Dagster is a modern data orchestrator that emphasises reproducibility, observability, and a strong developer experience (Elementl, 2024b).In parallel, most high-performance computing (HPC) centres continue to rely on Slurm for batch scheduling and resource governance (Yoo et al., 2003).The two ecosystems rarely meet in practice: Dagster projects often target cloud or single-node deployments, while Slurm users maintain bespoke submission scripts with limited reuse or visibility.This paper introduces dagster-slurm, an open-source integration that allows the same Dagster assets to run unchanged across laptops, CI pipelines, containerised Slurm clusters, and Tier-0 supercomputers.The project packages dependencies with Pixi (prefix.dev,2024), submits workloads through Slurm using Dagster Pipes (Elementl, 2024a), and streams logs plus scheduler metrics back to the Dagster UI.The key contribution is a unified compute resource (ComputeResource) that hides SSH transport (including password-only jump hosts and OTP prompts), dependency packaging, and queue configuration while still respecting Slurm's scheduling semantics.The project ships two production-ready execution modes-local for laptop/CI development and slurm for one-jobper-asset submissions-and two stable launchers: Bash for script-based workloads and Ray for multi-node distributed computing.Experimental support for Spark, session-based allocation reuse, and heterogeneous jobs is under active development.

Synthesizer is a fast, flexible, modular, and extensible Python package that empowers astronomers to turn theoretical galaxy models into realistic synthetic observations-including spectra, photometry, images, and spectral cubes-with a focus on interchangeable modelling assumptions.By offloading computationally intensive tasks to threaded C++ extensions, Synthesizer delivers both simplicity and speed, enabling rapid forward-modelling workflows without requiring users to manage low-level data processing and computational details.

Monte Carlo (MC) methods estimate high-dimensional integrals by computing sample averages at independent and identically distributed (IID) random points.Quasi-Monte Carlo (QMC) methods replace IID samples with low-discrepancy (LD) sequences which more uniformly cover the integration domain, leading to faster convergence and reduced computational requirements.Figure 1 visualizes IID and LD sequences.

Warwick et al., (2026). trud: An R interface to the NHS England Technology Reference data Update Distribution (TRUD) API. Journal of Open Source Software, 11(117), 9000, https://doi.org/10.21105/joss.09000

pytau is a Python package for batched Bayesian changepoint inference across parameter grids and datasets.It integrates with PyMC3 to provide uncertainty estimates that are critical for noisy neuroscience data with small sample sizes and low channel counts, and includes tools for preprocessing, model fitting, result visualization, and statistical analysis.The package has been successfully applied in published research examining taste processing (Baas-

The genome size of organisms present in an environment can provide many insights into evolutionary and ecological processes at play in that environment.The genomic revolution has enabled a rapid expansion of our knowledge of genomes in many living organisms, and most of that knowledge is classified and readily available in the databases of the National Center for Biotechnology Information (NCBI).The genomesizeR tool leverages the wealth of taxonomic and genomic information present in NCBI databases to infer the genome size of Archaea, Bacteria, or Eukaryote organisms identified at any taxonomic level.This R package provides three statistical methods for genome size prediction of a given taxon, or group of taxa.A straightforward 'weighted mean' method identifies the closest taxa with available genome size information in the taxonomic tree, and averages their genome sizes using weights based on taxonomic distance.A frequentist random effect model uses nested genus and family information to output genome size estimates.Finally, a third option provides predictions from a distributional Bayesian multilevel model which uses taxonomic information from genus all the way to superkingdom, therefore providing estimates and uncertainty bounds even for under-represented taxa.genomesizeR retrieves the taxonomic classification of input queries, estimates the genome size of each query, and provides 95% confidence intervals for each estimate.Some plotting functions are also provided to visualise the results.