Open Source Mac Research Software
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Research Software for Mac
View 15 business solutionsBrowse free open source Research software and projects for Mac below. Use the toggles on the left to filter open source Research software by OS, license, language, programming language, and project status.
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GNSS-SDR
An open source software-defined GNSS receiver
An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework. -
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Stellarium
GPL software which renders realistic skies in real time
Stellarium is a free GPL software which renders realistic skies in real time with OpenGL. It is available for Linux/Unix, Windows and macOS. With Stellarium, you really see what you can see with your eyes, binoculars or a small telescope. Stellarium is a free open source planetarium for your computer. It shows a realistic sky in 3D, just like what you see with the naked eye, binoculars or a telescope. Plugin system adding artifical satellites, ocular simulation, telescope control and more. Ability to add new solar system objects from online resources. Add your own deep sky objects, landscapes, constellation images, scripts, etc. Supernovae and novae simulation. Exoplanet locations. 3D sceneries. Skinnable landscapes with spheric panorama projection. -
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Device Activity Tracker
A phone number can reveal whether a device is active
Device Activity Tracker is a platform created to monitor and log the activity of digital devices across networks, giving users visibility into usage patterns, connection events, app launches, and interaction timelines that can be applied for security monitoring, parental oversight, productivity tracking, or device lifecycle analytics. It integrates with devices via sensors or APIs, continually capturing activity metrics and reporting them to a centralized dashboard that visualizes patterns over time, highlights anomalies, and correlates events across systems or users. Because it is designed with privacy and transparency in mind, the tracker offers configurable retention policies and granular consent controls, ensuring administrators can tailor what gets logged and how long data is stored. -
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Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. Megatron-LM is widely used in research and industry for pretraining GPT-, BERT-, T5-, and multimodal-style models, with tooling for checkpoint conversion and interoperability with Hugging Face. Overall, it is a production-grade system for organizations pushing the limits of large-scale language model training. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Linux command
Linux command encyclopedia search tool
Linux command encyclopedia search tool, the content includes Linux command manual, detailed explanation, study, and collection. The current warehouse has collected more than 570 Linux commands. It is a non-profit warehouse. It has generated a web site for easy use. Currently, the site does not have any advertisements. The content includes Linux command manuals, detailed explanations, and learning. Very worthy collection of Linux command quick reference manual. The copyright belongs to the original author, and does not assume any responsibility for any legal issues and risks. There is no commercial purpose. If you think that your copyright is infringed, please write to us. I cannot guarantee the correctness of the content. The risks caused by using the content of this site have nothing to do with me. When using this site, it means that you have accepted the terms of use and privacy terms of this site. -
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PageLM
PageLM is a community driven version of NotebookLM
PageLM is an open-source AI-powered education platform that transforms study materials into interactive learning experiences inspired in part by the NotebookLM style of knowledge interaction. It is built to help students, educators, and researchers turn documents and topics into more engaging forms of study rather than leaving content in static notes or isolated files. The platform includes a broad set of learning tools such as contextual chat, Cornell-style note generation, flashcards, quizzes, AI podcasts, voice transcription, homework planning, exam simulation, debate practice, and a personalized study companion. It supports uploaded documents including PDF, DOCX, Markdown, and TXT, allowing users to ground questions and generated materials in source content. On the technical side, it supports multiple model providers, multiple embedding back ends, WebSocket streaming for real-time generation, persistent content storage, and structured markdown outputs. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving). -
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OpenScience
The open-source AI workbench for scientific research
OpenScience is an open-source AI workbench for scientific research. It lets users give an agent a research goal, then have it read literature, form hypotheses, write and run code, run experiments, analyze results, and write up findings. The workspace runs in the browser and includes a file tree, editor, terminal, session history, and scientific rendering for molecules, structures, genomes, and plots. It supports frontier, open-weight, and local models through bring-your-own-key provider configuration. The project includes research agents, domain specialists, critique agents, literature-review agents, and more than 290 scientific skills. OpenScience is especially useful for machine learning, biology, physics, chemistry, and research automation workflows that need tool use and provenance. -
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WIKINDX
Virtual Research Environment / On-line Bibliography Manager
Reference management, bibliography management, citations and a whole lot more. Designed by academics for academics, under continuous development since 2003, and used by both individuals and major research institutions worldwide, WIKINDX is a Virtual Research Environment (an enhanced on-line bibliography manager) storing searchable references, notes, files, citations, ideas, and more. An integrated WYSIWYG word processor exports formatted articles to RTF, DOCX, and HTML. Plugins include a citation style editor and import/export of bibliographies (BibTeX, Endnote, RIS etc.). WIKINDX supports multiple attachments with each reference, multiple language localizations, and uses a template system to allow users to visually integrate WIKINDX into their sites. WIKINDX runs on a web server giving you and your research group ownership and global access from any web-enabled device. You manage your database, you own your data. WIKINDX can be test-driven at: https://testdrive.wikindx.com -
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KingJamesPureBibleSearch
GUI Application to Search and Count the Pure King James Bible
Study and analyze the Fingerprint of God in the mathematical structure, known as the King James Code, of the King James text of the Holy Bible. Allows instant real-time searches, with an autocompleter droplist to assist with words which come next. Jump to specific words, verses, or chapters by number, and see all possible count statistics of phrases within the text. Graphically visualize search results, cross-reference sources and word lexicons, and search foreign translations derived from the same Divine Masoretic/Textus Receptus Vine of Scripture. For more info and downloads, see http://www.purebiblesearch.com/ For details on the King James Code, see http://visitbethelchurch.com/ -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. Overall, this repository serves as an open research hub for sharing ideas and advancing the understanding of intelligent systems. -
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Open Science
Open Science is an open-source, local-first, model-agnostic research
Open Science is an open-source, local-first AI research workbench built for scientific discovery on macOS, Windows, and Linux. Researchers can describe a task in plain language and let an agent inspect files, execute Python or R, search the web, and call scientific data connectors. The system is model-agnostic, allowing users to connect supported providers, compatible gateways, or subscription-based backends. It produces reproducible reports, tables, figures, and other artifacts inside a unified project workspace. Users can inspect tool activity, approve sensitive actions, branch earlier conversations, and compare alternative research paths without losing prior work. Immutable artifact versions and detailed provenance connect results to code, inputs, execution history, environment evidence, conversation context, and review findings. -
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Biblatex is a LaTeX package which provides full-featured bibliographic facilities
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Hypernomicon
Hypertext-infused philosophy personal database software
Hypernomicon is a personal productivity/database application for researchers that combines structured note-taking, mind-mapping, management of files (e.g., PDFs) and folders, and reference management into an integrated environment that organizes all of the above into semantic networks or hierarchies in terms of debates, positions, arguments, labels, terminology/concepts, and user-defined keywords by means of database relations and automatically generated hyperlinks (hence ‘Hyper’ in the name). Hypernomicon keeps track of all these things in a highly structured, thoroughly indexed and user friendly relational database, automatically generates semantic hyperlinks between all of them, and presents this information in many different forms so that you are constantly informed of ways all of your information is related that you had not realized. -
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DSpace
Open Source "turn-key" institutional repository application
Open Source Digital Asset Management system that enables services for access, provision, stewardship and re-use of digital assets with a focus on educational and research materials For Support, please see: https://wiki.lyrasis.org/display/DSPACE/Support RELEASES: The most recent releases are now distributed via GitHub: https://github.com/DSpace/DSpace/releases MAILING LISTS: Mailing lists have all been moved to Google Groups: https://wiki.lyrasis.org/display/DSPACE/Mailing+Lists -
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RTSim is a simulator for real-time systems written in C++. It has been developed as an internal project at Scuola Superiore Sant'Anna as part of many research project, mainly thanks to the work of many PhD students.
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Local Deep Research
95% on SimpleQA (e.g. Qwen3.6-27B on a 3090)
Local Deep Research is an open-source AI-powered research assistant designed to perform deep, iterative investigations by combining large language models with multi-source search capabilities. It runs locally, giving users full control over their data, privacy, and infrastructure while supporting both local and cloud-based LLMs. The system breaks down complex queries into smaller steps, performs parallel searches across web and academic sources, and generates structured, citation-backed reports. It also supports personal document ingestion through vector search, enabling users to build a private, searchable knowledge base. The platform includes a web interface, Docker-based deployment, and flexible configuration options, making it accessible to both developers and researchers. Its architecture emphasizes transparency, customization, and reproducibility in AI-assisted research workflows. -
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eLabFTW
Open source electronic lab notebook for research labs
eLabFTW is a modern, open-source electronic laboratory notebook (ELN) that helps research teams store, organize, and manage experimental records and laboratory data from a central web interface. Designed for scientific environments, the software provides a secure, browser-accessible platform where users can log protocols, results, and observations in structured records that replace traditional paper notebooks. Beyond simple experiment journaling, eLabFTW includes an inventory database for tracking lab materials, reagents, and equipment, making it a versatile tool for laboratory management. It also supports collaboration across teams, enabling multiple users to access shared records and streamline project workflows. The software features advanced search, tagging, timestamping, and export tools, which are essential for reproducibility and long-term project tracking. -
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google-play-scraper
Node.js scraper to get data from Google Play
Node.js module to scrape application data from the Google Play store. Retrieves the full detail of an application. Retrieves a list of applications from one of the collections at Google Play. Retrieves a list of apps that results of searching by the given term. Returns the list of applications by the given developer name. Given a string returns up to five suggestions to complete a search query term. Retrieves a page of reviews for a specific application. Returns a list of similar apps to the one specified. Returns the list of permissions an app has access to. Retrieve a full list of categories present from the dropdown menu on Google Play. Since every library call performs one or multiple requests to an Google Play API or web page, sometimes it can be useful to cache the results to avoid requesting the same data twice. -
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Overhead Imagery Research Data Set (OIRDS) - an annotated data library & tools to aid in the development of computer vision algorithms
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moebinv
C++ libraries for manipulations in non-Euclidean geometry
These are two C++ libraries for symbolic, numeric and graphical manipulations in non-Euclidean geometry. There is GUI which allows to interact with these libraries by mouse clicks. On a dipper level the first library Cycle implements basic operations on cycles (quadrics) through FSCc construction. The second library Figure operates on ensembles of cycles connected by Moebius-invariant relations, e.g. orthogonality. Both libraries are based on the Clifford algebra capacities of the GiNaC computer algebra system (http://ginac.de). Besides C++ libraries there is a Python wrapper, which can be used in interactive mode (https://codeocean.com/capsule/7952650/). Both libraries work in arbitrary dimensions and signatures of metric. Additionally, there are some 2D/3D-specific routines including a visualisation to PostScript files through Asymptote (http://asymptote.sourcefourge.net) software. The source is written in literate programming NoWeb. -
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jMIR
Music research software
jMIR is an open-source software suite implemented in Java for use in music information retrieval (MIR) research. It can be used to study music in the form of audio recordings, symbolic encodings and lyrical transcriptions, and can also mine cultural information from the Internet. It also includes tools for managing and profiling large music collections and for checking audio for production errors. jMIR includes software for extracting features, applying machine learning algorithms, applying heuristic error error checkers, mining metadata and analyzing metadata. -
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xrayutilities
a package with useful scripts for X-ray diffraction physicists
xrayutilities is a python package used to analyze x-ray diffraction data. It can support with performing diffraction experiments and used for common steps in the data analysis. It can read experimental data from several data formats (spec, edf, xrdml, ...); convert them to reciprocal space for arbitrary goniometer geometries and different detector systems (point, linear as well as area detectors); for further processing the data can be gridded (transformed to a regular grid). More detailed description as well as documentation can be found at webpage http://xrayutilities.sourceforge.io/. Downloads for windows can be found on http://pypi.python.org/pypi/xrayutilities Development is performed on github: https://github.com/dkriegner/xrayutilities -
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Fuzzy machine learning framework
A library and a GUI front-end for fuzzy machine learning
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
