Meetings
BioHackSWAT4HCLS 2026
BioHackathon Europe 2026
BioHackathon Germany 2026
DBCLS BioHackathon 2026
ELIXIR INTOXICOM
Recent preprints
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BioHackEU24 report: Integrating Bioconductor packages with the ELIXIR Research Software Ecosystem using EDAM
This project seeks to enhance the ELIXIR Research Software Ecosystem (RSEc) by increasing the findability, accessibility, interoperability, and reusability (FAIR principles) of Bioconductor’s extensive collection of over 2,000 bioinformatics packages. By aligning Bioconductor metadata with the EDAM ontology and integrating detailed package descriptions into the bio.tools registry, we aim to improve the discoverability and usability of bioinformatics analysis tools. Short-term goals include mapping Bioconductor’s biocViews controlled vocabulary to EDAM concepts, developing a set of manually annotated “gold standard” packages, and evaluating tools for automated EDAM concept suggestions. Long-term, we intend to expand EDAM coverage across Bioconductor, phase out biocViews, and implement automated synchronisation with bio.tools. This initiative fosters collaboration between Bioconductor and ELIXIR, establishing a foundation for sustainable software management in European bioinformatics.Key results from the ELIXIR BioHackathon 2024 week include substantial progress in mapping the biocViews vocabulary to EDAM concepts, initiating the curation of a reference set of packages with manual annotations, integrating Bioconductor metadata into the ELIXIR Research Software Ecosystem (RSEc) with automated updates, and prototyping a tool for automated EDAM concept suggestions. Together, these achievements establish a strong foundation for further integration and refinement. -
Persistence, metadata collection and re-architecture of BioHackrXiv
In this paper, we present the work executed on re-architecting BioHackrXiv during the international ELIXIR BioHackathon Europe 2023 in Barcelona, Spain. BioHackrXiv is a scholarly publication service for biohackathons and codefests that target biology and the biomedical sciences in the spirit of pre-publishing platforms. -
An assessment of Croissant ML metadata descriptors for AI-ready datasets
To advance the use of machine learning to address humanity’s grand challenges such as the understanding of disease conditions and biodiversity loss in the anthropocene, it is important to promote FAIR AI-ready datasets, since data scientists and bioinformaticians spend 80% of their time in data finding and preparation. Metadata descriptors for datasets are pivotal for the creation of machine learning models as they facilitate the definition of strategies for data discovery, feature selection, data cleaning, and data pre-processing. ML-ready datasets, whether by design or after pre-processing, can be enriched with metadata so they become FAIRer, i.e., autonomously discoverable and processable by machines (machine-actionable). Croissant ML is an extension of schema.org to better describe ML-ready datasets, released early 2024 and already adopted by some ML-model platforms such as Hugging Face (see Croissant ML viewer documentation) and OpenML. However, as it commonly happens with metadata, there are some limitations to the amount of metadata that can be automatically extracted. How much Croissant metadata can be programmatically extracted from ML-ready datasets? And how could this automation be improved? In this project, we explored answers to these two questions. -
2024 OME-NGFF workflows hackathon
The 2024 OME-NGFF Workflows Hackathon, held at the BioVisionCenter at the University of Zurich, brought together an international group of researchers and developers to develop the ecosystem around the open, scalable, and FAIR bioimage file format OME-Zarr. Over five days, participants tackled key challenges in four main areas: (1) advancing the OME-Zarr specification, (2) enabling workflow interoperability by integrating OME-Zarr image processing tasks across multiple open-source frameworks, (3) expanding Java support for Zarr v3 and enhancing the compatibility of OME-Zarr with the popular bioimage analysis software Fiji, and (4) improving the Python resources supporting OME-Zarr. The event led to the release of OME-Zarr 0.5, which formalizes the adoption of Zarr v3 and introduces a sharding strategy to reduce file system overhead. This report provides an overview of the key discussions, outcomes, and future directions emerging from the hackathon, with the goal of fostering continued community engagement in developing OME-Zarr as a robust open bioimaging standard. -
Development of FAIR image analysis workflows and training in Galaxy
Although image analysis tools are available within the Galaxy platform, they remain underutilised. During the 2023 BioHackathon Europe, our efforts focused on enhancing the image analysis community in Galaxy by cataloguing and annotating tools and facilitating community discussions to establish naming conventions that promote standardisation. These initial efforts, detailed in the project outcomes, laid the foundation for the ongoing expansion of Galaxy’s image analysis capabilities.Building on these achievements, this year’s work aimed to exploit and demonstrate theGalaxy platform’s full potential to address the needs of the image analysis community.This project involved developing FAIR (Findable, Accessible, Interoperable, and Reusable)image analysis workflows, creating tutorials for the Galaxy Training Network (GTN) to providedocumentation, and fostering broader adoption and facilitating theapplication of these workflows across scientific domains. -
Secure Processing Environments as a Service in the de.NBI Cloud
Sensitive human data is crucial for biomedical research, enabling faster drug development and better understanding of diseases. The Biohackathon Germany project utilized ELIXIR Europe’s services and external tools to create Secure Processing Environments, ensuring high protection of sensitive data while facilitating research across Germany and Europe. -
Report: Workshop on connecting Knowledge Graphs with BioChatter
The workshop on connecting Knowledge Graphs (KGs) with BioChatter convened experts from biology, computer science, and bioinformatics to tackle challenges in integrating and accessing dispersed datasets in plant sciences. The goal was to create user-friendly interfaces for querying these datasets using natural language, bypassing the need for expertise in semantic technologies or query languages like SPARQL or Cypher.Key use cases included the BrAPI project, which aimed to simplify data retrieval from plant research datasets. While BioChatter effectively generated simple API queries, complex multi-step queries posed challenges, suggesting that programmatic approaches are better suited for such tasks. Integrating BrAPI with BioCypher enabled successful querying of KGs for questions like identifying studies involving specific plant varieties.The RDF adapter use case focused on enhancing the Plant Phenotyping Experiment Ontology (PPEO) by converting it into a BioCypher-compatible KG, thereby improving data interoperability and enabling LLMs to generate context-aware responses. The Mobile Element Knowledge Graph use case explored the relationship between transposable elements and gene regulation networks, utilizing BioChatter to assist users unfamiliar with Cypher.The Stress Knowledge Map (SKM) use case integrated a highly curated model of plant stress signaling with BioCypher and BioChatter, allowing natural language queries and improving access to complex biological data. The Chem and Plant KG use case aimed to integrate diverse scientific resources into a unified KG, enhancing data interoperability and accessibility.Challenges included the need for human-readable concepts within KGs to improve LLM interaction and aligning LLMs with user demands. Future work will focus on refining KG schemas, improving LLM integration, and expanding documentation to support broader adoption and utility in scientific research. The workshop highlighted the potential of combining KGs with LLMs to enhance data accessibility and drive new insights in biological and agricultural sciences.