README update

README.md

@@ -1,10 +1,10 @@
 # What is loadeR?
 
-loadeR is an R package for climate data access building on NetCDF Java. It allows loading  local or remote data (from OPeNDAP servers) and is fully integrated with the User Data Gateway ([UDG](http://www.meteo.unican.es/udg-wiki)). This package has been conceived to work in the framework of both seasonal forecasting and climate change studies. Thus, it considers ensemble members as a basic dimension of the two main data structures (`grid` and `station`). Find out more about this package at the [loadeR wiki](https://github.com/SantanderMetGroup/loadeR/wiki).
+loadeR is an R package for climate data access building on the NetCDF-Java API. It allows user-friendly data access either from local or remote locations (e.g. OPeNDAP servers) and it is fully integrated with the User Data Gateway ([UDG](http://www.meteo.unican.es/udg-wiki)), a Climate Data Service deployed and maintained by the [Santander Meteorology Group](http://www.meteo.unican.es). loadeR has been conceived to work in the framework of both seasonal forecasting and climate change studies. Thus, it considers ensemble members as a basic dimension of its two main data structures (`grid` and `station`). Find out more about this package at the [loadeR wiki](https://github.com/SantanderMetGroup/loadeR/wiki).
 
-This package is part of the [climate4R bundle](http://www.meteo.unican.es/climate4r), formed by `loadeR`, `transformeR`, `downscaleR` and `visualizeR`.
+This package is part of the [climate4R framework](http://www.meteo.unican.es/climate4r), formed by `loadeR`, `transformeR`, `downscaleR`, `visualizeR` and other packages dealing with climate data analysis and visualization.
 
-The recommended installation procedure (for loader and the companion loadeR.java packages) is to use the `install_github` command from the devtools R package (see the installation info in the wiki):
+The recommended installation procedure (for loader and the companion loadeR.java and climate4R.UDG packages) is to use the `install_github` command from the devtools R package (see the installation info in the wiki):
 
 ```r
 devtools::install_github(c("SantanderMetGroup/loadeR.java", "SantanderMetGroup/climate4R.UDG", "SantanderMetGroup/loadeR"))
@@ -18,9 +18,12 @@ devtools::install_github(c("SantanderMetGroup/loadeR.java", "SantanderMetGroup/c
 ---
 Reference and further information: 
 
-**[General description of the climate4R framework]** Iturbide et al. (2019) The R-based climate4R open framework for reproducible climate data access and post-processing. **Environmental Modelling and Software**, 111, 42-54. https://doi.org/10.1016/j.envsoft.2018.09.009
-Check out the companion notebooks for the two examples [GitHub](https://github.com/SantanderMetGroup/notebooks).
+**[General description of the climate4R framework]** Iturbide et al. (2019) The R-based climate4R open framework for reproducible climate data access and post-processing. *Environmental Modelling and Software*, 111, 42-54. https://doi.org/10.1016/j.envsoft.2018.09.009
+Check out the [companion notebooks](https://github.com/SantanderMetGroup/notebooks) with worked examples.
 
-**[Seasonal forecasting applications]** Cofiño et al. (2018) The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. **Climate Services**, 9, 33-43. http://doi.org/10.1016/j.cliser.2017.07.001
+**[Statistical Downscaling]** Bedia et al. (2020). Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment. *Geoscientific Model Development*, 13, 1711–1735. https://doi.org/10.5194/gmd-13-1711-2020
 
-**[Example of a sectoral application (fire danger)]** Bedia et al. (2018) Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe. **Climate Services**, 9, 101-110. http://doi.org/10.1016/j.cliser.2017.04.001
+
+**[Seasonal forecasting applications]** Cofiño et al. (2018) The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. *Climate Services*, 9, 33-43. http://doi.org/10.1016/j.cliser.2017.07.001
+
+**[Example of a sectoral application (fire danger)]** Bedia et al. (2018) Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe. *Climate Services*, 9, 101-110. http://doi.org/10.1016/j.cliser.2017.04.001