Resources

In 2026, a communication and dissemination strategy is no longer a box to tick at the end of a project proposal—it has become its very core. Across European funding programmes—from Horizon Europe to Erasmus+ and Creative Europe—how a project communicates with the world is almost as important as what it delivers. This shift is clearly […]

Anyone who has worked on a European project—whether under Horizon Europe or a shared management programme—eventually encounters the same phenomenon: documents pile up, versions multiply, files get sent via email, uploaded to various platforms, and before long, things descend into what many project coordinators describe in one sentence:“No one’s quite sure what the latest version

Artificial intelligence is no longer a novelty in the communication strategies of European-funded projects. It’s drafting texts, suggesting headlines, shaping social media posts, summarizing results, and even translating dense project documentation into language that’s more accessible to the public. For teams working under tight budgets and deadlines, AI seems like a logical—even inevitable—tool. But when

Science does not speak for itself. It never has. In A Brief Guide to Science Communication, physicist Donald W. Lincoln reflects on a lifelong journey—from a child growing up without scientific role models to a professional scientist committed to bringing science into the public sphere—and distils decades of experience into a thoughtful, pragmatic guide for

For decades, scientific communication followed a predictable format: paper, abstract, keywords, reference list. But digital transformation—accelerated by social media, open science, and new demands for public accountability—is steadily rewriting the rules. One of the clearest signs of this shift is the rise of the video abstract: a short scientific video that explains the essence, methodology,

In recent years, artificial intelligence has become more than a technological novelty. It now plays a direct role in scientific research—analysing vast datasets, summarising literature, proposing hypotheses, simulating experiments, and supporting the writing of scientific texts. This development is often described with clichés such as “revolution”, “automation”, “replacing humans”. Yet research points to something more