Computational models and huge amounts of available data allow us to predict the outbreak of COVID-19 globally and prepare respective measures to react to this crisis. Policy-makers, journalists and individuals refer to such calculations on a daily basis and they are prominently present in the public discourse.
Over the past 10 years, my colleagues and I have been doing research on research, on how academic knowledge is created (i.e. scholarly communication) and disseminated (i.e. research communication). We have been looking at how researchers collaborate and share data (here, here and here), how they perform quality checks (here), where they publish and how they engage with the public (here and here). Perhaps the most important insight I have gained
Juggling software, materials, and people In my third week of grad school, I found myself metaphorically elbows-deep in my human-robot interaction lab’s codebase. I was porting a robot teleportation interface from tablet to desktop. The goal: To run a psychology study exploring how young kids learned language skills with social robots. Jacqueline M. Kory-Westlund But comments were few and far between.
Introduction The way science is conducted is changing. The internet and other digital means alter the scale and pace of data-driven research and collaborative possibilities. Most modern scholarly research now relies to some extent on digital research infrastructures.
Einführung Seit der Veröffentlichung der Empfehlungen zur Spezifikation des Kerndatensatz Forschung (KDSF) durch den Wissenschaftsrat 2016 wird die Einführung von Forschungsinformationssystemen als zentrale Nachweis- und Berichtsinstrumente zu den eigenen Forschungsaktivitäten und -ergebnissen an vielen Hochschulen intensiv diskutiert.
There is more than one sort of scientific research infrastructure: those that provide technology and computational capacity, and those that are supplied by the community who works with it (Baron et al., 2017). We suggest that computing research infrastructures provide an information environment that support effective research, but of themselves are insufficient to provide the insights and understanding we desire.
The infrastructures that natural sciences run on are many and varied. A mixed garden of tailormade instruments and laboratories designed to handle highly specific tasks.
This interview is done in personal capacity and based on insights such as recently published in the “International Handbook on Responsible Innovation: A Global Resource”. In 2013, you introduced a vision for Responsible Research and Innovation.
What is the value of social science today? Arthur Lupia, University of Michigan, Photo: Svetluša Surova For me, the value of social science is ultimately about the quality of life. What social science does is that it helps us to take observations of the world and put them into a form where we can understand more about how the world works. We can then use social science to anticipate likely consequences of our actions.
In 1997, SciELO, the biggest Open Access database in Latin America got some initial funding from the São Paulo Research Foundation FAPESP. What triggered the launch of such an infrastructure and what were the main goals back then? Back then a few local scientists approached us with the idea to set up a repository for journals edited in Brazil.