break up success stories into files

content/03.01.--.HEADER.Data_Collection.md

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+### Data Collection 
+<!-- (Field Book, Climmob, other Phenoapps, ImageBreed, etc) -->
+
+<!-- * General use case description(s)
+* Specific tool examples 
+* Alternate solutions/ why is it better with BrAPI
+* future related use cases, areas to improve -->

content/03.01.01.Field_Book.md

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+#### Field Book
+
+<!-- Peter S: Stub paragraph to stimulate the writing process. Please edit, rewrite, or delete as needed. -->
+Phenotypic data collection is an essential part of the breeding process. Historically, gathering data in the field was done with pen and paper, or perhaps some version of a digital spreadsheet. The abundance and prevalence of smart phones has allowed the Field Book mobile app to enhance data collection. Field Book can create well-formed digital observation records from the moment they are taken. This can improve the efficiency of data collection and reduce human error. 
+
+In 2018, BrAPI was introduced into Field Book; specifically, the Core and Phenotyping modules. BrAPI was able to take things a step further by automating the flow of data from the Field Book mobile app to a central database server. This workflow allows data collection and storage to be expedited, removing the need of the user to transfer export files manually. Since Field Book’s adoption of BrAPI, many community servers have been integrated to simplify data storage. In this work flow, data is collected and stored completely digitally with little-to-no human involvement.

content/03.01.02.ClimMob.md

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+#### ClimMob
+
+Not all data can be collected by a single person, or even by a single organization. ClimMob is a tool to easily allow citizen scientists to assist in the data collection process. Although this data may not be as detailed as a focused scientific program, it can be very useful to collect simple data from a wide range of locations and environments.
+
+When it comes to BrAPI compatibility, ClimMob follows the same patterns established by Field Book. During a survey, all the farmer collected data is stored in a central ClimMob node. When the survey is complete, all the data is uploaded automatically via BrAPI to a central breeding database for long term storage and analysis. 

content/03.01.03.Image_Breed.md

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+#### ImageBreed
+
+High-throughput phenotyping has been gaining significant traction lately as a way to collect lots of data very quickly. Image collection from unmanned arial and ground vehicles (UAVs and UGVs) are a great way to collect a lot of raw data all at once, then analyze it later. ImageBreed is a image collection pipeline tool to support regular use of UAVs and UGVs. 
+
+When the raw images have been processed through the standardization pipelines in ImageBreed, useful phenotypes can be extracted from the images. The BrAPI standard is used to push these phenotypes back to a central breeding database where they can be analyzed with other data. In addition to this, ImageBreed also has the option to use BrAPI to upload the raw images to the central breeding database, or any other BrAPI compatible long term storage service. The BrAPI models in the current version of the standard (V2.1) are rudimentary, but effective. The ImageBreed team has put in some work to enhance the BrAPI image data standards. 

content/03.01.04.GridScore.md

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+#### GridScore
+
+Phenotypic data collection underpins scientific crop research and plant breeding. Knowledge gained from collected data and its analysis alongside data visualizations inform further phenotypic trials and ideally support research hypotheses. The importance of accuracy and efficiency in the collection of this data as well as the infrastructure to facilitate the flow of data from the field to a knowledge base cannot be underestimated. [GridScore](https://ics.hutton.ac.uk/get-gridscore/) [@doi:10.1186/s12859-022-04755-2] is a modern mobile application for phenotypic observations that harnesses technological advancements in the area of mobile devices to enrich the data collection process.
+
+BrAPI has further increased the value of GridScore by integrating it into the overarching workflow from trial creation, data collection, and its ultimate data storage for further processing. Specifically, trial designs as well as trait definitions can be imported into GridScore using BrAPI and a finalized trial can ultimately be exported via BrAPI to any compatible database.

content/03.02.--.HEADER.Data_Management.md

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+### Data Management 
+
+<!-- * General use case description(s)
+* Specific tool examples 
+* Alternate solutions/ why is it better with BrAPI
+* future related use cases, areas to improve -->

content/03.02.01.PHIS.md

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+#### PHIS 
+
+The Hybrid Phenotyping Information System ([PHIS](http://www.phis.inrae.fr/) [@doi:https://doi.org/10.1111/nph.15385]), based on the [OpenSILEX](https://github.com/OpenSILEX/) framework, is an ontology-driven information system based on semantic web technologies. PHIS is deployed in several field and greenhouse platforms of the national [PHENOME](https://www.phenome-emphasis.fr/) and European [EMPHASIS](https://emphasis.plant-phenotyping.eu/) infrastructure. It manages and collects data from 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. 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:https://doi.org/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 makes the indexing of PHIS in [FAIDARE](https://urgi.versailles.inra.fr/faidare/) very easy to implement.
+
+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 adherence 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/03.02.02.DeltaBreed.md

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+#### DeltaBreed
+
+<!-- Shawn Y.-->
+DeltaBreed is an open-source data management system designed and developed by Breeding Insight to support USDA-ARS specialty crop and animal breeders. DeltaBreed is a unified system for managing breeding data that connects a variety of BrAPI applications (see list below). BrAPI integration allows the complexity underlying interoperability to be hidden, shielding users from multifactorial differences between diverse applications. DeltaBreed, adhering to the BrAPI model, establishes data standards and validations for users and provides a singular framework for data management and user training.
+
+DeltaBreed users need not be aware of BrAPI or the specifics of underlying applications but will notice that BrAPI interoperability reduces the need for human-mediated file transfers and data manipulation. Field Book users, for example, can connect to their DeltaBreed program, authenticate, and pull studies and traits directly from DeltaBreed to Field Book on their data collection device. The subsequent step of pushing observations from Field Book to DeltaBreed is straightforward via BrAPI, but will not be implemented until repeated observation handling workflows are established to differentiate and validate repeated observations, such as accidental repeats, overwrite requests, time-series observations, and repeated sub-entity measures. Users can expect DeltaBreed observation handling to become more seamless with future development.
+
+**DeltaBreed Connected Applications**
+<< Submission is expected April 2024. We may need to trim this aspirational list down to reality in final edits.>>
+
++ BIMS <https://www.breedwithbims.org/>
++ BrAPI Java Server <https://test-server.brapi.org/brapi/v2/>
++ BrAPI Sync <https://github.com/IntegratedBreedingPlatform/brapi-sync>
++ BreedBase <https://breedbase.org/>
++ Diversity Arrays Technologies (DArT) genotyping services
++ Field Book <https://play.google.com/store/apps/details?id=com.fieldbook.tracker>
++ Gigwa <https://gigwa.southgreen.fr/gigwa/>
++ Mr Bean <https://github.com/AparicioJohan/MrBeanApp>
++ Pedigree Viewer <https://github.com/solgenomics/BrAPI-Pedigree-Viewer>

content/03.02.03.BMS.md

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+#### BMS
+
+The [Breeding Management System (BMS)](https://bmspro.io), developed by the [Integrated Breeding Platform (IBP)](https://integratedbreeding.net/), is a suite of tools designed to enhance the efficiency and effectiveness of plant breeding. BMS covers all stages of the breeding process, with the emphasis on germplasm management and [ontology](https://cropontology.org)-harmonized phenotyping. It also features analytics and decision-support tools. With its focus on interoperability, BMS integrates smoothly with BrAPI, facilitating easy connections with a broad array of complementary tools and databases, notably [Gigwa](https://southgreen.fr/content/gigwa) which is deployed together with the BMS to fulfill the genotyping data management needs of BMS users.
+
+The [brapi-sync](https://github.com/IntegratedBreedingPlatform/brapi-sync) tool, a significant component of BMS’s BrAPI capabilities, was developed by the IBP and released as a BrAPP for community use. Brapi-sync is designed to enhance collaboration among partner institutes within a network such as Innovation and Plant Breeding in West Africa ([IAVAO](https://www.iavao.org/en)), by enabling the sharing of germplasm and trials across BrAPI-enabled systems. This tool helps overcome traditional barriers to collaboration, ensuring data that was once isolated within specific programs or platforms can now be easily shared, integrated, and synchronized. 
+
+Additionally, brapi-sync improves data management by utilizing the externalReferences field to maintain links to the origin IDs of each entity it transmits. This not only retains the original context of the data but also establishes a traceability mechanism for accurate data source attribution and verification. Such practices are crucial for maintaining data integrity and fostering trust among collaborative partners, ensuring access to accurate, reliable, and current information.

content/03.02.04.Breedbase.md

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+#### Breedbase
+
+Breedbase is a comprehensive breeding data management system [@doi:10.1093/g3journal/jkac078] [@doi:10.1371/journal.pone.0240059] that implements a digital ecosystem for all breeding data, including trial data, phenotypic data, and genotypic data. Data acquisition is through tabled-based apps such as Fieldbook [@doi:10.2135/cropsci2013.08.0579] and related apps, such as Coordinate and InterCross apps, through drone imagery, Near Infra-Red Spectroscopy (NIRS), and other technologies. Search functions such as the Search Wizard interface provide powerful query capabilities, and various breeding-centric analysis tools are available, including mixed models, heritability, stability, PCA, and various clustering algorithms. The original impetus for creating Breedbase was the advent of new breeding paradigms based on genomic information such as genomic prediction algorithms [@doi:10.1093/genetics/157.4.1819] and the accompanying data management challenges, and complete genomic prediction workflow is integrated in the system.  The first instance was created for the NextGen Cassava project in 2012 as the Cassavabase (<https://cassavabase.org/>) database. Databases for other CGIAR root, tuber and banana (RTB) crops followed with database for yam (<https://yambase.org/>), sweet potato (<https://sweetpotatobase.org/>), banana (<https://musabase.org/>) as well as instances in labs and companies. The BrAPI interface [@doi:10.1093/bioinformatics/btz190] is crucial for Breedbase: Breedbase communicates via BrAPI with the data collection tablets, connection to other projects such as CLIMMOB [@doi:10.1016/j.compag.2023.108539], and many native tools use the BrAPI interface for accessing data. Users also appreciate the ability to connect to Breedbase instances using packages such as QBMS <https://icarda-git.github.io/QBMS/> for data import into R for custom analyses. Breedbase has been an early and continuous adopter of, and contributor to, the BrAPI standard.

content/03.02.05.BIMS.md

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+#### BIMS
+
+BIMS (Breeding Information Management System) [@doi:10.1093/database/baab054] is a free, secure, and online breeding management system which allows breeders to store, manage, archive, and analyze their private breeding program data. BIMS enables individual breeders to have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving for their germplasm, phenotype, genotype, and image data. BIMS is currently implemented in five community databases, the Genome Database for Rosaceae [@doi:10.1093/nar/gky1000], CottonGEN [@doi:10.3390/plants10122805], the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium, as well as a crop-independent website, <https://breedwithbims.org>. BIMS in these five community databases enables individual breeders to import publicly available data so that they can utilize public data in their breeding program. BIMS utilizes the Android App Field Book, enabling seamless data transfer between BIMS and the Field Book App through either files or BrAPI. Data transfer through BrAPI between BIMS and other resources such as BreedBase, GIGWA, and Breeder Genomics Hub is also on the way. 

content/03.02.06.Germinate.md

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+#### Germinate
+
+[Germinate](https://ics.hutton.ac.uk/get-germinate/) [@doi:10.1002/csc2.20248] is an open-source plant genetic resources database that combines and integrates various kinds of plant breeding data including genotypic data, phenotypic trials data, passport data, images, geographic information and climate data into a single repository. Germinate is tightly linked to the BrAPI specification and supports a majority of BrAPI endpoints for querying, filtering and submission.
+
+Germinate integrates and connects with other BrAPI-enabled tools such as GridScore for phenotypic data collection, Flapjack for genotypic data visualization and Helium for pedigree visualization, but, due to the nature of BrAPI, Germinate can act as a data repository for any BrAPI-compatible tool. Thanks to the interoperability provided by BrAPI the need for manual data handling becomes a rarity with the direct benefit of faster data processing, fewer to no human errors, data security and integrity.

content/03.02.07.PIPPA.md

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+#### PIPPA
+
+[PIPPA](https://pippa.psb.ugent.be) is a data management system used for collecting data from the [WIWAM](https://www.wiwam.be/) range of automated high throughput phenotyping platforms. These platforms have been deployed at different research institutes and commercial breeders across Europe in a variety of configurations with different types of equipment such as weighing scales, cameras and environment sensors. Examples are:
+
++ [Umea Plant Science Centre](https://www.upsc.se/plant-growth-facilities-at-upsc-and-slu-umea/325-upsc-tree-phenotyping-platform.html)
++ [Fondazione Edmund Mach](https://cri.fmach.it/en/Facilities/Technological-Facilities/Plant-Phenotyping#application_fields)
++ [Phenovision](https://www.psb.ugent.be/phenotyping/phenovision)
+
+Developed from 2016 onwards, the software features a web interface with functionality for setting up new experiments for the platform(s), planning imaging and irrigation treatments, linking metadata to pots (genotype, growth media, manual treatments), exporting data, importing data and visualizing data as charts. It also supports the integration of image analysis scripts and connections to a compute cluster for job submission.
+
+To share the phenotype data of the experiments linked to publications, an implementation of BrAPI 1.3 was developed on a separate public PIPPA server open to the public, which allowed read only access to the data in a standardized format. This endpoint was registered on [FAIDARE](https://urgi.versailles.inra.fr/faidare/) and allows the data to be found alongside data from other BrAPI endpoints.
+
+As the BrAPI ecosystem has matured, it created a clear path for the development of PIPPA as to how to share data in a manner according to the FAIR principles which are becoming standard in plant research data management best practices. In combination with the support for [MIAPPE](https://www.miappe.org/), these have served as guidelines in the current development, which is focussed on delivering a public BraPI 2.1 endpoint and making more high throughput datasets publicly available via BrAPI.

content/03.02.08.MGIS.md

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+#### 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 Build on the Drupal/Tripal technology, like BIMS and Florilège.  Since its inception, MGIS developers have actively participated in the Breeding API (BrAPI) community, pushing for the integration of Multicrop Passport Data (MCPD) into Germplasm module call of the API. MGIS thus provides passport data information on ITC banana genebank accessions (with GLIS DOI), synchronized with [Genesys](https://www.genesys-pgr.org/a/overview/v2YdWZGrZjD), but also enriches it by incorporating additional data from other germplasm collections worldwide. All those germplasm data are available through BrAPI germplasm module calls implementations. For genotyping data, MGIS incorporates GIGWA [@doi:10.1093/gigascience/giz051], which provides tailored implementations for BrAPI genotyping module calls. Furthermore, MGIS supports the implementation of a set of BrAPI phenotyping module calls, facilitating the exposing of morphological descriptors and trait information supported by ontologies like the Crop Ontology [@doi:10.1093/aobpla/plq008]. It is integrated with the Trait Selector BrAPP, developed as part of a project involving Breedbase [@doi:10.1093/g3journal/jkac078]. Uses cases between the Musa implementation of Breedbase, MusaBase, and MGIS to interlink genebank and breeding data.

content/03.03.--.HEADER.Federation_Infrastructure.md

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+### Federated Data Management Infrastructures
+<!-- 
+(AGENT, INCREASING, EURISCO, DataPLANT, NFDI4BioDiversity, FAIRAgro)
+
+* General use case description(s) -  AgrosystemIntegration of germplasm collections in context of data trustee models among private economy and public research, integration of ex-situ genebanks (EU H2020 projects AGENT, INCREASING), integrated agrosystems and plant research infrastructure
+* Specific tool examples - BraPI endpoints for AGENT, IPK-Genebank, MIAPPE ISA-TAB2BRAPI service
+* Alternate solutions/ why is it better with BrAPI - Schema.ORG lightweight meta data harvesting, ARCs as collaborative data decoration, API and publication pipeline
+* future related use cases, areas to improve - LIMS to BrAPI proxies -->