section reordered alphabetically (mostly)

content/03.01.01.Field_Book.md

@@ -6,4 +6,4 @@ Data from plant breeding and genetics experiments has traditionally been collect
 [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.
 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.
+Removing the need to manually transfer data files simplifies data exchange between these systems and reduces the opportunities for human error and data loss.

content/03.03.02.MGIS.md → content/03.03.05.MGIS.md

@@ -1,6 +1,6 @@
 #### MGIS
 
 <!-- authors: Mathieu Rouard, Valentin Guignon -->
-The Musa Germplasm Information System ([MGIS](https://www.crop-diversity.org/mgis/)) serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [@doi:10.1093/database/bax046]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [@doi:10.1186/s43170-020-00015-6]. It is built on the Drupal/Tripal technology, like BIMS [@doi:10.1093/database/baab054] and Florilège. 
+The [Musa Germplasm Information System (MGIS)](https://www.crop-diversity.org/mgis/) serves as a comprehensive community portal dedicated to banana diversity, a crop critical to global food security [@doi:10.1093/database/bax046]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [@doi:10.1186/s43170-020-00015-6]. It is built on the Drupal/Tripal technology, like BIMS [@doi:10.1093/database/baab054] and Florilège. 
 
 Since its inception, MGIS developers have actively participated in the BrAPI community. The MGIS team pushed for the integration of the Multi-Crop Passport Data (MCPD) standard into the Germplasm module of the API. MCPD support was added in BrAPI v1.3, and MGIS now provides passport data information on ITC banana genebank accessions (with GLIS DOI), synchronized with [Genesys](https://www.genesys-pgr.org/a/overview/v2YdWZGrZjD). MGIS also enriches the passport data by incorporating additional information from other germplasm collections worldwide. All the germplasm data is available through the BrAPI Germplasm module implementation. For genotyping data, MGIS integrates with Gigwa [@doi:10.1093/gigascience/giz051], which provides a tailored implementation of the BrAPI genotyping module. Furthermore, MGIS supports a set of BrAPI phenotyping endpoints, facilitating the exposure of morphological descriptors and trait information supported by ontologies like the Crop Ontology [@doi:10.1093/aobpla/plq008]. MGIS has integrated the Trait Selector BrAPP, and there are use cases implemented to interlink genebank and breeding data between MGIS and the breeding database MusaBase.

content/03.05.--.HEADER.Analytics.md

@@ -1,3 +1,5 @@
 ### Analytics
 
-While other tools listed above have the capability to do specialized analytics on certain types of data, general analytics tools can cover a wide range of data types and analytical models. The tools developed by the BrAPI community can pull in data from multiple BrAPI compatible data sources and provide enhanced analytical functionality. In many cases, there is no longer a need to import and export large data files to a local computational environment just to run standard analytical models. These tools are able to extract the data they need from a data source without much human intervention or human error. 
+Modern breeding programs can utilize data management systems to maintain both phenotypic and genotypic data. Numerous systems are available for adoption. To fully leverage the benefits of digitalization in this ecosystem, breeders need to utilize data from different sources to make efficient data-driven decisions. With increased computational power at their disposal, scientists can construct more advanced analysis pipelines by combining various data sources.
+
+The tools developed by the BrAPI community can pull in data from multiple BrAPI compatible data sources and provide enhanced analytical functionality. In many cases, there is no longer a need to import and export large data files to a local computational environment just to run standard analytical models. These tools are able to extract the data they need from a data source without much human intervention or human error. 

content/03.05.01.QBMS.md → content/03.05.02.QBMS.md

@@ -1,7 +1,6 @@
 #### QBMS
 
 <!-- Khaled -->
-Modern breeding programs can utilize data management systems to maintain both phenotypic and genotypic data. Numerous systems are available for adoption. To fully leverage the benefits of digitalization in this ecosystem, breeders need to utilize data from different sources to make efficient data-driven decisions. With increased computational power at their disposal, scientists can construct more advanced analysis pipelines by combining various data sources.
 
 The [QBMS](https://icarda-git.github.io/QBMS) [@doi:10.5281/zenodo.10791627] R package eliminates technical barriers scientists experience when using the BrAPI specification in their analysis scripts and pipelines. This barrier arises from the complexity of managing API backend processes, such as authentication, tokens, TCP/IP protocol, JSON format, pagination, stateless calls, asynchronous communication, database IDs, and more. To bridge this gap, the QBMS package abstracts the technical complexities, providing breeders with stateful functions familiar to them when navigating their GUI systems. It enables them to query and extract data into a standard data frame structure, consistent with their use of the R language, one of the most common statistical tools in the breeding community.
 

content/03.06.02.MIAPPE.md

@@ -0,0 +1,8 @@
+#### BrAPI2ISA
+
+<!-- Manuel & Bert -->
+Since the release of BrAPI 1.3, efforts have been made to incorporate support for the [MIAPPE](https://www.miappe.org/) (Minimal Information About a Plant Phenotyping Experiment) [@doi:10.1111/nph.16544] standard into the specification, achieving full compatibility in BrAPI 2.0. Consequently, BrAPI now includes all attributes necessary for MIAPPE compliance, adhering to standardized descriptions in accordance with MIAPPE guidelines. In some communities and projects, phenotyping data and metadata are archived and published as structured ISA-Tab files, validated using the [MIAPPE ISA configuration](https://github.com/ELIXIR-Belgium/isatab-validation) [@doi:10.1038/ng.1054]. Although ISA-Tab is easy to read for non-technical experts due to its file-based approach, it lacks programmatic accessibility, particularly for web applications. 
+
+[MIRA](https://github.com/USDA-ARS-GBRU/SugarcaneCrossingTool) enables the automatic deployment of a BrAPI server on a MIAPPE-compliant dataset in ISA-Tab format, facilitating programmatic access to these datasets. It is deployable from a Docker image with the dataset mounted. The tool leverages the mapping between MIAPPE, ISA-Tab, and BrAPI, eliminating the need for parsing or manual mapping of datasets compliant with (meta-)data standards. By providing programmatic access through BrAPI, MIRA facilitates the integration of phenotyping datasets into web applications.
+
+The [BrAPI2ISA](https://github.com/elixir-europe/plant-brapi-to-isa) service functions as a converter between a BrAPI-compatible server and the ISA-Tab format. The tool simplifies, automates, and facilitates the archiving of data, thereby enhancing data preservation and accessibility. The BrAPI2ISA tool is compatible with BrAPI 1.3 and welcomes community contributions to support the latest versions of BrAPI.