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πŸš€πŸ† International awards from NASA. Platform for bridging skilled contributors and open-source science projects. Leverages NLP/AI summarizations & categorizations + vector search matching algorithms.

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KAKEHASHI: Bridging Open Science and Skilled Contributors

KAKEHASHI Logo

KAKEHASHI (meaning "suspension bridge" in Japanese) is an innovative open science platform designed to seamlessly connect project creators with potential skilled collaborators. Leveraging the power of AI and vector database algorithms, KAKEHASHI acts as the perfect conduit for open-source enthusiasts to find projects they're passionate about.


πŸ“Œ Table of Contents

  1. Features
  2. Technologies Used
  3. Setup and Installation
  4. Usage
  5. Contributing
  6. License
  7. Contact

Features

  • AI/VectorDB Matching Algorithm: Swiftly matches users based on skills, project requirements, and interests.
  • User Profiles: Allows project creators and collaborators to highlight their skills, past projects, and interests.
  • Real-time Communication: Integrated chat and notification systems for immediate collaboration.
  • Responsive Design: Accessible on all devices, from desktops to mobiles.

Technologies Used

  • Core: Next.js 13
  • UI: Tailwind, RadixUI, Shadcn UI
  • Database: Firebase, PineconeDB

Setup and Installation

Prerequisites:

  • Node.js
  • Yarn or npm

Steps:

  1. Clone the repository:
git clone https://github.com/Engineers-for-Science/orcid-next-poc.git
cd orcid-next-poc
  1. Install dependencies:
yarn install
# or
npm install
  1. Set up Firebase and PineconeDB: Follow respective official documentation to set up and connect your project.

  2. Run the development server:

yarn dev
# or
npm run dev

Usage

  1. Signup/Signin to create a profile.
  2. For Project Creators: Create a new project, specify the skills required, and provide a brief description.
  3. For Contributors: Browse through available projects, view project details, and express interest in collaborating.
  4. Engage in real-time discussions with potential collaborators or project creators.

Contributing

If you're interested in contributing to KAKEHASHI, please create a branch and submit a pull request with details on the changes you made.


License

KAKEHASHI is open-source software licensed under the MIT license.


Contact

For questions, feedback, or discussions related to KAKEHASHI, please ask in the "issues" tab on Github.


Together, let's bridge the world of open science and skilled contributors. πŸŒ‰

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πŸš€πŸ† International awards from NASA. Platform for bridging skilled contributors and open-source science projects. Leverages NLP/AI summarizations & categorizations + vector search matching algorithms.

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