{
  "version": "v1.1.0",
  "entity": {
    "type": "organisation",
    "role": "maintainer",
    "name": "GeomScale Project",
    "email": "geomscale@gmail.com",
    "phone": "",
    "description": "GeomScale is a NumFOCUS-affiliated research and development project delivering open-source software for state-of-the-art algorithms at the intersection of data science, optimization, geometric computing, and statistical computing. We are a Google Summer of Code mentoring organization and part of the global scientific Python and open-source ecosystems.\n\nOur focus is on scalable algorithms for sampling from high-dimensional distributions, volume approximation, convex optimization, integration, and related tasks that are foundational for modern data science and machine learning. We bridge the gap between cutting-edge theory in high-dimensional geometry and optimization and robust, production-quality implementations that researchers and practitioners can rely on.\n\nApplications of GeomScale span computational biology (metabolic networks and systems biology), finance (portfolio optimization, risk and shock detection in large markets), Bayesian statistics, AI, and applied mathematics.",
    "webpageUrl": {
      "url": "https://geomscale.github.io/"
    }
  },
  "projects": [
    {
      "guid": "volesti",
      "name": "volesti",
      "description": "volesti is the core C++ library of GeomScale for practical volume approximation and random sampling in high dimensions.\n\nFeatures:\n- Volume approximation for convex polytopes and related convex bodies (exact and approximate methods)\n- Random sampling from high-dimensional convex sets using modern MCMC algorithms\n- Scales up to hundreds or thousands of dimensions depending on the problem\n- Forms the computational backbone for Rvolesti, dingo, and other GeomScale tools",
      "webpageUrl": {
        "url": "https://geomscale.github.io/software"
      },
      "repositoryUrl": {
        "url": "https://github.com/GeomScale/volesti",
        "wellKnown": "https://github.com/GeomScale/volesti/blob/develop/.well-known/funding-manifest-urls"
      },
      "licenses": [
        "spdx:LGPL-3.0"
      ],
      "tags": [
        "computational-geometry",
        "volume-computation",
        "sampling",
        "cpp",
        "scientific-computing",
        "learning",
        "inference",
        "data-science"
      ]
    },
    {
      "guid": "rvolesti",
      "name": "Rvolesti",
      "description": "Rvolesti is the R interface to the volesti C++ library and is distributed on CRAN as the R package volesti. It brings high-dimensional volume approximation, convex polytope sampling, rounding, rotation, and related geometric algorithms to R users. The package also includes methods relevant to computational finance, including copula estimation and portfolio-related analysis.",
      "webpageUrl": {
        "url": "https://geomscale.github.io/software"
      },
      "repositoryUrl": {
        "url": "https://github.com/GeomScale/Rvolesti",
        "wellKnown": "https://github.com/GeomScale/Rvolesti/blob/develop/.well-known/funding-manifest-urls"
      },
      "licenses": [
        "spdx:LGPL-3.0"
      ],
      "tags": [
        "r-language",
        "cran",
        "computational-geometry",
        "sampling",
        "volume-computation",
        "scientific-computing",
        "computational-finance",
        "bayesian-inference"
      ]
    },
    {
      "guid": "dingo",
      "name": "dingo",
      "description": "dingo is a Python library for sampling and analyzing metabolic networks. It uses high-dimensional MCMC sampling powered by volesti to explore the feasible flux space of large-scale metabolic models.\n\nApplications:\n- Metabolic flux analysis and flux variability analysis in systems biology\n- Unbiased description and visualization of metabolic network capabilities\n- Research workflows based on state-of-the-art geometric sampling",
      "webpageUrl": {
        "url": "https://geomscale.github.io/software"
      },
      "repositoryUrl": {
        "url": "https://github.com/GeomScale/dingo",
        "wellKnown": "https://github.com/GeomScale/dingo/blob/develop/.well-known/funding-manifest-urls"
      },
      "licenses": [
        "spdx:LGPL-3.0"
      ],
      "tags": [
        "computational-biology",
        "metabolic-networks",
        "python",
        "systems-biology",
        "sampling"
      ]
    }
  ],
  "funding": {
    "channels": [
      {
        "guid": "bank-transfer",
        "type": "bank",
        "address": "",
        "description": "Direct bank transfer (SEPA and international) can be arranged. Contact geomscale@gmail.com for account details, invoicing, and tax residency documents (Greece jurisdiction)."
      },
      {
        "guid": "paypal-or-provider",
        "type": "payment-provider",
        "address": "",
        "description": "A payment provider such as PayPal or Stripe can be used for one-time or recurring contributions. Contact us to coordinate larger institutional transfers."
      },
      {
        "guid": "github-sponsors",
        "type": "payment-provider",
        "address": "https://github.com/sponsors/GeomScale",
        "description": "Support GeomScale through GitHub Sponsors."
      },
      {
        "guid": "open-collective",
        "type": "payment-provider",
        "address": "https://opencollective.com/geomscale",
        "description": "Support GeomScale through Open Collective."
      }
    ],
    "plans": [
      {
        "guid": "annual-sustainability",
        "status": "active",
        "name": "Annual Sustainability Grant",
        "description": "Twelve months of core development, maintenance, and documentation for GeomScale and its key libraries (volesti, Rvolesti, dingo). Funding covers senior maintainer time, infrastructure, docs, community support, and contingency. Total request: $85,000/year. This support sustains a niche but critical open-source stack of algorithms, keeping them available for computational biology, finance, statistics, AI, and machine learning.",
        "amount": 110000,
        "currency": "USD",
        "frequency": "yearly",
        "channels": [
          "bank-transfer",
          "paypal-or-provider",
          "github-sponsors",
          "open-collective"
        ]
      },
      {
        "guid": "core-development-6m",
        "status": "active",
        "name": "6-Month Core Development Push",
        "description": "Six months of focused development on algorithmic improvements and language bindings.\n\nFocus areas:\n- Parallel and SIMD implementations in volesti for large-scale problems\n- New sampling algorithms (e.g., improved walks and advanced proposals) and better defaults\n- Python bindings for wider adoption\n- Performance engineering and benchmarking against alternative toolchains\n\nRequested amount: $50,000 (one-time).",
        "amount": 80000,
        "currency": "USD",
        "frequency": "one-time",
        "channels": [
          "bank-transfer",
          "paypal-or-provider",
          "github-sponsors",
          "open-collective"
        ]
      },
      {
        "guid": "documentation-and-tutorials",
        "status": "active",
        "name": "Documentation, Tutorials, and Examples",
        "description": "A comprehensive documentation and learning-materials push for GeomScale.\n\nDeliverables:\n- Unified API documentation for volesti, Rvolesti, and dingo\n- End-to-end tutorials for computational biology (metabolic networks) and finance (portfolio optimization, risk scenarios)\n- Jupyter notebooks for teaching high-dimensional sampling and volume computation\n- Talks and videos explaining use cases for data science and ML practitioners\n\nRequested amount: $25,000 (one-time).",
        "amount": 50000,
        "currency": "USD",
        "frequency": "one-time",
        "channels": [
          "bank-transfer",
          "paypal-or-provider",
          "github-sponsors",
          "open-collective"
        ]
      },
      {
        "guid": "goodwill-support",
        "status": "active",
        "name": "Goodwill and Unrestricted Support",
        "description": "Any amount of unrestricted support helps keep GeomScale sustainable and responsive to user needs. Funds are used for maintenance, bug fixes, and incremental improvements that are hard to fund via traditional research grants.",
        "amount": 0,
        "currency": "USD",
        "frequency": "one-time",
        "channels": [
          "bank-transfer",
          "paypal-or-provider",
          "github-sponsors",
          "open-collective"
        ]
      }
    ],
    "history": [
      {
        "year": 2023,
        "income": 0,
        "expenses": 0,
        "taxes": 0,
        "currency": "USD",
        "description": "NumFOCUS Small Development Grants and Google Summer of Code-related funding."
      },
      {
        "year": 2024,
        "income": 0,
        "expenses": 0,
        "taxes": 0,
        "currency": "USD",
        "description": "Google Summer of Code-related funding."
      },
      {
        "year": 2025,
        "income": 0,
        "expenses": 0,
        "taxes": 0,
        "currency": "USD",
        "description": "NumFOCUS Small Development Grants and Google Summer of Code-related funding."
      }
    ]
  }
}