Open Science

Open Science

New relationships between researchers and ‘researchees’? How target groups of data analytics see themselves

  Project start: 1 April 2020

  Research partner: GESIS – Leibniz Institute for the Social Sciences

 Dr. Katharina E. Kinder-Kurlanda

Open Science: New opportunities for shaping the relationship between researchers and ‘researchees’?

Researchers often use new data sources (digital data traces) and methods (machine learning), where, unlike in other types of survey, those studied usually do not know that they and their data become the object of research. Open Science concepts, in contrast, attempt to make research more transparent, comprehensible and participatory. The project deals with precisely this area of tension.

Open Science as an initiative in civil society

Open Science is a cross-disciplinary initiative developed in the scientific community to make the results of research freely and quickly accessible to the scientific community itself, but above all to the public and civil society, to citizens and practitioners, in the spirit of “Open Science”. New approaches, such as Open Access and Open Data, are intended to communicate scientific findings in a way that can be understood by all, thereby making research more transparent and comprehensible.

Digitalization research often uses new data sources, such as social media content and data traces generated by the use of Internet-of-Things technologies. New research methods of data science and machine learning are being used to analyze these data sources (as they are often used on social media platforms themselves) and thereby to investigate user behavior, and to mirror (or even predict) trends.

The project aims to investigate which models of collaboration with ‘researchees’ are possible, ethically justifiable and methodologically meaningful in the sense of Open Science in this research field.

Active design of digital data traces

The producers of digital data often behave as active and conscious producers of content. Being on social media or increasingly in the most varied everyday contexts (Internet of Things) that were previously not accessible to digital data acquisition means not only leaving data traces passively, but also actively creating and adapting them as an author, and consciously helping to shape the public sphere. Following on from existing literature, we can assume for example that the phenomenon of “produser” (Bruns 2008), i.e. the blurring of the roles of producer and user, shapes how data producers see themselves. Interest in generating attention in the sense of new attention economies could also be important: individual users employ different strategies to achieve online popularity (Marwick 2013, Tufekci 2013), which may have an impact on their role in research.

How do citizens assess data analytics?

The project investigates how users see themselves when it comes to the use of new methods of data science. Initial studies indicate that discourses on social media concerning the results of data analytics are complex and confrontational: users question the validity of big-data methods, and emphasize the context-dependency of data, e.g. they question whether statements are valid without more detailed knowledge of the situational context of the survey (Kinder-Kurlanda 2018, 2019).

We will therefore establish an initial classification of possible roles involved when researchers and ‘researchees’ cooperate in research projects with innovative data sources. For example, user-driven ‘data donations’ and Citizen Science approaches offer different possibilities to involve ‘researchees’ as active participants in research, and to use their detailed knowledge of specific technologies and their mode of operation to verify research results.

How the project will proceed

The project will be conducted in two steps:

1. Investigating how social media users see themselves

  • Analysis of disputes on social media, ignited by the presentation of results of analyses of user behavior (data analytics)
  • Mixed methods: qualitative (e.g. content analysis) and quantitative (e.g. interaction frequencies, and, where necessary, topic modeling)

2. Categorizing possible roles for social media users in research

  • Focus groups with researchers to discuss the categorizations
New models of cooperation between researchers and ‘researchees’

The project combines research with the development of best practice. Best-practice models are interesting for scientists who use new data and methods in their research, but also, say, for data curators who support research work. In working with innovative data sources, they all face the question of which data should and may be collected and used, and how.


Bruns, A. (2008). Blogs, Wikipedia, Second Life, and beyond: From production to produsage (Vol. 45). Peter Lang.

Kinder-Kurlanda, K. (2019): Alltagserfahrungen mit Algorithmen. In: Marion Hamm/Ute Holfelder/Christian Ritter/Alexandra Schwell/Ove Sutter (Hrsg.), Widerständigkeiten des Alltags. Beiträge zur einer empirischen Kulturanalyse. Für Klaus Schönberger zum 60. Geburtstag (S.143-149). Klagenfurt: Drava Verlag.

Kinder-Kurlanda, K. (2018). Algorithmen im Alltag. In: Jahrestagung der GWTF Gesellschaft für Wissenschafts- und Technikforschung „Verhalten und Vorhersage. Die techno-sozialen Zukünfte algorithmischer Bewertungssysteme“. Berlin, 15-16 November 2018.

Marwick, A. E. (2015). Instafame: Luxury selfies in the attention economy. Public culture, 27:1(75), 137-160.

Tufekci, Z. (2013). “Not this one” social movements, the attention economy, and microcelebrity networked activism. American Behavioral Scientist, 57(7), 848-870.