During the last years, there has been a push towards open science and transparency in the social sciences, including education research. A major reason is that many published results are not reproducible (Pashler & Wagenmakers 2012). As a consequence, researchers are asked to make their data and other research materials available, by funders, journals and their peers. Against this background, interest in the reuse of research data has increased significantly. This implies that data can be used beyond the project for which it was initially collected and refers to quantitative as well as qualitative data. There are various reasons for using educational data beyond the primary research project: Due to the large number of studies being carried out in the educational context, it is becoming increasingly difficult for researchers to gain access to schools for data collection. In addition, the collection and processing of this data is time-consuming and costly. Furthermore, data in the education sciences is mostly financed through public funds and should, therefore, be exploited and analyzed as much as possible. Moreover, scientific journals are increasingly demanding that the data on which articles are based should be made available. The latter is intended to create transparency and enable replications. In addition, funders are increasingly requesting that the data are made available to other researchers. Beyond research, secondary data is also very valuable for teaching purposes. Yet, there is still reluctance amongst researchers to share and reuse data. The first part of this presentation will elaborate on the opportunities and challenges of data sharing in the educational sciences. In a second part, the presentation will describe the role of research infrastructures, such as FORS (the Swiss Centre of Expertise in the Social Sciences), in facilitating data sharing, archiving and reuse. These infrastructures support researchers in preparing their data to be reused by others. It will be described how researchers can benefit from these infrastructures throughout the research process. In the final part, this presentation addresses the basic requirements and principles for data sharing. This includes the FAIR (findable, available, interoperable, reusable; Wilkinson et al. 2016) principles and consent.

Pashler, Harold and Eric-Jan Wagenmakers, 2012, “Editors’ Introduction to the Special Section on Replicability in Psychological Science: A Crisis of Confidence?”, Perspectives on Psychological Science, 7(6): 528–530. doi:10.1177/1745691612465253. Wilkinson, M. D. et al (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18.