It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

The vast and rapidly increasing supply of new data in the Earth sciences creates many opportunities to gain scientific insights and to answer important questions. Data analysis has always been an integral component of research and education in the Earth sciences, but mainstream Earth scientists may not yet be fully aware of many recently developed methods in computer science, statistics, and math. The fastest way to put these new methods of data analysis to use in the Earth sciences is for Earth scientists and data scientists to collaborate. However, those collaborations can be difficult to initiate and even more difficult to maintain and to guide to successful outcomes. Here we break down the collaboration process into steps and provide some guidelines that we have found useful for efficient collaboration between Earth scientists and data scientists.

When I think about the future of human-machine interactions, two entwined anxieties come to mind. First, there is the tension between individual and collective existence. Technology connects us to each other as never before, and in doing so makes explicit the degree to which we are defined and anticipated by others: the ways in which our ideas and identities do not simply belong to us, but are part of a larger human ebb and flow. This has always been true – but rarely has it been more evident or more constantly experienced. For the first time in human history, the majority of the world's population is not only literate – itself an achievement less than a century old – but also able to actively participate in written and recorded culture, courtesy of the connected devices pervading almost every country on earth. This is an astonishing, disconcerting, delightful thing: the crowd in the cloud becoming a stream of shared consciousness.

Collaboration between research scientists, particularly those with diverse backgrounds, is a driver of scientific innovation. However, finding the right collaborator is often an unscientific process that is subject to chance. This paper explores recommending collaborators based on repeating patterns of previous successful collaboration experiences, what we term prototypical collaborations. We investigate a method for discovering such prototypes to use them as a basis to guide the recommendation of new collaborations. To this end, we also examine two methods for matching collaboration seekers to these prototypical collaborations. Our initial studies reveal that though promising, improving collaborations through recommendation is a complex goal.