Reproducible Research and Development Environments for Deep Learning
Our service makes your research results reproducible with a single command, especially for statistics, signal processing, machine learning, and deep learning.
Reproducibility is a key requirement of scientific work. However, most research is difficult or impossible to reproduce. Code and data are rarely available, and development environments are often poorly documented or difficult to replicate. This poses a significant challenge to scientific integrity and the traceability of results.
Our deep learning reproducibility service helps universities and companies design research projects that can be easily and sustainably reproduced. Our solution is based on containerized, GPU-supported Jupyter environments, which provide a standardized, reproducible platform for experimentation.
Our services
We offer a comprehensive package to ensure the reproducibility of deep learning experiments. This package includes:
- Setting up a reproducible development environment: We have set up a standardized deep learning environment that can be accurately reproduced and version-controlled with just one command. It includes the programming languages and libraries Python, R, Julia, C/C++, Jupyter, PyTorch, TensorFlow, and Keras, which can be used within the same environment.
- Code standards and version control: We support the application of best practices for clean, documented code and Git-based version control.
- Referenced data sources: We assist with integrating and documenting external, persistently referenced data sources.
- Best Practices for Scientific Publications: We ensure that your research remains easy to understand, adaptable, and citable by using proven methods and tools.
Your benefits
With our solution, you benefit from:
- Increased research efficiency: Restoring the experimental environment is simple and saves time while avoiding errors caused by incomplete documentation.
- Sustainable scientific practice: Transparent and verifiable results strengthen scientific integrity, thus building confidence in your valuable work.
- Higher citability: A reproducible publication is preferred by colleagues at other research institutions, who then cite it more frequently.
Make your research results easily reproducible and increase the reach and adaptability of your scientific work!
Contact person
CHRISTOPH SCHRANZSalzburg Research Forschungsgesellschaft mbH
T: +43/662/2288-408 | Send an e-mail
