Helsinki Connect Lab promotes exploration of social, technical, experiential and imaginary dimensions of datafication and data-related practices. With datafication, we refer to the conversion of life into digital data and related socio-cultural and political-economic processes. 

Our research covers everyday lived experiences of automated decision-making and algorithmic systems, platform economy and its implications on the present and future of labour, and the public values at stake with controversial technologies. 

We are an interdisciplinary group of researchers working at the intersections of anthropology, sociology, consumer research, STS, communication and media studies. The group shares an interest in methods and methodological development, ranging from investigating exemplary cases, participatory approaches and data ethnographies to the uses of computational methods and large data sets in social research.

Helsinki Connect Lab is led by Prof. Minna Ruckenstein. 

Our research covers three thematic areas: 

1) Living with algorithms studies how datafication and related political-economic developments become part of the everyday lived experience. The research focuses on forms of self-tracking and everyday datafication that people engage in, recognizing that corporate surveillance and the related concerns over loss of privacy, injustices and inequalities co-exist with playful and pleasurable experiences of becoming datafied.

2) Infrastructures of datafication studies imaginary and experiential dimensions of infrastructural developments, as well as their political and economic underpinnings. The goal is to offer practical and conceptual support for querying how technology-driven processes and interventions are entangled with the society, as they shape, renew, and undermine public values.

3) Data explorations studies how data transforms into and is validated as evidence and knowledge, and what are the epistemological considerations that frame data, data analysis methods and data visualizations. The overall aim is to learn about data-capturing practices, and the kinds of questions that data explorations can answer, as well as their limitations. We employ both technical and non-technical ways to engage with research data and creatively combine quantitative and qualitative approaches.