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Complex algorithms will soon help clinicians make incredibly accurate determinations about our health from large amounts of information, premised on largely unexplainable correlations in that data.
Nature peers into the evidence for ‘psychographic targeting’.
The privacy backlash over Cambridge Analytica and Facebook may lead to explosive consequences for academics.
Six drug firms are paying to sequence the genes of every volunteer in the UK Biobank.
China is taking the idea of a credit score to the extreme, using big data to track and rank what you do - your purchases, your pastimes, your mistakes.
Equifax aside, companies are doing better at securing their info. But the phishers keep coming.
Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases.
Huge genetic databases are changing how scientists study disease.
Meta, a data science company, has been acquired by the Chan Zuckerberg Initiative, whose aim is to accelerate the pace of scientific advances.
The Big Data era, the impact of data science and its impact biological research and healthcare: interview with Phil Bourne.
But grave challenges remain before the promise of individually tailored medicine becomes reality.
Without access to large companies' datasets or the expertise to analyse them, research is confronted with a replication crisis and is vulnerable to commercial motivations.
An artificial intelligence system developed by researchers at DeepMind and the University of Oxford got so good by watching 5000 hours of BBC programmes.
Physicists and scientific computing experts prepare for an onslaught of petabytes.
Meta’s mission is to unlock all of the world’s scientific and technical information through artificial intelligence.
Iris is an Artificial Intelligence that starts out as a Science Assistant; helping you find the science you need. Over time she will learn, slowly but surely becoming a Scientist herself.
Technologies based on the electronic analysis of large amounts of works are still in their infancy, and the possibilities they might open up in the future are largely unpredictable.
Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed.
SSRN’s data actually represents the world of social science scholarship reasonably well.
The Commission today presented its blueprint for cloud-based services and world-class data infrastructure to ensure science, business and public services reap benefits of big data revolution.
Storing and processing genome data will exceed the computing challenges of running YouTube and Twitter.
To realize the full potential of large data sets, researchers must agree on better ways to pass data around, says Martin Bobrow.
An awesome list of (large-scale) public datasets on the Internet. (On-going collection)
Separating the true signal from the gigantic amount of noise is neither easy nor straightforward, but it is a challenge that must be tackled if information is ever to be translated into societal well-being.
