 This understanding of big data is rooted in a long history of researchers grappling with large and complex datasets, as exemplified by fields like astronomy, meteorology, taxonomy and demography (see the collections assembled by Daston 2017; Anorova et al. 2017; Porter &amp; Chaderavian 2018; as well as Anorova et al. 2010, Sepkoski 2013, Stevens 2016, Strasser 2019 among others). Similarly, biomedical research—and particularly subfields such as epidemiology, pharmacology and public health—has an extensive tradition of tackling data of high volume, velocity, variety and volatility, and whose validity, veracity and value are regularly negotiated and contested by patients, governments, funders, pharmaceutical companies, insurances and public institutions (Bauer 2008). Throughout the twentieth century, these efforts spurred the development of techniques, institutions and instruments to collect, order, visualise and analyse data, such as: standard classification systems and formats; guidelines, tools and legislation for the management and security of sensitive data; and infrastructures to integrate and sustain data collections over long periods of time (Daston 2017).