Update package description

DESCRIPTION

@@ -13,12 +13,14 @@ Authors@R: c(
 Author: Khaled Al-Shamaa [aut, cre], Mariano Omar Crimi [ctb], Zakaria
     Kehel [ctb], Johan Aparicio [ctb], ICARDA [cph]
 Maintainer: Khaled Al-Shamaa <k.el-shamaa@cgiar.org>
-Description: Linking data management systems to analytics is an important step in breeding 
-    digitization. Breeders can use this R package to Query the Breeding Management 
-    System(s) like 'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>, and 
-    'GIGWA' <https://southgreen.fr/content/gigwa> (using 'BrAPI' <https://brapi.org> calls) 
-    and help them to retrieve phenotypic and genotypic data directly into their analyzing 
-    pipelines.
+Description: This R package assists breeders in linking data systems with their analytic pipelines, 
+    a crucial step in digitizing breeding processes. It supports querying and retrieving 
+    phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 
+    'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>, and 
+    'GIGWA' <https://southgreen.fr/content/gigwa> (using 'BrAPI' <https://brapi.org> calls). 
+    Extra helper functions support environmental data sources, including 
+    'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 
+    'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database. 
 License: GPL (>= 3)
 URL: https://icarda-git.github.io/QBMS/
 BugReports: https://github.com/icarda-git/QBMS/issues

vignettes/bms_example.Rmd

@@ -11,7 +11,7 @@ vignette: >
 
 # QBMS
 <!-- https://shields.io/ -->
-Linking data management systems to analytics is an important step in breeding digitalization. Breeders can use this R package to Query the Breeding Management System(s) like [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls) and help them to retrieve phenotypic and genotypic data directly into their analyzing pipelines developed in R statistical environment.
+This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like [EBS](https://ebs.excellenceinbreeding.org/), [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls). Extra helper functions support environmental data sources, including [TerraClimate](https://www.climatologylab.org/terraclimate.html) and FAO [HWSDv2](https://gaez.fao.org/pages/hwsd) soil database.
 
 ## Breeding Management System
 Breeding Management System ([BMS](https://bmspro.io/)) is an information management system developed by the Integrated Breeding Platform to help breeders manage the breeding process, from programme planning to decision-making. The BMS is customizable for most crop breeding programs, and comes pre-loaded with curated ontology terms for many crops (bean, cassava, chickpea, cowpea, groundnut, maize, rice, sorghum, soybean, wheat, and others). The BMS is available as a cloud application, which can be installed on local or remote servers and accessed by multiple users.

vignettes/breedbase_example.Rmd

@@ -10,7 +10,7 @@ vignette: >
 ---
 
 ## QBMS
-Linking data management systems to analytics is an important step in breeding digitalization. Breeders can use this R package to Query the Breeding Management System(s) like [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls) and help them to retrieve phenotypic and genotypic data directly into their analyzing pipelines developed in R statistical environment.
+This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like [EBS](https://ebs.excellenceinbreeding.org/), [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls). Extra helper functions support environmental data sources, including [TerraClimate](https://www.climatologylab.org/terraclimate.html) and FAO [HWSDv2](https://gaez.fao.org/pages/hwsd) soil database.
 
 Well, because **Breedbase** supports the **BrAPI** standard, most of the QBMS functionalities work smoothly with minor changes. For example, you need to give special attention to the `set_qbms_config` parameters (check the example below). Currently, some functions are not supported when working with BreedBase, like `get_program_studies`, `get_germplasm_data`, and `list_trials` filtered by year!
 

vignettes/gigwa_example.Rmd

@@ -10,7 +10,7 @@ vignette: >
 ---
 
 ## QBMS
-Linking data management systems to analytics is an important step in breeding digitalization. Breeders can use this R package to Query the Breeding Management System(s) like [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls) and help them to retrieve phenotypic and genotypic data directly into their analyzing pipelines developed in R statistical environment.
+This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like [EBS](https://ebs.excellenceinbreeding.org/), [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls). Extra helper functions support environmental data sources, including [TerraClimate](https://www.climatologylab.org/terraclimate.html) and FAO [HWSDv2](https://gaez.fao.org/pages/hwsd) soil database.
 
 ## GIGWA
 [GIGWA](https://southgreen.fr/content/gigwa) is a web-based tool which provides an easy and intuitive way to explore large amounts of genotyping data by filtering the latter based not only on variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability perspectives. GIGWA can handle multiple databases and may be deployed in either single or multi-user mode. Finally, it provides a wide range of popular export formats.

vignettes/terraclimate_example.Rmd

@@ -10,7 +10,7 @@ vignette: >
 ---
 
 ## QBMS
-Linking data management systems to analytics is an important step in breeding digitalization. Breeders can use this R package to Query the Breeding Management System(s) like [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls) and help them to retrieve phenotypic and genotypic data directly into their analyzing pipelines developed in R statistical environment.
+This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like [EBS](https://ebs.excellenceinbreeding.org/), [BMS](https://bmspro.io/), [BreedBase](https://breedbase.org), and [GIGWA](https://southgreen.fr/content/gigwa) (using [BrAPI](https://brapi.org/) calls). Extra helper functions support environmental data sources, including [TerraClimate](https://www.climatologylab.org/terraclimate.html) and FAO [HWSDv2](https://gaez.fao.org/pages/hwsd) soil database.
 
 ## TerraClimate
 [TerraClimate](https://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2020. We plan to update these data periodically (annually).