includes visualization functions for mapping results and supports both uniform and stochastic spatial sampling strategies to balance computational efficiency with geographic coverage. Parallelization routines are compatible with NREL's open-source *GeoGridFusion* framework [@ford2025geogridfusion; @Tobin2025geogridfusion], allowing users to down-select meteorological datasets efficiently and strategically, and execute computations without high-performance computing access. This capability supports national- and global-scale analyses of degradation phenomena.
 
 ## Monte Carlo Framework
 Laboratory-to-field extrapolation carries significant uncertainty in kinetic parameters. PVDeg’s Monte Carlo engine samples parameter distributions and their correlations to generate thousands of realizations, producing confidence intervals on degradation rates rather than single deterministic values. This capability, described in [@springer2022futureproofing], can help quantify uncertainty in complex and non-linear module lifetime predictions, and identify which parameters most strongly affect reliability risk.
 
 ## Tutorials and Tools
-The tutorials and tools component of PVDeg consists of a comprehensive suite of Jupyter notebooks that demonstrate practical workflows for modeling PV degradation. These notebooks cover core degradation mechanisms, scenario setup, geospatial analysis, and uncertainty quantification, providing step-by-step guidance for both new and advanced users. Each tutorial is designed to be interactive and reproducible, enabling users to explore real-world datasets, customize parameters, and visualize results. The notebooks support comparative studies and integration with external meteorological data sources such as NSRDB and PVGIS. By leveraging these notebooks, users can efficiently learn, apply, and extend PVDeg’s capabilities for research and industry applications. These tools make many aspects of PVDeg accessible to novice Python programmers whose research focus is on the measurement of laboratory-based acceleration factors.
+The tutorials and tools component of PVDeg consists of a comprehensive suite of Jupyter notebooks that demonstrate practical workflows for modeling PV degradation. These notebooks cover core degradation mechanisms, scenario setup, geospatial analysis, and uncertainty quantification, providing step-by-step guidance for both new and advanced users. Each tutorial is designed to be interactive and reproducible, enabling users to explore real-world datasets, customize parameters, and visualize results. The notebooks support comparative studies and integration with external meteorological data sources such as NSRDB and PVGIS.
 
 ## Open datasets
-A growing component of PVDeg is its compilation of community-driven open datasets for PV degradation modeling. These databases include curated degradation parameters and material property data, such as kinetic coefficients for common degradation mechanisms, UV-albedo data, and permeation properties for materials (e.g., H$_2$O, O$_2$, acetic acid). The datasets are continuously expanded and updated, serving as a growing resource for users to access validated values for modeling and analysis. Users are encouraged to contribute their own data, enhancing the collective knowledge base and supporting reproducible research. The core PVDeg API also provides users with a means to seamlessly query these datasets and use them in their own modeling workflows, analysis, and investigations. The development and maintenance of these degradation databases and associated API calls also supports reproducible, reliable, and field-relevant degradation modeling for the PV community.
+A growing component of PVDeg is its compilation of community-driven open datasets for PV degradation modeling. These databases include curated degradation parameters and material property data, such as kinetic coefficients for common degradation mechanisms, UV-albedo data, and permeation properties for materials (e.g., H$_2$O, O$_2$, acetic acid). The datasets are continuously expanded and updated, serving as a growing resource for users to access validated values for modeling and analysis. Users are encouraged to contribute their own data, enhancing the collective knowledge base and supporting reproducible research. The core PVDeg API also provides users with a means to seamlessly query these datasets and use them in their own modeling workflows, analysis, and investigations. The development and maintenance of these degradation databases and associated API calls also support reproducible, reliable, and field-relevant degradation modeling for the PV community.
 
 # Research Impact Statement
 Since its first release as PV Degradation Tools [@Holsapple2020pvdegtools], PVDeg has been adopted in multiple studies across the PV reliability community:
+
 * Thermal Stability and IEC TS 63126 Compliance: Used to calculate effective standoff distances and generate public maps supporting the IEC TS 63126 standard [@IEC63126].
 * Light and Elevated Temperature Induced Degradation (LeTID): Integrated into the international interlaboratory comparison study of LeTID effects in crystalline-silicon modules [@karas2022letidstudy] and follow-up analyses of field-aged arrays [@repins2023longterm; @karas2024letid].
 * Geospatial Performance Modeling: Coupled with GeoGridFusion [@ford2025geogridfusion] to streamline weather-data storage and spatial queries for large-scale degradation simulations.