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 […]
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Open Source
These GitHub repositories contain open-source software developed by the Observatory on Social Media for various projects
How open source projects need to adapt to the AI coding era | We Love Open Source • All Things Open
A new Carnegie Mellon study shows AI coding tools boost velocity by 281% — then leave codebases harder to work with. Here's what open source communities need to do before the sugar rush wears off.
relevant_tools.csv · darkshapes/relevant_tools at main
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Anna’s Archive: LibGen (Library Genesis), Sci-Hub, Z-Library in one place - Anna’s Archive
The world’s largest open-source open-data library. Mirrors Sci-Hub, Library Genesis, Z-Library, and more.
From OSS to Open Source AI: an Exploratory Study of Collaborative...
AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...
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.
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
Denicek: Computational Substrate for Document-Oriented End-User Programming
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
Software Stewardship Lab
A non-profit research lab dedicated to ensuring the stability of the Open Source ecosystem we all rely on.
How to SNIFF out Good Scientific Software
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.
Malleable Software in the Age of AI - Geoffrey Litt
Substrates conference series - Substrates 2026
An increasing number of researchers see their work as interactive authoring tools or software substrates for interactive computational media. By talking about “authoring tools”, we remove the divide between programmers and users; “software substrates” let us look beyond conventional programming languages and systems; and “interactive computational media” promises a more malleable and adaptable notion of tools for thought we are striving for. This workshop aims to bring together a wide range of perspectives on these matters.
Home | Bellingcat's Online Investigation Toolkit
A toolkit for open source researchers
Introduction to Open Science
This course introduces the principles and practices of open science, with an emphasis on reproducible research workflows, transparent reporting, and collaborative scholarship. Students will critically examine reproducibility, explore tools that support openness (such as Git, GitHub, and Quarto), and apply these tools in hands-on assignments and a final open project. Students will also explore how open science practices vary across disciplines, including education, humanities, social sciences, industry and government, and STEM contexts. This course emphasizes applying open science principles to real-world data problems in alignment with the principles of producing reproducible research.