Stabilarity Research Platform Is Now Open
Every tool, every dataset, every model — available to researchers via API at no cost.
Ivchenko, O. (2026). Stabilarity Research Platform: An Open API Infrastructure for Reproducible Research in Medical AI, Geopolitical Risk, and Portfolio Optimization. Stabilarity Research Hub. Odessa National Polytechnic University.
DOI: 10.5281/zenodo.18928330
Abstract #
This paper presents the Stabilarity Research Platform — an open, API-accessible research infrastructure exposing validated machine learning models, geopolitical risk datasets, and decision optimization tools to the global research community at no cost. The platform implements FAIR data principles (Wilkinson et al., 2016)[1], providing composable, versioned endpoints for: (1) medical imaging classification across six diagnostic domains (Pesapane et al., 2022)[2]; (2) country-level geopolitical risk quantification using GDELT (Leetaru & Schrodt, 2013)[3] and ACLED (Raleigh et al., 2010) event data; (3) pharmaceutical portfolio optimization via the Decision Readiness Index (DRI) framework; (4) enterprise AI decision support tools; (5) universal intelligence benchmarking across eight cognitive dimensions; and (6) reference quality and trust infrastructure for reproducible citation management. All systems are versioned, documented, and accessible via personal API keys issued at no cost to community members, consistent with emerging norms in research software citation (Katz et al., 2021)[4].
graph TD
API["Stabilarity API v1
hub.stabilarity.com/api/v1/"]
MED["Medical AI Module
/scanlab/"]
GEO["Geopolitical Risk Module
/geo-risk/"]
DRI["DRI Optimizer Module
/dri/"]
DS["Decision Support Module
/enterprise-ai/"]
UIB["Benchmarking
UIB · Humanoid Sim"]
REF["Reference & Quality
Trust Analyzer · Evaluation"]
AUTH["Authentication
X-API-Key header"]
AUTH --> API
API --> MED
API --> GEO
API --> DRI
API --> DS
API --> UIB
API --> REF
MED --> M1["Chest X-ray · CT · Dermoscopy
Ophthalmology · Pathology · Brain MRI"]
GEO --> G1["Country Risk Scores
GDELT + ACLED · WSI Dashboard"]
DRI --> D1["Decision Readiness Index
Pharma Portfolio Optimization"]
DS --> E1["Enterprise ML Tools
Prediction · Classification"]
UIB --> U1["8-Dimension Composite Score
Resource-Normalized Ranking"]
REF --> R1["3,600+ References
DOI · STABIL Badges"]
style API fill:#000,color:#fff,stroke:#000
style AUTH fill:#e8f5e9,stroke:#2e7d32
We have been building research tools, training ML models, and assembling geopolitical and economic datasets for the past two years. Today, all of it is open — programmatically, via a unified API — free for every researcher who wants to build on top of what we have built. This approach aligns with the growing consensus that scientific data and tools should be made FAIR: Findable, Accessible, Interoperable, and Reusable (Stall et al., 2019)[5].
This is not a product launch. There is no pricing page, no trial period, no sales call. It is a research platform, built by researchers, opened to researchers. If you are a member of the Stabilarity community, your personal API key is already waiting for you.
Get Your Free API Key #
Every community member gets a personal key — 100 requests per minute, all endpoints, forever free.
View the full API documentation and find your key →
Not a member yet? Join the Stabilarity research community →
What Is Open #
- GET /api/v1/geo-risk/data/countries 87-country risk index powering regional classifier context
- GET /api/v1/geo-risk/status Service health
- GET /api/v1/geo-risk/data/countries Full country risk dataset (JSON)
- GET /api/v1/geo-risk/macro/current Live macro indicators
- GET /api/v1/geo-risk/chart/{type} Chart as PNG — types: timeseries, heatmap, world-map, forecast-comparison, regional-radar, top-stable, correlation-matrix, anomaly, region-bars
- GET /api/v1/scanlab/health Service health
- GET /api/v1/scanlab/models List all available models
- POST /api/v1/scanlab/predict Classify medical image — multipart/form-data: file + model
- GET /api/v1/scanlab/analytics Throughput + cost-effectiveness metrics
- GET /api/v1/hpf/health Service health
- GET /api/v1/hpf/sample Download sample portfolio CSV (21 SKUs)
- POST /api/v1/hpf/analyze Run full 6-module HPF pipeline on your portfolio data
flowchart LR
RAW["Raw Event Data
GDELT · ACLED · News"]
PROC["Data Processing Pipeline
Normalization · Deduplication"]
MODEL["Risk Quantification Model
Country-level scoring"]
CACHE["Dataset Cache
Versioned snapshots"]
API2["REST API Endpoint
/geo-risk/data/countries"]
VIZ["Visualization Endpoint
/geo-risk/chart/world-map"]
RAW --> PROC
PROC --> MODEL
MODEL --> CACHE
CACHE --> API2
CACHE --> VIZ
style RAW fill:#fff3cd,stroke:#856404
style MODEL fill:#cce5ff,stroke:#004085
style API2 fill:#d4edda,stroke:#155724
style VIZ fill:#d4edda,stroke:#155724
Quick Start #
Base URL: https://hub.stabilarity.com/api/v1/ — authenticate with X-API-Key header or ?api_key= param.
Interactive Sandbox + Full Docs #
Try every endpoint directly in the browser, copy cURL commands, and view request/response schemas — no setup needed.
Open the API Gateway documentation →
Our Commitment to Open Research #
Stabilarity exists to share results publicly whenever possible. Every tool built here, every dataset assembled, every model trained — these belong to the research community first. The API is not a product. It is an extension of that commitment. Members can build on our infrastructure freely, cite our work in their research, and contribute back to the platform. Questions or collaboration: contact@stabilarity.com
graph TD
INPUT["Researcher Request
Research question + API key"]
ROUTE["API Router
Endpoint selection"]
SCAN["ScanLab
Medical imaging inference"]
RISK["GeoRisk
Country risk dataset"]
OPT["DRI Optimizer
Portfolio decision framework"]
OUT1["JSON response +
confidence scores"]
OUT2["Risk scores +
trend data + charts"]
OUT3["DRI scores +
optimization recommendations"]
CITE["Research Citation
DOI: 10.5281/zenodo.18928330"]
INPUT --> ROUTE
ROUTE --> SCAN --> OUT1
ROUTE --> RISK --> OUT2
ROUTE --> OPT --> OUT3
OUT1 --> CITE
OUT2 --> CITE
OUT3 --> CITE
style INPUT fill:#e3f2fd,stroke:#1565c0
style CITE fill:#f3e5f5,stroke:#6a1b9a
Further Reading #
Beyond the Benchmark: What AI Looks Like When It Actually Works[7] — A journal-level examination of each platform system, how they compose, and why openness matters for research adoption. DOI: DOI pending — scientific review in progress
\nRelated reading #
- License Economics: How Open-Source Licensing Models Affect Enterprise Adoption Trust[8]
- Mid-Year Review: Top 3 Open-Source Breakthroughs of H1 2026[9]
References (9) #
- Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan-Willem; da Silva Santos, Luiz Bonino; Bourne, Philip E.; Bouwman, Jildau; Brookes, Anthony J.; Clark, Tim; Crosas, Mercè; Dillo, Ingrid; Dumon, Olivier; Edmunds, Scott; Evelo, Chris T.; Finkers, Richard; Gonzalez-Beltran, Alejandra; Gray, Alasdair J.G.; Groth, Paul; Goble, Carole; Grethe, Jeffrey S.; Heringa, Jaap; ’t Hoen, Peter A.C; Hooft, Rob; Kuhn, Tobias; Kok, Ruben; Kok, Joost; Lusher, Scott J.; Martone, Maryann E.; Mons, Albert; Packer, Abel L.; Persson, Bengt; Rocca-Serra, Philippe; Roos, Marco; van Schaik, Rene; Sansone, Susanna-Assunta; Schultes, Erik; Sengstag, Thierry; Slater, Ted; Strawn, George; Swertz, Morris A.; Thompson, Mark; van der Lei, Johan; van Mulligen, Erik; Velterop, Jan; Waagmeester, Andra; Wittenburg, Peter; Wolstencroft, Katherine; Zhao, Jun; Mons, Barend. (2016). The FAIR Guiding Principles for scientific data management and stewardship. doi.org. dcrtil
- Error: DOI Not Found. doi.org. drtil
- The GDELT Project. gdeltproject.org. ia
- Ali, Omer; Ishak, Mohamad Khairi; Bhatti, Muhammad Kamran Liaquat. (2021). Emerging IoT domains, current standings and open research challenges: a review. doi.org. dcrtil
- Stall, Shelley; Yarmey, Lynn; Cutcher-Gershenfeld, Joel; Hanson, Brooks; Lehnert, Kerstin; Nosek, Brian; Parsons, Mark; Robinson, Erin; Wyborn, Lesley. (2019). Make scientific data FAIR. doi.org. dcrtil
- Ardila, Diego; Kiraly, Atilla P.; Bharadwaj, Sujeeth; Choi, Bokyung; Reicher, Joshua J.; Peng, Lily; Tse, Daniel; Etemadi, Mozziyar; Ye, Wenxing; Corrado, Greg; Naidich, David P.; Shetty, Shravya. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. doi.org. dcrtil
- Stabilarity Research Hub. (2026). Beyond the Benchmark: What AI Looks Like When It Actually Works. tib
- Stabilarity Research Hub. License Economics: How Open-Source Licensing Models Affect Enterprise Adoption Trust. tb
- Stabilarity Research Hub. (2026). Mid-Year Review: Top 3 Open-Source Breakthroughs of H1 2026. tb