Mirko Tobias Schäfer / Assistant Professor
University of Utrecht Department for Media and Culture Studies
Mirko Tobias Schäfer / Assistant Professor
University of Utrecht Department for Media and Culture Studies
When I was writing my PhD thesis, I conducted interviews, collected data, sifted through the material of my corpus, and of course recorded everything in a coherent document. Discussing my findings and theoretical conceptualization with my fellow colleagues Marianne van den Boomen and Imar de Vries was to the closest I came to collaboration. This all changed when Thomas Boeschoten and I started the Utrecht Data School in 2013. All of a sudden, research projects were more complex and required input from team members with different skills. For one of our first research projects, we needed someone to help us scrape timelines from the Twitter accounts all members of the Dutch parliament and the 400 accounts they interacted with most frequently (Overheul, Boeschoten, Schäfer 2014). Since then, our research projects became more complex and more interdisciplinary.
In our book Datafied Society: Studying Culture through Data, Karin van Es and I emphasize the need for humanities scholars to develop new skills in order to address their new research objects appropriately (Schäfer, Van Es 2017). In our case that would be datafication, algorithms, and data practices, and hence we had to learn about datasets, and to develop skills in data analysis, network analysis, and data visualization. However, we also learned that cooperation between different disciplines and expertise intensified in our new research projects. In addition to colleagues from philosophy (mostly ethics), law, political sciences or other disciplines, we needed team members who had a mastery of data management and could automatize data collection from diverse web platforms, and we needed help from colleagues in statistics, linguistics, and computer sciences. This could still qualify for interdisciplinary cooperation in academic research projects, but our approach goes further than this.
In contrast to the experiences recounted by most of our colleagues from other humanities departments, our research effort was more team-driven, rather than being centered on individual achievement. The participation of team members, listed in the old-fashioned academic system as “support staff”, also appeared to be relevant on a conceptual level. They were not merely facilitating technical infrastructure and providing services; their insights and expertise also informed the research process and the findings. Summarizing, we have increasingly begun to characterize research as:
Traditionally, university staff is divided into research and support, where the latter serves merely the primary process of research. However, we have begun to see how these roles are blurring, and researchers are also carrying out traditional support tasks from project management to fundraising, and support staff participate in research efforts. At Utrecht Data School and Datafied Society, the blurring between support-staff and research-staff manifests in the capacity of several team members to successfully write and publish peer-reviewed research papers despite being labeled as mere support staff.
One of our team members, Iris Muis, devotes most of her working time to the role of project manager, keeping track of the progress of the several projects, allocating resources, managing people, and scheduling future projects. In addition, she publishes jointly with our research staff and presents at conferences. As head of our DEDA team, she also plans and moderates most of our DEDA workshops, where we help external partners to assess their data projects for ethical pitfalls. She is also a DEDA developer, identifying shortcomings and developing improvements of our data ethics impact assessment. Arthur Vankan, who is an instructor for our Practicum, an undergraduate course on data analysis, has not limited his role to teaching. He advises colleagues on their own research projects, develops research and learning opportunities with external partners and interfaces between student-teams and external partners, making sure their cooperation is effective and productive. Working for a data analysis and consulting firm, he is also a part-time university employee. The expertise he developed in his occupation outside the university is essential for his teaching and our research. Sander Prins, who specialized within UDS in data management, data collection, and all matters software and infrastructure, is also essential to research projects where his participation manifests in rich data, more insights and better findings. We very often find that our research projects are successful because people with different expertise and background achieve something together none of them could have achieved on their own. These three examples are don’t provide a comprehensive picture of our entire research group, but indicate how the traditional division between support and research activities is fading.
Also, the divisions between student, teacher and researcher are becoming less distinct at UDS and Datafied Society. Students actively participate in research projects, starting already at the undergraduate level with hands-on applied research projects in the Practicum. As research interns or student assistants they continue to participate in applied and basic research projects. Here, they publish jointly with research staff and present findings at international research conferences. We also find the prerequisite of a PhD for long-term contracts just as dated as the division between research and support staff. Not everybody has the ambition to obtain an advanced degree and the PhD is surely not necessary to be a good teacher or a fully functioning member in an interdisciplinary research team.
We all have several roles at Utrecht Data School and Datafied Society, many of which may freely change. One of the most striking features of Utrecht Data School and Datafied Society is our connection to societal sectors. We approach research from an entrepreneurial perspective, meaning that we deliberately develop services and products that enable us to immerse ourselves deeply into the respective areas that we want to investigate (Schäfer, Van Schie 2019). We had to develop skills for fundraising, business development, and most importantly learn to identify opportunities that would serve both our research interest and meet the funding requirements. Almost everybody on the team has to interface and to collaborate with external partners and researchers from different disciplines. It took some time, but we trained ourselves to keep our research interests in sight while negotiating the interests of the various stakeholders in our research projects. This is quite a departure from a traditional research model where a researcher writes a proposal, and if lucky receives a grant, and then carries out her research in a more closely defined role. Our openness and the many connections we maintain, also demand a different university infrastructure for support.
As our projects interface dynamically and intensively with external partners, the traditional support infrastructure often cannot keep up with this pace, and are at times not even qualified and too bureaucratic to respond effectively to our needs. Therefore, it is essential that team members also carry out support tasks. A blurring between support staff and research staff within the team is inevitable. We could share expertise more effectively if universities would understand that more than one group or department can benefit from it. E.g. our data scientists, our data manager, or our business developer could be valuable for other groups as well.
Naturally, we oppose this short-sighted and ineffective division of university employees in either research or support staff. In order to excel, research teams will consist of both. We also think that there will be team members participating in research projects who do not have advanced degrees and who don’t need them. It does not mean that the PhD becomes devaluated, but it means that it is not a viable prerequisite for functioning in a research environment. We are also convinced that university faculty will include more employees who, next to their university jobs hold a position in an organization outside academia.
The traditional organization of universities in departments, centers and faculties does not yet support such a modus operandi. However, the future academy as anticipated in concepts such as the 4th Generation University (Steinbuch 2016), the New ABC of Research (Shneiderman2016), or represented in keywords such as altac or team science, paints a very different image. Here, researchers from different disciplines work in dynamic open spaces, are more organized in networks than in departments, collaborate in temporary teams, interface with societal sectors and work together with practitioners, designers, artists and policy makers. We believe that our work at Utrecht Data School and Datafied Society is pioneering this approach for the humanities.
Image: CC by gastev
Work at Utrecht Data School and Datafied Society is most often a team effort. Our practices require collaboration between team members with different backgrounds and expertise. However, incentives for academic excellence in the humanities are almost exclusively tailored to reward individual performance of singular scholars. While this system has its merits, it certainly has limits. Our research on datafication taught us that as a team we can achieve together what none of us could have done alone. A post on teamwork, blurring disciplines and fading boundaries between academia and societal sectors.