jonathan@researchcomputingteams.org
This resource first appeared in issue #63 on 26 Feb 2021 and has tags Managing A Team: Data Teams, Strategy: Working across an organization, Strategy: Working with Stakeholders, Strategy: Working with Decision Makers
Ten simple rules for starting (and sustaining) an academic data science initiative - Micaela S. Parker, Arlyn E. Burgess, Philip E. Bourne, PLOS Computational Biology
Many research computing centres are trying to figure out how to launch or scale up a data science core facility or research institute. Creating anything new within an organization is a challenge, even when the winds are in your favour. Parker, Burgess, and Bourne offer some very sage advice on not just starting up a data science effort in particular, but creating something new within an organization more broadly.
Some key points made are:
All of these are about having a very clear understanding of what you want to achieve, the organizational environment and which teams have skills or capabilities that can be complementary to that and building on the enthusiasm of others. Another key point:
As you’re creating the thing, you need to make sure that the incentives within your piece of the organization don’t work against what you’re trying to build. A data science facility within a team that doesn’t consider data, software or workflow work “real science” is going to have a hard time retaining people.
