Research Computing and Data is a tough field that combines the challenges of many disciplines.
Like IT or commercial software development, we need to deliver real usable tools to support our users; but like research, our projects are often extremely open-ended, with complexity coming not from unvalidated requirements but the uncertaintainty of the new, requiring experimentation and discovery. Like academia, we often work with team members who are trainees, not employees; like nonprofits we are called on to enact real changes with ongoing programmes or products while funded only by budgets dependent on multiple short-term grants.
But while there are many websites and podcasts, newsletters and tutorials, on the bytes and flops and Mbit/s of research computing, there is very little out there on the genuinely hard day-to-day work of designing, building and managing R&D computing teams, projects, and software. Commercial or open-source software development, research, nonprofit, IT, business - our field is too different for advice from those fields to routinely directly carry over, even if there are lessons we can learn from them.
