Merge branch 'ahulsekemp-main'

content/02.introduction.md

@@ -10,11 +10,11 @@ Since its first publication in 2019 [@doi:10.1093/bioinformatics/btz190], BrAPI
 
 An API is a technical connection between two pieces of software. Just as a graphical user interface (GUI) or a command line interface (CLI) allows a human user to interact with a piece of software, an API allows one software application to interact with another. A REST-style (or RESTful) web service is a type of API commonly used in modern web infrastructure. REST is a technical architecture that describes the stateless transmission of data between applications. Typically, RESTful web service APIs are implemented using the standard HTTP protocol that most of the modern internet is built upon. These implementations generally use JavaScript Object Notation (JSON) to represent the data being transferred. Both HTTP and JSON are programming language agnostic, very stable, and highly flexible. This means BrAPI can be implemented in almost any piece of software and can solve a wide range of use cases.
 
-Data repositories and service providers that are BrAPI compatible have mapped their internal data structures to the BrAPI standard models, allowing them to share data with the outside world in a standardized format. Similarly, they can accept new data from external sources and automatically map the new data to their existing database. Client application developers can take advantage of this standardization by building tools and connectors that integrate with all BrAPI-compatible data repositories. Visualization, reporting, analytics, data collection, and quality control tools can be built once and shared with other organizations that follow the standard. As the number of BrAPI-compatible databases, tools, and organizations grows, so does the value of implementing the standard into any given application.
+Data repositories and service providers that are BrAPI compatible have mapped their internal data structures to the BrAPI standard models, allowing them to share data with the outside world in a standardized format. Similarly, they can accept new data from external sources and automatically map the new data to their existing database. Client application developers can take advantage of this standardization by building tools and connectors that integrate with all BrAPI-compatible data repositories. Visualization, reporting, analytics, data collection, and quality control tools can be built once and shared with other organizations that follow the standard. This type of BrAPI-compatible, easily sharable tool is often referred to as a BrAPP, meaning BrAPI Application. BrAPPs are simple tools that are entirely reliant on BrAPI for their data requirements, and often fit on a single web page. A single BrAPP can be easily shared and used by many organizations and systems, as long as those organizations have the required BrAPI endpoints available. As the number of BrAPI-compatible databases, tools, and organizations grows, so does the value of implementing the standard into any given application.
 
 ### Project Updates
 
-Over its lifetime, the BrAPI project has grown and changed substantially. The total size of the specification has almost quadrupled since the release of version v1.0 in 2017, increasing from 51 endpoints in v1.0 to 201 endpoints in v2.1. Because of this growth, the specification documents were reorganized into four modules: BrAPI-Core, BrAPI-Phenotyping, BrAPI-Genotyping, and BrAPI-Germplasm {@fig:domains}. While early versions of the specification focused on read-only phenotype data, the specification now has representation from most of the major concepts related to breeding. The newest specification has also been updated to be internally consistent, easier to navigate, and allow for read, write, and update capabilities.
+Over its lifetime, the BrAPI project has grown and changed substantially. The total size of the specification has almost quadrupled since the release of version v1.0 in 2017, increasing from 51 endpoints in v1.0 to 201 endpoints in v2.1. Because of this growth, the specification documents were reorganized into four modules: BrAPI-Core, BrAPI-Phenotyping, BrAPI-Genotyping, and BrAPI-Germplasm. Figure {@fig:domains} is a simplified domain map of the whole BrAPI data model, showing what kinds of data are defined in each module. While early versions of the specification focused on read-only phenotype data, the specification now has representation from most of the major concepts related to breeding. The newest specification has also been updated to be internally consistent, easier to navigate, and allow for read, write, and update capabilities.
 
 ![A simplified domain map of the whole BrAPI data model, divided into organizational modules. A more detailed Entity Relationship Diagram (ERD) is available on brapi.org.](images/BrAPI_Domains_v2-1_vertical.png){#fig:domains width="100%"}
 

content/03.00.HEADER.Success.md

@@ -1,7 +1,7 @@
 ## Results
 <!-- success stories highlighting BrAPI usefulness in breeding cycle. Perhaps reference the original BrAPI paper where possible use cases were proposed.  -->
 
-Below are a number of short success stories from the BrAPI community. These tools, applications, and infrastructure projects serve as another indicator of community growth and success over the past 5-10 years. These stories clearly illustrate all the different ways the BrAPI standard can be used productively and in practice. Figure {@fig:apps} contains a summary of the tools described below.
+Below are a number of short success stories from the BrAPI community. These tools, applications, and infrastructure projects serve as another indicator of community growth and success over the past 5 years. These stories clearly illustrate all the different ways the BrAPI standard can be used productively and in practice. Figure {@fig:apps} contains a summary of many of the currently available BrAPI-compliant tools, and each will be further described below.
 
 ![A summary of all the tools described below and the general areas each tool is designed to handle](images/BrAPI_Paper_Applications_Chart.png){#fig:apps width="100%"}
 

content/03.01.--.HEADER.Phenotyping.md

@@ -7,4 +7,5 @@ Collected data, subsequent analyses, and data visualizations all impact and shap
 The BrAPI specification supports phenotypic data throughout the entire breeding pipeline, including collection, analyses, publication, and archiving.
 The BrAPI community has developed several BrAPI-compatible tools to facilitate data standardization, efficient storage, and curation of phenotypic data and trait metadata.
 Ongoing development efforts are creating tools to manage images and other high-throughput phenotypic data sources, further enhancing the precision and efficiency of plant breeding research.
-By supporting the collection and storage of phenotypic data accurately and efficiently, BrAPI compatible tools simplify the conversion of phenotypes into insights that are necessary to help digitize and boost modern breeding and genetics research programs.
+By supporting the collection and storage of phenotypic data accurately and efficiently, BrAPI compatible tools (as outlined below) simplify the conversion of phenotypes into actionable insights that are necessary to help digitize and boost modern breeding and genetics research programs.
+The following set of BrAPI-compatible tools were developed to support some aspect of the phenotyping process.

content/03.01.01.Field_Book.md

@@ -3,7 +3,8 @@
 <!-- Trevor and Chaney -->
 
 Data from plant breeding and genetics experiments has traditionally been collected using pen and paper, but this approach often results in transcription errors and delayed analysis.
-[Field Book](fieldbook.phenoapps.org/) [@doi:10.2135/cropsci2013.08.0579], a highly-customizable Android app, was developed to help scientists digitize and organize their phenotypic data as measurements are collected.
+[Field Book](fieldbook.phenoapps.org/) [@doi:10.2135/cropsci2013.08.0579], a highly-customizable Android app, was developed to help scientists digitize and organize their phenotypic data as measurements and images are collected.
 This effectively improves data collection speed, reduces errors, and enables larger and more robust breeding populations and data sets. 
+
 Field Book has added support for BrAPI to streamline data transfer to and from BrAPI-compatible servers.
-Removing the need to manually transfer data files simplifies data exchange between these systems and reduces the opportunities for human error and data loss.
+This improvement has removed the need to manually transfer data files, simplifies data exchange between these systems, and reduces the opportunities for human error and data loss.

content/03.01.02.GridScore.md

@@ -4,9 +4,9 @@
 <!-- Make distinct from Field Book, highlight what makes GridScore different -->
 <!-- Better flow is needed between Field Book and GridScore -->
 
-[GridScore](https://ics.hutton.ac.uk/get-gridscore/) [@doi:10.1186/s12859-022-04755-2] is a multi-platform, web-based application for recording phenotypic observations that harnesses mobile devices to enrich the data collection process. 
+[GridScore](https://ics.hutton.ac.uk/get-gridscore/) [@doi:10.1186/s12859-022-04755-2] is a web-based application for recording phenotypic observations that harnesses mobile devices to enrich the data collection process. 
 The GridScore interface closely mirrors the look and feel of printed field plans, creating an intuitive user experience. 
-GridScore performs a wide range of functions, including data validation, data visualization, georeferencing, cross-platform support, and data synchronization across multiple devices. 
+GridScore performs a wide range of functions, including data validation, data visualization, georeferencing, multi-platform support, and data synchronization across multiple devices. 
 The application's data collection approach employs a top-down view onto the trial and offers field navigation mechanisms using barcodes, QR codes, or guided walks that take the data collector through the field in one of 16 pre-defined orders.
 
 BrAPI has further increased the value of GridScore by integrating it into the overarching plant breeding workflow. 

content/03.01.04.Image_Breed.md

@@ -1,7 +1,7 @@
 #### ImageBreed
 
-Unoccupied aerial and ground vehicles (UAVs and UGVs) enable the high throughput collection of images and other sensor data in the field, but the rapid processing and management of these data is often a bottleneck for breeding programs seeking to deploy these technologies for time-sensitive decision making.
+Unoccupied aerial and ground vehicles (UAVs and UGVs) enable the high throughput collection of images and other sensor data in the field, but the rapid processing and management of these datasets are often a bottleneck for breeding programs seeking to deploy these technologies for time-sensitive decision making.
 [ImageBreed](https://imagebreed.org/) [@doi:10.1002/ppj2.20004] is an open-source, BrAPI-compliant image processing tool that supports the routine use of UAVs and UGVs in breeding programs through standardized pipleines.
 It creates orthophotomosaics, applies filters, assigns plot polygons, and extracts ontology-based phenotypes from raw UAV-collected images.
-The BrAPI standard is used to push these phenotypes back to a central breeding database where they can be analyzed with other experiment data.
+The BrAPI standard is used to push these phenotypes back to a central BrAPI-compliant breeding database where they can be analyzed with other experiment data.
 The ImageBreed team has collaborated with others in the community to enhance the BrAPI image data standards, which it uses to upload raw images to a central breeding database, or any other BrAPI-compatible long term storage service.

content/03.01.05.PHIS.md

@@ -7,7 +7,7 @@ It manages and collects data from basic phenotyping and high-throughput phenotyp
 PHIS unambiguously identifies the objects and traits in an experiment and establishes their types and relationships via ontologies and semantics.
 
 Since its inception, PHIS has been designed to be BrAPI compliant, encompassing the Core, Phenotyping, and Germplasm BrAPI modules. 
-This enables integration with other BrAPI-compliant systems and platforms, simplifying the exchange of accession and phenotyping data across systems. PHIS is actively integrated with the genebank accessions management system [OLGA](https://crb-plantes-olga.fr/public/frontend/auth/login), and is indexed by the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://urgi.versailles.inrae.fr/faidare].
+This enables integration with other BrAPI-compliant systems and platforms, simplifying the exchange of accession and phenotyping data across systems. PHIS is actively integrated with the [OLGA](https://crb-plantes-olga.fr/public/frontend/auth/login) genebank accessions management system , and is indexed by the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://urgi.versailles.inrae.fr/faidare].
 BrAPI-enabled interoperability promotes a more coherent and efficient approach to the management and use of phenotyping data on various platforms and research initiatives within the European scientific community.
 BrAPI compliance also ensures that PHIS is compatible with other standards such as the Minimal Information About a Plant Phenotyping Experiment ([MIAPPE](https://www.miappe.org/) [@doi:10.1111/nph.16544]).
-By integrating BrAPI requirements into its structure, PHIS not only meets the standards of the phenotyping field, but also strengthens its capacity for interoperability and effective collaboration in the wider context of plant breeding and related fields. 
+By integrating BrAPI requirements into its structure, PHIS strengthens its capacity for interoperability and effective collaboration in the wider context of plant breeding and related fields. 

content/03.01.06.PIPPA.md

@@ -1,7 +1,7 @@
 #### PIPPA
 
 <!-- Rafael -->
-[PIPPA](https://pippa.psb.ugent.be) [@https://pippa.psb.ugent.be] is a data management system used for collecting data from the Weighing, Imaging and Watering Machines ([WIWAM](https://www.wiwam.be/)) [@https://www.wiwam.be] range of automated high-throughput phenotyping platforms. 
+[PIPPA](https://pippa.psb.ugent.be) [@https://pippa.psb.ugent.be] is a data management system used for collecting data from the Weighing, Imaging and Watering Machines ([WIWAM](https://www.wiwam.be/)) [@https://www.wiwam.be], whcih are a range of automated high-throughput phenotyping platforms. 
 These platforms have been deployed by research institutes and commercial breeders across Europe. 
 They can be set up in a variety of configurations with different types of equipment including weighing scales, cameras, and environment sensors. 
 The software features a web interface with functionality for setting up new experiments, planning imaging and irrigation treatments, linking metadata (genotype, growth media, manual treatments) to pots, and importing, exporting, and visualizing data. 

content/03.02.--.HEADER.Genotyping.md

@@ -1,4 +1,4 @@
 ### Genotyping
 
 <!-- Ajay -->
-Genotyping has become a cornerstone of most breeding processes, but managing the data can be challenging. Understanding different genotyping protocols for various crops is crucial due to the unique genetic structures of each species. Techniques such as SNP genotyping, Genotyping-by-Sequencing (GBS), SSRs, Whole Genome Sequencing (WGS), and array-based genotyping each offer specific advantages depending on the crop and research objectives. BrAPI supports genotypic data by utilizing existing standards such as VCF [@doi:10.1093/bioinformatics/btr330] and the GA4GH Variants schema [@https://github.com/ga4gh-metadata/SchemaBlocks]. The BrAPI community has developed compatible tools for storing, searching, visualizing, and analyzing genotypic data, making it easier to integrate and utilize this information in breeding programs. Mastery of the various genotyping protocols ensures efficient and effective breeding, while BrAPI-compliant tools streamline data management and analysis, enhancing the ability to make data-driven decisions in developing superior crop varieties.
+Genotyping has become a cornerstone of most breeding processes, but managing the data can be challenging. Understanding different genotyping protocols for various crops is crucial due to the unique genetic structures of each species. Techniques such as single nucleotide polymorphism (SNP) genotyping, Genotyping-by-Sequencing (GBS), SSRs, Whole Genome Sequencing (WGS), and array-based genotyping each offer specific advantages depending on the crop and research objectives. BrAPI supports genotypic data by utilizing existing standards such as the variant call format (VCF) [@doi:10.1093/bioinformatics/btr330] and the GA4GH Variants schema [@https://github.com/ga4gh-metadata/SchemaBlocks]. The BrAPI community has developed compatible tools for storing, searching, visualizing, and analyzing genotypic data, making it easier to integrate and utilize this information in breeding programs. Mastery of the various genotyping protocols ensures efficient and effective breeding, while BrAPI-compliant tools streamline data management and analysis, enhancing the ability to make data-driven decisions in developing superior crop varieties.