New PLOS Computational Biology paper: "Ten Quick Tips to SNIFF Out Sustainable and Secure Scientific Software"
Ten quick tips to SNIFF out sustainable and secure scientific software
Modern computational biology depends heavily on open-source software tools, analysis pipelines, and containerized workflows developed and shared by the research community. While there is extensive guidance (including Quick Tips and Simple Rules articles) on how to build robust and sustainable scientific software, far less has been written for researchers in the role of software users evaluating whether an existing tool is reliable, secure, and sustainable enough for their work. Here we present ten quick tips to help researchers critically assess the tools they adopt. Our tips are organized around a framework that centers on key evaluation features: source, network, interaction, fit, and fragility (SNIFF). These dimensions prompt researchers to consider who maintains a tool and why, whether it is embedded in a broader ecosystem, how actively its developers and users engage, whether it matches the intended use case and licensing requirements, and how robust its dependencies and security practices are. By applying these tips, researchers can make more informed decisions, reduce the risk of relying on abandoned or insecure software, and contribute to a more sustainable scientific software ecosystem.
Linking the world's research to the code it runs on - OpenAlex blog
Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means […]
Introducing COSIG: The Collection of Open Science Integrity Guides
Investigating the integrity of published scientific papers is key to the scientific process, but the necessary knowledge is in short supply. We present COSIG, an open collection of meta-scientific guides enabling anyone to perform forensic peer review.
The Bazaar of Scientific Knowledge | shishyko!
Is the current form of the scientific paper still optimal in 2025? How do we preserve, and efficiently leverage, the uncut gems of the scientific process?
Welcome to the Tidyverse
Wickham et al., (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
SCI — Software Carbon Intensity | Green Software Foundation
The global standard for calculating and reducing the carbon emissions of your software. ISO/IEC 21031:2024.
oss-security - Dirty Frag: Universal Linux LPE
Please check out the Open Source Software Security Wiki, which is counterpart to this mailing list.
Scimeto - Automated Manuscript Integrity Checker
Upload your academic manuscript and get instant analysis of citations, references, statistics, retractions, and more. 21 plugins, 9 citation styles, free for researchers.
Konrad Hinsen's blog
How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it.
#bioinformatics #computationalbiology #aiforscience #singlecell #spatialtranscriptomics #opensource #codex #claudecode #mcp #agenticai | Herry Z.
BioNexus: A Scientific Reliability Layer for Agentic Biology As coding agents such as OpenAI Codex, Claude Code, and Cursor become increasingly capable of running bioinformatics workflows, a new problem emerges: Knowing how to run an analysis does not mean knowing when that analysis is scientifically valid. BioNexus is an open-source project exploring a reliability layer for biological coding agents rather than another standalone AI scientist. Its current architecture focuses on: Machine-readable Capability Contracts — define input semantics, scientific preconditions, canonical backends, refusal conditions, and evidence requirements. EvidenceCard 2.1 — separates execution state from scientific evidence and grades conclusions as PRELIMINARY, SUPPORTED, ROBUST, REPLICATED, FRAGILE, or ABSTAIN. Scientific Intent & Invariant Routing — detects invalid analyses such as pseudoreplication, incompatible data representations, or missing backends. BioNexus Eval — evaluates routing, unsafe claims, refusal behavior, and scientific outcomes. RunBundle Artifact Contract — packages inputs, parameters, results, provenance, environment, and evidence into reproducible machine-readable runs. The long-term goal is simple: Make scientific reliability a first-class infrastructure layer for agentic biology. BioNexus is still evolving, and I am actively looking for contributors in bioinformatics, single-cell/spatial omics, scientific benchmarking, MCP, reproducibility, and AI coding agents. PRs, issues, benchmark datasets, scientific critiques, and failure cases are especially welcome. GitHub: HERRY423/BioNexus #Bioinformatics #ComputationalBiology #AIForScience #SingleCell #SpatialTranscriptomics #OpenSource #Codex #ClaudeCode #MCP #AgenticAI
Introducing the Open-Source Codex Security CLI
Codex Security helps security and engineering teams find, confirm, and fix vulnerabilities. Use its command-line interface (CLI) to scan repositories you own or have permission to assess, review findings over time, and check changes before they land. Quickstart Guide for an interactive scan Cloud set-up for connected GitHub repositories Link to the public repo: Codex Security This is an early release, and we’re listening to your feedback as we continue improving it. The Codex Security CLI ...
Improving Science That Uses Code
Abstract. As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being
VaultGemma: The world's most capable differentially private LLM
Amer Sinha, Software Engineer, and Ryan McKenna, Research Scientist, Google Research
