Database , 2025, 1–15 DOI: https://doi.org/10.1093/database/baaf048 Original Article BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community Peter Selby 1 ,1 , Rafael Abbeloos 2 , Anne-Francoise Adam-Blondon 3 ,4 , Francisco J. Agosto-Pérez 1 , Michael Alaux 4 , Isabelle Alic 5 , Khaled Al-Shamaa 6 , Johan Steven Aparicio 7 , Jan Erik Backlund 8 , Aldrin Batac 8 ,9 , Sebastian Beier 10 ,11 , Gabriel Besombes 5 , Alice Boizet 12 ,13 , Matthijs Brouwer 14 , Terry Casstevens 15 , Arnaud Charleroy 5 , Keo Corak 16 , Chaney Courtney 17 , Mariano Crimi 8 , Gouripriya Davuluri 18 , Kauê de Sousa 19 , Jeremy Destin 4 , Stijn Dhondt 2 , Ajay Dhungana 20 , Bert Droesbeke 21 , Manuel Feser 18 ,22 , Mirella Flores-Gonzalez 23 , Valentin Guignon 19 , Corina Habito 8 , Asis Hallab 10 ,24 , Jenna Hershberger 17 , Puthick Hok 25 , Amanda M. Hulse-Kemp 16 , Lynn Carol Johnson 15 , Sook Jung 26 , Paul Kersey 27 , Andrzej Kilian 25 , Patrick König 18 , Suman Kumar 18 , Josh Lamos-Sweeney 1 , Laszlo Lang 24 , Matthias Lange 18 , Marie-Angélique Laporte 19 , Taein Lee 26 , Erwan Le Floch 4 , Francisco López 28 , Brandon Madriz 29 , Dorrie Main 26 , Marco Marsella 28 , Maud Marty 4 , Célia Michotey 4 , Zachary Miller 15 , Iain Milne 30 , Lukas A. Mueller 23 , Moses Nderitu 31 , Pascal Neveu 5 , Nick Palladino 32 , Tim Parsons 32 , Cyril Pommier 4 , Jean-François Rami 12 ,13 , Sebastian Raubach 30 , Trevor Rife 17 , Kelly Robbins 1 , Mathieu Rouard 19 , Joseph Ruff 27 , Guilhem Sempéré 33 ,34 , Romil Mayank Shah 35 , Paul Shaw 30 , Becky Smith 30 , Nahuel Soldevilla 8 ,9 , Anne Tireau 5 , Clarysabel Tovar 8 ,9 , Grzegorz Uszynski 25 , Vivian Bass Vega 24 , Stephan Weise 18 , Shawn C. Yarnes 32 , The BrAPI Consortium 36 1 Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA 2 VIB Agro-incubator, Vlaams Instituut voor Biotechnologie (VIB), 9850 Nevele, Belgium 3 IFB-core, Institut Français de Bioinformatique (IFB), CNRS, INSERM, INRAE, CEA, 91057 Evry, France 4 Université Paris-Saclay, INRAE, BioinfOmics, URGI, 78026, Versailles, France 5 MISTEA, University of Montpellier, INRAE, Institut Agro, 34000 Montpellier, France 6 International Center for Agricultural Research in the Dry Areas (ICARDA), Dalia Bldg, Bashir El Kassar Street, VFPM + GJW, Bayrut, Lebanon 7 International Center for Tropical Agriculture (CIAT), Cali, Valle del Cauca, Colombia 8 Integrated Breeding Platform , 56237 El Batán, Texcoco, México, México 9 Leafnode LLC, 56237 El Batán, Texcoco, México, México 10 Institute of Bio- and Geosciences (IBG-4: Bioinformatics), CEPLAS, Forschungszentrum Jülich GmbH, Wilhelm Johnen Straße, 52428 Jülich, Germany 11 Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany 12 CIRAD, UMR AGAP Institut, 34980, Montpellier, France 13 AGAP Institut, CIRAD, INRAE, Institut Agro, Université de Montpellier, Montpellier, 34980, France 14 Wageningen University and Research, Droevendaalsesteeg, Wageningen, 2, 6708 PB, Netherlands 15 Buckler Lab and Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA 16 USDA-ARS Genomics and Bioinformatics Research Unit, 5601 Sunnyside Avenue, Beltsville, MD 20705, USA 17 Department of Plant and Environmental Sciences, Clemson University, 16 N Clemson Ave, Clemson, SC 29631, USA 18 Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany 19 Bioversity International, Parc Scientifique Agropolis II, 34397 Montpellier, France 20 College of Agriculture, Louisiana State University (LSU), 106 Martin D. Woodin Hall Baton Rouge, LA 70803, USA 21 VIB Data Core, Vlaams Instituut voor Biotechnologie (VIB), 9052 Ghent, Belgium 22 Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Postfach 10 01 31, 33501 Bielefeld, Germany 23 The Boyce Thompson Institute, Ithaca, NY 14853, USA 24 Bingen Technical University of Applied Sciences, Berlinstraße, Bingen am Rhein, 109, 55411, Germany Received 5 February 2025; Revised 24 April 2025; Accepted 13 June 2025 © The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 https://doi.org/10.1093/database/baaf048 https://orcid.org/0000-0001-7151-4445 https://orcid.org/0000-0002-0177-3887 https://orcid.org/0000-0002-3412-9086 https://orcid.org/0000-0002-5059-253X https://orcid.org/0000-0001-9356-4072 https://orcid.org/0000-0002-8961-6068 https://orcid.org/0000-0002-7668-3798 https://orcid.org/0000-0002-2177-8781 https://orcid.org/0009-0004-1359-2145 https://orcid.org/0000-0003-4096-6689 https://orcid.org/0000-0001-8183-0484 https://orcid.org/0000-0001-7602-0487 https://orcid.org/0009-0009-7575-3617 https://orcid.org/0000-0002-4129-3319 https://orcid.org/0009-0005-2308-0773 https://orcid.org/0000-0002-7571-7845 https://orcid.org/0000-0003-4402-2191 https://orcid.org/0009-0007-2639-6604 https://orcid.org/0000-0003-0522-5674 https://orcid.org/0000-0001-6546-1818 https://orcid.org/0000-0002-7759-1617 https://orcid.org/0000-0003-0903-6811 https://orcid.org/0000-0002-2421-5431 https://orcid.org/0000-0002-3147-6867 https://orcid.org/0000-0001-9670-9433 https://orcid.org/0000-0001-8103-2722 https://orcid.org/0000-0003-3968-2769 https://orcid.org/0000-0002-8948-6793 https://orcid.org/0009-0005-5832-7190 https://orcid.org/0009-0009-8936-4532 https://orcid.org/0000-0002-4316-078X https://orcid.org/0000-0002-8461-9745 https://orcid.org/0000-0002-2000-2586 https://orcid.org/0000-0002-1010-6859 https://orcid.org/0000-0002-1162-2724 https://orcid.org/0000-0003-0334-8785 https://orcid.org/0009-0001-8764-6466 https://orcid.org/0000-0003-1877-1703 https://orcid.org/0000-0002-5454-4527 https://orcid.org/0000-0002-4126-0859 https://orcid.org/0000-0001-8640-1750 https://orcid.org/0000-0003-4189-7793 https://orcid.org/0009-0009-1645-297X https://orcid.org/0000-0001-6209-2455 https://orcid.org/0000-0002-9040-8733 https://orcid.org/0000-0002-5679-3877 https://orcid.org/0000-0001-5659-247X https://orcid.org/0000-0002-5974-6523 https://orcid.org/0000-0001-9522-9585 https://orcid.org/0000-0003-0284-1885 https://orcid.org/0000-0001-7429-2091 https://orcid.org/0000-0002-0202-1150 https://orcid.org/0000-0002-8968-3383 https://orcid.org/0000-0001-8501-6922 https://orcid.org/0009-0002-2476-9888 https://orcid.org/0000-0003-4031-9131 https://orcid.org/0000-0002-1090-0403 https://creativecommons.org/licenses/by/4.0/ 2 Selby et al. 25 Diversity Arrays Technology (DArT), University of Canberra Kirinari Street, Bruce, ACT, 2617, Australia 26 Department of Horticulture, Washington State University, Pullman, WA 99164, USA 27 Royal Botanic Gardens, Kew, Richmond, London, TW9 3AE, England, UK 28 International Treaty on Plant Genetic Resources for Food and Agriculture, FAO, Viale delle Terme di Caracalla, 00153 Rome, Italy 29 MrBot Software Solutions, Cartago, 30501, Costa Rica 30 Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, Scotland 31 SEQART AFRICA, Old Naivasha road, 00100, Nairobi, Kenya 32 Breeding Insight, Cornell University, Ithaca, NY 14853, USA 33 CIRAD, UMR INTERTRYP, Montpellier, France INTERTRYP, Univ Montpellier, CIRAD, IRD, French Institute of Bioinformatics (IFB), Montpellier, 34980, France 34 South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, 34980 Montpellier, France 35 College of Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 27695, USA 36 The BrAPI Consortium, Cornell University, Ithaca, NY 14853, USA ∗Corresponding author. Plant Breeding and Genetics Section, School of Integrative Plant Science, 102D Beebe Hall, 10 Arboritum Road, Cornell University, Ithaca, NY 14853, USA. E-mail: ps664@cornell.edu Citation details: Selby, P., Abbeloos, R., Adam-Blondon, A.-F., et al. BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community. Database (2025) Vol. 2025: article ID baaf048; DOI: https://doi.org/10.1093/database/baaf048 Abstract Population growth and the impacts of climate change are placing increasing pressure on global agriculture and breeding programmes. Re- cent advancements in phenotyping techniques, genotyping technologies, and predictive modelling are accelerating genetic gains in breeding programmes, helping researchers and breeders develop improved crops more efficiently. However, these advancements have also led to an overwhelming torrent of fragmented data, creating significant challenges in data integration and management. To address this issue, the Breed- ing Application Programming Interface (BrAPI) project was established as a st andardized dat a model for breeding data. BrAPI is an international, community-driven effort that facilit ates interoperabilit y among dat abases and tools, improving the sharing and interpretation of breeding-related data. This open-source standard is software-agnostic and can be used by anyone interested in breeding , phenot yping , germplasm, genot yping , and agronomy data management. This manuscript provides an overview of the BrAPI project, highlighting the significant progress made in the development of the data standard and the expansion of its community. It also presents a showcase of the wide variety of BrAPI-compatible tools that have been built to enhance breeding and research activities, demonstrating how the project is advancing agricultural innovation and data management practices. I B v f t i p t m l t t u t t o f d c c s s i f d c i t i i w D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 ntroduction reeding programmes aim to deliver improved lines or culti- ars, the most fundamental input for farming, and are thus oundational for maintaining a productive agricultural sys- em amidst the pressing challenges of climate change. Breed- ng efforts are time- and resource-intensive, with progress de- endent on efficient programme logistics and accurate selec- ion decisions. While breeding programmes can benefit from odern and emerging breeding techniques like genomic se- ection, machine learning, and high-throughput phenotyping, he successful implementation of these methods depends on he ability to efficiently collect, manage, and analyse large vol- mes of carefully curated genomic and phenomic data [ 1 ]. Ex- racting actionable knowledge from these complex datasets is ime-consuming, often prohibiting the adoption of new meth- ds, especially by under-resourced breeding programme. To acilitate the collection, management, and analysis of these atasets, it is essential to transition to digital tools. Histori- ally, independent applications were designed to address spe- ific problems, but in many cases, this led to separate software olutions for each breeding programme task and created data ilos. The Breeding Application Programming Interface (BrAPI) s a standardized, web service application programming inter- ace (API) specification for breeding and related agricultural ata [ 2 ]. Since the project’s inception in 2014, BrAPI has be- ome an essential part of the digital infrastructure for breed- ng, providing a domain-specific open data standard tailored o the needs of breeding and genetics projects. BrAPI enables nteroperability between breeding software platforms, allow- ng groups to seamlessly share data and software tools both ithin and across breeding programmes. It eases the merging of datasets of different types and provides access to shared trait ontologies, phenotypic data, genotypes, seed inventories, and other essential components for collaborative breeding ef- forts. Since its first publication in 2019 [ 2 ], BrAPI has seen a significant increase in community services, compatible tools, and participating organizations. The community has orga- nized numerous hackathons to evolve the specification, re- sulting in continuous improvements and enhancements. This report includes a short technical description of the standard and a showcase of the applications, services, and tools avail- able from the BrAPI community. It is the intention of this manuscript to demonstrate the value of BrAPI to the wider scientific community as an effective and efficient means to col- laborate and exchange data. How it works An API is a technical connection between two pieces of soft- ware. 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. BrAPI is based on the represen- tational state transfer (REST) technical architecture, which describes the stateless transmission of data between appli- cations [ 3 ]. Typically, REST-style (or RESTful) web service APIs are implemented using the standard HTTP protocol that most of the modern internet is built upon. These implemen- tations 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 mailto:ps664@cornell.edu https://doi.org/10.1093/database/baaf048 Real-world applications for data integration 3 Figure 1. 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. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/ any piece of software and can solve a wide range of use cases. Data repositories and service providers that are BrAPI com- patible 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 ac- cept new data from external sources and automatically map the new data to their existing database. The service providers publish a set of RESTful web service paths, also called ‘end- points’, for authorized client applications to use to access the services. Client application developers can take advantage of this standardization by building tools and connectors that in- tegrate with all BrAPI-compatible data repositories. Visual- ization, reporting, analytics, data collection, and quality con- trol tools can be built once and shared with other organiza- tions 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 or- ganizations have the required BrAPI service endpoints avail- able. 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, increas- ing from 51 endpoints in v1.0 to 201 endpoints in v2.1. Be- cause of this growth, the specification documents were reor- ganized into four modules: BrAPI-Core, BrAPI-Phenotyping, BrAPI-Genotyping, and BrAPI-Germplasm. Figure 1 is a sim- plified domain map of the current BrAPI data model, showing what kinds of data are defined in each module. The BrAPI specification follows a simple versioning scheme which attempts to balance the static nature of a standard with the ability to fix issues and innovate over time. The basic strategy involves major version and minor version up- dates. Minor versions are always made backwards compatible within a major version. Major versions are not guaranteed to be backwards compatible and usually come with sweep- ing structural changes to the whole specification. For exam- ple, a version v1.1 client application should still work with a 8263863 by guest on 01 O ctober 2025 4 Selby et al. v s t h c a r a o s j a b v m d a p p m b i u d t u m t S f p a C T v r t m t p i t c s B t z t R B B t a t i i ersion v1.3 server, but is not guaranteed to work with a v2.0 erver. Each new version of the specification is built almost en- irely from community suggestions, and the whole community as an opportunity to review a new version before it is offi- ially released. This ensures that specification enhancements re driven by real use cases and the needs of the community. The major version update from v1.3 to v2.0 allowed for the estructuring into the modules described in Figure 1 , as well s a number of critical enhancements. While the v1.X versions f the specification focused on read-only phenotype data, the pecification now has representation from most of the ma- or concepts related to breeding and allows for read, write, nd update capabilities. The v2.0 specification was updated to e more internally consistent, easier to navigate, and to pro- ide more robust search capabilities. Major updates were also ade to align BrAPI with some of the other relevant breeding ata standards, including the GA4GH Variants Schema [ 4 ] nd the Minimal Information About a Plant Phenotyping Ex- eriment (MIAPPE) guidelines [ 5 ]. This addition ensures com- rehensive and standardized documentation of experimental etadata to improve data interoperability and reuse. As BrAPI has matured, so have the tools, services, and li- raries that work with the specification. Each new version s released with a change log to guide developers as they pgrade, an Entity Relationship Diagram (ERD) to visually escribe the data model, and a JSON Schema data model o be used for automated development efforts. For groups sing Java, JavaScript, Python, R, or Drupal, community- aintained libraries are available with full BrAPI implementa- ions ready to be integrated into existing code. The BrAPI Test erver is updated to support every version of the specification or testing purposes. Finally, there are resource pages on the roject website that showcase BrAPI-compatible applications nd data resources available in the community. ommunity growth he international BrAPI Community consists of software de- elopers, biologists, and other scientists working on BrAPI- elated projects and data sources. This community sustains he BrAPI project, builds implementations, maintains develop- ent tools, and provides input to enhance the specification. As he project has grown, so too has the community. The BrAPI roject started in June 2014 with fewer than ten people com- ng together to discuss the idea and has since grown to more han 200 members. The BrAPI Hackathons are a major staple of the BrAPI ommunity [ 6 ]. Twice a year, the community gathers in per- on or virtually to discuss the specification and collaborate on rAPI-related projects. These events have proven to be vital o the long-term growth of the community; for some organi- ations, the hackathon is the only time during the year when hey can collaboratively work on BrAPI projects. esults elow, we present a number of short success stories from the rAPI community. These tools, applications, and infrastruc- ure projects serve as another indicator of community growth nd success over the past 5 years. The vast majority of these ools have been developed and maintained by organizations ndependent of the BrAPI project, further demonstrating the mpact of the project. These stories clearly illustrate all the different ways the BrAPI standard can be used productively and in practice. Figure 2 contains a summary of many of the currently available BrAPI-compliant tools, and each will be further described below. The list presented here showcases the breadth of BrAPI capabilities, but it is not an exhaustive list of BrAPI-compliant tools. A more comprehensive list can be found on the Compatible Software page of the BrAPI project website. Phenotyping Phenotyping is fundamental to plant breeding and genetics re- search, providing the data breeders rely on to make informed selection decisions. Effective phenotyping requires a strong bi- ological framework and robust data collection methods to en- sure successful outcomes. The BrAPI specification supports phenotypic data throughout their lifecycle, including collec- tion, analyses, publication, and archiving. To facilitate stan- dardized data management, the BrAPI community has devel- oped several BrAPI-compatible tools that streamline data cu- ration, storage, and metadata integration. Ongoing develop- ment efforts are creating tools to manage images and other high-throughput phenotypic data sources, further enhancing the precision and efficiency of breeding research. By enabling the seamless transfer of phenotypic data, BrAPI-compatible tools simplify the conversion of raw observations into action- able insights, accelerating the digitization of modern breed- ing and genetics research programmes. The following set of BrAPI-compatible tools were developed to support various as- pects of the phenotyping process. Field book Data from plant breeding and genetics experiments has tradi- tionally been collected using pen and paper, but this approach often results in transcription errors and delayed analysis. Field Book [ 7 ], 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. This improve- ment 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. GridScore GridScore [ 8 ] is a web-based application for recording phe- notypic 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, geo- referencing, multiplatform support, and data synchronization across multiple devices. The application’s data collection ap- proach 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 predefined orders. BrAPI has further increased the value of GridScore by inte- grating it into the overarching plant breeding workflow. Trial designs and trait definitions can be imported into GridScore D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 Real-world applications for data integration 5 Figure 2. A summary of all the tools described below and the general areas each tool is designed to handle. The ‘Generation/Collection’ column indicates that an application is used to input or create new data. The ‘Storage’ column indicates the tool stores that type of data. The ‘Visualization’ column indicates that the application has a way of presenting data to a human user. The ‘Analysis’ column indicates the tool performs calculations to provide new insight. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 using BrAPI, and a finalized trial can be exported via BrAPI to any compatible database. ClimMob ClimMob [ 9 ] is a software suite designed to facilitate de- centralized agricultural research through citizen science and choice experiments. It enables large-scale participatory trials where farmers and other stakeholders evaluate and rank dif- ferent crop accessions or agronomic practices based on their preferences and field performance. While these data may lack the detail of centralized experiments, they can provide robust evidence on performance across a wide range of environments with increased external validity. Applications of ClimMob include crop variety testing, evaluating agronomic practices, and investigating climate resilience strategies. The platform supports experimental design, data collection through mobile 6 Selby et al. a i s m e a A d a I U a s m p s B t s fi n i c o w s b s P P a w p n t a s e o B G o e P s d m u i c s m t o P P i c pps, and data analysis to provide actionable insights, mak- ng on-farm trials accessible to farmers, breeders, and other takeholders. ClimMob uses BrAPI to retrieve curated germplasm infor- ation from breeding databases for trial design, subsequently nabling the automatic upload of ClimMob-collected data to central breeding database for long-term storage and analysis. nalysed data can also be pushed from ClimMob to breeding atabases, providing breeders with insights into the potential doption of the tested crop varieties. mageBreed noccupied aerial and ground vehicles (UAVs and UGVs) en- ble the high throughput collection of images and other sen- or data in the field, but the rapid processing and manage- ent of these datasets are often a bottleneck for breeding rogrammes seeking to deploy these technologies for time- ensitive decision-making. ImageBreed [ 10 ] is an open-source, rAPI-compliant image processing tool that supports the rou- ine use of UAVs and UGVs in breeding programmes through tandardized pipelines. It creates orthophotomosaics, applies lters, assigns plot polygons, and extracts ontology-based phe- otypes from raw UAV-collected images. The BrAPI standard s used to push these phenotypes back to a central BrAPI- ompliant breeding database where they can be analysed with ther experiment data. The ImageBreed team has collaborated ith others in the community to enhance the BrAPI image data tandards, which it uses to upload raw images to a central reeding database or any other BrAPI-compatible long-term torage service. HIS HIS [ 11 ], the Hybrid Phenotyping Information System, is n ontology-driven information system based on semantic eb technologies and the OpenSILEX framework. PHIS is de- loyed in several field and greenhouse platforms of the French ational PHENOME and European EMPHASIS infrastruc- ures. It manages and collects data from basic phenotyping nd high-throughput phenotyping experiments on a daily ba- is. PHIS unambiguously identifies the objects and traits in an xperiment and establishes their types and relationships via ntologies and semantics. Since its inception, PHIS has been designed to be rAPI compliant, encompassing the Core, Phenotyping, and ermplasm BrAPI modules. This enables integration with ther BrAPI-compliant systems and platforms, simplifying the xchange of accession and phenotyping data across systems. HIS is actively integrated with the OLGA genebank acces- ions management system and is indexed by the FAIDARE ata portal [ 12 ]. BrAPI-enabled interoperability promotes a ore coherent and efficient approach to the management and se of phenotyping data on various platforms and research nitiatives within the European scientific community. BrAPI ompliance also ensures that PHIS is compatible with other tandards such as MIAPPE [ 5 ]. By integrating BrAPI require- ents into its structure, PHIS strengthens its capacity for in- eroperability and effective collaboration in the wider context f plant breeding and related fields. IPPA IPPA [ 13 ] is a data management system used for collect- ng data from the Weighing, Imaging, and Watering Ma- hines (WIWAM) [ 14 ], which 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 soft- ware features a web interface with functionality for setting up new experiments, planning imaging and irrigation treat- ments, linking metadata (genotype, growth media, manual treatments) to pots, and importing, exporting, and visualizing data. It also supports the integration of image analysis scripts and connects to a compute cluster for job submission. To share the phenotypic data from PIPPA experiments linked to publications, an implementation of BrAPI v1.3 was developed which allowed read-only access to the data in the BrAPI standardized format. This server was registered on FAIDARE, allowing the data to be found alongside data from other BrAPI-compatible repositories. Throughout its development, the PIPPA project has adhered to guidelines set forth by BrAPI and the MIAPPE scientific standard. Current efforts are focused on delivering a public BrAPI v2.1 endpoint and increasing the availability of public high-throughput datasets via BrAPI. Trait selector BrAPP The Trait Selector BrAPP is a JavaScript-based application used to visually search and select traits from an ontology. The Trait Selector employs a visual aid, an image of a plant, to connect plant anatomy with relevant trait ontology terms. In- stead of scrolling through a long list of possible traits, the user can click on pieces of the image to show the traits associated with specific plant structures. The Trait Selector BrAPP can be used to quickly find specific traits or to identify accessions that have a specific phenotype of interest. The Trait Selector BrAPP has been successfully added to Cassavabase [ 15 ] and MGIS [ 16 ], and it can be integrated into any website or system with a BrAPI-compatible data source. A breeding database would need to only implement the BrAPI endpoints for Traits, Observations, and Variables, while a genebank would require only Traits and Germplasm Attributes. Genotyping Genotyping has become a cornerstone of most breeding processes, but managing the data can be challenging. The choice of genotyping platform depends largely on the crop species, research objectives, and available resources. Tech- niques such as single nucleotide polymorphism (SNP) geno- typing, genotyping-by-sequencing (GBS), simple sequence re- peats (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) [ 17 ] and the GA4GH Variants schema [ 4 ]. The BrAPI community has developed compatible tools for storing, searching, visualizing, and analysing genotypic data, making it easier to integrate and utilize this information in breeding pro- grammes. These BrAPI-compliant tools streamline data man- agement and analysis, enhancing the breeders’ ability to make data-driven decisions in developing superior crop varieties. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 Real-world applications for data integration 7 DArT sample submission The Diversity Arrays Technology (DArT) genotyping lab is heavily used worldwide for plant genotyping. With over 1200 available organisms and species, a client base on every conti- nent, and many millions of samples processed, DArT provides services for several generic and bespoke genotyping technolo- gies 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 lab- oratory, quality, and analytical processes. DArT has been a part of the BrAPI community since its in- ception. DArT developers have worked with the BrAPI com- munity, contributing to various aspects of the API specifica- tion. One key aspect was establishing a standard API for send- ing sample metadata to the lab for genotyping. This solution eliminates much of the human error involved with sending samples to an external lab and allows for an automated pro- cess 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 produc- tion, and it is compatible with the BrAPI v2.1 specification. Further details about DArT’s ordering system can be found at DArT Ordering System and also at DArT Help. DArTView DArTView is a desktop application for marker data cura- tion via metadata filtering. DArTView enables genotype vari- ant data visualization designed such that users can easily identify trends or correlations within their data. The pri- mary goal of the tool is to overcome tedious manual cal- culation of marker data through common spreadsheet ap- plications 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 en- sures easy integration with other tools and pipelines that would use DArTView for marker filtering and exploration. Initially developed by DArT, the tool is gaining popular- ity within the breeding community, especially in Africa. Fu- ture releases will focus on enhancing the BrAPI compatibility, making it accessible to more breeders and researchers. 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. DivBrowse DivBrowse [ 18 ] is a web platform for exploratory data analy- sis of large genotyping studies. The software can be run stan- dalone or integrated as a plugin into existing web portals. At its core, DivBrowse combines the convenience of a genome browser with features tailored to germplasm diversity anal- ysis. DivBrowse provides visual access to VCF files obtained through genotyping experiments and can handle hundreds of millions of variants across thousands of samples. It is able to display genomic features such as nucleotide sequence, asso- ciated gene models, and short genomic variants. DivBrowse also calculates and displays variant statistics such as minor allele frequencies, the proportion of heterozygous calls, and the proportion of missing variant calls. Dynamic PCAs can be performed on a user-specified genomic area to provide infor- mation on local genomic diversity. DivBrowse has an interface to BLAST + tools [ 19 ] installed on Galaxy servers [ 20 ], which can be used to directly access genes or other genomic features from the results of custom BLAST queries. DivBrowse em- ploys the BrAPI-Genotyping module to serve genotypic data as a BrAPI endpoint and to get genotypic data from other BrAPI endpoints. Flapjack Flapjack [ 21 ] is a multiplatform desktop application for data visualization and breeding analysis (e.g. pedigree verification, marker-assisted backcrossing and forward breeding) using high-throughput genotype data. Data can be imported into Flapjack from any BrAPI-compatible data source with geno- type data available. Flapjack Bytes is a smaller, lightweight, and fully web-based counterpart to Flapjack that can be easily embedded into a database website to provide similar visualiza- tions online. Traditionally supporting its own text-based data formats, Flapjack’s use of BrAPI has streamlined the end-user experience for data import. Work is underway to determine the best methods to exchange analysis results using future ver- sions of the API. Gigwa Gigwa is a Java EE web application that provides a means to centralize, share, finely filter, and visualize high-throughput genotyping data [ 22 ]. Built on top of MongoDB, it is scalable and can support working smoothly with datasets containing billions of genotypes. It is installable as a Docker image or as an all-in-one bundle archive. It is straightforward to de- ploy on servers or local computers and has thus been adopted by numerous research institutes from around the world. No- tably, Gigwa serves as a collaborative management tool and a portal for exploring public data for genebanks and breeding programmes at some CGIAR centres [ 23 ]. The total amount of data hosted and made widely accessible using this system has continued to grow over the last few years. 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 compatibil- ity with the Flapjack visualization tool [ 21 ]. Over time, Gigwa has established itself as the first and most reliable implemen- tation of the BrAPI-Genotyping module. 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, SnpClust, QBMS). Some use-cases require Gigwa to also consume data from other BrAPI servers. This requirement led to the implementa- tion of BrAPI client features within Gigwa. A close collabo- ration was established with the Integrated Breeding Platform (IBP) team and their widely used breeding management sys- tem (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. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 8 Selby et al. P T p a U ( s fi g g p d t b q u u t o p n G G b c a c s c o d f i t B g A T m a E P t i m [ F b m b n t o o f t p g r HG he Practical Haplotype Graph (PHG) is a graph-based com- utational framework that represents large-scale genetic vari- tion and is optimized for plant breeding and genetics [ 24 ]. sing a pangenome approach, each PHG stores haplotypes the sequence of part of an individual chromosome) to repre- ent the collective genes of a species. This allows for a simpli- ed approach for dealing with large-scale variation in plant enomes. The PHG pipeline provides support for a range of enomic analyses and allows for the use of graph data to im- ute complete genomes from low-density sequence or variant ata. Users can access the haplotype data either with direct calls o the PHG embedded server or indirectly using the rPHG li- rary from an R environment. The PHG server accepts BrAPI ueries to return information on sample lists and the variants sed to define the graph’s haplotypes. In addition, PHG users tilize the BrAPI variant sets endpoint query to return links o VCF files containing haplotype data. Work on the PHG is ngoing, and it is expected to support additional BrAPI end- oints that allow for fine-tuned slicing genotypic data in the ear future. ermplasm management ermplasm data management is essential for research or reeding programmes, national genebanks, and international ollaborations. A substantial number of new accessions, vari- nts and lines are developed every year and preserved in ollections around the world. Germplasm are an important ource of biological diversity for breeders to further develop rops. To support these efforts, BrAPI allows for the exchange f germplasm passport data, pedigree trees, and crossing meta- ata. The BrAPI community has developed compliant tools or storing, searching, and visualizing this metadata, facilitat- ng efficient management. Additionally, there are plans to es- ablish federated networks of genebank data connected via rAPI, enhancing global accessibility and collaboration in ermplasm management. GENT he aim of the AGENT project, funded by the European Com- ission, is to develop a concept for the digital exploitation nd activation of plant genetic resources (PGRs) throughout urope [ 25 ]. In the global system for ex situ conservation of GRs, material is being conserved in about 1750 collections otalling ∼5.8 million accessions [ 26 ]. Unique and permanent dentifiers in the form of DOIs are available for more than 1.9 illion accessions via the Global Information System (GLIS) 27 ] of the International Treaty on Plant Genetic Resources for ood and Agriculture (ITPGRFA). Each DOI is linked to some asic descriptive data that facilitates the use of these resources, ainly passport data. However, a data space beyond the most asic information is needed that includes genotypic and phe- otypic data. This space will help to answer questions about he global biological diversity of plant species, the detection f duplicates, the tracking of provenance for the identification f genetic integrity, the selection of the most suitable material or different purposes, and to support further applications in he field of data mining or AI. In this context, the AGENT roject activates and utilizes the PGRs from European ex situ enebanks according to the FAIR principles [ 28 ] and tests the esources in practice using two important crops, barley and wheat. Thirteen European genebanks and five bioinformatics centres are working together and have agreed on standards and protocols for data flow and data formats for the collec- tion, integration, and archiving of genotypic and phenotypic data [ 29 ]. The BrAPI specification is one of the agreed standards that are detailed in the AGENT guidelines for dataflow [ 30 ]. The implemented BrAPI interface enables the analysis of current and historic genotypic and phenotypic information. This will drive the discovery of genes, traits, and knowledge for future missions, complement existing information for wheat and bar- ley, and use the new data standards and infrastructure to pro- mote better access and use of PGR for other crops in Euro- pean genebanks. The AGENT database backend aggregates curated passport data, phenotypic data, and genotypic data on wheat and barley accessions of 18 project partners. This data is accessible via BrAPI endpoints and explorable in a web por- tal. Genotyping data uses the DivBrowse [ 18 ] storage engine and its BrAPI interface. Soon, the BrAPI implementation will be expanded to enable the integration of analysis pipelines in the AGENT infrastructure, such as the FIGS + pipeline devel- oped by ICARDA [ 31 ]. In addition, the data collected by the AGENT project will be integrated into the European Search Catalogue for Plant Genetic Resources (EURISCO) [ 32 ]. Florilège Florilège is a web portal designed primarily for the general public to access public PGRs held by the Biological Resource Centers across France, as part of France’s National Research Institute for Agriculture, Food and Environment (INRAE). Through this portal, users can browse accessions from over 50 plant genera spread across 19 genebanks. It allows users to view available seeds and plant material, including options for ordering material. Florilège provides centralized access to the various French collections of PGRs available to the public. Florilège retrieves accession information from several BrAPI-compliant systems. Key among these are OLGA, a genebank accessions management system, and GnpIS, an IN- RAE data repository for PGRs, phenomics, and genetics [ 33 , 34 ]. Using BrAPI to gather data from these systems reduced development efforts and enabled standardized data retrieval. As a result, BrAPI has become the de facto standard within the French PGRs community for exchanging information. Dur- ing development, the Florilège team also proposed several en- hancements to the BrAPI specifications themselves, such as ad- ditional support for Collection objects or improved reference linking, to better accommodate their specific use case. GLIS The GLIS on Plant Genetic Resources for Food and Agricul- ture (PGRFA) of the International Treaty on PGRFA (ITP- GRFA) is a web-based, BrAPI-compliant global entry point for PGRFA data [ 27 ]. It allows users and third-party systems to access information and knowledge on scientific, technical, and environmental matters to strengthen PGRFA conserva- tion, management, and utilization activities. The system and its portal also enable recipients of PGRFA to make available all non-confidential information on germplasm according to the provisions of the Treaty and facilitate access to the results of their research and development. Thanks to the adoption of Digital Object Identifiers (DOIs) for Multi-Crop Passport Descriptors (MCPD) of PGRFA accessions, the GLIS Portal provides access to 1.9 million D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 Real-world applications for data integration 9 PGRFA in collections conserved worldwide. Of these, over 1.5 million are accessible for research, training and plant breeding in the food and agriculture domain. The Scientific Advisory Committee of the ITPGRFA has repeatedly welcomed efforts on interoperability among germplasm information systems. In this context, the GLIS Por- tal adopted the BrAPI v1.3 in 2022. Integrating BrAPI among the GLIS content negotiators facilitates queries and the ex- change of content for data management in plant breeding. The Portal also offers other protocols (XML, DarwinCore, JSON and JSON-LD) to increase data and metadata connectivity. In the near future, depending on the availability of resources, up- grading to BrAPI v2 is planned. Helium Helium [ 35 ] is a pedigree visualization platform designed to account for the unique characteristics of plant pedigrees. A pedigree is a representation of the genetic relationships be- tween discrete individuals, linking individual plants or lines with their parents and progeny. Plant pedigrees inform cross- ing decisions, are required for variety releases, and are often used to check for potential genotyping or phenotyping errors [ 36 ]. The accurate representation of pedigrees and the ability to pull pedigree data from different data sources are impor- tant in plant breeding and genetics. Helium provides ways to visualize and interact with this complex data in meaningful ways. From its original desktop interface, Helium has devel- oped into a web-based visualization platform implementing BrAPI calls to allow users to import data from other BrAPI- compliant databases. The ability to pull data from BrAPI- compliant data sources has significantly expanded Helium’s capability and utility within the community. Helium is used in projects ranging in size from tens to tens of thousands of lines and across a wide variety of crops and species. While orig- inally designed for plant data [ 37 ], it has also found utility in other non-plant projects [ 38 ] highlighting its broad utility. BrAPI also allows Helium to provide direct dataset links to collaborators, allowing the original data to be held with the data provider and utilizing Helium for its visualization func- tionality. Our current Helium deployment includes example BrAPI calls to a barley dataset at the James Hutton Institute to allow users to test the system and features it offers. MGIS The Musa Germplasm Information System (MGIS) serves as a comprehensive community portal dedicated to banana di- versity, a crop critical to global food security [ 16 ]. MGIS offers detailed information on banana germplasm, focusing on the collections held by the CGIAR International Banana Genebank (ITC) [ 39 ]. It is built on the Drupal/Tripal technol- ogy, like BIMS [ 40 ] and Florilège. Since its inception, MGIS developers have actively partic- ipated in the BrAPI community. The MGIS team pushed for the integration of the 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. MGIS also enriches the passport data by incor- porating additional information from other germplasm collec- tions worldwide. All the germplasm data is available through the BrAPI-Germplasm module implementation. For genotyp- ing data, MGIS integrates with Gigwa [ 22 ], which provides a tailored implementation of the BrAPI genotyping module. Fur- thermore, MGIS supports a set of BrAPI-Phenotyping mod- ule endpoints, facilitating the exposure of morphological de- scriptors and trait information supported by ontologies like the Crop Ontology [ 41 ]. MGIS has integrated the Trait Selec- tor BrAPP, and there are use cases implemented to interlink genebank and breeding data between MGIS and the breeding database MusaBase. Breeding and genetics data management While specialty data management is important for some use cases, often breeders want a central repository or access point of critical data. General breeding and genetics data manage- ment systems and web portals support some level of phe- notypic, genotypic, and germplasm data, as well as trial, equipment, and people management. By enabling BrAPI sup- port, these larger systems can connect with smaller tools and specialty systems to provide more functionality under the same UI. There are several breeding data management sys- tems developed in the BrAPI community, each with their own strengths. BIMS The Breeding Information Management System (BIMS) [ 40 ] is a free, secure, online BMS, which allows breeders to store, manage, archive, and analyse their private breeding pro- gramme data. BIMS enables individual breeders to have com- plete control of their own breeding data along with access to tools such as data import, export, analysis, and archiving for their germplasm, phenotype, genotype, and image data. BIMS is currently implemented in five community databases, the Genome Database for Rosaceae [ 42 ], CottonGEN [ 43 ], the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium, where it enables individual breeders to import publicly available data. BIMS is also im- plemented in the public database breedwithbims.org that any breeder can use. BIMS primarily utilizes BrAPI to connect with Field Book [ 7 ], enabling seamless data transfer between data collection and subsequent management in BIMS. BIMS can receive data from BreedBase [ 15 ] via BrAPI as well, and data transfer be- tween BIMS and other resources such as GIGWA [ 22 ], and the Breeder Genomics Hub [ 44 ] is under development. BMS The BMS, developed by the IBP, 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 em- phasis on germplasm management and ontology-harmonized phenotyping (i.e. with the Crop Ontology). It also features an- alytics and decision-support tools. With its focus on interoper- ability, BMS integrates smoothly with BrAPI, facilitating easy connections with a broad array of complementary tools and databases. Notably, the BMS is often deployed together with Gigwa to fulfil the genotyping data management needs of BMS users. The 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 en- hance collaboration among partner institutes within a net- work such as Innovation and Plant Breeding in West Africa (IAVAO). The tool enables the sharing of germplasm and trial D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 10 Selby et al. m t o n m m l b t p f B B m t a d I S t q a c r a m a g B s b B d h h d D D t p S D t s fi J t m t d l a s H w h B g etadata across BrAPI-enabled systems. It helps overcome raditional barriers to collaboration, ensuring data that was nce isolated within specific programmes or platforms can ow be easily shared, integrated, and synchronized. Additionally, Brapi-sync improves data management by aintaining links to the original source of each entity it trans- its. This retains the original context of the data and estab- ishes a traceability mechanism for accurate data source attri- ution and verification. Such practices are crucial for main- aining data integrity and fostering trust among collaborative artners, ensuring access to accurate, reliable, and current in- ormation. reedbase reedbase is a comprehensive, open-source breeding data anagement system [ 15 ,45 ] that implements a digital ecosys- em for all breeding data, including trial data, phenotypic data, nd genotypic data. Data acquisition is supported through ata collection apps such as Fieldbook [ 7 ], Coordinate, and nterCross, as well as through drone imagery, Near Infra-Red pectroscopy (NIRS), and other technologies. Search func- ions, such as the Search Wizard interface, provide powerful uery capabilities. Various breeding-centric analysis tools are vailable, including mixed models, heritability, stability, prin- ipal component analysis (PCA), and various clustering algo- ithms. The original impetus for creating Breedbase was the dvent of new breeding paradigms based on genomic infor- ation, such as genomic prediction algorithms [ 46 ] and the ccompanying data management challenges. Thus, a complete enomic prediction workflow is integrated into the system. The BrAPI interface is crucial for Breedbase. Breedbase uses rAPI to connect with the data collection apps, other projects uch as ClimMob [ 9 ], and native BrAPPs built into the Breed- ase webpage. Users also appreciate the ability to connect to reedbase instances using packages such as QBMS [ 47 ] for ata import into R for custom analyses. The Breedbase team as been part of the BrAPI community since its inception and as continuously adopted and contributed to the BrAPI stan- ard. eltaBreed eltaBreed is an open-source breeding data management sys- em designed and developed by Breeding Insight to sup- ort U.S. Department of Agriculture—Agricultural Research ervice (USDA-ARS) speciality crop and animal breeders. eltaBreed differs from other related systems in that it is cus- omizable to small breeding teams and generalized enough to upport the workflows of diverse species. DeltaBreed is a uni- ed system that integrates BrAPI applications, like the BrAPI ava Test Server, Gigwa, and the Pedigree Viewer BrAPP, hrough a common UI. BrAPI integration shields users from ultifactorial differences existing between various applica- ions. DeltaBreed, adhering to the BrAPI model, establishes ata standards and validations for users and provides a singu- ar framework for data management and user training. BrAPI lso enables DeltaBreed users to connect to external tools with eparate UIs, like Field Book, QBMS, Mr Bean, BreedBase, and elium. DeltaBreed users need not be aware of BrAPI specifics but ill notice that BrAPI interoperability reduces the need for uman-mediated file transfers and data manipulation. Field ook users, for example, can connect to their DeltaBreed pro- ram, authenticate, and pull studies and observation variables directly from DeltaBreed to Field Book on their data collec- tion device. The subsequent step of pushing observations from Field Book to DeltaBreed is straightforward via BrAPI but is pending implementation until data quality validations are put in place; these include improved data transaction handling and differentiation of intentional and inadvertent repeated measures. FAIDARE FAIDARE [ 48 ] is a data discovery portal providing a biologist- friendly search system over a global federation of 40 plant re- search databases. It allows users to identify data resources us- ing a full-text search approach combined with domain-specific filters. Each search result contains a link back to the original database for visualization, analysis, and download. The in- dexed data types are broad and include genomic features, se- lected bibliography, QTL, markers, genetic variation studies, phenomic studies, and PGRs. This inclusiveness is achieved thanks to a two-stage indexation data model. The first index, more generic, provides basic search functionalities and relies on five fields: name, link back URL, data type, species, and ex- haustive description. To provide more advanced filtering, the second-stage indexation mechanism takes advantage of BrAPI endpoints to get more detailed metadata on germplasm, geno- typing studies and phenotyping studies. The FAIDARE indexation mechanism relies on a public software package [ 49 ] that allows data resource managers to request the indexation of their database. This BrAPI client is currently able to extract data from any BrAPI v1.3 and v1.2 endpoint, and the development of BrAPI v2.x indexation will be initiated in 2025. Since not all databases are willing to im- plement BrAPI endpoints, it is possible to generate metadata as static BrAPI-compliant JSON files, using the BrAPI stan- dard as a file exchange format. The FAIDARE architecture has been designed by elaborat- ing on the BrAPI data model in combination with the GnpIS Software Architecture [ 33 ]. It uses an Elasticsearch NoSQL engine that searches and serves enriched versions of the BrAPI JSON data model. FAIDARE also includes a BrAPI endpoint using all indexed metadata. It has been adopted by several communities, including the ELIXIR and EMPHASIS Euro- pean infrastructures, and the WheatIS of the Wheat Initiative. Several databases are added each year to the FAIDARE global federation, adding to both the portal and BrAPI adoption. Germinate Germinate [ 50 ,51 ] is an open-source PGRs database that com- bines and integrates various types of plant breeding data in- cluding genotypic, phenotypic, passport, image, geographic, and climate data into a single repository. Germinate is tightly linked to the BrAPI specification and supports the majority of BrAPI endpoints for querying, filtering, and submission. Germinate connects with other BrAPI-enabled tools such as GridScore for phenotypic data collection, Flapjack for geno- typic data visualization, and Helium for pedigree visualiza- tion. Additionally, due to the nature of BrAPI, Germinate can act as a data repository for any BrAPI-compatible tool. The interoperability provided by BrAPI reduces the need for man- ual data handling, providing the direct benefits of faster data processing, fewer human errors, and improved data security and integrity. D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 Real-world applications for data integration 11 Analytics Modern breeding programmes have multiple decision points requiring analysis and integration of various data types. While there are numerous breeding and genetics data management systems (above), certain programme tasks could be simplified by the development of specific streamlined analysis systems. These analysis systems better enable certain tasks by utilizing data from different sources to make efficient data-driven deci- sions. 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 pro- vide 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 analyt- ical models. These tools are able to extract the data they need from various data sources without much human intervention or human error. They can also provide simple user interaction to enhance decision support for the breeders and researchers. G-Crunch G-Crunch is an upcoming user-facing tool to make simple, repeatable analysis requests. The lightweight UI can be used to specify and filter incoming data, select specific analysis cri- teria, and trigger any analytics pipeline that is built into the specific framework instance. G-Crunch is currently built on top of the open-source Analytics Framework project and can run pipelines using tools such as Sommer and ASREML. Each piece of the data and pipeline can be separately specified, which can allow flexibility when running complex analysis. A ‘test’ analysis can be run on small data sets with a small or local analytics engine, then quickly redirect G-Crunch to a larger dataset and a larger computational framework. This mitigates the complications of moving data around and intro- ducing errors from manually triggering the analysis steps. G-Crunch relies on BrAPI endpoints to access phenotypic and genotypic data sources, as well as an API currently im- plemented in the Analytics Framework to start and track pro- cesses. G-Crunch, as a tool, could not feasibly exist without BrAPI. The support of BrAPI interfaces allows G-Crunch to use one unified request method and adapt to the user’s exist- ing network of BrAPI-compliant tools. This lowers the barrier to entry for adoption and makes analysis pipelines easily re- peatable. QBMS Many plant breeders and geneticists analyse their datasets using the R statistical programming language, but this re- quires the import of data into an R environment. BrAPI en- ables access to pull datasets into R from compatible databases, but API backend processes, such as authentication, tokens, TCP/IP protocol, JSON format, pagination, stateless calls, asynchronous communication, and database IDs, are complex for users to navigate. The QBMS R package eliminates techni- cal barriers scientists experience when using the BrAPI spec- ification in their analysis scripts and pipelines by providing breeders with stateful functions familiar to them when navi- gating their GUI systems [ 47 ]. QBMS enables users to query and extract data into a dataframe, a common structure in the R language, providing an intuitive connection with breeding data management systems. The community has built extended solutions on top of QBMS, incorporating the package into R-Shiny BrAPPs such as MrBean [ 52 ] (described below). QBMS is open-source and available on the official CRAN repository, where it has gar- nered over 16,000 downloads. Mr.Bean MrBean [ 52 ] is a GUI designed to assist breeders, statisticians, and individuals involved in plant breeding programmes with the analysis of field trials. By utilizing innovative method- ologies such as SpATS for modelling spatial trends and au- tocorrelation models to address spatial variability, MrBean proves highly practical and powerful in facilitating faster and more effective decision-making. Modelling genotype-by- environment interaction poses its challenges, but MrBean of- fers the capability to explore various variance-covariance ma- trices, including factor analytic, compound symmetry, and het- erogeneous variances. This aids in the assessment of genotype performance across diverse environments. MrBean boasts flexibility in importing different file types, yet for users managing their data within data management systems, the process of downloading from their systems and importing it into MrBean can be cumbersome. To address this issue, QBMS was integrated into the back end. This feature prompts users to input the URL of a BrAPI-compatible server, enter their credentials (if necessary), and select the specific trial they wish to analyse. Subsequently, users can seamlessly access their dataset through BrAPI and utilize it across the entire Mr- Bean interface. SCT The Sugarcane Crossing Tool (SCT) is a lightweight R-Shiny dashboard application designed to receive, process, and visu- alize data from a linked BreedBase [ 15 ] instance. This appli- cation is being developed collaboratively with members of the Sugarcane Integrated Breeding System, who have advocated for an application that assists them in designing crosses based on queried information from a list of available accessions. By leveraging existing community resources, the team has been able to develop a simple, BrAPI-enabled application without possessing extensive programming knowledge or experience. The SCT is presented as an inspiration for similarly positioned scientists to consider developing custom applications for spe- cific tasks. The crossing tool utilizes a modified version of the BrAPI-R library to access a compliant database, and it employs stan- dard R/JavaScript packages to aggregate and visualize data. Modules within the application allow breeders to query the database (through BrAPI) for information relevant to their decision-making process. This includes the number and sex of flowering accessions, deep pedigree and relatedness infor- mation, summarized trial data, and the prior frequency and success of potential cross combinations. Future versions of this tool will provide additional decision support (e.g. ranked potential crosses) to enhance the accuracy and efficiency of crossing. ShinyBrAPPs The ShinyBrAPPs code repository contains a number of useful tools, built using the R-Shiny framework and the BrAPI-R open-source library. The R-Shiny framework allows user communities to quickly prototype and produce applica- tions that are finely tailored to their needs, thus improving D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 12 Selby et al. a t I e n p p t u c b fi y s c d s t r d s t a y i n c d G A d e g c m l e d B B a B c M fi b B l t u t fi p s c i d doption and daily use of data management systems. An in- ernational collaboration of developers from CIRAD and the BP have been working together as part of the IAVAO breed- rs community to develop these ShinyBrAPPs in support of ational breeding programmes in western Africa. These ap- lications are typically connected to BMS and/or Gigwa and rovide tools for specific use cases. BrAPI compliance offers hese systems the opportunity to add functionalities in a mod- lar way through the development of external plugin appli- ations that can quickly fulfil specific needs for this group of reeders and scientists. So far, four applications have been developed covering the elds of trial data quality control, single trial statistical anal- sis, breeding decision support, and raw genotyping data vi- ual inspection. The ‘TDxPLOR’ (trial data explorer) appli- ation retrieves data from multilocation trials and displays ata counts and summary boxplots for all variables mea- ured in different studies. It also provides an interactive dis- ribution plot to easily select observations that require cu- ation and a report of candidate issues that need to be ad- ressed by the breeder. ‘STABrAPP’ is an application for ingle-trial mixed- model analysis. It basically provides a GUI o the Statgen-STA R package. The ‘BrAVISE’ application is decision support tool helping breeders to run GxE anal- sis, select germplasm according to their various character- stics and save lists of selected germplasm to the BMS. Fi- ally, the ‘snpclust’ tool enables a user to check and manually orrect the clustering of fluorescence-based SNP genotyping ata. eneral infrastructure dopting BrAPI compatibility into an existing system can be ifficult sometimes. The BrAPI Community has developed sev- ral tools to make adoption easier, built to support other pro- rammers and developers. This includes things like prebuilt ode libraries, connectors to other technology standards, and appers to alternate data types or data files. The goal is to ower the barrier to entry for the BrAPI community, making it asier for other groups to get started and connect their existing ata to the standard. rAPIMapper rAPIMapper is a full BrAPI implementation designed to be convenient wrapper for any breeding-related data source. rAPIMapper is provided as a Docker application that can onnect to a variety of external data sources, including ySQL or PostgreSQL databases, generic REST services, flat les (XML, JSON, CSV/TSV/GFF3/VCF, YAML), or any com- ination of these. It provides an administration UI to map rAPI data models to external data sources. The interface al- ows administrators to select the BrAPI specification versions o use and which endpoints to enable. Data mapping config- ration import and export features simplify upgrades to fu- ure BrAPI versions; administrators only have to map missing elds or make minor adjustments. BrAPIMapper supports the rimary BrAPI features, including paging, deferred search re- ults, user lists, and authentication. Access restrictions to spe- ific endpoints can be managed through the administration nterface as well. This tool aims to accelerate BrAPI services eployment while ensuring specification compliance. MIRA and BrAPI2ISA In some communities and projects, phenotyping data and metadata are archived and published using files in ISA formats and validated using the MIAPPE ISA configuration [ 53 ]. Al- though 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 is a tool developed by IPK and extended during community hackathons to ad- dress this challenge. MIRA acts as an intermediary between a web application and the ISA-formatted dataset by automat- ing the deployment of a BrAPI v2.1 server in a Docker con- tainer. Leveraging the MIAPPE mapping between the ISA data model and the BrAPI data model, MIRA facilitates the integra- tion of data and metadata from phenotyping experiments and makes the ISA data programmatically accessible. The BrAPI server implements all the BrAPI endpoints required to access MIAPPE-compliant data and metadata. The BrAPI2ISA service is the counterpart to MIRA, func- tioning as a converter between a BrAPI-compatible server and the ISA-Tab format. The tool simplifies, automates, and facil- itates the archiving of data, thereby enhancing data preserva- tion and accessibility. The BrAPI2ISA tool is compatible with BrAPI v1.3 and welcomes community contributions to sup- port the latest versions of BrAPI. GraphQL data-warehouse GraphQL is a newer web service API architecture that offers an alternative to REST by enabling clients to retrieve exactly the data they need through a single, flexible query. Whereas REST focuses on representing individual resources, GraphQL defines a network, or graph, of data entities. The BrAPI data model can be extracted from the original RESTful documen- tation and repurposed in a GraphQL system. Using the Zen- dro software generator, a fully functional, cloud-capable data warehouse can be created from the current version of the BrAPI data models. This generated warehouse offers simi- lar functionalities through its GraphQL API that BrAPI of- fers, including secure access to create, read, update, and delete (CRUD) operations standardized across all BrAPI data mod- els. Zendro supports many underlying database systems, of- fering flexibility during installation and integration. The BrAPI GraphQL server is particularly rich in data search and discovery features. Logical filters allow for exhaus- tive search queries constructed as logical triplets consisting of a BrAPI model property, a logical operator, and a value (e.g. ‘studyName equals “Nursery Study”’). Searches can be ex- tended over relationships between data models, thus enabling a user to traverse the data model graph and query the ware- house for exactly the required data. An example data ware- house is publicly available and offers full read access in the UI and through the GraphQL API. The example warehouse is populated with public CassavaBase data [ 15 ] to demonstrate BrAPI-compliant data examples based on real data. Three in- teractive scientific example plots are available to explore the data. Discussion BrAPI for breeders While the BrAPI technical specification is designed to be read and used by software developers, its underlying purpose is to support the work of breeders and other scientists by D ow nloaded from https://academ ic.oup.com /database/article/doi/10.1093/database/baaf048/8263863 by guest on 01 O ctober 2025 Real-world applications for data integration 13 making routine processes faster, easier, and cheaper. BrAPI offers a convenient path to automation, interoperability, and data integration for software tools in breeding, genetics, phe- nomics, and other related agricultural domains. By integrating the tools described above, breeders and scientists can spend less time on data management and more time focusing on sci- ence. For many, the ultimate goal is the development of a dig- ital data ecosystem: a collection of software tools and appli- cations that can all work together seamlessly. In this scenario, data is digitally collected, automatically sent to quality control systems, batch analysed to provide actionable insights, and fi- nally stored in accessible databases for long-term applications. As tools continue to adopt the BrAPI standard, this vision is beginning to approach reality. Looking ahead The BrAPI project leadership and community are committed to building standards to support new use cases and technolo- gies as they are adopted by breeders and other scientists, po- tentially including drone imaging data, spectroscopy, LIDAR, metabolomics, transcriptomics, agronomics, high-throughput phenotyping, pangenomics, and machine learning-based anal- ysis. Each of these technologies will have unique challenges, generate different types of data, and require substantial thought and discussion before being added to the BrAPI speci- fication. This process has already begun for several data types, with small groups working to build generic data models and proposed communication standards. As these community ef- forts are completed, they will become part of a future version of the BrAPI standard, enabling further interoperability and simplifying data exchange. Expanding the BrAPI specification is important for the com- munity, but this growth should not reinvent or compete with existing functional standards. Additions to the BrAPI specifi- cation are reviewed thoroughly by the community to make sure BrAPI is compliant with existing standards and data structures. For example, the community has requested com- pliance with the GFF3 standard for genomic data and the GeoTIFF standard for aerial image data. Pieces of these exist- ing popular data structures might be integrated into the over- all BrAPI standard documentation. In some cases, BrAPI will only reference other standards instead of including them in the specification. For example, there have been community discus- sions around developing connections with the NOAA CDO standard for weather data or the Galaxy Analytics API for analytics pipeline controls and information. These standards are perfectly adequate on their own, and recreating them in the BrAPI standard would be redundant. Conclusion The BrAPI project only exists because of the community of software engineers, biologists, and other scientists who sup- port and use it. While there were many tools and use cases pre- sented here, it is not an exhaustive list of all BrAPI-compliant systems. As long as the standard continues to be supported, the community will continue to expand. As more groups con- tinue to make their tools BrAPI compliant, others will see the value in implementing BrAPI into their own tools, al- lowing the community to strengthen and grow. By provid- ing an open standard for breeding data and the infrastructure and community to support it, the BrAPI project is doing its part to support a productive agricultural system amidst the pressing challenges of climate change. If this manuscript is your first introduction to the BrAPI project, the authors invite you to join the community. More information is available at brapi.org . Acknowledgements This work is also supported by � The U.S. Department of Agriculture, National Institute of Food and Agriculture (grant number 2022-51181-38449). � The U.S. Department of Agriculture, National Institute of Food and Agriculture National Research Support Project 10. � The U.S. Department of Agriculture (grant numbers 8062-21000-043-004-A, 8062-21000-052-002-A, and 8062- 21000-052-003-A). � The Innovation Lab for Crop Improvement Cornell (grant number 7200AA19LE00005). � The Foundation for Food & Agriculture Research (grant number CA20-SS-0000000103). � The European Union’s Horizon 2020 Research and Inno- vation Programme under (grant number 862613). � The ELIXIR initiative, the research infrastructure for life science data. � The German Ministry of Research and Education (grant number FKZ 031B1302A). � The German Research Foundation DFG (grant number 442032008) (NFDI4Biodiversity). 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D ow nlo https://doi.org/10.1093/genetics/157.4.1819 https://icarda.github.io/QBMS https://doi.org/10.5281/zenodo.16638223 https://github.com/elixir-europe/plant-brapi-etl-faidare https://doi.org/10.1002/csc2.20248 https://doi.org/10.2135/cropsci2016.09.0814 https://doi.org/10.3389/fpls.2023.1290078 https://doi.org/10.1038/ng.1054 https://creativecommons.org/licenses/by/4.0/ Introduction Results Discussion Conclusion Acknowledgements Funding Data Availability References