BioHackrXiv

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BioHackrXiv is a preprint server powered by OSF. It was created with the aim of reporting on the work done during BioHackathons, CodeFests, Sprints or similar […] 🌉 bridged from ⁂ https://fediscience.org/@biohackrxiv, follow @ap.brid.gy to interact

Figure 1: Schematic representation of the Tidyomics hackathon aims. 1. We introduced tidyAnnData to provide tidy operators for AnnData objects; 2. We fixed major bugs in information accessibility for Tidyomics core packages; 3. We updated and extended Tidyomics packages such as DFplyr and tidybulk; 4. We provide a comprehensive and concise vignette for the tidySingleCellExperiment package. This plot was created by Google Nano Banana 2.

network of nodes and edges, with each node being a BioHackrXiv preprint, and the edges citations with CiTO intent annotations. The nodes have the first author surname and publication year, and the edges have the label of the CiTO citation intent. The graph has a few isolated networks, and 30 nodes in totel. The largest network has 17 articles and their 21 citations between these preprints.

One of the figures from the preprint. It shows a 2d image of tissue, colored with a red stain, showing the red and white image that we see. At the top there is the text "Overlay: Terra Image and Segmentation".

Figure 1: Comparison of Zarr, bioimaging and single-cell/spatial omics packages across scverse and Bioconductor ecosystems that interface with SpatialData objects on disk (“Database-icon” by Zahra Ibrahem is licensed under CC BY-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/?ref=openverse.)

Schematic with many boxes in various UML types. At the top we see a purple box with various stakeholders (JRC, ELIXIR, OECD, EIRENE, NWO, etc), and a blue box below this with various activities (AOP Wiki, Omics AOP, Methods AOP, AOP-DB, AOPXplorer, etc), supported by a layer reflection data warehouses (without much details). On the right side we see three headers (Macro level: landscape, Meso level: regimes, and Micro level (niches).

Figure 1: Mid-week reporting poster used during BioHackathon Europe 2025 to communicate early design decisions, illustrate EDAM branches, and motivate the use of the Model Context Protocol to mitigate LLM hallucinations during ontology-driven tool annotation.

Figure 2.10-1: Distribution of species and proteins across different taxa (Human, Virus, Prokaryote, Eukaryote) in the ProteinGym benchmark.

It shows each of the four in separate colorful boxplots. Not easy to explain, and plz read the article for the details.

Figure 3:Principal component analysis (PCA) of bio.tools entries with a GitHub repository basedon the numbers of contributors, forks (and network count), commits, pulls, releases, open issues,subscribers, watchers, stargazers and the average time to close issues. The colors represent thebio.toolsmaturitylevel, i.e.,Emerging,MatureorLegacy.

Figure 1: Curated crosswalks between MoDALIA and Schema.org metadata models for training materials.

The figure shows schema.org on the left side, SSSOM icon below an bidirectional arrow in the middle in a screenshot of a MoDALIA predicate definition on the right.

Part of Figure 1:Photo of project poster for the mid-week presentation. The photo shows a A0 sheet notes from the meeting, including country flags, some key words, and the output of brainstorming.