Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API

Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF software Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API Julian Frey, Zoe Schindler, Muhammad Ali, Joshua Braun-Wimmer, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927956/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Standardisation, accessibility, and reproducibility remain persistent challenges in data‑intensive scientific research. The Galaxy platform addresses these issues by providing a web‑based environment for executing, sharing, and publishing computational workflows, yet programmatic access has been largely centred on the Python ecosystem through BioBlend. Methods Here, we introduce GalaxyR, a native R package that provides a comprehensive and structured interface to the Galaxy application programming interface. GalaxyR enables users to manage histories, upload and retrieve data, execute tools and workflows, monitor jobs, and inspect results directly from within the R environment. By integrating Galaxy’s scalable computational infrastructure with R’s widely adopted data analysis ecosystem, GalaxyR facilitates automated, reproducible, and resource‑efficient workflows without requiring local high‑performance computing resources. The package supports both HTTPS‑ and FTP‑based data transfer, robust history and dataset management, and programmatic workflow orchestration across Galaxy instances. Results An application example demonstrates the use of GalaxyR for large‑scale processing of drone-based laser scanning data, where computationally intensive tree‑level segmentation was delegated to the Galaxy infrastructure, while workflow control and preprocessing were handled in R within a few lines of code. Conclusions GalaxyR thus bridges a critical gap for R users, significantly expanding access to Galaxy‑based analyses and enabling scalable, reproducible research across bioinformatics, ecology, remote sensing, and related data‑driven disciplines. software cloud computing R programming language application programming interface Figures Figure 1 Figure 2 Background Standardisation, accessibility, interoperability, and reusability of analytical tools remain central challenges in the life sciences and related data-driven research fields [ 1 – 3 ]. Addressing these challenges is essential for ensuring reproducibility, transparency, and long-term usability of computational analyses [ 2 , 3 ]. The Galaxy platform represents one of the most comprehensive and successful efforts to tackle these issues by providing a unified platform for the execution, sharing, and publication of scientific workflows [ 4 ]. Originally established as a resource for biomedical research, Galaxy has since evolved into a general-purpose computational workbench widely used across biological, medical, ecological, remote sensing, and other earth system sciences [ 4 , 5 ]. Galaxy is best known for its user-friendly, web-based interface that enables researchers to perform complex analyses without requiring programming expertise [ 6 ]. Beyond this no-code usage model, the platform provides access to substantial computational and storage resources, along with a rich ecosystem of community-maintained research data management and analysis tools and workflows. These capabilities support standardised and repeatable analyses, facilitate data and method sharing and publication, and promote reproducible research practices [ 4 ]. At the same time, Galaxy has been designed with automation in mind and exposes nearly all of its functionality through a well-documented representational state transfer (REST) application programming interface (API) [ 7 , 8 ]. The REST API is implemented as an OpenAPI v3 [ 9 ], which ensures a standardised API design, automatic documentation generation, and code generation for multiple programming languages. Direct interaction with a REST API, however, can be challenging for many scientists, particularly those whose primary expertise lies outside software engineering [ 8 ]. As a result, high-level software libraries that abstract the underlying API and integrate Galaxy functionality into familiar programming environments are of substantial value to the research community. Such abstractions are well established in the Python ecosystem [ 10 ] through the BioBlend package [ 8 ], but not for the R programming environment [ 11 ]. The RGalaxy package [ 12 ] provides functionality to integrate R functions into Galaxy tools, but allows no direct interaction with the API. The R GalaxyConnector [ 13 ] allows uploading and downloading files from R to a Galaxy instance, but additional functionality, such as workflow and tool invocations, is absent. The lack of integration between Galaxy and R is notable given the widespread use of R across many scientific domains in which Galaxy plays an important role, including statistics-driven analyses, ecological modelling, and data-intensive life sciences [ 14 , 15 ]. To address this limitation, we developed GalaxyR, a native R package that provides a structured and well-documented interface to the Galaxy API. GalaxyR enables users to programmatically upload data, create and manage histories, invoke tools and workflows, monitor execution, and retrieve results and metadata directly from within R. By integrating the Galaxy functionality into the R environment, the package allows researchers to automate complex analytical workflows while continuing to work within a familiar and widely adopted scientific programming framework. Importantly, because analyses can be executed on public Galaxy servers, GalaxyR enables users to overcome the need for dedicated local computing infrastructure, making large-scale or computationally intensive analyses accessible even to users without specialised hardware. This includes workflows that rely on substantial memory, parallel computation, or specialised accelerators, such as graphics processing units, for artificial intelligence models, thereby lowering technical barriers and broadening access to advanced analytical methods. Institutions might also choose to set up their own Galaxy instances to manage their tools, computational resources and load. Implementation of the GalaxyR package The GalaxyR package is designed to support reproducible, scriptable, and scalable computational workflows for data-intensive scientific analyses, particularly in bioinformatics [ 5 ], geospatial data sciences [ 16 – 18 ] and related fields. GalaxyR implements API-key-based authentication and centralised configuration of Galaxy connection parameters via environment variables, allowing seamless interaction with a specified Galaxy instance. It supports dynamic resolution of the Galaxy base URL and integrates credential handling for both HTTPS- and FTP-based data transfers. These design choices make GalaxyR suitable for use in interactive sessions as well as automated analysis pipelines (Fig. 1 ). GalaxyR enables workflow invocations with just a few lines of code. The main functionality is implemented as an S4 class that holds all relevant information for executing the next steps and can therefore be piped through the workflow in a comfortable way (Fig. 1 ). GalaxyR provides comprehensive support for Galaxy history management. Users can create new histories, list existing histories, and compute disk usage for individual histories in a manner that is robust to differences across Galaxy server versions. Disk usage is reported in both raw byte counts and human-readable formats, facilitating resource monitoring in large-scale analyses. Data upload is supported through multiple mechanisms. Local files can be uploaded via HTTPS using Galaxy’s built-in upload tools, with optional polling until the uploaded datasets are fully processed and ready for downstream use. For large files or environments where HTTPS uploads are restricted, FTP-based uploads are also supported. These capabilities allow GalaxyR to accommodate a wide range of data sizes and deployment scenarios. Single tools and workflows installed on the Galaxy instance can be listed, filtered, and queried for detailed metadata, including input and output specifications. Tool and workflow identifiers can be resolved by name, facilitating reproducible tool selection across Galaxy instances. Tools and workflows can then be executed with fully specified input parameter sets, closely mirroring Galaxy’s internal JSON-based execution model. GalaxyR includes functionality to monitor the execution by polling Galaxy jobs until they reach a terminal state, thereby ensuring reliable synchronisation between R scripts and Galaxy’s asynchronous execution model. Upon completion, GalaxyR retrieves the identifiers of output datasets generated by the execution, enabling seamless chaining of analyses. GalaxyR also provides functionality for dataset inspection and management. For one or multiple Galaxy datasets, the package can retrieve metadata including dataset name, file type, size in bytes, human-readable size, processing state, and deletion status. This information is returned in tabular form, making it straightforward to integrate into downstream analyses or reporting workflows. Result datasets produced by tools or workflows can be downloaded directly to local files, enabling further processing outside of Galaxy. Finally, the package includes utilities for dataset cleanup, allowing individual datasets or collections of datasets to be deleted programmatically, with optional permanent purging to free disk space. Requests are paced to avoid overloading the Galaxy server, reflecting an emphasis on robustness and responsible API usage. Application example Forests provide essential ecosystem services – carbon sequestration, biodiversity support, water regulation, and timber production – yet their complex three‑dimensional structure makes reliable, large‑scale monitoring a persistent challenge [ 19 ]. The structural mapping of forests by modern remote sensing techniques such as Light Detection and Ranging (LiDAR) has made great progress in recent decades, but processing such big datasets still proves to be difficult [ 20 ]. In spring 2025, we conducted 113 flights using a multirotor uncrewed aerial vehicle UAV (DJI Matrice 350, DJI, China) equipped with a LiDAR sensor (DJI L2, DJI, China) over a forested area in the central‑western part of Germany. The objective was to obtain a high‑resolution representation of forest structure, with a particular focus on single‑tree attributes such as tree height. This close‑range remote sensing approach produced centimetre‑accurate, dense three‑dimensional point clouds representing individual LiDAR returns within the forest canopy and understory (Fig. 2 ). The resulting dataset covered more than 600 hectares of mountainous forest and comprised approximately 4.7 billion points, corresponding to more than 460 gigabytes of compressed data after initial preprocessing. Extracting single‑tree parameters from data of this scale required the application of an instance segmentation model specifically designed for tree‑level analysis [ 21 ], which exceeded the available local computing resources. Within the R programming environment, the lidR package provides a high‑performance tiling and processing framework for large point cloud datasets, including support for overlapping tiles [ 22 ]. We used lidR to reorganise our data into tiles with an edge length of 200 metres, replacing the original structure of one file per flight with overlapping coverage at tile boundaries. An additional buffer of 10 metres was added around each tile to prevent edge artefacts during processing (Fig. 2 ). The computationally intensive single‑tree instance segmentation was then executed on the European Galaxy servers, using the GalaxyR package directly from within the lidR tiling workflow. After downloading the segmentation results from the Galaxy instance, all remote data were deleted, and trees outside the tile boundaries were removed automatically. Using this approach, we were able to process the complete dataset and segment more than 250,000 individual trees within a total runtime of 72 hours. The R code necessary for this analysis is just about 40 lines long, underscoring how efficient tasks can be automated using GalaxyR (Appendix 1). This application example illustrates how combining R and Galaxy enables efficient, scalable analysis of large, complex datasets that exceed local computational capabilities. By using R as an orchestration and preprocessing environment and delegating computationally intensive tasks to Galaxy, researchers can seamlessly integrate local data handling with remote, high-performance execution. The tight coupling provided by GalaxyR allows workflows to be defined, parameterised, and monitored directly from R, while Galaxy transparently manages data transfer, scheduling, and execution on suitable infrastructure. This division of labour leverages the strengths of both environments: R for flexible data manipulation and workflow control, and Galaxy for robust, reproducible execution at scale. Discussion While GalaxyR provides a broad set of functions for interacting with Galaxy histories, tools, workflows, and the retrieval of structured provenance records (RO-Crate) [ 23 ], several capabilities described below are not yet supported. As a result, users who require these functionalities must currently rely on Galaxy’s web interface or external tools. Support for more complex data structures is limited. Although workflows and tools that operate internally on grouped or paired datasets can be executed, GalaxyR does not yet provide dedicated functions to create, inspect, or manipulate such dataset groupings directly from R [ 7 ]. This limits the ability to programmatically manage analyses that rely heavily on structured collections of input data. In addition, GalaxyR primarily focuses on file‑based data stored in Galaxy histories. Direct interaction with remote data sources, such as externally hosted repositories or federated storage systems accessible through Galaxy, is not yet implemented. As large‑scale and distributed data access becomes increasingly important, extending GalaxyR to better support remote data integration represents a natural direction for future development. Future work on GalaxyR will therefore focus on expanding support for improving the handling of complex dataset structures and enabling more seamless access to remote data resources. Conclusion In summary, GalaxyR provides an end-to-end R interface to the Galaxy API, covering history management, data upload, tool execution, workflow orchestration, result retrieval, and resource monitoring. Its design emphasises reproducibility, automation, and compatibility across Galaxy instances, making it a suitable foundation for integrating Galaxy-based analyses into scientific R workflows and computational pipelines. This significantly strengthens the ability of the R users’ community to utilise the platform for various applications, from simple tool usage for tools not available on R to complex automation tasks or big data analyses with high computational demands. Abbreviations API Application Programming Interface REST Representational State Transfer FAIR Findable, Accessible, Interoperable, Reusable UAV Unmanned/ uncrewed Aerial Vehicle LiDAR Light Detection and Ranging TLS Terrestrial Laser Scanning HPC High-Performance Computing GPU Graphics Processing Unit Declarations Availability and requirements Project name: GalaxyR Project home page: https://github.com/JulFrey/GalaxyR Operating system(s): Platform independent Programming language: R Other requirements: R4.2 or higher License: GNU GPL Any restrictions to use by non-academics: none GalaxyR is published on the CRAN R package archive. Additionally, a Conda package ( https://anaconda.org/conda-forge/r-galaxyr ) and a Docker container https://quay.io/repository/biocontainers/r-galaxyr are available. Acknowledgements We want to warmly thank Björn Grüning for his extensive supervision and aid in implementing tools in Galaxy and providing the Conda package and the containerised version of the GalaxyR package. Funding The development of GalaxyR was funded by the Baden-Württemberg Stiftung within the project “WaldAgil” (Agiles Forstmanagement durch ein digitales Multi-User-Entscheidungssystem für eine klimaresiliente Waldwirtschaft). The LiDAR data collection for this research was funded by the Sattelmühle Stiftung, Germany, within the project "Präzisionsinventur Sattelmühle". KG and TK acknowledge support by the German Research Foundation (DFG) under the project UAV-mounted dual-wavelength LiDAR for leaf water content retrieval (LeafH2O, project no. 541018379). Further Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials: All code is published. The LiDAR data sets exceed the limits of public repositories and are therefore available upon request. Competing interests: The authors declare that they have no competing interests. Author contributions JF has conceptualised the software. JF and ZS have written the code. MA and JBW have conducted the LiDAR campaign. MA, JBW and JF have analysed the LiDAR data. KG, JWJ and DL implemented the Galaxy tools for the application example. KK, EL, YW and MW tested and improved the software. TK and TS supervised the project. JF wrote the manuscript. MA, ZS and JF prepared the figures. All authors actively improved the manuscript. References Barker M, Chue Hong NP, Katz DS, Lamprecht A-L, Martinez-Ortiz C, Psomopoulos F, et al. Introducing the FAIR Principles for research software. Sci Data. 2022;9:622. https://doi.org/10.1038/s41597-022-01710-x . Grüning B, Chilton J, Köster J, Dale R, Soranzo N, Beek M, van den, et al. Practical Computational Reproducibility in the Life Sciences. Cell Syst. 2018;6:631–5. https://doi.org/10.1016/j.cels.2018.03.014 . Wratten L, Wilm A, Göke J. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods. 2021;18:1161–8. https://doi.org/10.1038/s41592-021-01254-9 . The Galaxy Community. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res. 2024;52:W83–94. https://doi.org/10.1093/nar/gkae410 . Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46:W537–44. https://doi.org/10.1093/nar/gky379 . Bacon WA, Srikakulam SK, Batut B, Zierep P, Bretaudeau A, Grüning B et al. Ten Common Misconceptions About Galaxy (and Why They Are Wrong! 2025. https://doi.org/10.20944/preprints202509.0823.v1 Børnich C, Grytten I, Hovig E, Paulsen J, Čech M, Sandve GK. Galaxy Portal: interacting with the galaxy platform through mobile devices. Bioinformatics. 2016;32:1743–5. https://doi.org/10.1093/bioinformatics/btw042 . Sloggett C, Goonasekera N, Afgan E. BioBlend: automating pipeline analyses within Galaxy and CloudMan. Bioinformatics. 2013;29:1685–6. https://doi.org/10.1093/bioinformatics/btt199 . Chilton J, Grüning B. Galaxy Code Architecture. Galaxy Training Network. 2024. https://training.galaxyproject.org/training-material/topics/dev/tutorials/architecture/slides-plain.html . Accessed 28 Jan 2026. Van Rossum G, Drake FL Jr. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. RGalaxy. https://www.bioconductor.org/packages//2.12/bioc/vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html . Accessed 20 Feb 2026. r-galaxy-connector. 2025. Joshi J, Cumbo F, Blankenberg D. R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy. 2025;:2025.12.22.695980. https://doi.org/10.64898/2025.12.22.695980 Turaga N, Freeberg MA, Baker D, Chilton J, Team G, Nekrutenko A et al. A guide and best practices for R/Bioconductor tool integration in Galaxy. 2016. https://doi.org/10.12688/f1000research.9821.1 Cholewińska P, Wojnarowski K, Szeligowska N, Pokorny P, Hussein W, Hasegawa Y, et al. Presence of microplastic particles increased abundance of pathogens and antimicrobial resistance genes in microbial communities from the Oder river water and sediment. Sci Rep. 2025;15:16338. https://doi.org/10.1038/s41598-025-01136-6 . Royaux C, Norvez O, Jossé M, Arnaud E, Sananikone J, Pavoine S, et al. From Biodiversity Observation Networks to Datasets and Workflows Supporting Biodiversity Indicators, a French Biodiversity Observation Network (BON) Essential Biodiversity Variables (EBV) Operationalization Pilot using Galaxy and Ecological Metadata Language. Biodivers Inf Sci Stand. 2022. https://doi.org/10.3897/biss.6.94957 . Royaux C, Mihoub J-B, Jossé M, Pelletier D, Norvez O, Reecht Y, et al. Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. GigaScience. 2025;14:giae122. https://doi.org/10.1093/gigascience/giae122 . FAO and UNEP. The State of the World’s Forests 2020: Forests, biodiversity and people. Rome, Italy: FAO and UNEP; 2020. https://doi.org/10.4060/ca8642en . Coops NC, Tompalski P, Goodbody TRH, Queinnec M, Luther JE, Bolton DK, et al. Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends. Remote Sens Environ. 2021;260:112477. https://doi.org/10.1016/j.rse.2021.112477 . Wielgosz M, Puliti S, Xiang B, Schindler K, Astrup R, SegmentAnyTree:. A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sens Environ. 2024;313:114367. https://doi.org/10.1016/j.rse.2024.114367 . Roussel J-R, Auty D, lidR. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. 2017. Soiland-Reyes S, Sefton P, Crosas M, Castro LJ, Coppens F, Fernández JM, et al. Packaging research artefacts with RO-Crate. Data Sci. 2022;5:97–138. https://doi.org/10.3233/DS-210053 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8927956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"software","associatedPublications":[],"authors":[{"id":599160669,"identity":"a6a2c980-f7b1-45a5-83a0-1d668ca4dce4","order_by":0,"name":"Julian 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17:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927956/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103826636,"identity":"7e9dffc5-e07d-419d-9ac7-e2037c342480","added_by":"auto","created_at":"2026-03-03 11:43:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGalaxyR example command flow from initialisation (yellow) to results download (pink). The code shows the original R commands from the GalaxyR package.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8927956/v1/4a2f0c243ef97a7f2b546488.png"},{"id":103826627,"identity":"81878852-6c8e-4ae8-92e6-4df9ff0ae00e","added_by":"auto","created_at":"2026-03-03 11:42:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":951783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorkflow of the case study. A LiDAR dataset has been collected in multiple flights utilising a multirotor UAV on a 600 ha forested landscape (rose box). In the R environment, flights have been retiled to minimise overlap (blue box) and automatically uploaded to the Galaxy Europe platform using GalaxyR for AI-based instance segmentation of trees (yellow cloud). Afterwards, the segmented point clouds have been downloaded and analysed locally to create a single tree map of the whole area (blue box).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8927956/v1/ca4bf918a8b87dc9e5d2b30f.png"},{"id":104400980,"identity":"1dcd4b69-3d34-4f94-9957-4f20be4c9a73","added_by":"auto","created_at":"2026-03-11 12:11:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1535406,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927956/v1/a7b2939b-b734-405a-9c0d-c447b48ae84b.pdf"},{"id":103826593,"identity":"55d7af68-9fa7-4809-81da-8e4367eb2bf5","added_by":"auto","created_at":"2026-03-03 11:42:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16943,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8927956/v1/6ae7ef0871230f0e59cfe29c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API","fulltext":[{"header":"Background","content":"\u003cp\u003eStandardisation, accessibility, interoperability, and reusability of analytical tools remain central challenges in the life sciences and related data-driven research fields [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Addressing these challenges is essential for ensuring reproducibility, transparency, and long-term usability of computational analyses [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Galaxy platform represents one of the most comprehensive and successful efforts to tackle these issues by providing a unified platform for the execution, sharing, and publication of scientific workflows [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Originally established as a resource for biomedical research, Galaxy has since evolved into a general-purpose computational workbench widely used across biological, medical, ecological, remote sensing, and other earth system sciences [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGalaxy is best known for its user-friendly, web-based interface that enables researchers to perform complex analyses without requiring programming expertise [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Beyond this no-code usage model, the platform provides access to substantial computational and storage resources, along with a rich ecosystem of community-maintained research data management and analysis tools and workflows. These capabilities support standardised and repeatable analyses, facilitate data and method sharing and publication, and promote reproducible research practices [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. At the same time, Galaxy has been designed with automation in mind and exposes nearly all of its functionality through a well-documented representational state transfer (REST) application programming interface (API) [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The REST API is implemented as an OpenAPI v3 [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], which ensures a standardised API design, automatic documentation generation, and code generation for multiple programming languages.\u003c/p\u003e \u003cp\u003eDirect interaction with a REST API, however, can be challenging for many scientists, particularly those whose primary expertise lies outside software engineering [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a result, high-level software libraries that abstract the underlying API and integrate Galaxy functionality into familiar programming environments are of substantial value to the research community. Such abstractions are well established in the Python ecosystem [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] through the BioBlend package [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], but not for the R programming environment [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The RGalaxy package [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] provides functionality to integrate R functions into Galaxy tools, but allows no direct interaction with the API. The R GalaxyConnector [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] allows uploading and downloading files from R to a Galaxy instance, but additional functionality, such as workflow and tool invocations, is absent. The lack of integration between Galaxy and R is notable given the widespread use of R across many scientific domains in which Galaxy plays an important role, including statistics-driven analyses, ecological modelling, and data-intensive life sciences [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address this limitation, we developed GalaxyR, a native R package that provides a structured and well-documented interface to the Galaxy API. GalaxyR enables users to programmatically upload data, create and manage histories, invoke tools and workflows, monitor execution, and retrieve results and metadata directly from within R. By integrating the Galaxy functionality into the R environment, the package allows researchers to automate complex analytical workflows while continuing to work within a familiar and widely adopted scientific programming framework. Importantly, because analyses can be executed on public Galaxy servers, GalaxyR enables users to overcome the need for dedicated local computing infrastructure, making large-scale or computationally intensive analyses accessible even to users without specialised hardware. This includes workflows that rely on substantial memory, parallel computation, or specialised accelerators, such as graphics processing units, for artificial intelligence models, thereby lowering technical barriers and broadening access to advanced analytical methods. Institutions might also choose to set up their own Galaxy instances to manage their tools, computational resources and load.\u003c/p\u003e "},{"header":"Implementation of the GalaxyR package","content":"\u003cp\u003eThe GalaxyR package is designed to support reproducible, scriptable, and scalable computational workflows for data-intensive scientific analyses, particularly in bioinformatics [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], geospatial data sciences [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] and related fields.\u003c/p\u003e\u003cp\u003eGalaxyR implements API-key-based authentication and centralised configuration of Galaxy connection parameters via environment variables, allowing seamless interaction with a specified Galaxy instance. It supports dynamic resolution of the Galaxy base URL and integrates credential handling for both HTTPS- and FTP-based data transfers. These design choices make GalaxyR suitable for use in interactive sessions as well as automated analysis pipelines (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eGalaxyR enables workflow invocations with just a few lines of code. The main functionality is implemented as an S4 class that holds all relevant information for executing the next steps and can therefore be piped through the workflow in a comfortable way (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGalaxyR provides comprehensive support for Galaxy history management. Users can create new histories, list existing histories, and compute disk usage for individual histories in a manner that is robust to differences across Galaxy server versions. Disk usage is reported in both raw byte counts and human-readable formats, facilitating resource monitoring in large-scale analyses.\u003c/p\u003e\u003cp\u003eData upload is supported through multiple mechanisms. Local files can be uploaded via HTTPS using Galaxy’s built-in upload tools, with optional polling until the uploaded datasets are fully processed and ready for downstream use. For large files or environments where HTTPS uploads are restricted, FTP-based uploads are also supported. These capabilities allow GalaxyR to accommodate a wide range of data sizes and deployment scenarios.\u003c/p\u003e\u003cp\u003eSingle tools and workflows installed on the Galaxy instance can be listed, filtered, and queried for detailed metadata, including input and output specifications. Tool and workflow identifiers can be resolved by name, facilitating reproducible tool selection across Galaxy instances. Tools and workflows can then be executed with fully specified input parameter sets, closely mirroring Galaxy’s internal JSON-based execution model. GalaxyR includes functionality to monitor the execution by polling Galaxy jobs until they reach a terminal state, thereby ensuring reliable synchronisation between R scripts and Galaxy’s asynchronous execution model. Upon completion, GalaxyR retrieves the identifiers of output datasets generated by the execution, enabling seamless chaining of analyses.\u003c/p\u003e\u003cp\u003eGalaxyR also provides functionality for dataset inspection and management. For one or multiple Galaxy datasets, the package can retrieve metadata including dataset name, file type, size in bytes, human-readable size, processing state, and deletion status. This information is returned in tabular form, making it straightforward to integrate into downstream analyses or reporting workflows. Result datasets produced by tools or workflows can be downloaded directly to local files, enabling further processing outside of Galaxy. Finally, the package includes utilities for dataset cleanup, allowing individual datasets or collections of datasets to be deleted programmatically, with optional permanent purging to free disk space. Requests are paced to avoid overloading the Galaxy server, reflecting an emphasis on robustness and responsible API usage.\u003c/p\u003e"},{"header":"Application example","content":"\u003cp\u003e \u003c/p\u003e\u003cp\u003eForests provide essential ecosystem services – carbon sequestration, biodiversity support, water regulation, and timber production – yet their complex three‑dimensional structure makes reliable, large‑scale monitoring a persistent challenge [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The structural mapping of forests by modern remote sensing techniques such as Light Detection and Ranging (LiDAR) has made great progress in recent decades, but processing such big datasets still proves to be difficult [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn spring 2025, we conducted 113 flights using a multirotor uncrewed aerial vehicle UAV (DJI Matrice 350, DJI, China) equipped with a LiDAR sensor (DJI L2, DJI, China) over a forested area in the central‑western part of Germany. The objective was to obtain a high‑resolution representation of forest structure, with a particular focus on single‑tree attributes such as tree height. This close‑range remote sensing approach produced centimetre‑accurate, dense three‑dimensional point clouds representing individual LiDAR returns within the forest canopy and understory (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe resulting dataset covered more than 600 hectares of mountainous forest and comprised approximately 4.7\u0026nbsp;billion points, corresponding to more than 460 gigabytes of compressed data after initial preprocessing. Extracting single‑tree parameters from data of this scale required the application of an instance segmentation model specifically designed for tree‑level analysis [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], which exceeded the available local computing resources.\u003c/p\u003e\u003cp\u003eWithin the R programming environment, the lidR package provides a high‑performance tiling and processing framework for large point cloud datasets, including support for overlapping tiles [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. We used lidR to reorganise our data into tiles with an edge length of 200 metres, replacing the original structure of one file per flight with overlapping coverage at tile boundaries. An additional buffer of 10 metres was added around each tile to prevent edge artefacts during processing (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The computationally intensive single‑tree instance segmentation was then executed on the European Galaxy servers, using the GalaxyR package directly from within the lidR tiling workflow.\u003c/p\u003e\u003cp\u003eAfter downloading the segmentation results from the Galaxy instance, all remote data were deleted, and trees outside the tile boundaries were removed automatically. Using this approach, we were able to process the complete dataset and segment more than 250,000 individual trees within a total runtime of 72 hours. The R code necessary for this analysis is just about 40 lines long, underscoring how efficient tasks can be automated using GalaxyR (Appendix 1).\u003c/p\u003e\u003cp\u003eThis application example illustrates how combining R and Galaxy enables efficient, scalable analysis of large, complex datasets that exceed local computational capabilities. By using R as an orchestration and preprocessing environment and delegating computationally intensive tasks to Galaxy, researchers can seamlessly integrate local data handling with remote, high-performance execution. The tight coupling provided by GalaxyR allows workflows to be defined, parameterised, and monitored directly from R, while Galaxy transparently manages data transfer, scheduling, and execution on suitable infrastructure. This division of labour leverages the strengths of both environments: R for flexible data manipulation and workflow control, and Galaxy for robust, reproducible execution at scale.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile GalaxyR provides a broad set of functions for interacting with Galaxy histories, tools, workflows, and the retrieval of structured provenance records (RO-Crate) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], several capabilities described below are not yet supported. As a result, users who require these functionalities must currently rely on Galaxy\u0026rsquo;s web interface or external tools.\u003c/p\u003e \u003cp\u003eSupport for more complex data structures is limited. Although workflows and tools that operate internally on grouped or paired datasets can be executed, GalaxyR does not yet provide dedicated functions to create, inspect, or manipulate such dataset groupings directly from R [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This limits the ability to programmatically manage analyses that rely heavily on structured collections of input data.\u003c/p\u003e \u003cp\u003eIn addition, GalaxyR primarily focuses on file‑based data stored in Galaxy histories. Direct interaction with remote data sources, such as externally hosted repositories or federated storage systems accessible through Galaxy, is not yet implemented. As large‑scale and distributed data access becomes increasingly important, extending GalaxyR to better support remote data integration represents a natural direction for future development.\u003c/p\u003e \u003cp\u003eFuture work on GalaxyR will therefore focus on expanding support for improving the handling of complex dataset structures and enabling more seamless access to remote data resources.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, GalaxyR provides an end-to-end R interface to the Galaxy API, covering history management, data upload, tool execution, workflow orchestration, result retrieval, and resource monitoring. Its design emphasises reproducibility, automation, and compatibility across Galaxy instances, making it a suitable foundation for integrating Galaxy-based analyses into scientific R workflows and computational pipelines. This significantly strengthens the ability of the R users\u0026rsquo; community to utilise the platform for various applications, from simple tool usage for tools not available on R to complex automation tasks or big data analyses with high computational demands.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAPI Application Programming Interface\u003c/p\u003e\u003cp\u003eREST Representational State Transfer\u003c/p\u003e\u003cp\u003eFAIR Findable, Accessible, Interoperable, Reusable\u003c/p\u003e\u003cp\u003eUAV Unmanned/ uncrewed Aerial Vehicle\u003c/p\u003e\u003cp\u003eLiDAR Light Detection and Ranging\u003c/p\u003e\u003cp\u003eTLS Terrestrial Laser Scanning\u003c/p\u003e\u003cp\u003eHPC High-Performance Computing\u003c/p\u003e\u003cp\u003eGPU Graphics Processing Unit\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability and requirements\u003c/h2\u003e\n\u003cp\u003eProject name: GalaxyR\u003cbr\u003eProject home page: \u003cu\u003ehttps://github.com/JulFrey/GalaxyR\u003c/u\u003e\u003cbr\u003e\u0026nbsp;Operating system(s): Platform independent\u003cbr\u003e\u0026nbsp;Programming language: R\u003cbr\u003e\u0026nbsp;Other requirements: R4.2 or higher\u003cbr\u003e\u0026nbsp;License: GNU GPL\u003cbr\u003e\u0026nbsp;Any restrictions to use by non-academics: none\u003c/p\u003e\n\u003cp\u003eGalaxyR is published on the CRAN R package archive. Additionally, a Conda package (\u003cu\u003ehttps://anaconda.org/conda-forge/r-galaxyr\u003c/u\u003e) and a Docker container \u003cu\u003ehttps://quay.io/repository/biocontainers/r-galaxyr\u003c/u\u003e are available.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe want to warmly thank Bj\u0026ouml;rn Gr\u0026uuml;ning for his extensive supervision and aid in implementing tools in Galaxy and providing the Conda package and the containerised version of the GalaxyR package.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe development of GalaxyR was funded by the Baden-W\u0026uuml;rttemberg Stiftung within the project \u0026ldquo;WaldAgil\u0026rdquo; (Agiles Forstmanagement durch ein digitales Multi-User-Entscheidungssystem f\u0026uuml;r eine klimaresiliente Waldwirtschaft). The LiDAR data collection for this research was funded by the Sattelm\u0026uuml;hle Stiftung, Germany, within the project \u0026quot;Pr\u0026auml;zisionsinventur Sattelm\u0026uuml;hle\u0026quot;. KG and TK acknowledge support by the German Research Foundation (DFG) under the project UAV-mounted dual-wavelength LiDAR for leaf water content retrieval (LeafH2O, project no. 541018379).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFurther Declarations\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003cbr\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003cbr\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All code is published. The LiDAR data sets exceed the limits of public repositories and are therefore available upon request.\u003cbr\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eJF has conceptualised the software. JF and ZS have written the code. MA and JBW have conducted the LiDAR campaign. MA, JBW and JF have analysed the LiDAR data. KG, JWJ and DL implemented the Galaxy tools for the application example. KK, EL, YW and MW tested and improved the software. TK and TS supervised the project. JF wrote the manuscript. MA, ZS and JF prepared the figures. All authors actively improved the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarker M, Chue Hong NP, Katz DS, Lamprecht A-L, Martinez-Ortiz C, Psomopoulos F, et al. Introducing the FAIR Principles for research software. Sci Data. 2022;9:622. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-022-01710-x\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eGr\u0026uuml;ning B, Chilton J, K\u0026ouml;ster J, Dale R, Soranzo N, Beek M, van den, et al. Practical Computational Reproducibility in the Life Sciences. Cell Syst. 2018;6:631\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cels.2018.03.014\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eWratten L, Wilm A, G\u0026ouml;ke J. Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers. Nat Methods. 2021;18:1161\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41592-021-01254-9\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eThe Galaxy Community. The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update. Nucleic Acids Res. 2024;52:W83\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkae410\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eAfgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46:W537\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gky379\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eBacon WA, Srikakulam SK, Batut B, Zierep P, Bretaudeau A, Gr\u0026uuml;ning B et al. Ten Common Misconceptions About Galaxy (and Why They Are Wrong! 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20944/preprints202509.0823.v1\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eB\u0026oslash;rnich C, Grytten I, Hovig E, Paulsen J, Čech M, Sandve GK. Galaxy Portal: interacting with the galaxy platform through mobile devices. Bioinformatics. 2016;32:1743\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btw042\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eSloggett C, Goonasekera N, Afgan E. BioBlend: automating pipeline analyses within Galaxy and CloudMan. Bioinformatics. 2013;29:1685\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btt199\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eChilton J, Gr\u0026uuml;ning B. Galaxy Code Architecture. Galaxy Training Network. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://training.galaxyproject.org/training-material/topics/dev/tutorials/architecture/slides-plain.html\u003c/span\u003e\u003c/span\u003e. Accessed 28 Jan 2026.\u003c/li\u003e\n\u003cli\u003eVan Rossum G, Drake FL Jr. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024.\u003c/li\u003e\n\u003cli\u003eRGalaxy. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioconductor.org/packages//2.12/bioc/vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html\u003c/span\u003e\u003c/span\u003e. Accessed 20 Feb 2026.\u003c/li\u003e\n\u003cli\u003er-galaxy-connector. 2025.\u003c/li\u003e\n\u003cli\u003eJoshi J, Cumbo F, Blankenberg D. R2G2: A Python-R Framework for Seamless Integration of R/Bioconductor Tools into Galaxy. 2025;:2025.12.22.695980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.64898/2025.12.22.695980\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eTuraga N, Freeberg MA, Baker D, Chilton J, Team G, Nekrutenko A et al. A guide and best practices for R/Bioconductor tool integration in Galaxy. 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12688/f1000research.9821.1\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eCholewińska P, Wojnarowski K, Szeligowska N, Pokorny P, Hussein W, Hasegawa Y, et al. Presence of microplastic particles increased abundance of pathogens and antimicrobial resistance genes in microbial communities from the Oder river water and sediment. Sci Rep. 2025;15:16338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-01136-6\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eRoyaux C, Norvez O, Joss\u0026eacute; M, Arnaud E, Sananikone J, Pavoine S, et al. From Biodiversity Observation Networks to Datasets and Workflows Supporting Biodiversity Indicators, a French Biodiversity Observation Network (BON) Essential Biodiversity Variables (EBV) Operationalization Pilot using Galaxy and Ecological Metadata Language. Biodivers Inf Sci Stand. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3897/biss.6.94957\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eRoyaux C, Mihoub J-B, Joss\u0026eacute; M, Pelletier D, Norvez O, Reecht Y, et al. Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology. GigaScience. 2025;14:giae122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gigascience/giae122\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eFAO and UNEP. The State of the World\u0026rsquo;s Forests 2020: Forests, biodiversity and people. Rome, Italy: FAO and UNEP; 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4060/ca8642en\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eCoops NC, Tompalski P, Goodbody TRH, Queinnec M, Luther JE, Bolton DK, et al. Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends. Remote Sens Environ. 2021;260:112477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2021.112477\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eWielgosz M, Puliti S, Xiang B, Schindler K, Astrup R, SegmentAnyTree:. A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. Remote Sens Environ. 2024;313:114367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2024.114367\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eRoussel J-R, Auty D, lidR. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. 2017.\u003c/li\u003e\n\u003cli\u003eSoiland-Reyes S, Sefton P, Crosas M, Castro LJ, Coppens F, Fern\u0026aacute;ndez JM, et al. Packaging research artefacts with RO-Crate. Data Sci. 2022;5:97\u0026ndash;138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/DS-210053\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"software, cloud computing, R programming language, application programming interface","lastPublishedDoi":"10.21203/rs.3.rs-8927956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStandardisation, accessibility, and reproducibility remain persistent challenges in data‑intensive scientific research. The Galaxy platform addresses these issues by providing a web‑based environment for executing, sharing, and publishing computational workflows, yet programmatic access has been largely centred on the Python ecosystem through BioBlend.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHere, we introduce GalaxyR, a native R package that provides a comprehensive and structured interface to the Galaxy application programming interface. GalaxyR enables users to manage histories, upload and retrieve data, execute tools and workflows, monitor jobs, and inspect results directly from within the R environment. By integrating Galaxy\u0026rsquo;s scalable computational infrastructure with R\u0026rsquo;s widely adopted data analysis ecosystem, GalaxyR facilitates automated, reproducible, and resource‑efficient workflows without requiring local high‑performance computing resources. The package supports both HTTPS‑ and FTP‑based data transfer, robust history and dataset management, and programmatic workflow orchestration across Galaxy instances.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn application example demonstrates the use of GalaxyR for large‑scale processing of drone-based laser scanning data, where computationally intensive tree‑level segmentation was delegated to the Galaxy infrastructure, while workflow control and preprocessing were handled in R within a few lines of code.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGalaxyR thus bridges a critical gap for R users, significantly expanding access to Galaxy‑based analyses and enabling scalable, reproducible research across bioinformatics, ecology, remote sensing, and related data‑driven disciplines.\u003c/p\u003e","manuscriptTitle":"Introducing GalaxyR: an easy-to-use R implementation of the Galaxy API","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 11:41:45","doi":"10.21203/rs.3.rs-8927956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"308592006717547018184706535985809743197","date":"2026-03-02T09:14:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T10:08:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T10:35:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-24T02:05:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T02:04:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2026-02-20T17:00:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a06fb3f-ac3c-4a75-947d-e52417a03490","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T11:41:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 11:41:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927956","identity":"rs-8927956","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}