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Publicly available AI models and local infrastructure can reduce AI's environmental footprint while giving researchers greater control over the tools they use.
Some scientists want to scale back their research to reduce carbon emissions. Instead of this being equated with a need to scale down research infrastructures and data centres, we need to take action to ensure these facilities are sustainable.
CERN has been a model for how to support large-scale research collaboration. Given the challenges facing democracy today related to the information environment, a similar level of effort is required for research on the information environment.
About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors. However, few companies are talking about the issue.
How can libraries help to prevent tracking in science, thereby protecting the data of the researchers and, in an idealistic sense, scientific freedom?
Unlike many valuable resources, real-time data is both abundant and growing rapidly. But it also needs to be handled with great care.
Smartphones, sensors and consumer habits reveal much about society. Too few people have a say in how these data are created and used.
Resist seamless dataism and de-automate your life with Miriam Rasch's recommended reading.
In the introductory talk of this event, the speakers argue that the role of data in society needs to be grasped as not only a development of capitalism, but as the start of a new phase in human history that rivals in importance the emergence of historic colonialism.
Businesses and entrepreneurs are racing to deploy blockchain technology against all manner of problems, and perceived opportunities.
Big data are difficult to handle. These tips and tricks can smooth the way.
The initiative, "Project Nightingale," gives the tech giant the ability to analyze personal health information from Ascension, a Catholic hospital system.
The U.S. health care system uses commercial algorithms to guide health decisions. A study found evidence of racial bias in one widely used algorithm, such that black patients assigned the same level of risk by the algorithm are sicker than white patients.
A key dimension of our current era is Big Data, the rapid rise in produced data and information; a key frustration is that we are nonetheless living in an age of ignorance, as the real knowledge and understanding of people does not seem to be substantially increasing. This development has critical consequences.
When social scientists think about big data, they often think in terms of quantitative number crunching. However, the growing availability of ‘big’ qualitative datasets presents new opportunities for qualitative research.
Up to now, STI (Science, Technology, Innovation) studies are either rich but small scale (qualitative case studies) or large scale and under-complex. However, progress in the STI research field depends in our view on the ability to do large-scale studies with often many variables specified by relevant theories: There is a need for studies which are at the same time big and rich. To enable that, combining and integration of STI data and beyond is needed – in order to exploit the huge amount of data that are ‘out there’ in an innovative and meaningful way.
The aim of the Semantically Mapping Science (SMS) platform as the technical core within the RISIS EU project is to produce richer data to be used in social research – through the integration of heterogeneous datasets, ranging from tabular statistical data to unstructured data found on the Web.
Fitibit's wristbands have collected 150 billion hours' worth of heart-rate data from people around the world. For the first time, the company offered a look inside that data, to see how lifestyle, location, age, and gender affects our health and longevity.
Here’s how China rules using data, AI, and internet surveillance.
Machine learning on mountain of safety data improves automated assessments.
