Without sufficient information about researchers data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental publications are used to analyse the ways in which computer scientists share research data. All journal articles published by researchers in the computer science department of the case studys university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Furthermore, a rudimentary classification of the main study types exhibited in the investigated articles was developed to accommodate the multidisciplinary nature of the case departments research agenda. Human interaction and intervention studies often collected original data, whereas research on novel computational methods and life sciences more frequently used openly available data. Articles that made data available for reuse were most often in life science studies, whereas data sharing was least frequent in human interaction studies. The use of open code was most frequent in life science studies and novel computational methods. The findings highlight that multidisciplinary research organisations may include diverse subfields that have their own cultures of data sharing, and suggest that research information system-based methods may be valuable additions to the questionnaire and interview methodologies eliciting insight into researchers data sharing. The collected data and coding framework are provided as open data to facilitate future research.
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