Find open-source science resources

Auto-generates clean, customizable academic CVs from open research data (OpenAlex, ORCID, Crossref, DataCite, Open Editors Plus). A single canonical CV object drives every output format (HTML, PDF, DOCX, LaTeX, Markdown); citations render through CSL; and the account holder is matched by persistent identifier (ORCID / OpenAlex ID) rather than name string. Free for individuals, open-source, and FAIR by design.

A static web application presents an interactive knowledge graph of single-cell long-read RNA sequencing literature synthesized from seven source papers. Users navigate mind-tree, network graph, guided learning-path, and Sankey views linking platforms, protocols, methods, and software. A benchmark tab provides 34 question-answer pairs with category and difficulty filters, exportable as JSON or CSV for LLM and agent evaluation.

Interactive explorer for single-cell transcriptomics data enabling visualization of UMAP/t-SNE embeddings, differential expression analysis, and cross-dataset comparison through a fast web-based interface; widely adopted for exploring atlas-scale single-cell datasets and integrating with AI/ML analysis workflows (773+ stars, MIT License)

FlowVision is offline flow cytometry analysis software for Windows and macOS. It supports FCS 2.0, 3.0, 3.1 and 3.2 file formats, polygon/rectangle/ellipse/quadrant gating with auto-fit (snap to cluster), spillover compensation, biexponential and hyperlog scales, MFI statistics (median, geometric mean, CV%), multi-file batch analysis with per-file gate overrides, and hierarchical gating. Spectral unmixing supports linear, NNLS, and Poisson-weighted least squares algorithms, with autofluorescence extraction and spillover spreading matrix. UMAP dimensionality reduction with reproducible seed and landmark mode for high-parameter panels. Imports FlowJo .wsp (compensation matrix) and exports gates to FlowJo .wsp and Gating-ML 2.0 (ISAC open standard) for interoperability with FlowJo, R/flowWorkspace/CytoML, and FCS Express.

Quantum chemisttry web platform that brings all the necessary tools to perform quantum chemistry in a user-friendly web interface.

JavaScript library that can be used to generate interactive and highly customizable web-based genome browsers.

JavaScript library for drawing canvas-based gene diagrams.

D3 JavaScript based genome viewer. Constructs SVGs.

The Open Neuroscience Graph (openneuroscience.org) is an open-access, curated knowledge graph that maps the open science ecosystem in neuroscience as a browsable digital garden. Built from an Obsidian vault and published as a static website using Quartz, the project replaces traditional linear presentation with a networked structure of interlinked Markdown notes. Bidirectional links, full-text search, and an integrated graph visualization allow users to navigate thematic relationships dynamically rather than sequentially. The complete source material is openly available to sustain, replicate and extend the resource, includding all Markdown content, media attachments, Quartz configuration files, and site customizations. Researchers, educators, and open-science practitioners may explore the site directly, download the vault for offline use in Obsidian, or fork the material to build new, derivative knowledge bases. PID=https://doi.org/10.5281/zenodo.20181900

Mol Biology Tools is a free browser-based collection of molecular biology utilities for routine sequence analysis, primer design, Sanger sequencing primer planning, cloning setup, and wet-lab calculations. The site includes tools for PCR primer design, Sanger primer design and primer walking, primer binding checks, restriction site analysis, reverse complement generation, ORF and protein translation, codon optimization, ligation calculations, molarity calculations, dilution calculations, and multi-solute solution recipe preparation. The tools run in the browser and are intended for quick experimental planning without requiring logins or server-side sequence upload.

Web application and service for visualizing small- to medium-scale models of gene regulatory networks. It automatically lays out either an unweighted or weighted network graph based on an Excel input spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows. It is best-suited for visualizing networks of fewer than 35 nodes and 70 edges and has general applicability.