Research relies on the collection and analysis of data to develop explanations about the phenomena it seeks to represent (Borgman, 2015). Currently, many research disciplines are developing new data sharing and data use practices. One of these disciplines is biology, where novel ideas and practices around data are being established. This paper focuses on data and science within synthetic biology, and specifically on the International Genetically Engineered Machine (iGEM), a leading synthetic biology competition. It begins with a review of open-science as a social and technical movement, then we introduce synthetic biology and the iGEM competition specifically, to show how open-data concepts are being encouraged and meeting resistance in this forum.
Incidentally, while exploring this, we were inspired to pursue more specific investigation of iGEM's knowledge infrastructure through our collaboration network project.
In conducting a network analysis of iGEM collaborations and laying the groundwork for future study of this phenomenon, Waterloo iGEM appreciated the value of consulting the knowledge of, distributing the labour among, and reciprocating aid with colleagues outside of our immediate in-group. Not only did we participate in inter-iGEM team collaborations, but we also deliberately promoted intra-iGEM team collaborations between our three subteams (Lab and Design, Math Modelling, and Policy and Practices). We documented our work in a dataset codebook, network analysis paper, and strategic plan for future work in this area, as well as will signal subteam collaborations throughout our wiki. We hope, after providing further evidence of this practice’s significance and intricacies, decisions can be better made to improve interactions within the synbio community. As resources are shared and trust is built among actors, we expect to see net global increase of quality in iGEM work.
- Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, Massachusetts: The