Open source software and workflows: a driving force for innovation in life sciences

Open source software and workflows A driving force for innovation in life sciences Authors ELIXIR Hub Despoina Sousoni Mihail Anton Andrew Smith University of Duisburg-Essen Mahnoor Shahid Lydia Adungo Hannes Rothe 3 6 Open source software tools and workflows to support innovation 7 How open source tools support bioinformatics companies 8 Why companies are turning to open source software and workflows 10 Strategic approaches to open source software and workflows in industry 12 Example strategies from four interviewed companies 14 How companies integrate impact metrics with open source software and workflows Contents 15 Global community collaboration and contributions 16 Galaxy’s global reach across life science domains 18 Maintenance activity reflected in tool availability 20 Open source sustainability: operationalising collaboration and innovation 22 Methods 4 Key terms Open source software 1 https://en.wikipedia.org/wiki/Free_and_open-source_software 2 Wilkinson,S.R.,Aloqalaa,M.,Belhajjame,K.etal.ApplyingtheFAIRPrinciplestocomputationalworkflows.SciData12,328(2025).https://doi.org/10.1038/s41597-025-04451-9 Software with source code available to the public to inspect, modify, enhance and redistribute, according to the chosen license. More precisely, this concept is referred to as ‘free and open source software’1 (FOSS). Workflows In this report, ‘workflow’ refers to a computational workflow – a special kind of software expressed in a specific language targeted at handling multi-step, multi-code data pipelines, data analyses and other data-handling operations, especially through the efficient use of computational resources to transform data inputs into desired outputs. Workflows increasingly incorporate machine learning models and are critical for integrating and deploying software code and data analysis2. Workflow management systems Specialised software systems that help scientists design, manage, execute and partially or fully automate their computational workflows from start to finish. 5 Open source software tools and workflows to support innovation Large, complex datasets are fundamental to life science research, and extracting meaningful insights from them is essential for developing new products and services. This requires robust analytical workflows and sustainably maintained, professional-grade software. These tools enable data aggregation, analysis and infrastructure support, while promoting reproducibility, portability, trust and productivity, often in alignment with FAIR principles2,3. While open source software (OSS) has been an important pillar for research and development for decades, the increasing ubiquity of machine learning (ML) has further emphasised the importance of workflows, as manual tasks increasingly shift towards to algorithm-driven processes4,5. Life scientists in industry and academia use open source software and workflows (OSSW) to build analytical pipelines tailored to specific research purposes, including clinical studies and the development of diagnostic or therapeutic applications. In turn, these scientists often contribute expertise to design, validate and enrich open workflows, fostering a community of practice and a wealth of open knowledge with diverse applications and methodologies. Such collective efforts produce invaluable assets for research across industry and academia. Investigating current practices in OSSW is crucial for understanding trends and developing best practices for sharing and using open software and workflows. In this report, we conducted a qualitative analysis based on 15 interviews with the representatives from bioinformatics companies, alongside a quantitative analysis of empirical data collected from GitHub and the Galaxy platform. The findings underscore the importance of OSSW in driving innovation, fostering collaboration and enhancing transparency in research. 3 FAIRResearchSoftwarePrinciples(https://everse.software/RSQKit/fair_rs),ResearchSoftwareQualityKit(https://doi.org/10.5281/zenodo.14892767) 4 Baird,A.,&Maruping,L.M.(2021).ThenextgenerationofresearchonISuse:AtheoreticalframeworkofdelegationtoandfromagenticISartifacts.MISquarterly,45(1). https://doi.org/10.25300/MISQ/2021/15882 5 Stelmaszak,M.,Möhlmann,M.,&Sørensen,C.(2024).Whenalgorithmsdelegatetohumans:Exploringhuman-algorithminteractionatUber.MISquarterly,49(1).  https://doi.org/10.25300/MISQ/2024/17911 6 How open source tools support bioinformatics companies OSS plays a crucial role in research across academia and industry. Over the past decade, OSS has transformed industries such as bioinformatics, biotechnology6 and pharmaceuticals7 by increasing reliance on professionals skilled in software engineering, research data management and AI, giving rise to so-called ‘invert firms’8. In these firms, employees not only bring technical expertise but also equip companies with open tools and workflows that accelerate product and service innovation. Many organisations now strategically build their business and operational models around open source, while others contribute to and use them extensively9. As with open data initiatives, OSS has played an important role in enhancing openness and transparency,10 and in shaping new companies in the bioinformatics domain11. Advanced computational analysis – particularly involving ML – has become critical in life science product and service development12. Workflows encode algorithmic approaches to load and transform data, train, test, package and deploy ML models. Also, they facilitate the monitoring and governance of ML models, enhancing reproducibility and trust13,14. As the role of ML becomes more pivotal15, these workflows are becoming a key part of OSSW. 6 Vassilakopoulou,P.,Skorve,E.,&Aanestad,M.(2019).Enablingopennessofvaluableinformationresources:Curbingdatasubtractabilityandexclusion.InformationSystems Journal,29(4),768–786.https://doi.org/10.1111/isj.12191 7 Priego,L.P.,&Wareham,J.(2024).DataCommoningintheLifeSciences.MISQuarterly,48(2).https://doi.org/10.25300/MISQ/2023/17439 8 Parker,G.,VanAlstyne,M.,&Jiang,X.(2017).Platformecosystems.MISQuarterly,41(1),255-266.http://dx.doi.org/10.2139/ssrn.2861574 9 Shaikh, M., & Vaast, E. (2016). Folding and unfolding: Balancing openness and transparency in open source communities. Information Systems Research, 27(4), 813-833. https://doi.org/10.1287/isre.2016.0646 10 Lifshitz-Assaf,H.(2018).DismantlingknowledgeboundariesatNASA:Thecriticalroleofprofessionalidentityinopeninnovation.AdministrativeScienceQuarterly,63(4),746-782. https://doi.org/10.1177/0001839217747876 11 Rothe,H.,Lauer,K.B.,Talbot-Cooper,C.,&SivizacaConde,D.J.(2023).Digitalentrepreneurshipfromcellulardata:Howomicsaffordtheemergenceofanewwaveofdigital venturesinhealth.ElectronicMarkets,33(1),48.https://doi.org/10.1007/s12525-023-00669-w 12 Lou,B.,&Wu,L.(2021).AIonDrugs:CanArtificialIntelligenceAccelerateDrugDevelopment?EvidencefromaLarge-ScaleExaminationofBio-PharmaFirms.Management InformationSystemsQuarterly,45(3),1451-1482.http://dx.doi.org/10.2139/ssrn.3524985 13 Sculley,D.,Holt,G.,Golovin,D.,etal.(2015).Hiddentechnicaldebtinmachinelearningsystems.Advancesinneuralinformationprocessingsystems,28.  https://dl.acm.org/doi/10.5555/2969442.2969519 14 Walsh,I.,Fishman,D.,Garcia-Gasulla,D.,etal.(2021).DOME:recommendationsforsupervisedmachinelearningvalidationinbiology.NatureMethods,18(10),1122-1127. https://doi.org/10.1038/s41592-021-01205-4 15 Xu,Y.,Liu,X.,Cao,X.,etal.(2021).Artificialintelligence:Apowerfulparadigmforscientificresearch.TheInnovation,2(4).  https://doi.org/10.1016/j.xinn.2021.100179 Open source code, workflows and software – developed and maintained by the global community – play a crucial role in highly specialised, innovationdriven fields like drug discovery and development.” — Interviewee, Ardigen Open source tools and public databases, being state of the art and widely validated by the scientific community, facilitate rapid advancements in research with minimal costs, particularly in the field of drug discovery.” — Interviewee, Knowing01s For a resource-constrained startup, adopting standardised tools and workflows allows us to focus on our value proposition, rather than diverting resources to baseplate code and infrastructure. By integrating open source tools, which benefit from continuous community support and evolution, we can significantly reduce development overhead and accelerate our time to market.” — Interviewee, Cambrium 7 Why companies are turning to open source software and workflows Productivity through cost reduction Open source tools boost productivity by providing code at little or no cost. Developers benefit from the collective expertise of global communities, and access to OSSW significantly improves the efficiency of private code development16,17 by reducing development time and expense – ultimately driving product improvements or service quality. OSS enhances innovation by enabling the company to build on existing tools, thereby reducing development time. Also, exposure to open source tools fosters skill development.” — Interviewee, SeQone Trust and credibility Legitimacy is critical in life sciences, due to stringent regulatory requirements and significant risks associated with many applications. By adopting OSS, companies can foster trust through the use of community-vetted OSSW. This approach not only showcases their capacity to develop high-quality code in an open environment, but also enhances credibility by aligning with prominent research institutions18. Community-vetted tools offer reliability, as bugs are often caught and fixed by a broader user base.” — Interviewee, Cambrium 16 Eilhard,J.,&Ménière,Y.(2009).Alookinsidetheforge:Developerproductivityandspilloversinopensourceprojects.SSRNElectronicJournal,1316772;  http://dx.doi.org/10.2139/ssrn.1316772 17 Perez-Riverol,Y.,Bittremieux,W.,Noble,W.S.,etal.(2025).Open-SourceandFAIRResearchSoftwareforProteomics.JournalofProteomeResearch,24(5),2222-2234.  https://doi.org/10.1021/acs.jproteome.4c01079 18 Marsan,J.,Carillo,K.D.A.,&Negoita,B.(2020).Entrepreneurialactionsandthelegitimationoffree/opensourcesoftwareservices.JournalofInformationTechnology,35(2), 143–160.https://doi.org/10.1177/0268396219886879 19 Faraj,S.,vonKrogh,G.,Monteiro,E.,&Lakhani,K.R.(2016).Specialsectionintroduction–Onlinecommunityasspaceforknowledgeflows.InformationSystemsResearch,27(4), 668–684.https://doi.org/10.1287/isre.2016.0682 20 Fürstenau,D.,Baiyere,A.,Schewina,K.,Schulte-Althoff,M.,&Rothe,H.(2023).Extendedgenerativitytheoryondigitalplatforms.InformationSystemsResearch,34(4),1686–1710. https://doi.org/10.1287/isre.2023.1209 Productivity through community engagement Adopting open source code allows developers to tap into the insights and feedback of a broad, diverse community. Developers can use reported OSSW issues or limitations to refine and validate their solutions19. This collaborative environment not only drives innovation, but also accelerates the overall improvement and reliability of software, often leading to the discovery of novel approaches20. Building a community of experts dedicated to contributing to open source tools demands significant commitment. In return, transparency builds trust and the company gains recognition from the community for its research expertise, fostering commercial partnerships.” — Interviewee, Helical AI 8 Customer acquisition and visibility When companies contribute to OSSW, they engage directly with established communities on platforms like GitHub or Galaxy. This proactive participation increases their reach and connects them with prospective users and clients9. The company uses open source tools and data when relevant because they are well documented and peer reviewed, and this helps the company build credibility with clients, especially in the academic world, where transparency is valued.” — Interviewee, Saphetor Transparency Open workflows provide full transparency in how code, data and ML models are handled and executed. These benefits extend to software testing and quality assurance, ensuring consistency throughout the software development cycle. With computational approaches and ML increasingly permeating life sciences, the ability to monitor and control data and models is increasingly critical. Public tracking, community discussions and collaborative maintenance of workflow changes further strengthen reproducibility. Sharing and version tracking workflows openly or among communities via registries such as WorkflowHub promotes transparent reuse and reliability of published results21. Integrating ML models into the same open framework makes them highly interchangeable; reusable workflows reduce redundant coding.” — Interviewee, Helical AI Using open source tools like Nextflow and nf-core allows organisations to run bestpractice pipelines off the shelf, which are reproducible and can be executed on various infrastructures.” — Interviewee, Seqera 21 Gustafsson,O.J.R.,Wilkinson,S.R.,Bacall,F.etal.WorkflowHub:aregistryforcomputationalworkflows.SciData12,837(2025).https://doi.org/10.1038/s41597-025-04786-3 22 Allen,J.P.(2012).Democratizingbusinesssoftware:Smallbusinessecosystemsforopensourceapplications.CommunicationsoftheAssociationforInformationSystems,30(1), 28.https://doi.org/10.17705/1CAIS.03028 23 Lindberg,A.,Berente,N.,Howison,J.,&Lyytinen,K.(2024).DiscursiveModulationinOpenSourceSoftware:HowOnlineCommunitiesShapeNoveltyandComplexity.Management InformationSystemsQuarterly,48(4),1395–1422.https://doi.org/10.25300/MISQ/2023/16872 Mission-driven goals OSS has long been associated with democratisation, enabling broad audiences, including under-resourced groups and small businesses to use, share, modify and contribute to high-quality software22. For some companies, engagement with OSS is not just a technical choice, but a commitment to broader societal impact. By embedding their mission and values into code or licensing agreements, these organisations help shape the future of products and services23. Employees are motivated by the company’s mission to enable open science and open source development, and enjoy working in an environment that values transparency and community contribution.” — Anonymous 9 Galaxy’s global reach across life science domains The Galaxy platform is a cornerstone of bioinformatics and life science data analysis, providing a collaborative space to execute, develop and disseminate workflows and software tools25. Behind this diverse platform of tools lies an intricate web of sharing patterns, contributor behaviour and evolving tool maintenance practices, all essential for understanding how knowledge and technology propagate across the platform. While Galaxy’s primary focus is life sciences, its global distribution – supported by a network of OSSW repositories on GitHub – offers a clear overview of the diversity of tools used in this domain. To shed light on how workflows and tools are shared within Galaxy, we analysed the platform’s ecosystem, identifying the key trends and behaviours that define its community-driven model. The basis of this analysis is a curated dataset of around 6800 Galaxy ToolShed repositories26 owned by 636 distinct ToolShed owners. In 25 TheGalaxyCommunity.TheGalaxyplatformforaccessible,reproducible,andcollaborativedataanalyses:2024update,NucleicAcidsResearch,2024,  https://doi.org/10.1093/nar/gkae410 26 Thisdatasetincludesallpublicly-availablerepositoriesfromtheGalaxyToolShedasof26February2025,retrievedviatheToolShedAPI. 27 Theweightscorrespondtotheprobabilitiesgeneratedbythetopicmodel,whereeachprobabilityindicatesthelikelihoodthatarepositorybelongstoaspecificcategory. Galaxy, each tool is typically developed and maintained in a dedicated repository, mapped into 15 distinct primary domains. Because many tools span multiple research domains, we used a weighted distribution (rather than simple headcounts) to identify the most prominent domains27. Key insight Galaxy is not merely a collection of isolated tools; it is a dynamic, interconnected ecosystem where tools are frequently integrated across diverse life sciences subdomains. This integration drives collaboration and cross-functional reuse in ways that simple metrics cannot fully capture. Weighted distribution of Galaxy tools by category Sequence assembly & alignment Genomic & variant analysis Small contribution categories Formatting & conversion Data preprocessing & cleaning Workflow automation & meta-tools Metagenomics & microbiome analysis Feature extraction & annotation Visualization & reporting tools Quantification & statistical analysis Machine learning & predictive models Molecular structure & chemoinformatics Other Data integration & merging Network & pathway analysis Structural biology & simulation tools Text & literature mining 1000 tools 100 tools 24.8% 16.4% 15.3% 15.2% 10.1% 9.4% 4.7% 4.0% 3.1% 2.8% 2.6% 2.0% 1.3% 1.3% 0.9% 0.7% 0.5% 16 Top 50 most downloaded tools by category To assess the impact of Galaxy’s most popular tools, we analysed the top 50 most-downloaded tools and their primary categories. Each tool was assigned to its primary category based on the highest weight, giving a clear perspective on where each tool predominantly belongs28. The analysis revealed a classic long-tail distribution observed in other platform ecosystems, where some ‘superstar’ workflows are complemented by a long tail of other workflows with low to moderate levels of use. The most downloaded tools that belong in diverse categories showcase how Galaxy supports a wide 28 Foreachtool,thetopicmodelgeneratesaprobabilitydistributionacrossseveralpotentialcategories.Thetoolisthenassignedexclusivelytothesinglecategorythatreceivedthe highestprobabilityscore. range of scientific needs. The most frequently downloaded repositories belong to categories such as Workflow Automation & Meta-tools, Formatting & Conversion and Genomic & Variant Analysis. Standout tools – including FastQC (Genomic & Variant Analysis), data_manager_manual (Workflow Automation) and collection_column_join (Data Integration & Merging) – have become indispensable in many research pipelines. Their widespread adoption highlights the Galaxy community’s emphasis on reliability, automation and effective data management. Sequence assembly & alignment Machine learning & predictive models Feature extraction & annotation Genomic & variant analysis Formatting & conversion Data preprocessing & cleaning Workflow automation & meta-tools 50000 downloads Data integration & merging Text & literature mining Categories Tools cufflinks collection_column_join diff ncbi_blast_plus package_atlas_3_10 samtools_calmd smagexp_datatypes trimmomatic package_readline_6_3 samtools_mpileup bam_to_sam column_maker cutadapt data_manager_fetch_genome_dbkeys_all_fast a data_manager_sam_fasta_index_builder fastp multiqc package_bzlib_1_0 package_libpng_1_6_7 package_ncurses_5_9 package_ncurses_6_0 package_zlib_1_2_8 query_tabular sam_to_bam samtools_rmdup tabular_to_fasta bedtools deseq2 fastqc featurecounts package_fastqc_0_11_4 samtools_stats blast_datatypes bowtie2 bwa data_manager_bwa_mem_index_builder picard rgrnastar samtools_flagstat samtools_idxstats samtools_slice_bam samtools_sort compose_text_param text_processing a_selenium_test_repo data_manager_bowtie2_index_builder data_manager_manual package_fontconfig_2_11_1 package_samtools_1_2 17 Maintenance activity reflected in tool availability Our analysis of OSSW on Galaxy shows that active maintenance correlates closely with tool availability on Galaxy instances – a prominent trend across all subdomains. The most popular subdomains (by installations) also demonstrate a strong pattern of frequent updates and active revisions. Notably, there is a strong positive correlation (0.88) between the number of tool revisions and total downloads (typically via installations on Galaxy instances). Trust is earned through consistent maintenance and refinement to meet evolving needs. In essence, we see that there is trust in tools that keep evolving. Regular revisions that enhance or expand functionality, combined with continuous integration systems, appear to foster confidence among Galaxy administrators and the broader community. This likely contributes to higher adoption rates, supporting the growth and impact identified in the independently-commissioned report on the sustainability of Galaxy29. 29 Jain,S.(2025).GalaxySustainabilityReport(Version1).Zenodo.https://doi.org/10.5281/zenodo.16030329 Key message Active maintenance fosters trust – and trust drives usage. For example, Sequeone, which provides an end-toend solution for clinicians and biologists, releases new versions monthly based on customer feedback. It also maintains a dedicated support team and robust security infrastructure, adding value far beyond algorithms. Similarly, another company highlights that clients ‘very much appreciate’ stable software, so much so that they are willing to ‘pay for keeping it stable in the long run’. This underscores the direct link between reliability, ongoing maintenance and user adoption. In our industry, trust and transparency are paramount, and while we share similarities with our competitors, our unique advantage lies in our specialised skillset, customised solutions and active participation in open-source communities that build trust with our customers and the community.” — Interviewee, Ardigen Revision count vs. downloads Revisionsandtooldownloadscorrelateindifferentresearchcategories. Data processing Times downloaded 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Times downloaded Revision count 1.4 1e6 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 1000 2000 3000 4000 Revision count 0 1000 2000 3000 4000 Sequence & genomic analysis Advanced analytics & biology Visualization & workflow Data integration & merging Machine learning & predictive models Other Network & pathway analysis Visualization & reporting tools Text & literature mining Quantification & statistical analysis Molecular structure & chemoinformatics Structural biology & simulation tools Formatting & conversion Data preprocessing & cleaning Sequence assembly & alignment Metagenomics & microbiome analysis Feature extraction & annotation Genomic & variant analysis Workflow automation & meta-tools 18 Top ten contributing ToolShed owners in the Galaxy platform An analysis of ownership patterns within the Galaxy platform and its tools shows a striking concentration of OSS development. The top ten contributing ToolShed owners are responsible for an impressive 56.6% of the tools in the ToolShed. However, most of these are groups of people, which explains their broad activity 30 Hoffmann,M.,Nagle,F.,&Zhou,Y.(2024).TheValueofOpenSourceSoftware.HarvardBusinessSchoolStrategyUnitWorkingPaper,(24-038).  https://dx.doi.org/10.2139/ssrn.4693148 across categories29. This finding shows that small core groups of ToolShed owners play a significant role in sustaining and advancing open source ecosystems30, highlighting the importance of collaborative, long-term maintenance for high-quality tools. recetox rnateam nml ecology ebi-gxa q2d2 devteam galaxyp bgruening iuc Network & pathway analysis Sequence assembly & alignment Machine learning & predictive models Quantification & statistical analysis Feature extraction & annotation Genomic & variant analysis Molecular structure & chemoinformatics Formatting & conversion Data preprocessing & cleaning Visualization & reporting tools Workflow automation & meta-tools Structural biology & simulation tools Metagenomics & microbiome analysis Data integration & merging Other Text & literature mining 1000 tools ToolShed owners Categories ThetoptencontributingToolShedownersinGalaxyarenotconfinedtoasingleniche;instead,theyactivelycontributetoawiderange of essential domains. 19 Open source sustainability: operationalising collaboration and innovation OSSW have become integral to the life sciences, underpinning everything from basic research to commercial applications. Organisations of all sizes rely on OSSW to develop products and services. The question is no longer whether to use open source, but how to deploy it strategically to ensure sustainability, security and innovation. This study demonstrates that industry approaches to OSSW differ based on the extent to which companies align their core products and services with OSSW and integrate community contributions into their business models. Sustainable OSSW adoption requires a combination of open collaboration and robust operational models. Companies that prioritise OSSW and community engagement often see benefits in market growth, talent acquisition and quality improvement. Others integrate OSSW selectively to reduce costs or accelerate development, balancing flexibility with strategic independence. All approaches, however, face common challenges: ongoing maintenance costs, the need to adapt to evolving technologies and appropriate IP protection. Open source technology, combined with the operational backing of managed services, give development teams significant leverage. This approach allows them to capitalise on mature, widely-adopted open source tools, reducing the burden of foundational development and maintenance. At the same time, the peace of mind offered by managed services frees them to concentrate their energy and resources on building a product.” — Interviewee, Cambrium Despite heavy reliance on open source tools, modifications are always needed to match specific requirements.” — Interviewee, Saphetor Finding the balance between the needs of the community and the specific requirements of individual customers, without diverging from the main open source values of the company, can be complicated but is highly feasible.” — Interviewee, Seqera 20 There is growing recognition that sustainable and secure open science practices are essential. Collaboration remains the most efficient and costeffective path forward, benefiting all stakeholders, even in commercially-driven environments. Communitydriven projects like Nextflow exemplify the power of collective effort in creating robust, open solutions that benefit the research community, regardless of sector. Challenges around sustainability, scalability and security persist, prompting innovation in both open source adoption and revenue generation. As one Saphetor interviewee noted, ‘Iwantsciencetobeasopen aspossibleforthegoodofscienceandsocietyasawhole’. This statement reflects a broader ethos: open science and specifically, in this case, OSS, is crucial for advancing knowledge and benefiting society. The examples in this report show it is possible to embed open source into business models, while balancing community benefit with commercial viability. This means investing in active maintenance, adopting common standards and developing governance models that reward both contribution and consumption. 31 https://research-software-ecosystem.github.io 32 https://everse.software/about/objectives 33 https://elixir-europe.org/about-us/how-funded/eu-projects/steers 34 https://elixir-europe.org/about-us/how-funded/eu-projects/steers/wp3 ELIXIR plays a central role in this ecosystem. Through initiatives like the Research Software Ecosystem31, a project that centralises high-quality OSSW metadata, ELIXIR members curate and connect metadata for computational biology tools, workflows and libraries, making them more FAIR. Horizon Europe projects32,33 led by ELIXIR to promote the adoption and implementation of software best practices34, – particularly for OSSW – and to elevate research software, including OSSW, to a central role in the scientific process. These efforts strengthen reproducibility and trust, reduce duplication and lower integration costs, fueling the growth of OSSW. For the life sciences community, this is an opportunity to move beyond isolated codebases towards a connected, sustainable knowledge infrastructure. By collaborating across academia, industry and infrastructures, we can ensure open source remains a catalyst for discovery, a driver of efficiency and a foundation for long-term competitiveness. ELIXIR will continue to act as a convenor, standards-setter and enabler in this space, fostering both the openness that accelerates science and the structures that keep it sustainable. 21 Methods Computational analysis The dataset of Galaxy Platform was collected from the Galaxy Tool-shed (26 February 2025) via the Toolshed API. Due to limited data access, only five metrics were available for analysis: tool name, total downloads, revisions count, unique owners and description. Based on their descriptions, tools were grouped into 15 primary categories using a topic modeling approach. Their primary categories were generated through the modelling process and were not derived from the Galaxy taxonomy. To assess company engagement and collaboration, 531 public GitHub repositories from 15 interviewed companies were extracted using the GitHub GraphQL API. The analysis compared how companies of different sizes engage with the open source community, focusing on key metrics such as average stars, forks, commits and contributors. To analyse global collaboration through GitHub, we identified the geographic location of each company, whereas the location of each contributor was extracted from their public GitHub profile. The intensity of collaboration from a specific region was measured by counting the total number of contributors originating from that location. This study did not differentiate between CI/CD workflows and analytical or ML workflows; we note their different purposes and some shared and distinct features. All data generated and analysed in this study are publicly available. The complete dataset, including metadata and documentation, is accessible through Zenodo at https://doi.org/10.5281/zenodo.17637118. Interview study From a pool of 678 relevant companies founded between 2014 and 2024, we contacted 258 organisations with accessible founder or management contacts in the health data sector. Fifteen companies agreed to interviews between November 2024 to March 2025. Sessions (30–60 minutes) were held via Zoom or in person at major conferences such as BioTechX 2024 and the Festival of Genomics 2025. Participating companies were categorised by size – small, mid and large – based on their LinkedIn employee counts. In line with EU Recommendation 2003/361, we considered a company as small (less than 50 employees and/or less than €10m EUR turnover), medium (less than 250 employees and/ or less than €50m EUR turnover) and large (above these thresholds). The interviews followed a set of questions that had been developed around workflows and OSS regarding use, active sharing and business-model relevance (value creation, delivery, capture). Responses were analysed based on thematic coding to interpret the data. Interviewees represented their companies throughout, and they reviewed all quoted material. All quotations are used with company consent, and most companies permitted direct attribution. The interviewees that only wanted to share their personal views and experiences preferred to be referenced as anonymous. We note a sampling bias – most companies had some existing knowledge of ELIXIR, potentially increasing their likelihood of engagement in open source activities. This bias may have influenced our interpretation, particularly regarding the open source ingestion strategy. While the interviews addressed OSS and workflows, we adopted a flexible approach to the definition of FOSS. This allowed for a wide range of discussions on how companies capture value from OSS without needing to provide it for free. 22 Credits and Acknowledgements With special thanks to all who contributed including the interviewed companies and those within the ELIXIR community who provided helpful feedback. Hinxton, UK, November 2025 Published under the CC BY 4.0 licence. http://doi.org/10.5281/zenodo.17570133 ELIXIR HUB [email protected] South Building Wellcome Genome Campus Hinxton CB10 1SD, UK