 This entry stressed how the emerging emphasis on big data signals the rise of a data-centric approach to research, in which efforts to mobilise, integrate, disseminate and visualise data are viewed as central contributions to discovery. The emergence of data-centrism highlights the challenges involved in gathering, classifying and interpreting data, and the concepts, technologies and institutions that surround these processes. Tools such as high-throughput measurement instruments and apps for smartphones are fast generating large volumes of data in digital formats. In principle, these data are immediately available for dissemination through internet platforms, which can make them accessible to anybody with a broadband connection in a matter of seconds. In practice, however, access to data is fraught with conceptual, technical, legal and ethical implications; and even when access can be granted, it does not guarantee that the data can be fruitfully used to spur further research. Furthermore, the mathematical and computational tools developed to analyse big data are often opaque in their functioning and assumptions, leading to results whose scientific meaning and credibility may be difficult to assess. This increases the worry that big data science may be grounded upon, and ultimately supporting, the process of making human ingenuity hostage to an alien, artificial and ultimately unintelligible intelligence.