Despite the collaborative nature of scientific research, a key component — data analysis — can be a lonely burden. Undertaken by researchers who largely lack formal training in data and open science, such analyses are often bespoke efforts that scientists must perform on their own, reinventing the wheel as they do so. Moreover, when we become faculty members, lecturers and project managers, we can feel unqualified to establish more responsible data practices and unsupported in this endeavour, despite mounting need. We found a sustainable approach to establish more responsible data practices in our research groups through Openscapes, a mentorship programme originally funded by the open-source software company Mozilla in Mountain View, California, and operated by the National Center for Ecological Analysis and Synthesis (NCEAS) in Santa Barbara, California. Openscapes has helped us to supercharge our research, and we have advice on how others can ignite change in their own teams.
As we originally understood it, open science had little relevance to or benefit for our daily research — largely because it was not clear how to implement it. We narrowly interpreted the concept to mean only sharing data on publication, and we assumed data science was applicable only to big data and machine learning. Existing software tools that could automate data analysis seemed out of reach as we quietly handcrafted our own approaches to write code and analyse data.
Now, we have reframed data analysis as a collaborative effort rather than an individual burden. We regularly discuss our data challenges as a team, starting with the expectation that better approaches and tools exist and that we can find them together.
Our idea of open data science blends R developer Hadley Wickham’s definition of data science — “turn[ing] raw data into understanding” — with open science tools and practices, such as using collaborative version-control platforms for code and project management. Empowered by our new perspective, we are establishing such practices in our groups by creating workflows that facilitate reproducibility and data sharing, and that streamline code organization and collaboration. All of our approaches are centred around an ‘open’ ethos.
This transition requires a shift in mindset as much as an investment in skill development and team-building. Here are three ideas for how research groups can get started, and a plan for kick-starting this change in ten weeks (see ‘A ten-week plan for open data science’).
1. Normalize data discussions
Create digital and physical spaces where group members — despite having differing research questions and expertise — feel comfortable discussing data challenges and seeking, offering and accepting guidance from one another. Scheduling regular data-centric meetings demonstrates that this is a priority and promotes a more open culture; naming these meetings can give them value and identity. (The Ocean Health Index team at NCEAS calls these meetings Seaside Chats.)
You do not have to be an expert to initiate conversations about data in your research group. You do, however, need to become comfortable enabling group members to learn from, with and for each other — which means encouraging them to engage with coding communities both online and in person. For instance, they might follow #rstats discussions on Twitter, attend or organize in-person coding clubs and ‘hacky hours’, or contribute documentation and tutorials to open-source projects. Encouraging horizontal leadership within your research group is crucial for seeding better data practices and for evolving alongside the ‘software-scape’.
2. Identify and address shared needs
Start by discussing the software and workflows that group members use for reproducibility, collaboration and communication. What software is used for data analysis, data storage and documentation, for instance? How do members share data and methods, and request feedback? And how do members learn to use these tools?
Once your team’s needs are known, choosing paths forward will require identifying and acting on shared priorities in the research group, such as organizing scripts, improving metadata and building skill sets. Skill-building opportunities can include online tutorials and videos, workshops, skill-sharing meet ups and university courses. The goal is not perfection, but incremental improvement through attainable goals.
3. Think ahead
Think of yourself as your most important future collaborator because you are most likely to build on your own work. Consider the broader community — your research group, co-authors and colleagues you have yet to meet — to be potential important collaborators, who might not have been involved in the original effort. What will make it easier for ‘future you’ to continue a project that has been idle for several months? What can you do to help ‘future us’ bring in members to contribute to and build on the work?
More broadly, consider how you can better prepare students to contribute to science. This means not only establishing practices encouraging open data science in your research group, but also championing it broadly. Declare its importance in meetings and presentations, and on social media; support coding meet-ups and training sessions by providing space and funding; and develop university courses and hire dedicated scientific computing staff to support research groups of all sizes.
Through Openscapes, we learnt that open data science is empowering and achievable, and saves precious time for our future selves, both as individuals and as teams. We also realized that it is never too late to engage in the practice, and that the best way to get started is to talk about current approaches to data and code, no matter the perception of expertise or quality. In our experience, investment in open data science can promote resilient workflows and reinforce a culture of inclusion, trust and innovation. Frank discussions help to identify needs that can be addressed through workshops and online tutorials that provide a diversity of entry points, because no one size fits all.
Upgrading workflows, building coding skills and creating space for discussing data takes time and effort. But by valuing teamwork and building trust, we have found that incorporation of open data science can promote more collaborative, transparent and meaningful research, and can help to ignite systemic change from the inside out. We welcome you to join us.
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