The Knowledge Complexity Project KPLEX
Institute of Social and Cultural Anthropology
In the framework of the K-PLEX project, researchers at FU Berlin have examined how data acquisition is conducted in a broad range of scientific disciplines. On the basis of the cross-disciplinary topic “emotions”, the research focus was on epistemological and methodological approaches, the collection, production, and analysis of data, the biases resulting out of research questions, and the organization of research in the social sciences, the humanities and the natural sciences. Findings are based on a survey with 123 responses and 15 expert interviews.
Research findings point to the challenges scholars are facing in their research, be it in terms of methodological biases, the development of overarching theoretical frameworks or the possibility of reusing data collected by other researchers.
The results lay open that datafication – the rendering of real-world phenomena into data – inevitably leads to a reduction of the complexity of the research object “emotions”. Conceptual gaps such as between the experience and the expression of emotions are explored with regard to the choice of research objects and the selection of methods. The findings demonstrate how divergent epistemologies and analytical scales create barriers to data aggregation and integration.
The potentials for sharing and reusing data are high when confined to the same scientific discipline because of familiarity with research questions, methodologies and data structures. In contrast, cross-disciplinary integration of data demands “thick description” of these data by means of metadata, extensive annotations or the provision of context information. Only if these prerequisites are met, the mutual understanding between differing “epistemic cultures” can be enhanced, e.g. between anthropologists working with ethnographic data collected in the field, and psychologists working with neurocognitive data created during laboratory experiments.
Research participants showed cautiousness with respect to Big Data opening up new research possibilities. Big Data are not collected according to a specific research question or methodology and are thus antecedent to the epistemological process. This can be seen as a major difference between Big Data and research data. Moreover, Big Data are investigated in an exploratory process dominated by serendipitous findings, an approach that runs counter to scientists’ conception of a steered navigation of the research process. However, the analysis of Big Data stemming from social media can contribute to raising new research questions and provides hints to emotional waves within collectives.
The findings regarding applied science laid open the heightened interest of the big tech companies in emotion research. Advances in human-machine interaction aim at improving the emotion recognition and expression of chatbots and robots. Private companies hope to receive a better acceptance of their products.
The recommendations directed at researchers, universities and research organisations as well as funding organisations pertain to the interdisciplinary composition of research units and the establishment of integrative research approaches. In order to increase the reusability of research data, close attention should be paid to the development of standard formats as well as the interlinking of data and research contexts. The recommendations also refer to the training of data scientists and the creation of textbook material on how data were reused and analysed in meaningful ways.
The report of this research project can be accessed online at https://hal.archives-ouvertes.fr/hal-01761214.
In the framework of the K-PLEX project, the Institute of Social and Cultural Anthropology has collaborated with three European partners.
Further information on the “Knowledge Complexity” project (K-PLEX) can be found online at https://kplex-project.com/.