Research & Scientific Software
Software that makes specialist research usable.
Bring structure, usability and maintainability to specialist software while preserving the domain knowledge and assumptions that give it meaning.
Start with the research workflow.
Understand how people prepare inputs, execute analysis, interpret results and share findings. A workflow may depend on scripts, notebooks, specialist tools and tacit knowledge that is not visible in a product demonstration.
Identify the people and decisions the software must support. Requirements may differ between researchers, analysts and external users. The interface should expose the controls and limitations relevant to each role without obscuring the underlying method.
Connect models, datasets and interfaces deliberately.
Define supported inputs, units, versions and data transformations before designing the application boundary. Validation should help people distinguish an invalid input, an execution failure and a result that requires specialist interpretation.
Keep model logic and domain assumptions separate from presentation and infrastructure where practical. That makes changes easier to assess and helps avoid an interface update silently changing scientific behaviour. Data provenance and licensing need to remain traceable through the workflow.
Reproducibility and maintainability.
Record the code, configuration, dependencies and data versions needed to reproduce an execution. The appropriate level of reproducibility depends on the use case, but it should be an explicit requirement rather than a claim inferred from version control alone.
Use reference inputs and results, meaningful automated checks and documented environment setup. Plan for dependency changes, supported platforms and the people who will maintain the software after the original authors or delivery team move on.
Separate scientific and engineering responsibilities.
Software engineering can improve reliability, interfaces, deployment and maintainability. It does not by itself prove the scientific correctness, accuracy or suitability of a model for a particular use.
Agree the domain expertise, validation evidence and acceptance responsibilities required. Any clinical, safety-critical or regulated use needs a specifically assessed scope and appropriate specialist involvement; a general research-software capability is not an assurance of suitability for those settings.
Validation and human review in the working product.
Decide how uncertainty, limitations and review requirements are communicated in the actual workflow. A result should not appear definitive merely because it is presented in a polished interface.
Provide appropriate audit information, error handling and review steps, with responsibility for decisions kept explicit. Operational monitoring can show whether software is running as expected, while domain review addresses whether the outputs are meaningful for the intended task.
Specialist support when you need it.
Start the conversation
Discuss the requirement and the right next step.
Describe the workflow, model or dataset and the people who need to use it. We can discuss the engineering scope and expertise required.