Grant: Collaborative Research and Training Experience

The rapid rise of information technologies coincides with a period of dramatic change in the Earth's ecosystems. We have the potential to harness the power of this data revolution to understand how species and ecosystems will be affected by global drivers of change such as climate, land use, pollution and biological invasions, and mitigate against these effects. However, this potential can only be realized if we: (1) rescue historical data in ecology, evolution and environmental science (for simplicity, EEE) that set baselines for change, often collected before today's data management standards and therefore at high risk of being lost; and (2) train the EEE researchers of tomorrow in the necessary skills to manage and synthesize large amounts of relevant data, and then to analyse these complex datasets in collaborative teams. Employers are now requiring this new skillset, which applies tools from data science, archival science and team management to EEE-specific tasks. There is a cultural shift underway that recognizes that the most productive researchers incorporate data and collaboration skills at every step in their reproducible scientific workflow, and both academic and non-academic employers are prioritizing such skills in hiring. In our proposed training program, we will first formally teach EEE graduate students skills in data management, synthesis statistics and collaboration, providing a new national certification in these competencies. We will then strategically pair students with willing senior researchers or organizations holding historical EEE datasets of high importance to our partners in government (six), non-profit organizations (six), and biological field stations (three), but which were collected prior to current data management standards. Preserving these datasets will require the specialized data management tools we have taught the students, and the high societal value of this task will be motivating. Trainees will then collaboratively analyse the datasets, using their new teamwork and synthesis skills. The result will be a new generation of scientists with cutting-edge training, equipped with the historical data needed to advance our understanding of global change.