How to build a successful OHI technical team
by Julie Lowndes
At the core of Ocean Health Index (OHI) assessments lie dozens of datasets and the technical teams to process them. Technical teams are tasked with collaboratively gathering and organizing datasets, preparing data in a standard format, and coding reproducibly to calculate OHI scores. Thus, building the correct technical team plays an instrumental role in the success of the assessment. So what skills should you consider when building your technical team?
Well, two years ago, I thought having an experienced R coder was a non-negotiable requirement, but now my views have shifted. Instead, I think having at least two scientists with the interest and motivation to learn the open data science tools we use for OHI is the recipe for success.
So, how did I get to this point and why did my views change?
My views started to shift in 2013 when our methods for OHI global assessments proved ineffective to efficiently repeat our own analyses. Our team turned to open data science tools – R, RStudio (including the tidyverse and RMarkdown), git, and GitHub – to streamline our workflow and learned how to code as a team.
Most of our team came from a marine science background with no formal coding training, so this was a new and challenging experience. We taught ourselves how to code through many online resources, including RStudio webinars, Software Carpentry, Stats 545, and tutorials by the #rstats community. Through our struggles and successes, we realized our story was an important one to share with all scientists, and we published Our path to better science in less time using open data science tools in Nature Ecology and Evolution last year (Lowndes et al. 2017).
So, when you are building your own team, instead of only looking for scientists with coding experience, look for scientists that are motivated to develop these skills. There are many science-based decisions within all data analysis and it is important to have a scientific background and coding skills in the same person.
And what’s so cool about these data science tools is all the acquired skills are applicable to other projects. GitHub and RStudio were not created for the Ocean Health Index, we merely use them for the Ocean Health Index. Just like you use Google Docs for multiple projects, you can use these tools for anything you want to communicate, whether it is analysis-driven or not.
Realizing that your team will likely need to develop these collaborative open data science skills to conduct your OHI assessment, we developed two hands-on training guides based on some of the materials we used to teach ourselves (listed above and also here). The first book is an introduction to open data science, and is not specific to the Ocean Health Index: it teaches the tools and workflows that we describe in Lowndes et al. 2017. The second book is OHI-specific; it is an introduction to the OHI Toolbox software. As we lead trainings using these materials, we have started recording and posting them on our OHI-Science YouTube channel (stay tuned for edited versions)!
Knowing these tools and resources exist is a good starting point, but what concrete advice will help you build your technical team?
From our past experiences and challenges, here are my suggestions for building a successful technical team. Remember it takes patience and time to overcome the conceptual and technical challenges involved in developing computing skills – but the payoff is huge.
- Hire at least two people for the technical team – it is more efficient to work together and they can also help each other learn (a win-win situation!).
- Hire people with scientific backgrounds – there are many conceptual, science-based decisions to make within OHI analyses, so you need scientists with knowledge of marine systems.
- Allow time for them to learn and practice these skills – give them the time to do it right so that it is well-documented and communicated this time, and repeatable next time. It’s best to have the technical team start data science training at the same time high-level goal planning happens.
We are currently doing this ourselves. Not only have we focused our OHI+ technical trainings on data science, but on January 26, we began our Global Fellows program and welcomed three graduate students to the OHI team: Ellie Campbell, Iwen Su, and Camila Vargas. While these Global Fellows have different technical backgrounds and have never worked with OHI, they all had one thing in common: they are environmental scientists with the interest and motivation to learn open data science practices and tools.
Over nine months, the Global Fellows will learn open data science tools and the OHI Toolbox using our training materials, and then calculate the seventh annual OHI Global scores. By October, we will have OHI Global scores calculated for 2018 and will have tested our training materials. But most importantly, we will have trained three graduate students in these open data science practices and tools that are incredibly powerful and empowering.
Stay tuned for updates from our OHI Global Fellows and OHI+ technical teams!