{ "version": "https://jsonfeed.org/version/1.1", "title": "BioHackrXiv Preprints", "description": "Preprints for BioHackathons", "home_page_url": "https://index.biohackrxiv.org//", "feed_url": "https://index.biohackrxiv.org//feed.json", "icon": "https://index.biohackrxiv.org//assets/images/chem-bla-ics_logo.png", "language": "en", "authors": [ { "name": "BioHackrXiv", "url": "https://biohackrxiv.org/" } ], "items": [ { "id": "https://doi.org/10.37044/osf.io/ncrkm_v1", "url": "https://index.biohackrxiv.org//2026/04/10/ncrkm.html", "title": "Minimal information standardization of phenomic experimental data in animals", "content_html": "

The current landscape of animal phenomics is characterised by a substantial lack of standardisation, hindering data reuse, reproducibility, and interoperability across\nstudies, all of which are particularly important in light of the 3Rs principles for animal experiments (replace, reduce, refine). Within ELIXIR, the Domestic Animals\nGenome and Phenome Focus Group emerged to establish standardised practices that enhance the quality and interoperability of animal research data. In this context, the\nISA model presents a robust, domain-agnostic framework well-established in the life sciences for describing experimental metadata. Notably, other scientific communities,\nsuch as the ELIXIR Plant and Metabolomics Communities (MIAPPE, PhenoMeNal), have successfully leveraged the ISA model to improve the consistency and usability of their\nmetadata. Our project aims to develop a minimal information checklist tailored specifically for phenomics, facilitating the integration of diverse datasets, including\nrecirculation systems in agriculture, and fostering collaborative research efforts. We will focus on various goals.Identifying essential aspects of animal phenotyping,\ninformed by existing frameworks and community input. We aim to produce a concise and practical checklist that can be readily adopted by researchers, and promote a\nculture of standardisation.Mapping the checklist to the ISA model ensures alignment with established standards, promotes interoperability and facilitates data reuse\nwhile improving the overall quality of research outputs. Adopting existing ISA tools streamlines the implementation of our metadata checklist, providing user-friendly\ninterfaces for researchers to manage, document, and share animal phenotyping data efficiently.

", "summary": "The current landscape of animal phenomics is characterised by a substantial lack of standardisation, hindering data reuse, reproducibility, and interoperability across studies, all of which are particularly important in light of the 3Rs principles for animal experiments (replace, reduce, refine). Within ELIXIR, the Domestic Animals Genome and Phenome Focus Group emerged to establish standardised practices that enhance the quality and interoperability of animal research data. In this context, the ISA model presents a robust, domain-agnostic framework well-established in the life sciences for describing experimental metadata. Notably, other scientific communities, such as the ELIXIR Plant and Metabolomics Communities (MIAPPE, PhenoMeNal), have successfully leveraged the ISA model to improve the consistency and usability of their metadata. Our project aims to develop a minimal information checklist tailored specifically for phenomics, facilitating the integration of diverse datasets, including recirculation systems in agriculture, and fostering collaborative research efforts. We will focus on various goals.Identifying essential aspects of animal phenotyping, informed by existing frameworks and community input. We aim to produce a concise and practical checklist that can be readily adopted by researchers, and promote a culture of standardisation.Mapping the checklist to the ISA model ensures alignment with established standards, promotes interoperability and facilitates data reuse while improving the overall quality of research outputs. Adopting existing ISA tools streamlines the implementation of our metadata checklist, providing user-friendly interfaces for researchers to manage, document, and share animal phenotyping data efficiently.", "date_published": "2026-04-10T00:00:00+00:00", "date_modified": "2026-04-10T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Sarah Oranna Fischer-Zielke", "url": "https://orcid.org/0000-0002-6218-7275" }, { "name": "Rica Johanna Rehfeld", "url": "https://orcid.org/0009-0007-3289-5872" }, { "name": "McKinley Santiago", "url": "https://orcid.org/0009-0009-1160-5041" }, { "name": "Emily Clark", "url": "https://orcid.org/0000-0002-9550-7407" }, { "name": "Daniel Arend", "url": "https://orcid.org/0000-0002-2455-5938" }, { "name": "Manuel Feser", "url": "https://orcid.org/0000-0001-6546-1818" } ] }, { "id": "https://doi.org/10.37044/osf.io/tsxby_v1", "url": "https://index.biohackrxiv.org//2026/03/31/tsxby.html", "title": "Evolving FAIR Image Analysis in Galaxy for Cross-domain and AI-ready Applications", "content_html": "

The increasing adoption of image-based technologies across life sciences, environmental research, and related domains has increased the demand for\ninteroperable, reproducible, and FAIR-compliant image analysis infrastructures. At ELIXIR BioHackathon Europe 2025, Project 9, “Evolving FAIR Image\nAnalysis in Galaxy for Cross-domain and AI-ready Applications”, addressed these challenges by enhancing the Galaxy platform for bioimage analysis\nwith a focus on semantic interoperability, content-based reproducibility validation, and user-centered onboarding tutorials.To advance semantic\ninteroperability, we developed a curated vocabulary based on the EDAM Bioimaging ontology, which was applied to annotate tutorials on the Galaxy\nTraining Network, improving discoverability and aligning with evolving community standards. For reproducibility and AI-readiness, we integrated\nthe International Standard Content Code (ISCC) via the ISCC-SUM tool suite, enabling format-independent content-based validation, dataset\ndeduplication, and assessment of data similarity for robust model training. Finally, usability improvements included a comprehensive onboarding\ntutorial for newcomers, enhanced integration with OMERO and BioImage Archive, and generally improved tool interoperability, including support for\nGeoJSON-based spatial annotations. Collectively, these developments establish a scalable, cross-domain image analysis framework within Galaxy,\npromoting FAIR-aligned practices while enabling reproducible and AI-ready workflows.

", "summary": "The increasing adoption of image-based technologies across life sciences, environmental research, and related domains has increased the demand for interoperable, reproducible, and FAIR-compliant image analysis infrastructures. At ELIXIR BioHackathon Europe 2025, Project 9, “Evolving FAIR Image Analysis in Galaxy for Cross-domain and AI-ready Applications”, addressed these challenges by enhancing the Galaxy platform for bioimage analysis with a focus on semantic interoperability, content-based reproducibility validation, and user-centered onboarding tutorials.To advance semantic interoperability, we developed a curated vocabulary based on the EDAM Bioimaging ontology, which was applied to annotate tutorials on the Galaxy Training Network, improving discoverability and aligning with evolving community standards. For reproducibility and AI-readiness, we integrated the International Standard Content Code (ISCC) via the ISCC-SUM tool suite, enabling format-independent content-based validation, dataset deduplication, and assessment of data similarity for robust model training. Finally, usability improvements included a comprehensive onboarding tutorial for newcomers, enhanced integration with OMERO and BioImage Archive, and generally improved tool interoperability, including support for GeoJSON-based spatial annotations. Collectively, these developments establish a scalable, cross-domain image analysis framework within Galaxy, promoting FAIR-aligned practices while enabling reproducible and AI-ready workflows.", "date_published": "2026-03-31T00:00:00+00:00", "date_modified": "2026-03-31T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Diana Chiang", "url": "https://orcid.org/0000-0002-5857-1477" }, { "name": "Pavankumar Videm", "url": "https://orcid.org/0000-0002-5192-126X" }, { "name": "David Lopez Tabernero", "url": "https://orcid.org/0000-0002-9541-3961" }, { "name": "Maarten W. Paul", "url": "https://orcid.org/0000-0002-7990-6010" }, { "name": "Alireza Heidari", "url": "https://orcid.org/0000-0003-0315-4403" }, { "name": "Martin Etzrodt", "url": "https://orcid.org/0000-0003-1928-3904" }, { "name": "Beatriz Serrano-Solano", "url": "https://orcid.org/0000-0002-5862-6132" }, { "name": "Leonid Kostrykin", "url": "https://orcid.org/0000-0003-1323-3762" } ] }, { "id": "https://doi.org/10.37044/osf.io/5psfj_v1", "url": "https://index.biohackrxiv.org//2026/03/20/5psfj.html", "title": "Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon", "content_html": "

The Carnegie Mellon University-NVIDIA Federated Learning Hackathon for Biomedical Applications (January 7-9, 2026) convened researchers\nfrom academia, government, and industry to implement federated frameworks for disease subtyping, genetic association studies, and\nmultimodal clinical prediction using NVIDIA FLARE. This preprint presents ten projects spanninggenome-wide association analyses,\nhistopathology harmonization, pangenome construction, ancestry deconvolution, rare disease stratification, cancer subtyping, polygenic\nrisk score aggregation, and multimodal fusion. These proofs of principle collectively demonstrate both the versatility of federated\nlearning for biomedical applications and the technical considerations required for successful deployment.

", "summary": "The Carnegie Mellon University-NVIDIA Federated Learning Hackathon for Biomedical Applications (January 7-9, 2026) convened researchers from academia, government, and industry to implement federated frameworks for disease subtyping, genetic association studies, and multimodal clinical prediction using NVIDIA FLARE. This preprint presents ten projects spanninggenome-wide association analyses, histopathology harmonization, pangenome construction, ancestry deconvolution, rare disease stratification, cancer subtyping, polygenic risk score aggregation, and multimodal fusion. These proofs of principle collectively demonstrate both the versatility of federated learning for biomedical applications and the technical considerations required for successful deployment.", "date_published": "2026-03-20T00:00:00+00:00", "date_modified": "2026-03-20T00:00:00+00:00", "tags": ["CMU26"], "authors": [ { "name": "James Mu", "url": "https://orcid.org/0009-0008-1598-9292" }, { "name": "Aditya Kumar Karna", "url": "https://orcid.org/0009-0000-0365-5748" }, { "name": "Telaprolu Kumar Koushik", "url": "https://orcid.org/0009-0006-5026-5201" }, { "name": "Jeff Winchell", "url": "https://orcid.org/" }, { "name": "Tyler Jay Yang", "url": "https://orcid.org/" }, { "name": "Caiwei Maggie Zhang", "url": "https://orcid.org/" }, { "name": "Jasmine Baker", "url": "https://orcid.org/" }, { "name": "Espen Hagen", "url": "https://orcid.org/" }, { "name": "Enamul Hoq", "url": "https://orcid.org/" }, { "name": "Kyulin Kim", "url": "https://orcid.org/" }, { "name": "Konstantinos Koukoutegos", "url": "https://orcid.org/" }, { "name": "Peter Lawson", "url": "https://orcid.org/" }, { "name": "Chantera Lazard", "url": "https://orcid.org/0009-0006-1367-3812" }, { "name": "Qianqian Liang", "url": "https://orcid.org/" }, { "name": "Robert Loughnan", "url": "https://orcid.org/" }, { "name": "Diya Patidar", "url": "https://orcid.org/" }, { "name": "Chunduru Sri Abhijit", "url": "https://orcid.org/" }, { "name": "Vibha Acharya", "url": "https://orcid.org/0000-0001-6598-0052" }, { "name": "Rahaf M. Ahmad", "url": "https://orcid.org/" }, { "name": "Anna Boeva", "url": "https://orcid.org/" }, { "name": "Jingyao Chen", "url": "https://orcid.org/" }, { "name": "Ioannis Christofilogiannis", "url": "https://orcid.org/0009-0008-5906-0776" }, { "name": "Mariona Jaramillo Civill", "url": "https://orcid.org/" }, { "name": "Heena Dalal", "url": "https://orcid.org/" }, { "name": "Alina Devkota", "url": "https://orcid.org/" }, { "name": "Amrit Gaire", "url": "https://orcid.org/" }, { "name": "Dhruv Gor", "url": "https://orcid.org/" }, { "name": "Aryan Sharan Guda", "url": "https://orcid.org/" }, { "name": "Prashnna Gyawali", "url": "https://orcid.org/" }, { "name": "Seungjin Han", "url": "https://orcid.org/" }, { "name": "Jiahao He", "url": "https://orcid.org/" }, { "name": "Yuan-Ting Hsieh", "url": "https://orcid.org/" }, { "name": "Mengying Hu", "url": "https://orcid.org/" }, { "name": "Peiran Jiang", "url": "https://orcid.org/" }, { "name": "Pu Kao", "url": "https://orcid.org/" }, { "name": "Adam Kehl", "url": "https://orcid.org/" }, { "name": "Arnav Kharbanda", "url": "https://orcid.org/0009-0007-9195-9960" }, { "name": "Yajushi Khurana", "url": "https://orcid.org/" }, { "name": "KUSHAL KOIRALA", "url": "https://orcid.org/0009-0009-7935-4533" }, { "name": "Sumeet Kothare", "url": "https://orcid.org/" }, { "name": "Jędrzej Kubica", "url": "https://orcid.org/" }, { "name": "Seohyun Lee", "url": "https://orcid.org/" }, { "name": "Zilinghan Li", "url": "https://orcid.org/" }, { "name": "Yosen Lin", "url": "https://orcid.org/" }, { "name": "William Lu", "url": "https://orcid.org/" }, { "name": "Jialan Ma", "url": "https://orcid.org/" }, { "name": "Samarpan Mohanty", "url": "https://orcid.org/0009-0001-1309-7425" }, { "name": "Abraham G. Moller", "url": "https://orcid.org/" }, { "name": "Derek Mu", "url": "https://orcid.org/" }, { "name": "Shreyan Balaji Nalwad", "url": "https://orcid.org/" }, { "name": "Shreya Nandakumar", "url": "https://orcid.org/" }, { "name": "Hieu Ngo", "url": "https://orcid.org/" }, { "name": "Bhanvi Paliwal", "url": "https://orcid.org/" }, { "name": "Isha Parikh", "url": "https://orcid.org/" }, { "name": "Zillur Rahman", "url": "https://orcid.org/" }, { "name": "Arunannamalai Sujatha Bharath Raj", "url": "https://orcid.org/" }, { "name": "Nikita Rajesh", "url": "https://orcid.org/" }, { "name": "Shivank Sadasivan", "url": "https://orcid.org/" }, { "name": "Ushta Samal", "url": "https://orcid.org/" }, { "name": "Srikant Sarangi", "url": "https://orcid.org/" }, { "name": "Andrew Scouten", "url": "https://orcid.org/0009-0004-6418-7158" }, { "name": "Aastha Shah", "url": "https://orcid.org/" }, { "name": "Sanjnaa Sridhar", "url": "https://orcid.org/" }, { "name": "Suratha Sriram", "url": "https://orcid.org/" }, { "name": "Mrunali Abhijit Thokadiwala", "url": "https://orcid.org/" }, { "name": "Jacob Thrasher", "url": "https://orcid.org/" }, { "name": "Jeffrey Wang", "url": "https://orcid.org/" }, { "name": "Yiman Wu", "url": "https://orcid.org/" }, { "name": "Zhenghao Xiao", "url": "https://orcid.org/" }, { "name": "Qiyu Yang", "url": "https://orcid.org/" }, { "name": "Zhaoyi You", "url": "https://orcid.org/" }, { "name": "Jiayi Zhao", "url": "https://orcid.org/0009-0008-2597-6196" }, { "name": "Jiayan Zhou", "url": "https://orcid.org/0000-0001-5974-087X" }, { "name": "Zheqian Zhu", "url": "https://orcid.org/" }, { "name": "Pravesh Parekh", "url": "https://orcid.org/" }, { "name": "Huajin Wang", "url": "https://orcid.org/0000-0003-0121-4257" }, { "name": "Melanie Gainey", "url": "https://orcid.org/" }, { "name": "Sean Davis", "url": "https://orcid.org/" }, { "name": "Beryl Rabindran", "url": "https://orcid.org/" }, { "name": "Holger R. Roth", "url": "https://orcid.org/" }, { "name": "Ben Busby", "url": "https://orcid.org/0000-0001-5267-4988" } ] }, { "id": "https://doi.org/10.37044/osf.io/ey4c5_v1", "url": "https://index.biohackrxiv.org//2026/03/13/ey4c5.html", "title": "Tools to develop constraint-based models in R: adapting existing toolboxes", "content_html": "

As part of the BioHackathon Europe 2025, we here report on the progress of the hacking team preparing tools to develop constraint-based\nmodels in R for the Systems Biology community. This preliminary development relies on the adaptation of existing toolboxes. In this project,\nwe proposed the (re)development of an R based framework for developing and simulating constraint-based models. We proposed to expand\nthe Sybil library for model simulation with the functionalities for model reconstruction and analysis available in the widely used RAVEN\ntoolbox in Matlab. The outcome will facilitate constraint based modelling to experimental scientists, thereby contributing to bridge the\ngap between data users and data generators. It will also be more FAIR by being usable with non-proprietary software, and align with\nsoftware best practices as collected by the ELIXIR Tools Platform. We will work towards increased reproducibility by also considering\nimplementation of FROG analysis in R. Moreover, as a tool developed by the ELIXIR Systems Biology Community for the wider community,\nthe long-term maintenance burden is spread across a wider membership.Two weeks before the BioHackathon, we discovered a new tool in R\nallowing the simulation of models, called cobrar (https://github.com/Waschina/cobrar). Which calls for an assessment of its current\nstate and definition of new development areas.

", "summary": "As part of the BioHackathon Europe 2025, we here report on the progress of the hacking team preparing tools to develop constraint-based models in R for the Systems Biology community. This preliminary development relies on the adaptation of existing toolboxes. In this project, we proposed the (re)development of an R based framework for developing and simulating constraint-based models. We proposed to expand the Sybil library for model simulation with the functionalities for model reconstruction and analysis available in the widely used RAVEN toolbox in Matlab. The outcome will facilitate constraint based modelling to experimental scientists, thereby contributing to bridge the gap between data users and data generators. It will also be more FAIR by being usable with non-proprietary software, and align with software best practices as collected by the ELIXIR Tools Platform. We will work towards increased reproducibility by also considering implementation of FROG analysis in R. Moreover, as a tool developed by the ELIXIR Systems Biology Community for the wider community, the long-term maintenance burden is spread across a wider membership.Two weeks before the BioHackathon, we discovered a new tool in R allowing the simulation of models, called cobrar (https://github.com/Waschina/cobrar). Which calls for an assessment of its current state and definition of new development areas.", "date_published": "2026-03-13T00:00:00+00:00", "date_modified": "2026-03-13T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Jesubukade Ajakaye", "url": "https://orcid.org/0000-0002-8966-4422" }, { "name": "Mihail Anton", "url": "https://orcid.org/0000-0002-7753-9042" }, { "name": "Iván Domenzain", "url": "https://orcid.org/0000-0002-5322-2040" }, { "name": "Tanisha Malpani", "url": "https://orcid.org/0009-0007-8065-8492" }, { "name": "Sebastien Moretti", "url": "https://orcid.org/0000-0003-3947-488X" }, { "name": "Rahuman S. Malik Sheriff", "url": "https://orcid.org/0000-0003-0705-9809" }, { "name": "Maria Suarez-Diez", "url": "https://orcid.org/0000-0001-5845-146X" }, { "name": "Silvio Waschina", "url": "https://orcid.org/0000-0002-6290-3593" } ] }, { "id": "https://doi.org/10.37044/osf.io/8ktd6_v1", "url": "https://index.biohackrxiv.org//2026/02/24/8ktd6.html", "title": "Bidirectional bridge: GitHub ⇄ bio.tools", "content_html": "

Research software metadata can be found across many code repositories and software registries. Here, we describe the tooling for a\nbidirectional bridge between the software development platform GitHub and the ELIXIR bio.tools registry of life sciences software\ntools and data resources. The developed bridge maps and improves metadata records across these two platforms, thereby benefiting\nboth and helping make research software more FAIR: findable, accessible, interoperable, and reusable. Specifically, the bridge\nenables production of high-quality, rich bio.tools entries from the content already available in GitHub repositories, and uses\nbio.tools records to suggest improvements to GitHub repositories through pull requests or issues. This includes adding missing\ninformation and standardized descriptions for increased compliance with Software Management Plans. The bidirectional bridge makes\nextensive use of existing APIs (GitHub, bio.tools, Europe PMC) and large language models (LLMs) to enrich metadata on both\nplatforms. By automating metadata extraction, improvement suggestion, and integration, the bridge reduces the manual overhead\nrequired to FAIRify research software, lowering barriers for researchers to contribute or maintain well-annotated, reusable software.

", "summary": "Research software metadata can be found across many code repositories and software registries. Here, we describe the tooling for a bidirectional bridge between the software development platform GitHub and the ELIXIR bio.tools registry of life sciences software tools and data resources. The developed bridge maps and improves metadata records across these two platforms, thereby benefiting both and helping make research software more FAIR: findable, accessible, interoperable, and reusable. Specifically, the bridge enables production of high-quality, rich bio.tools entries from the content already available in GitHub repositories, and uses bio.tools records to suggest improvements to GitHub repositories through pull requests or issues. This includes adding missing information and standardized descriptions for increased compliance with Software Management Plans. The bidirectional bridge makes extensive use of existing APIs (GitHub, bio.tools, Europe PMC) and large language models (LLMs) to enrich metadata on both platforms. By automating metadata extraction, improvement suggestion, and integration, the bridge reduces the manual overhead required to FAIRify research software, lowering barriers for researchers to contribute or maintain well-annotated, reusable software.", "date_published": "2026-02-24T00:00:00+00:00", "date_modified": "2026-02-24T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Mariia Steeghs-Turchina", "url": "https://orcid.org/0000-0002-0852-4752" }, { "name": "Anna Niehues", "url": "https://orcid.org/0000-0002-9839-5439" }, { "name": "Ana Mendes", "url": "https://orcid.org/0009-0008-5170-0927" }, { "name": "Erik Jaaniso", "url": "https://orcid.org/0009-0003-4246-6546" }, { "name": "Ove Johan Ragnar Gustafsson", "url": "https://orcid.org/0000-0002-2977-5032" }, { "name": "Walter Baccinelli", "url": "https://orcid.org/0000-0001-8888-4792" }, { "name": "Vedran Kasalica", "url": "https://orcid.org/0000-0002-0097-1056" }, { "name": "Sam Cox", "url": "https://orcid.org/0000-0002-9841-9816" }, { "name": "Ivan Topolsky", "url": "https://orcid.org/0000-0002-7561-0810" }, { "name": "Magnus Palmblad", "url": "https://orcid.org/0000-0002-5865-8994" }, { "name": "Veit Schwämmle", "url": "https://orcid.org/0000-0002-9708-6722" } ] }, { "id": "https://doi.org/10.37044/osf.io/un6cd_v1", "url": "https://index.biohackrxiv.org//2026/01/26/un6cd.html", "title": "BH25DE report: On the path to machine-actionable training materials", "content_html": "

The fragmentation of training materials across research infrastructures often results in unsustainable resource\nduplication and significant barriers to upskilling. This work aims to enable developers to build systems that\neffectively discover relevant materials by promoting a federated, FAIR-compliant strategy for open training. The\nproject operated across three interrelated streams: metadata interoperability, material analysis, and the\ndefinition and representation of learning paths in a machine readable manner.We demonstrated content federation\nvia the mTeSS-X platform, enabling cross-instance exchange and preparing for future integration with the EOSC\nfederation. To enhance interoperability, we indexed relevant ontologies and curated semantic crosswalks between\nestablished metadata models, specifically MoDALIA and Schema.org/Bioschemas. These mappings were implemented\nwithin the open-source OERbservatory Python package, providing a facility for exchanging data between platforms\nsuch as DALIA and TeSS. For material analysis, we utilised Large Language Models (LLMs) and explored vectorisation\ntechniques to calculate similarity, allowing for the identification of related materials and the potential for\nfuture deduplication of records across registries.To address the lack of machine-actionable trajectories across\nrelated or sequential materials, we proposed new Bioschemas profiles specifically for learning paths. By extending\nSchema.org types, including Course and Syllabus, we developed a schema that supports modular and linear orderings\nof training materials. This model was validated using SPARQL queries on knowledge graphs derived from real-world\nexamples like the Galaxy Training Network. Such advancements provide a foundation for automated path generation\nand improved discoverability within training catalogues, and serves as a use case and strategy with broader\napplicability beyond those materials.

", "summary": "The fragmentation of training materials across research infrastructures often results in unsustainable resource duplication and significant barriers to upskilling. This work aims to enable developers to build systems that effectively discover relevant materials by promoting a federated, FAIR-compliant strategy for open training. The project operated across three interrelated streams: metadata interoperability, material analysis, and the definition and representation of learning paths in a machine readable manner.We demonstrated content federation via the mTeSS-X platform, enabling cross-instance exchange and preparing for future integration with the EOSC federation. To enhance interoperability, we indexed relevant ontologies and curated semantic crosswalks between established metadata models, specifically MoDALIA and Schema.org/Bioschemas. These mappings were implemented within the open-source OERbservatory Python package, providing a facility for exchanging data between platforms such as DALIA and TeSS. For material analysis, we utilised Large Language Models (LLMs) and explored vectorisation techniques to calculate similarity, allowing for the identification of related materials and the potential for future deduplication of records across registries.To address the lack of machine-actionable trajectories across related or sequential materials, we proposed new Bioschemas profiles specifically for learning paths. By extending Schema.org types, including Course and Syllabus, we developed a schema that supports modular and linear orderings of training materials. This model was validated using SPARQL queries on knowledge graphs derived from real-world examples like the Galaxy Training Network. Such advancements provide a foundation for automated path generation and improved discoverability within training catalogues, and serves as a use case and strategy with broader applicability beyond those materials.", "date_published": "2026-01-26T00:00:00+00:00", "date_modified": "2026-01-26T00:00:00+00:00", "tags": ["BH25DE"], "authors": [ { "name": "Phil Reed", "url": "https://orcid.org/0000-0002-4479-715X" }, { "name": "Nick Juty", "url": "https://orcid.org/0000-0002-2036-8350" }, { "name": "Petra Steiner", "url": "https://orcid.org/0000-0001-8997-2620" }, { "name": "Leyla Jael Castro", "url": "https://orcid.org/0000-0003-3986-0510" }, { "name": "Charles Tapley Hoyt", "url": "https://orcid.org/0000-0003-4423-4370" }, { "name": "Oliver Knodel", "url": "https://orcid.org/0000-0001-8174-7795" }, { "name": "Martin Voigt", "url": "https://orcid.org/0000-0001-5556-838X" }, { "name": "Roman Baum", "url": "https://orcid.org/0000-0001-5246-9351" }, { "name": "Dilfuza Djamalova", "url": "https://orcid.org/0009-0004-7782-2894" }, { "name": "Jacobo Miranda", "url": "https://orcid.org/0009-0005-0673-021X" }, { "name": "Alban Gaignard", "url": "https://orcid.org/0000-0002-3597-8557" } ] }, { "id": "https://doi.org/10.37044/osf.io/2jgk4_v1", "url": "https://index.biohackrxiv.org//2026/01/22/2jgk4.html", "title": "METRICS - Monitoring of Key Performance Indicators for ELIXIR Services", "content_html": "

Key Performance Indicators (KPIs) are increasingly requested by a diverse range of stakeholders across the research\necosystem. Funders want to measure the impact of projects and related services they fund, or research organisations\nwant to track the service use for informed decision making. Service providers themselves are also interested in\nmonitoring their services to gather feedback and improve service quality. KPIs are a simple, but powerful tool for\nthese purposes.As part of the BioHackathon Europe 2025, we report on the activities of the METRICS project, which\naddresses the need for consistent and transparent evaluation of services across ELIXIR and related initiatives\nusing KPIs. The project brings together experts from multiple ELIXIR Nodes and scientific domains to identify,\nharmonise, and semantically model KPIs that reflect service quality, usage, sustainability, and impact. By exploring\nexisting evaluation frameworks, and processes, the team aims to design a flexible yet coherent foundation for KPI\nmonitoring of ELIXIR services. This report summarises the project’s motivation, current landscape analysis, and\ninitial steps toward developing an ontology-driven framework for KPI representation, fostering interoperability\nand supporting evidence-based management of life science infrastructures.

", "summary": "Key Performance Indicators (KPIs) are increasingly requested by a diverse range of stakeholders across the research ecosystem. Funders want to measure the impact of projects and related services they fund, or research organisations want to track the service use for informed decision making. Service providers themselves are also interested in monitoring their services to gather feedback and improve service quality. KPIs are a simple, but powerful tool for these purposes.As part of the BioHackathon Europe 2025, we report on the activities of the METRICS project, which addresses the need for consistent and transparent evaluation of services across ELIXIR and related initiatives using KPIs. The project brings together experts from multiple ELIXIR Nodes and scientific domains to identify, harmonise, and semantically model KPIs that reflect service quality, usage, sustainability, and impact. By exploring existing evaluation frameworks, and processes, the team aims to design a flexible yet coherent foundation for KPI monitoring of ELIXIR services. This report summarises the project’s motivation, current landscape analysis, and initial steps toward developing an ontology-driven framework for KPI representation, fostering interoperability and supporting evidence-based management of life science infrastructures.", "date_published": "2026-01-22T00:00:00+00:00", "date_modified": "2026-01-22T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Nils-Christian Lübke", "url": "https://orcid.org/0009-0009-4801-9978" }, { "name": "Helena Schnitzer", "url": "https://orcid.org/0000-0002-6382-9452" }, { "name": "Julia Koblitz", "url": "https://orcid.org/0000-0002-7260-2129" }, { "name": "Saskia Lawson-Tovey", "url": "https://orcid.org/0000-0002-8611-162X" }, { "name": "Nicola Soranzo", "url": "https://orcid.org/0000-0003-3627-5340" }, { "name": "Karel Berka", "url": "https://orcid.org/0000-0001-9472-2589" }, { "name": "Séverine Duvaud", "url": "https://orcid.org/0000-0001-7892-9678" }, { "name": "Kristyna Kvizdova", "url": "https://orcid.org/0009-0000-9827-1359" }, { "name": "Manuel Feser", "url": "https://orcid.org/0000-0001-6546-1818" }, { "name": "Anna Golobardes Vilarasau", "url": "https://orcid.org/" }, { "name": "Gavin Farrell", "url": "https://orcid.org/0000-0001-5166-8551" }, { "name": "Adel Bouhraoua", "url": "https://orcid.org/0000-0001-9531-6339" }, { "name": "Espen Åberg", "url": "https://orcid.org/0000-0002-2280-7978" }, { "name": "David Lloyd", "url": "https://orcid.org/" }, { "name": "Sebastian Beier", "url": "https://orcid.org/0000-0002-2177-8781" }, { "name": "Vedran Kasalica", "url": "https://orcid.org/0000-0002-0097-1056" }, { "name": "Walter Baccinelli", "url": "https://orcid.org/0000-0001-8888-4792" }, { "name": "Mijke Jetten", "url": "https://orcid.org/0000-0001-9114-2896" }, { "name": "Laura Chabot", "url": "https://orcid.org/" }, { "name": "Grégory Gimenez", "url": "https://orcid.org/" }, { "name": "Daniel Arend", "url": "https://orcid.org/0000-0002-2455-5938" } ] }, { "id": "https://doi.org/10.37044/osf.io/m37f2_v1", "url": "https://index.biohackrxiv.org//2026/01/06/m37f2.html", "title": "QPX: Pathway analysis environment", "content_html": "

Building on our work at DBCLS BioHackathon 2023 (BH23), where we introduced QPX and promoted pathway modeling with WikiPathways (Pico et al., 2008)\nusing PathVisio (Kutmon et al., 2015), we now focused on creating new pathway diagrams for diverse species and registering them in WikiPathways with\nfunctional annotations. In parallel, we deployed WikiPathways node data into Elasticsearch to enable fast and flexible search and integration of\npathway information.

", "summary": "Building on our work at DBCLS BioHackathon 2023 (BH23), where we introduced QPX and promoted pathway modeling with WikiPathways (Pico et al., 2008) using PathVisio (Kutmon et al., 2015), we now focused on creating new pathway diagrams for diverse species and registering them in WikiPathways with functional annotations. In parallel, we deployed WikiPathways node data into Elasticsearch to enable fast and flexible search and integration of pathway information.", "date_published": "2026-01-06T00:00:00+00:00", "date_modified": "2026-01-06T00:00:00+00:00", "tags": ["BH25JP"], "authors": [ { "name": "Hidemasa Bono", "url": "https://orcid.org/0000-0003-4413-0651" }, { "name": "Naoya Oec", "url": "https://orcid.org/0000-0002-7491-4994" }, { "name": "Airu Hayashi", "url": "https://orcid.org/" }, { "name": "Chiharu Fujita", "url": "https://orcid.org/" }, { "name": "Kotaro Uchida", "url": "https://orcid.org/" }, { "name": "Ryo Mameda", "url": "https://orcid.org/0009-0007-5830-6482" }, { "name": "Sora Yonezawa", "url": "https://orcid.org/0009-0004-1874-3117" }, { "name": "Kazuki Nakamae", "url": "https://orcid.org/0000-0002-4469-664X" }, { "name": "Ryo Nozu", "url": "https://orcid.org/0000-0002-1099-3152" } ] }, { "id": "https://doi.org/10.37044/osf.io/jfpsx_v1", "url": "https://index.biohackrxiv.org//2025/12/31/jfpsx.html", "title": "BioHackEU25 Report Project 16: MiCoReCa (Microbiome Community Resource Catalogue) - Towards Centralized Curation And Integration Of Microbiome Bioinformatics Resources", "content_html": "

The rapid growth of microbiome research has led to the development of numerous bioinformatics tools and databases, but information about them remains fragmented across disparate,\noften outdated cataloging efforts, hindering resource discovery and utilization. To address this critical gap, the ELIXIR Microbiome Community proposes the development of MiCoReCa\n(Microbiome Community Resource Catalogue), a comprehensive, dynamic, open-access catalogue of microbiome-related bioinformatics resources (tools, workflows, training, standards,\nand databases). Leveraging our community’s expertise, this initiative will utilize standardized ontologies like EDAM and cross-reference established platforms like bio.tools and\nWorkflowHub to create a centralized, findable inventory. A key feature is the community-driven process for identifying and curating missing ontological terms and metadata,\nensuring MiCoReCa’s accuracy and relevance in collaboration with partner platforms. Furthermore, the catalogue will integrate links to training materials from TeSS to support\nappropriate tool usage, and connect with OpenEBench for benchmarking capabilities. This project will not only provide a vital resource for the microbiome field, enhancing\nresearch efficiency and reproducibility, but will also establish a sustainable, adaptable infrastructure potentially applicable to other ELIXIR Communities. This effort\nrepresents a significant contribution by the ELIXIR Microbiome Community to streamline microbiome bioinformatics.

", "summary": "The rapid growth of microbiome research has led to the development of numerous bioinformatics tools and databases, but information about them remains fragmented across disparate, often outdated cataloging efforts, hindering resource discovery and utilization. To address this critical gap, the ELIXIR Microbiome Community proposes the development of MiCoReCa (Microbiome Community Resource Catalogue), a comprehensive, dynamic, open-access catalogue of microbiome-related bioinformatics resources (tools, workflows, training, standards, and databases). Leveraging our community’s expertise, this initiative will utilize standardized ontologies like EDAM and cross-reference established platforms like bio.tools and WorkflowHub to create a centralized, findable inventory. A key feature is the community-driven process for identifying and curating missing ontological terms and metadata, ensuring MiCoReCa’s accuracy and relevance in collaboration with partner platforms. Furthermore, the catalogue will integrate links to training materials from TeSS to support appropriate tool usage, and connect with OpenEBench for benchmarking capabilities. This project will not only provide a vital resource for the microbiome field, enhancing research efficiency and reproducibility, but will also establish a sustainable, adaptable infrastructure potentially applicable to other ELIXIR Communities. This effort represents a significant contribution by the ELIXIR Microbiome Community to streamline microbiome bioinformatics.", "date_published": "2025-12-31T00:00:00+00:00", "date_modified": "2025-12-31T00:00:00+00:00", "tags": ["BioHackEU25"], "authors": [ { "name": "Vivek Ashokan", "url": "https://orcid.org/0009-0006-1470-3999" }, { "name": "Clara Emery", "url": "https://orcid.org/0009-0003-9572-6671" }, { "name": "Agnès Barnabé", "url": "https://orcid.org/0000-0002-8420-7556" }, { "name": "Valentin Loux", "url": "https://orcid.org/0000-0002-8268-915X" }, { "name": "Christina Pavloudi", "url": "https://orcid.org/0000-0001-5106-6067" }, { "name": "Paul Zierep", "url": "https://orcid.org/0000-0000-0000-0000" }, { "name": "Nikolaos Strepis", "url": "https://orcid.org/0000-0000-0000-0000" }, { "name": "Bérénice Batut", "url": "https://orcid.org/0000-0001-9852-1987" } ] }, { "id": "https://doi.org/10.37044/osf.io/hw2fj_v1", "url": "https://index.biohackrxiv.org//2025/12/23/hw2fj.html", "title": "Enhancement of the Interoperability of Trait Data on Genetic Resources between Japan and France", "content_html": "

Japan’s National Agriculture and Food Research Organization initiated a collaborative research project with France’s National Research Institute\nfor Agriculture, Food and Environment to evaluate wheat genetic resources and to identify materials with desirable traits using standardized\ncriteria. This paper presents the current status of trait data standardization between the two organizations and outlines a direction for\nstandardization. Trait data for genetic resources in Japan and France are managed using independently developed standards. The lack of mapping\nstandards hinders data integration and interoperability. To support experts in the mapping process, we developed a tool that translates trait\nterms. A generative AI-based translation tool appears to be applicable for collecting relevant information to support mapping between trait\nterms, as well as translating newly submitted Japanese trait terms into English.

", "summary": "Japan’s National Agriculture and Food Research Organization initiated a collaborative research project with France’s National Research Institute for Agriculture, Food and Environment to evaluate wheat genetic resources and to identify materials with desirable traits using standardized criteria. This paper presents the current status of trait data standardization between the two organizations and outlines a direction for standardization. Trait data for genetic resources in Japan and France are managed using independently developed standards. The lack of mapping standards hinders data integration and interoperability. To support experts in the mapping process, we developed a tool that translates trait terms. A generative AI-based translation tool appears to be applicable for collecting relevant information to support mapping between trait terms, as well as translating newly submitted Japanese trait terms into English.", "date_published": "2025-12-23T00:00:00+00:00", "date_modified": "2025-12-23T00:00:00+00:00", "tags": ["BH23JP"], "authors": [ { "name": "Akane Takezaki", "url": "https://orcid.org/0009-0008-3547-0391" }, { "name": "", "url": "https://orcid.org/0000-0002-5719-7559" }, { "name": "Celia Michotey", "url": "https://orcid.org/0000-0003-1877-1703" }, { "name": "Raphael Flores", "url": "https://orcid.org/0000-0002-0278-5441" }, { "name": "Cyril Pommier", "url": "https://orcid.org/0000-0002-9040-8733" } ] } ] }