Conference Agenda

“The end of theory”. This was the title of an article in Wired Magazine ten years ago. No theory needed, the data deluge allows us to extract any knowledge by exploring data using artificial intelligence (AI). This new empiricism received attention and criticism. In Earth sciences, including my field of climate science, there has been a focus on both theory and data over the past century. Based on theory, there is a quest for resolving finer scales in numerical models as physical processes interact seamlessly from the planetary to the molecular scales. Still, to simulate the most important drivers of weather and climate, at least a thousand-fold more computational resources are needed. Does AI come to the rescue? Of course. Many processes are not fully understood. Interactions and processes beyond the physical domain often don’t have a basic theory. There is already work ongoing using AI in heuristic modelling for radiative transfer, turbulence and cloud characteristics. Not only can such studies aid in increasing understanding, they can also enable faster simulations. The integration of data is an even bigger challenge. There is so much data out there to take advantage of. Using data from unconventional sensors in meteorology, such as my cell phone, is still at its infancy, let alone using social media data. It takes a much wider perspective on data and simulations to advance science and to advance informed decision-making.

All of this seems daunting. Taking examples form daily weather forecasts and future climate I will open some black boxes of AI.

Open Science provides a range of potential benefits including more transparent and efficient research, and greater opportunities for sharing and re-use of scientific outputs. With this increased sharing of research assets e.g. data, workflows, software etc. comes the need for new approaches to how researchers are credited for these contributions to the research effort, especially those outputs that are less tangible or do not conform to the requirements for the traditional scholarly publication. This paucity of reward and recognition for making research assets available discourages researchers from making their research open and accessible.

But this lack of incentive is not the only reason that researchers are reluctant to adopt and implement open science practices. Scientists also have major reservations such as others unfairly benefitting from their shared research assets with little additional effort/input at a time when they already face fierce competition for funding and employment.

By creating a framework for career credit that recognises and rewards a more diverse range of scientific outputs, there will be a greater willingness among researchers to make their results and associated research assets more open and accessible. However, to do this we need to redesign or modify the existing scientific reward systems to make open science practices the norm for researchers.