The Openscapes Champions Program is not a typical training workshop for individuals. Through Openscapes you focus on your own work with your team. As Openscapes introduces modern concepts, tooling, and examples from your peers for collaborative open data science, your team focuses on where you are and where you want to go together.
One of the biggest benefits we hear from past Champions is that Openscapes helps them “find their team” by identifying common needs and solutions, which makes science feel less lonely. While teams do not require a shared project, teams should not be arbitrarily constructed. Teams should be colleagues who already interact around research and will continue to, for example a research lab or group.
CHAMPIONS PROGRAM: TEAMS
What is a team/How do I choose my team?
There is a lot of flexibility in choosing your team for the Openscapes Champions program. We designed it with academic research groups in mind, to provide resilience where there is high turnover and folks might be struggling with similar challenges around data analysis while working on different projects. But we’re increasingly working with research groups of different kinds and needs! We expect a range of technical ability and training experience; most importantly is selecting folks that have interest in making daily practices more efficient and developing collaboration and leadership skills.
Teams do not need to have a specific shared research project to participate. The idea is that by developing shared open data science habits within the research group, it will be easier to onboard new folks and offboard knowledge/data/code/etc when folks leave. And, as more and more research groups work this way, it can ease transitions between research groups (i.e. when students graduate and take a position with a different research group).
The Champions Program is structured for teams with the intent that research group leads (faculty, lecturers, program managers, etc) and members (students, post-docs, analysts, lab managers, etc.) participate as a team together. This is a strategy so that the lead does not necessarily have to be an expert/comfortable with open data science to enable their team to design open data science workflows. Group leads and members participate together so that:
- everyone sees and values what is possible with open data science
- leads enable members to invest time in learning skills and developing shared workflows as part of their jobs
- members have guidance, agency, and support to incorporate open data science concepts into their work
- everyone champions open data science and contributes to growing the community of practice within the research group, institution, and beyond, in whatever capacity their roles and circumstances (time limitations & responsibilities) allow.
Choosing your team can be based on interests around data analysis and around leadership. There are no prerequisite skills to participate, just an interest to learn and contribute. The “homework” between Champions sessions and beyond is to meet with your full research group (beyond workshop participants, and optional for the lead) to establish shared workflows and habits within the research group.
How you define “team” is completely up to you and having one person be in the cohort and using in-between session “seaside chats” to bring back the information to another group is 100% fine. That is common in the cohorts. In my first Openscapes cohort, I was the only person from my project. My personal goal was to use the Openscapes structure to help a team that I am on figure out how to tackle some off-boarding tasks due to a retirement. During my second cohort, there were 2 team members in the cohort and 2 not in the cohort. We focused on standardizing our data to get ready for our GitHub served data package. For the 3rd cohort I am doing, 2⁄3 of our team is in the cohort as we start to get organized for a major revamp of our report into a reproducible workflow. - Eli Holmes (NOAA NWFSC, NMFS-Openscapes)
Does a team need a shared research project?
No, the idea is to help individuals within a research group see themselves as part of a team. With this team mindset, your most important collaborator is Future You. Working with Future You in mind helps you spend more time on answering awesome research questions rather than trying to make sense of
data_analysis_finalv2b.xls. And a team mindset also includes Future Us in the research group, so that onboarding and offboarding is more efficient and so that participation in science can be more inclusive and equitable.
Is attendance required for all Cohort Calls?
Yes; we do try to have everyone on the team attend all Cohort Calls – but know that sometimes that’s not possible. The Cohort Calls are synchronous: in 90 mins, we teach for ~30 and the rest of the time is you discussing and applying open data science ideas to your own work with your team, and learning from your fellow champions. All the lessons, slides, and also recordings are available if people have to miss, but everyone should sign up expecting to be at the Cohort Calls.
Do I as the PI need to attend the Cohort Calls, or just my team?
It’s important that team leads attend with their team. The Champions program is designed to reduce the burden for PIs in the long term and develop leadership within and across their teams. With this investment now, team leads and members will build resilient and enduring practices for their research group, even as that research group changes into the future. The idea is that together, the whole team sees what’s possible with open data science and builds confidence, ownership, and a shared pathway forward. Then it’s the entire team, not just the PI, who puts it to practice in between sessions.
CHAMPIONS PROGRAM: COHORTS
Does a cohort need a shared research project or to be closely related?
No, the cohort does not need a shared project; each team will work on what they need to for their own research. Teams within a cohort do not need to be closely related, but having teams somewhat related is good for community building (i.e. environmental science or psychology).
Can we have smaller teams so more teams can participate in a cohort?
From our experiences so far think that ~8 teams with 4-5 participants per team (maximum of ~40 participants) is a good size so there is time for everyone to be engaged and contributing (and not feeling like a passive webinar). Also, having a good amount of members per team is really key to the whole model of Openscapes: we want to reduce the amount of burden for PIs to have to relay info to their research groups. Having more members participate as a team in the Openscapes program helps more team members feel agency to build resilient systems with less transmission time.