Data are essential to define, understand, establish urgency, and address global issues encapsulated in the Sustainable Development Goals. Within the University of Groningen, Campus Fryslân and the CIT investigate how we can develop sophisticated data practices that can help address these issues.

Campus Fryslân offers a four-year scholarship to complete a PhD on Valuing Big Data. The PhD student will be connected to the Data Research Centre of Campus Fryslân and supervised by Dr JA Beaulieu, Dr O. Gstrein, and Prof R.P. Stolk. The PhD student will be enrolled in the Graduate School of Campus Fryslân (GSCF) and in the graduate school WTMC. PhD candidates can benefit from affiliations at research institutes of the University of Groningen, among others, as appropriate to the PhD Project.

Topic Description
- Background
We live in a digital world in which our dependence on technology is increasing and in which datafication of our activities is a growing dynamic. In this context, many of our actions produce traces that are recorded and monitored by a multitude of sensors, cameras, microphones and all kinds of technological devices that communicate this rich input into virtual clouds. An increasing number of decisions depend on data-driven systems: from reliance of physicians to recognize illnesses and patterns of diseases, to public institutions which govern societies, to the reliance of private companies to develop business intelligence to retrieve information from a weather or traffic app. Such data-based evidence originates from creating and analyzing large sets of data: ‘Big Data’. The use of large amounts of data allows us to draw detailed inferences about the lives of individuals and groups. On the basis of this seemingly novel source, algorithms are designed to seek and identify patterns that enables actors to make predictions and even decisions by applying Artificial Intelligence (AI) technologies. Clearly, this approach to knowledge has implications and it is urgent to consider its development and application critically.

- Main issue
The use of Big Data is reshaping the ways in which research is being done. This leads to fundamental changes in how we as society explore and understand the world, as well as what we define as useful findings. Arguably, Big Data changes methods (e.g. machine learning in data analytics) and challenges the whole notion of theory driven research (e.g. correlation rather than causation, a data set does not represent a sample but the whole population) in favor of a radical empiricist approach. In other words, Big Data changes methods, methodology and epistemic assumptions much more fundamentally than just questions of internal and external validity. How much insight is added with increasing amounts of data? Does the augmented diversity of larger data sets hamper their scientific value? Is the relationship between ‘Big Data’ and ‘Big Insights’ appropriately understood, or even understandable? Finally, what are the consequences of aiming for greater collection and circulation for our notions of individual privacy and collective autonomy, property, creativity and freedom? The answers might have huge implications for future research practices, both for data collection as well as data analysis.

As more and more areas in and beyond research use Data Science and Big Data as part of knowledge generation, the data sets used are extended both with more variables to account for more potential modifiers and intermediates, as well as with more observations to detect smaller effects. The current paradigm therefore conforms to the assumption that “bigger is better”. This line of thought has fueled the construction of the European Open Science Cloud (EOSC), in order to make data from research projects from all over Europe available for re-use in a FAIR data format that allows computer algorithms to harvest these data. Another example is the recent report of the Royal Netherlands Academy of Sciences on the “reuse of public data”. This initiative aims to make all governmental data available for scientific research. Is more data always better, and if so, for whom? Are the current concepts and regulatory frameworks that we use to evaluate the use of data, specifically when considering privacy and proportionality, sufficient to effectively safeguard fundamental rights and human dignity?

- Main research topic
These considerations lead to the project’s main research topic, Valuing Big Data. This project will help address how do we value the size of data. Proposals that address value as a multi-layered concept that connects ethical, epistemological, and legal modes of valuation are of particular interest.

- Scope of project
The proposed PhD project (4 years) will explore how we value the size of data with regards to issues of privacy, regulation, and the transformation of data into evidence. This will provide insight into how we value size from a scientific perspective as well as how we value the size of data from a social, ethical and epistemological perspective. This work will be based on empirical knowledge of ongoing data intensive research at the University of Groningen, and include cases in medicine, intelligence gathering in security contexts, as well as ecology. The project will result in a conceptual framework based on interdisciplinary research to understand how we value data, as well as practical guidelines for researchers using Big Data.

The proposed research will benefit from the broad institutional setup of the UG, with expertise in computer science, human rights law, data protection and privacy law, science and technology studies, ethics, epistemology, interdisciplinary data science and much more. The project will be based within the Data Research Centre of Campus Fryslân. Principal Investigators are Dr Oskar Gstrein, Dr Anne Beaulieu and prof Ronald Stolk (UMCG/Clinical Epidemiology, also CIO of the university).

Embedding of PhD
• The candidate will be part of the Data Research Centre, Campus Fryslân
• There are ongoing collaborations with related research projects in other faculties of the UG, including the UMCG

- Profile of candidate
The candidate must have an intrinsic interest in research data with an interdisciplinary background, including a solid component in social science and demonstrable affinity with the areas of research involved in the project (science and technology studies, quantitative social science, data science, epistemology, human rights law and ethics).

More information related to qualifications, conditions, applications, etc. available HERE