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Code for the book "Artificial Intelligence: A Modern Approach"
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Code for the book "Artificial Intelligence: A Modern Approach"
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Exploring cyberspace, sharing in its study, & helping to pioneer its development
Building m-Learning applications
Analytical solutions for Next-Generation Sequencing data
Amplifying Research Imapct through Open Source
Develop translational data science.
CircuitVerse is an easy to use digital logic circuit simulator which aims at providing a platform to create, share and learn digital circuits. It can run on almost any device without the need for installing any software. The platform has been designed for use by students, professionals and hobbyists alike. The vision is to develop a community around the platform that will aid students to self-learn digital logic design. The platform is currently used by several universities worldwide. Apart from the simulator, users can create, learn, collaborate and share their work. Teachers can create groups and host assignments on the platform. The platform’s impact has been more evident than ever in the Covid 19 pandemic as CircuitVerse enabled schools and colleges to move their courses online.
Fast, Accurate, Flexible Open Source Speech Recognition
Tool development in bioinformatics, computational biology, data visualization
Game libraries, frameworks, and development toolkits
DBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. It allows for a global and unified access to Knowledge Graphs.
EMBL-EBI is a global centre for biological data, developing and maintaining open data resources and open-source software that support life science research worldwide. Our services are used daily by researchers across academia, healthcare, and industry, and span genomics, transcriptomics, metagenomics, molecular interactions, chemistry, and functional annotation.
GeomScale is a research and development project that delivers open source code for state-of-the-art algorithms for problems at the intersection of data science, optimization, geometric, and statistical computing. The current focus of GeomScale is on scalable algorithms for sampling from high-dimensional distributions, integration, convex optimization, and their applications. One of our ambitions is to fill the gap between theory and practice by turning state-of-the-art theoretical tools in geometry and optimization to state-of-the-art implementations. Towards this goal, we will deliver various innovative solutions in a variety of application fields, like finance, computational biology, and statistics that will extend the limits of contemporary computational tools. GeomScale aims in serving as a building block for an international, interdisciplinary, and open community in high dimensional geometrical and statistical computing. The main development is currently performed in volesti, a generic open source C++ library, with R and python interfaces (the latter is hosted in package dingo), for high-dimensional sampling, volume approximation, and copula estimation for financial modelling. In particular, the current implementation scales up to hundred or thousand dimensions, depending on the problem. To our knowledge it is the most efficient software package for sampling and volume computation to date. It is, in several cases, orders of magnitude faster compared to packages that solve the same problems. It can be used to compute challenging multivariate integrals and to approximate optimal solutions in optimization problems. It has already found important applications in systems biology by analyzing large metabolic networks (e.g., the latest human network) and in FinTech by detecting shock events and by evaluating portfolios performance in stock markets with thousands of assets. Other application areas include AI and in particular approximate weighted model integration. Recent studies has shown a potential application of volesti methods in trustworthy AI, static analysis of programs and differential privacy.
The International Neuroinformatics Coordinating Facility (INCF; www.incf.org) is an international organization launched in 2005, following a proposal from the Global Science Forum of the OECD.
Integrating biological data sources, making it easy to query and analyse data
Computational thinking for all with free visual coding apps The Catrobat project develops useful frameworks to create games, animations, or apps easily within a short time. This set of mobile creativity tools for smartphones is inspired by the well-known Scratch framework by the Lifelong Kindergarten Group at the MIT Media Lab. The motivation behind the project is that programming is an important cultural technique on the same level as mathematics and physics, from a practical as well as from a philosophical point of view. Our aim thus is to popularize the skills needed to program from an early age in a fun and engaging way that will facilitate the spread of its adoption among young people all over the world.
U.S. IOOS is a national and regional partnership working to provide ocean, coastal and Great Lakes observations, data, tools, and forecasts to improve safety, enhance the economy, and protect our environment. Our primary purpose is to provide free and open data about the state of our oceans and Great Lakes to our users. These data are fundamental to understanding the health of our marine ecosystems, to monitor the climate signal as captured in oceanographic conditions, and to provide predictions about the future state of our oceans and coasts.
Robotics applications are typically distributed, made up of a collection of concurrent asynchronous components which communicate using some middleware (ROS messages, DDS...). Building robotics applications is a complex task. Integrating existing nodes or libraries that provide already solved functionality, and using several tools may increase the software robustness and shorten the development time. JdeRobot provides several tools, libraries and reusable nodes. They have been written in C++, Python or JavaScript.
Open for Innovation
Style and grammar checker
Learning Equality is an education technology nonprofit focused on fostering student-centered learning and advancing organizations working with underserved teachers and learners globally. Through community-driven innovation and strategic partnerships, we provide offline-first, open-source digital tools and implementation support to help them thrive. We create and support open-source technology that directly and focally addresses the infrastructural and resource equity gaps that further marginalize learners with limited Internet. We started by enabling offline access to Khan Academy's videos and exercises and grew to become a key player in the global education technology landscape. For the past 13 years we’ve focused on boosting learning outcomes in some of the most challenging contexts, supporting disconnected learning in refugee camps, rural schools, out-of-school programs, and more. Learning Equality develops and maintains Kolibri, an adaptable set of open tools specially designed to support teaching and learning with tech but without the Internet for the third of the world that still lacks access to connectivity. Kolibri is centered around an offline-first learning platform that runs on a variety of low-cost and legacy devices. It is complemented by a curricular tool, a library of open educational materials, and a toolkit of resources to support training and implementation. These tools are open and available in a variety of languages to better support learners and educators globally. As a community-driven nonprofit, Learning Equality works closely to co-design Kolibri with a core network of collaborators, including national NGOs, UN agencies, government, and corporate partners. We also adopt a needs-based approach, constantly gathering insights from our community to inform the development of our tools. Through its do-it-yourself adoption model and strategic collaborations, Learning Equality has conservatively reached 13 million learners and educators in every country in the world. Given our offline access and distribution model, we learn about usage of Kolibri through a combination of telemetry ping backs of high level, aggregate, anonymized statistics, data from partners, and use of our Kolibri Data Portal. Since instances of Kolibri never need to connect to the Internet to be used, we do not know about usage across every instance, which is why this is an informed estimate from years of data.
Learning Unlimited's mission is to create educational opportunities for high school and middle school students, and to provide leadership and teaching opportunities for college students. Learning Unlimited strives to engage students of all ages in an educational setting to share and discover their passions. To accomplish this, Learning Unlimited links local universities with their communities. Our open source work revolves around building and maintaining website infrastructure for these chapters to use.
Open to the core - collaborative engineering for open source silicon and tools
Contributor counts are accepted projects per cycle.
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