Designing science through collaboration, trust, and a little fun: studying the translation of big data into social and health impact

Rationale
Collaboration between social and natural scientists is thought to be a valuable, even necessary, component of the effective and responsible translation of research. Within the cross-disciplinary Data to Knowledge group such collaboration has been an intrinsic part of developing a data science tool called DataSHIELD. DataSHIELD enables the analysis of distributed data sources whilst (1) preserving the privacy and confidentiality of the participants the data was obtained from, and (2) enabling fine control of how and what data is shared to protect the intellectual property of the data owners. While the evolution of DataSHIELD has benefited from the insights of all disciplines involved, it has also generated a few thorny issues, a lot of learning and a little fun along the way. Science is a fundamentally human activity and collaboration is one of its contemporary forms yet we don’t fully understand how cross-disciplinary collaboration functions, what its consequences are and who benefits.

Aims & Objectives
This PhD project will focus on understanding how technologies of trust and collaboration function to produce scientific knowledge. It will also investigate how entangled power relations, in the context of large international research consortia, structure these collaborative relations.

Methods
The PhD will (1) examine retrospective ethnographic data (visual and textual) which records the development of DataSHIELD; (2) undertake a prospective ethnographic study of ‘designing useable science’ in the next stages of DataSHIELD’s development. This will be collaborative in nature, with the PhD student acting as both observer and participant.

Data collection will include video ethnography of international and local meetings/workshops and interviews with researchers, research users, research participants and end users. Visual, audio and textual media will form the data for analysis. Analysis will combine thematic analysis with theory driven interpretations and data-dependent techniques (eg. discourse, conversation, or narrative analysis) as appropriate. Findings from the ethnography will be shared with the team on an ongoing basis in order to drive improvements in user experience/usability. Fieldnotes recording this process will also form data for analysis.