# Boehringer Ingelheim — developer ecosystem report

> Boehringer Ingelheim is one of the world's largest pharmaceutical companies, and the largest private one. Headquartered in Ingelheim, it operates globally

- GitHub org: `Boehringer-Ingelheim`
- Category: other
- Overall score: 35.0 / 100
- Overall rank: #598
- Last refreshed: 2026-08-14

## Pillar scores

| Pillar | Score / 100 | Rank | Weight in overall |
| --- | --- | --- | --- |
| Code & Polish | 52.0 | #395 | ~22% |
| Education | 52.0 | #707 | ~18% |
| AI readiness | 9.0 | #879 | part of Reach |
| Builder experience | 0.0 | #1 | ~12% |
| Community | 28.0 | #555 | ~16% |
| Reach | 40.0 | #491 | ~18% |
| Momentum | 37.0 | #386 | ~14% |

## Links

- GitHub: https://github.com/Boehringer-Ingelheim
- Website: https://boehringer-ingelheim.com
- Docs: https://docs.boehringer-ingelheim.com

## Public GitHub totals

| Metric | Value |
| --- | --- |
| Repos analyzed | 12 |
| Stars | 378 |
| Forks | 92 |
| Open issues | 193 |
| Contributors | — |

## Top repositories

| Repo | Stars | Language | Description |
| --- | --- | --- | --- |
| [pyPept](https://github.com/Boehringer-Ingelheim/pyPept) | 98 | Python | pyPept: a python library to generate atomistic 2D and 3D representations of peptides |
| [dso](https://github.com/Boehringer-Ingelheim/dso) | 48 | Python | Data Science Operations (dso) command line tool |
| [stain-transfer](https://github.com/Boehringer-Ingelheim/stain-transfer) | 30 | Python | Comparative evaluation of image-to-image translation methods for stain transfer in histopathology |
| [anomaly-detection-in-histology](https://github.com/Boehringer-Ingelheim/anomaly-detection-in-histology) | 23 | Python | Learning image representations for anomaly detection: application to discovery of histological alterations in drug devel |
| [deep-learning-based-quantification-of-NAFLD-NASH](https://github.com/Boehringer-Ingelheim/deep-learning-based-quantification-of-NAFLD-NASH) | 15 | Jupyter Notebook | Automated liver NAFLD/NASH scoring |
| [FutureManager](https://github.com/Boehringer-Ingelheim/FutureManager) | 15 | R | A R/shiny extension for forking expensive calculations |
| [prettier-config](https://github.com/Boehringer-Ingelheim/prettier-config) | 14 | JavaScript | This is the shared prettier configuration used at Boehringer Ingelheim for code formatting. |
| [TorsionProfiler](https://github.com/Boehringer-Ingelheim/TorsionProfiler) | 13 | Python | Torsion profile calculation and analysis made easy! Get quick access to various methods. |
| [MVPapp](https://github.com/Boehringer-Ingelheim/MVPapp) | 12 | R | Model Visualization Platform - Shiny App to Explore Pharmacometrics Models |
| [toscaci](https://github.com/Boehringer-Ingelheim/toscaci) | 11 | C# | tosca command line tool and service that simplify tosca integration with Continuous Integration tools |
| [BayesianMCPMod](https://github.com/Boehringer-Ingelheim/BayesianMCPMod) | 10 | R | Simulate, analyze, and evaluate Bayesian MCPMod trials with normally and binary distributed endpoints. |
| [oncomsm](https://github.com/Boehringer-Ingelheim/oncomsm) | 9 | R | Bayesian multi-state models for the analysis of oncology trials |

## Methodology

Scores are computed from public signals only (GitHub, package registries, docs sites, community platforms). Pillar definitions: https://www.smoower.com/docs.md

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