How can libraries help to prevent tracking in science, thereby protecting the data of the researchers and, in an idealistic sense, scientific freedom?

Resist seamless dataism and de-automate your life with Miriam Rasch's recommended reading.

Big data are difficult to handle. These tips and tricks can smooth the way.

To the companies selling tests, the ever-evolving nature of ancestry reports is more of a feature than a bug. To consumers, it can mean an identity crisis.

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.

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.