general edits

content/03.01.05.PHIS.md

@@ -3,6 +3,6 @@
 <!-- Isabelle -->
 [PHIS](http://www.phis.inrae.fr/) [@doi:10.1111/nph.15385], the Hybrid Phenotyping Information System, is an ontology-driven information system, based on the [OpenSILEX](https://github.com/OpenSILEX/) framework. PHIS is deployed in several field and greenhouse platforms of the French national [PHENOME](https://www.phenome-emphasis.fr/) and European [EMPHASIS](https://emphasis.plant-phenotyping.eu/) infrastructures. It manages and collects data from basic phenotyping and high throughput phenotyping experiments on a day to day basis. PHIS unambiguously identifies all the objects and traits in an experiment, and establishes their types and relationships via ontologies and semantics.
 
-PHIS has been designed to be BrAPI compliant. PHIS adheres to the standards and protocols specified by BrAPI and implements various services aligning with the BrAPI standards, encompassing the Core, Phenotyping, and Germplasm modules. This enables integration and compatibility with BrAPI-compliant systems and platforms, such as OLGA, a genebank accessions management system, to retrieve accession information. This prerequisite served as the basis for formalizing the data model, while also facilitating compatibility 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. The fact that data within a PHIS instance can be queried through BrAPI services enables indexing of PHIS in the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://hal.inrae.fr/hal-04425516].
+PHIS was designed to be BrAPI compliant since its inception. PHIS adheres to the standards and protocols specified by BrAPI and implements various services aligning with the BrAPI standards, encompassing the Core, Phenotyping, and Germplasm modules. This enables integration and compatibility with BrAPI-compliant systems and platforms, such as the genebank accessions management system [OLGA](https://crb-plantes-olga.fr/public/frontend/auth/login), to retrieve accession information. This prerequisite served as the basis for formalizing the data model, while also facilitating compatibility 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. The fact that data within a PHIS instance can be queried through BrAPI services enables indexing of PHIS in the [FAIDARE](https://urgi.versailles.inrae.fr/faidare/) data portal [@https://hal.inrae.fr/hal-04425516].
 
 Furthermore, as PHIS offers BrAPI-compliant web services, it simplifies the integration and data exchange with other European information systems that handle phenotyping data. The adhesion to BrAPI standards ensures a common interface and compatibility, facilitating communication and collaboration between PHIS and other systems in the European context. This interoperability not only eases data sharing, but also 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.

content/03.01.07.Trait_Selector_BrAPP.md

@@ -4,7 +4,7 @@
 <!-- the "BrAPP" description part should better fit at the beginning of the section. -->
 <!-- BrAPPs are simple tools developed by the BrAPI community that are entirely reliant on BrAPI for their data requirements. Often, they are JavaScript based applications or visualizations that fit on a single web page. This means a single BrAPP can be easily shared and used by many organizations and systems, as long as those organizations have the standard BrAPI endpoints available. -->
 
-The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) is used to search and select useful traits, using a visual aid to help the user find exactly what they need. Instead of searching through a long list of possible traits, the user is presented with a cartoon image of a species. They can then click on pieces of the image to show traits associated to that part of the plant. For a breeder, they might use it to quickly find specific traits to study. For a genebank user, they might use it to find varieties that have a specific trait they are interested in.
+The [Trait Selector BrAPP](https://github.com/solgenomics/BrAPI-Trait-selector) is used to search and select meaningful traits, with a visual aid to help the user find exactly what they need. Instead of searching through a long list of possible traits, the user is presented with a cartoon image of a species. They can then click on pieces of the image to show traits associated to that part of the plant. For a breeder, they might use it to quickly find specific traits to study. For a genebank user, they might use it to find varieties that have a specific trait they are interested in.
 
 The Trait Selector can be integrated into any website or system, assuming there is a BrAPI compatible data source available to connect to. A breeding database would need to only implement the BrAPI endpoints for Traits, Observations, and Variables, while a genebank would require Traits and Germplasm Attributes. Any BrAPI server with either of these sets of endpoints implemented could use this BrAPP. CassavaBase and MGIS are two successful examples of the Trait Selector BrAPP in use. 
 

content/03.02.01.DArT.md

@@ -1,8 +1,8 @@
 #### DArT Sample Submission
 
 <!-- Grzegorz Uszynski and Puthick Hok - Diversity Arrays Technology DArT -->
-The DArT genotyping lab is heavily used world wide when it comes to plant genotyping. With over 1200 available organisms and species, client base on every continent and already many million samples processed, DArT provides services for several generic and bespoke genotyping technologies and solutions. Processes of sample tracking and fast data delivery are at the core of the ordering system developed at DArT. The ordering system is tightly integrated with DArTdb - DArT's custom LIMS operational system, which drives laboratory, quality, and analytical processes.
+The [Diversity Arrays Technology (DArT)](https://www.diversityarrays.com/) genotyping lab is heavily used world wide when it comes to plant genotyping. With over 1200 available organisms and species, a client base on every continent, and many millions of samples processed, DArT provides services for several generic and bespoke genotyping technologies and solutions. Processes of sample tracking and fast data delivery are at the core of the ordering system developed at DArT. The ordering system is tightly integrated with DArTdb - DArT's custom LIMS operational system, which drives laboratory, quality, and analytical processes.
 
-Diversity Arrays Technology was a part of BrAPI community since its inception. DArT developers have worked with the BrAPI community contributing to various aspects of the API specification. One key aspect was establishing a standard API for sending sample metadata to the lab for genotyping. This solution eliminates much of the human error involved with sending samples to an external lab and also allows for an automated process of sample batch transfers. Beyond sample submission, the current implementation also allows for an order status verification, automated data discovery, and data downloads. Data are delivered as standard data packages with self-describing metadata.
+Diversity Arrays Technology has been a part of BrAPI community since its inception. DArT developers have worked with the BrAPI community contributing to various aspects of the API specification. One key aspect was establishing a standard API for sending sample metadata to the lab for genotyping. This solution eliminates much of the human error involved with sending samples to an external lab, and also allows for an automated process of sample batch transfers. The current implementation also allows for an order status verification, automated data discovery, and data downloads. Data are delivered as standard data packages with self-describing metadata.
 
 The current BrAPI implementation at DArT is in production and it is compatible with the newest BrAPI specification. Further details about DArT's ordering system can be found at [DArT Ordering System](https://ordering.diversityarrays.com) and also at [DArT Help](https://help.diversityarrays.com/docs/ordering).

content/03.02.02.DArTView.md

@@ -1,6 +1,6 @@
 #### DArTView
 
 <!-- Moses N -->
-[DArTView](https://software.kddart.com/kdxplore/dartview/dartviewdocs/KDXplore-DartView.html) is a desktop application for marker data curation via metadata filtering. DArTView enables genotype variant data visualization and users can easily identify trends or correlations within their data using the tool. Its primary goal is to overcome tedious manual calculation of marker data through common spreadsheet applications like Excel. Users are able to import marker data from csv files, but DArTView has been recently enhanced to be BrAPI compatible. Users can now use any BrAPI compatible server as an input data source. BrAPI provides a consistent data standard across databases and data resources. DArTView's compatibility with BrAPI also ensures easy integration with other tools and pipelines that would use DArTView for marker filtering and exploration.
+[DArTView](https://software.kddart.com/kdxplore/dartview/dartviewdocs/KDXplore-DartView.html) is a desktop application for marker data curation via metadata filtering. DArTView enables genotype variant data visualization designed such that users can easily identify trends or correlations within their data. The primary goal of the tool is to overcome tedious manual calculation of marker data through common spreadsheet applications like Excel. Users are able to import marker data from csv files, but DArTView has been recently enhanced to be BrAPI compatible. BrAPI provides a consistent data standard across databases and data resources, which allows DArTView to use any BrAPI-compatible server as an input data source.  DArTView's compatibility with BrAPI also ensures easy integration with other tools and pipelines that would use DArTView for marker filtering and exploration.
 
-Initially developed by Diversity Arrays Technology (DArT), the tool is gaining popularity within the breeding community, especially in Africa. Future releases will focus on enhancing the BrAPI compatibility, making it accessible to more breeders and researchers in the region. A web enabled version of DArTView is in development. This new version will allow for further collaboration opportunities with other interested partners who would like to integrate it as part of their pipelines.
+Initially developed by DArT, the tool is gaining popularity within the breeding community, especially in Africa. Future releases will focus on enhancing the BrAPI compatibility, making it accessible to more breeders and researchers in the region. A web enabled version of DArTView is in development. This new version will allow for further collaboration opportunities with other interested partners who would like to integrate it as part of their pipelines.

content/03.02.03.DivBrowse.md

@@ -1,7 +1,7 @@
 #### DivBrowse
 
 <!-- Sebastian B -->
-[DivBrowse](https://divbrowse.ipk-gatersleben.de/) [@doi:10.1093/gigascience/giad025] is a web platform for exploratory data analysis of huge genotyping studies. The software can be run standalone or integrated as a plugin into existing data web portals. It provides a powerful interactive visualization of variant call matrices with hundreds of millions of variants and thousands of samples. It enables easy data import and export by using well established, standardized, bioinformatics file formats. 
+[DivBrowse](https://divbrowse.ipk-gatersleben.de/) [@doi:10.1093/gigascience/giad025] is a web platform for exploratory data analysis of huge genotyping studies. The software can be run standalone or integrated as a plugin into existing data web portals. It provides a powerful interactive visualization of genotypic matrices that can handle hundreds of millions of variants and thousands of samples. It enables easy data import and export by using well established, standardized, bioinformatics file formats. 
 
 At its core, DivBrowse combines the convenience of a genome browser with features tailored to the diversity analysis of germplasm. It is able to display genomic features such as nucleotide sequence, associated gene models, and short genomic variants. DivBrowse provides visual access to large VCF files obtained through genotyping experiments. In addition, DivBrowse also calculates and displays variant statistics such as minor allele frequencies, proportion of heterozygous calls, and proportion missing variant calls. Dynamic Principal Component Analyses (PCA) can be performed on a user specified genomic area to provide information on local genomic diversity. 
 

content/03.02.05.GIGWA.md

@@ -5,6 +5,6 @@
 
 The Gigwa development team has been involved in the BrAPI community since 2016 and took part in designing the genotype-related section of the BrAPI standard. Gigwa's first BrAPI-compliant features were designed for compatibility with the Flapjack visualization tool [@doi:10.1093/bioinformatics/btq580]. Over time, Gigwa has established itself as the first and most reliable implementation of the BrAPI-Genotyping endpoints. Local collaborators and external partners used it as a reference solution to design a number of tools taking advantage of the BrAPI-Genotyping features (e.g., [BeegMac](https://webtools.southgreen.fr/BrAPI/Beegmac/), [SnpClust](https://github.com/jframi/snpclust), [QBMS](https://github.com/icarda-git/QBMS)). 
 
-Additional use-cases required Gigwa to also consume data from other BrAPI servers. This led to the implementation of BrAPI client features within Gigwa. A close collaboration was established with the Integrated Breeding Platform team developing the widely used Breeding Management System (BMS). This collaboration means both applications are now frequently deployed together; Gigwa pulling germplasm or sample metadata from BMS, and BMS displaying Gigwa-hosted genotypes within its own UI.
+Some use-cases require Gigwa to also consume data from other BrAPI servers. This requirement led to the implementation of BrAPI client features within Gigwa. A close collaboration was established with the Integrated Breeding Platform team their widely used Breeding Management System (BMS). This collaboration means both applications are now frequently deployed together; Gigwa pulling germplasm or sample metadata from BMS, and BMS displaying Gigwa-hosted genotypes within its own UI.
 <!-- 
 Community members typically write adhoc scripts federating data from multiple BrAPI sources using BrAPI client libraries available for R, python, and other programming languages.  For instance, phenotypes from one data source and genotypes from another in order to run various kinds of analyses such as GWAS, genomic selection or phylogenetic investigations. The most generic and widely-used of those pipelines are at least publicly distributed, and possibly web-interfaced using solutions like R-Shiny. This provides new, excitingly useful, online services, based on Gigwa-hosted data. -->

content/03.02.06.PHG.md

@@ -1,6 +1,6 @@
 #### PHG
 
 <!-- Lynn J. -->
-The [Practical Haplotype Graph](https://www.maizegenetics.net/phg) (PHG) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics [@doi:10.1093/bioinformatics/btac410]. Using a pangenome approach, each PHG stores haplotypes (the sequence of part of an individual chromosome) to represent the collected genes of a species. This allows for a simplified approach for dealing with large scale variation in plant genomes. The PHG pipeline provides support for a range of genomic analyses and allows for the use of graph data to impute complete genomes from low density sequence or variant data.
+The [Practical Haplotype Graph (PHG)](https://www.maizegenetics.net/phg) is a graph-based computational framework that represents large-scale genetic variation and is optimized for plant breeding and genetics [@doi:10.1093/bioinformatics/btac410]. Using a pangenome approach, each PHG stores haplotypes (the sequence of part of an individual chromosome) to represent the collected genes of a species. This allows for a simplified approach for dealing with large scale variation in plant genomes. The PHG pipeline provides support for a range of genomic analyses and allows for the use of graph data to impute complete genomes from low density sequence or variant data.
 
 Users access the crop databases either with direct calls to the PHG embedded server or indirectly using the rPHG library from an R environment. The PHG server accepts BrAPI queries to return information on sample lists and the variants used to define the graph's haplotypes. In addition, PHG users utilize the BrAPI Variant Sets endpoint query to return links to VCF files containing haplotype data. Work on the PHG is ongoing and it is expected to support additional BrAPI endpoints that allow for fine tuned slicing genotypic data in the near future.