Social Responsibility

Put computing resources to good use

Research workloads vary enormously. A short round of data cleanup does not need to tie up a high-spec machine for months, and a long-running analysis should not keep getting interrupted on a personal laptop. We look at the task, the data volume, and the number of users before talking about configurations.

If your existing equipment is already enough, we'll say so honestly. When new compute is genuinely needed, we'll then discuss whether shared, dedicated, or on-premises capacity makes the most sense.

Helping small teams get started

Many labs do not have anyone whose job is to keep servers running. Rather than handing over a long list of parameters, we would rather explain environments like RStudio, Jupyter, and Python clearly, along with how to use them day to day, so researchers can get back to their own work as soon as possible.

Respecting the boundaries of research data

Sample data, unpublished results, and project materials belong to the research team, and the team decides how they are used. When we discuss a solution, we first confirm where the data can live, who needs access, and whether there are institutional requirements to follow.

For projects involving sensitive data, or with explicit requirements to stay on an internal network, on-premises compute can be discussed; access permissions and delivery are governed by the specific project agreement.