We asked whether the design of a Science Data Repository (SDR) can influence data sharing behaviour in small scientific collaborations. We hypothesised that an SDR can influence data-sharing behaviour when its design considers the context of data-sharing. We proposed an alternative approach to those documented in the literature, employing a combination of socio-technical empirical and analytical methods for context capturing, and choice architecture for context incorporation. To evaluate the approach we applied it to design features in a Scientific Data Repository for a population of small scientific collaborations within the Life Sciences. The application of this thesis' approach consisted of an exploratory case study, a review of factors associated with data sharing, the definition of design claims, and implementation of a set of design features. We collected data using interviews with members of the collaborations and designers of the SDR; as well as obtaining the data-logs from the collaborations' SDR. We evaluated the resulting design features using an asynchronous web experiment. We found that using the empirical approach to context capturing we are able to effectively identify factors associated with data sharing in the small scientific collaborations. Moreover, we identified a number of limitations on the application of the analytical approach to context capturing. Furthermore, we found that the Choice Architecture based procedure for context incorporation can define effective design features in Science Data Repositories. In this work, we show that we can facilitate data-sharing by incorporating context into the design of a Science Data Repository, and identified a set of restrictions to use our approach. The approach proposed in this thesis can be used by practitioners wishing to improve data sharing in an SDR. Contributions, such as the survey of factors associated with data sharing behaviour, can be used by researchers to understand the problems associated with data sharing in small scientific collaborations.

The thesis explores the potential of one way of facilitating and stimulating collaboration in Library and Information Science (LIS), through a specific scientific collaboration activity: creating, sharing and reusing data collection instruments, such as interview guides, questionnaires, and observation protocols. The four studies reported in the thesis can be read as a linear narrative, each study building on the previous and contributing to the following ones. Together the four studies describe the process exploring social and contextual aspects of LIS; developing requirements and designing a working prototype collaboratory; and evaluating how the prototype collaboratory was perceived by LIS professionals. Overall, the results show that whereas the benefits of an LIS collaboratory reported by the study participants focused on the greater good for LIS, the challenges reported focused on the individuals’ perspectives. Hence, a tension exists between supporting the greater good, and challenges for individuals concerning sharing and reusing data collection instruments in an LIS collaboratory. The thesis emphasizes the implications for the LIS discipline when new ways of working with data collection instruments would be introduced; the implications of addressing needs of a diverse target audience; and the implications for further design iterations of an LIS collaboratory, including rewarding contributions, and ensuring quality content in a collaboratory. / <p>Academic dissertation for the Degree of Doctor of Philosophy in Library and Information Science at the University of Gothenburg and the University of Borås to be publicly defended on Wednesday 11 June 2014 at 13.15 in lecture room E310, University of Borås, Allégatan 1, Borås</p>

Für die Konzeption sozio-technischer Systeme zur wissenschaftlichen Erkenntnisgenerierung ist das Wissen über die spezifischen Inhalte und Bedingungen der Arbeit einer Fachgemeinschaft essenziell. Im Kontext der Automatisierung von Wissensproduktion ist unklar, welche fachspezifischen Faktoren die Möglichkeiten einer Arbeitsteilung zwischen Mensch und Maschine beeinflussen. In der vorliegenden Arbeit wird eine grundlegende Voraussetzung für die Automatisierung von Forschungsprozessen, nämlich die Formalisierbarkeit typischer Handlungen und Wissensbestände, hinsichtlich ihrer fachspezifischen Bedingungen untersucht. Dafür wurde ein qualitativer Vergleich der Evidenzkonstruktion zweier Fachgebiete, der Editionsphilologie und der Klimaforschung, durchgeführt. Um deren Forschungsprozesse systematisch vergleichen und Zusammenhänge zwischen den Eigenschaften eines Forschungsprozesses und den Möglichkeiten seiner Formalisierbarkeit empirisch untersuchen zu können, wurde ein Vergleichsrahmen entwickelt, der auf dem wissenschaftssoziologischen Konzept der epistemischen Bedingungen beruht. Die fachspezifischen Bedingungen des Forschungshandelns stellen einen Erklärungsansatz für Varianten der Wissensproduktion und damit auch für unterschiedlich formalisierbare Forschungsprozesse dar. Es konnte gezeigt werden, dass insbesondere der Grad an Kodifizierung des Wissens einen wesentlichen Einfluss auf das Auftreten bzw. die Abfolge unterschiedlich formalisierbarer Handlungstypen und Wissensbestände hat. Der Anteil persönlicher Perspektive in der Evidenzkonstruktion und der Grad der Zerlegbarkeit eines Forschungsprozesses sind ebenfalls wichtige Faktoren für die Möglichkeiten der Delegation von Handlungen an Maschinen. Desweiteren konnte gezeigt werden, dass selbst bei einem hohen Formalisierungsgrad das informelle menschliche Handeln das wesentliche Komplement automatisierter Abläufe darstellt und dass die Formalisierbarkeit einer zeitlichen Dynamik unterliegt. / Knowing about the field-specific content and conditions of work in a scientific discipline is essential for the design of socio-technical systems used for the production of scientific knowledge. In the context of automated knowledge production, it remains unclear which field-specific factors influence the possibilities to distribute labour between humans and machines. This study analyses a fundamental prerequisite for the automation of research processes, namely the possibility to formalise typical actions and knowledge, with regard to its field-specific conditions. A qualitative approach is used to compare the construction of evidence in two scientific fields, textual studies and climate research. In order to systematically compare research processes and to empirically investigate correlations between the properties of a research process and the possibilities of its formalisation, a comparative framework based on the sociological concept of epistemic conditions was developed. Field-specific conditions of doing research represent an explanatory approach for variants of knowledge production and thus for variant degrees of formalised processes. Results show that the degree of codification of knowledge has a significant influence on the occurrence and on the sequence of types of action and of knowledge resources with variant degrees of formalisation. In addition, the role of personal interpretation in problem formulation and construction of empirical evidence and the degree of decomposability of a research process are decisive factors for being able to delegate actions to machines. Furthermore, the study shows that a high degree of formalisation requires informal human action to complement automated processes and that formalisability is subject to temporal dynamics during research processes.