Research Software

Our objectives:

• Automating data processes: We implement the necessary tools to automate repetitive data tasks to enhance efficiency and accuracy in research workflows. By leveraging technology, we streamline data collection, processing, analysis, and reporting, allowing researchers to focus more on their core work.
• Ensuring data traceability: Data integrity is paramount in research. Our team develops tools that establish clear data lineage, making it easy to track data from its source to its usage. This promotes transparency and reproducibility in research findings.
• Software engineering guidelines: We define and promote software engineering best practices within our team and across the organization. By establishing guidelines for code quality, documentation, testing, and version control, we ensure the development of robust and maintainable software products.
• Open-source commitment: We embrace the principles of open-source development. All our software products are released under open-source licenses, fostering collaboration, transparency, and innovation within the research community. Our code is maintained in our GitHub and Gitlab

Our team specializes in the development of software solutions tailored to the unique needs of research projects. From web applications to functionality supporting existing tools such as our LIMS. The software team employs a variety of technologies and programming languages to create user-friendly, portable, and scalable software products. We follow agile methodologies to iterate quickly and respond to changing requirements, ensuring the timely delivery of high-quality solutions.

Our projects:

• LIMS extensions: Extending the functionality of our LIMS and simplifying data processes, e.g., purchasing
• The Data Catalog: We are extending and adapting CKAN, an open-source data management system, as our data catalog to provide access to all data linked to a project and collect metadata related to that project.
• Data provenance: A collection of functionality to support data traceability across the entire data life cycle and the interaction with large-scale lab instruments and analytical pipelines.

Collaboration is at the heart of our approach. We work closely with the Research Data Management team to understand data requirements and governance policies. Additionally, we collaborate with the Data Science team supporting their advanced analytics and machine learning capabilities with tools and functionality to reliably access data and store results in DTU Biosustain’s data infrastructure.

Our commitment:
We are committed to delivering high-quality software solutions that empower researchers to tackle complex challenges and drive impactful discoveries. Our team continuously seeks feedback from users to improve and refine our products, ensuring they remain at the forefront of research innovation.