As I’m writing this, on Easter 2020, the big wave of COVID-19 infections spreads as a slow-motion tsunami over our planet, wreaking havoc in many families, communities and businesses all over the world. Knowing a thing or two about contagion dynamics, I started following this pandemic when there were still just 881 confirmed cases in Wuhan, hoping, or actually expecting an adequate response.

Introduction The current COVID-19 crisis has prompted hand-wringing and self-reflection among some museum professionals. What, they are asking, is the point of a museum that remains closed to the public? How can museums remain relevant if people can’t visit them? Can exhibitions, which take years to plan and execute, be transferred to the digital realm, in order to keep museums open virtually?

Agata Komendant-Brodowska Since covid-19 started to spread, I’ve reacted twofold. Of course, this is a huge crisis, so, on the one hand I’ve been worried, sad and scared. On the other hand, there are a lot of massive social processes happening right now in front of our eyes, almost in ‘fast forward’ mode, so I’ve also been observing what is happening as a researcher interested in social dynamics.

[et_pb_section bb_built=”1″ admin_label=”section”][et_pb_row admin_label=”row”][et_pb_column type=”4_4″][et_pb_text admin_label=”Intro” background_layout=”light” text_orientation=”left” use_border_color=”off” border_color=”#ffffff” border_style=”solid”] Tracking Open Access in times of COVID-19 Here, we measure in near time the number of publications on COVID-19 and Sars-CoV-2 and the share of Open Access publications.

Evgeny Bobrov, Open Data and Research Data Management Officer, at QUEST Center, Berlin Institute of Health (BIH), (© BIH/Thomas Rafalzyk) The COVID-19 pandemic requires swift reactions, which in turn makes the sharing of all available knowledge on the virus as quickly and as freely as possible an imperative.

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.

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.