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2	Background Fraud cases in science: Peer review no guarantee Purification measures Harbingers of future of data quality control Responsibility of quality control at stake Networked Research and Digital Information: Interaction ICTs and knowledge creation Interdisciplinary social science Research methods Research themes
3	Increasing role data Exponential rise of amounts of data Research increasingly data oriented and dependent Developments vary by discipline Quality control of data more crucial Ethics of research also focused on data (human subjects) Different configurations
4	Conceptual issues Definition of data Units of information in research that can be isolated from their context of production in order to be used in another context. In digitized science: digital records of scientific measurements or observations. Distinction raw data and processed data blurred definition of data always context specific and related to research question Flows of data instead of data bits (Hilgartner) Data structure is field specific Quality as constructed and context specific Quality control both produces and represents quality
5	Data Sharing Data Sharing as a Good Thing (policy) Good Stewardship of public knowledge Strong value chains of innovation The creation of value from international co-operation Quality control implicit Data Sharing as Extra Work (practice) privacy of subjects; too much work being scooped long-running squabbles paper work losing volunteers career collaboration with industry
6	Data Sharing Configurations Different actors: Peer to peer Data archives and repositories Centralized data production Different mechanisms: Face to face Mediated by ICTs Different data types
7	Peer to Peer Data Sharing Discrete research groups Data location not self-evident Researcher is keeper/steward of the data Data tied to specific research project Trust among researchers key element
8	Data Archives Centralized repository Data annotated and formatted (meta-data) Focused on one field or sub-field Uncertain budgets due to system of research funding
9	Centralized data production “Big science” institutions or networks Close coordination of data production Data sharing not a separate issue: data availability limited to groups involved in production Highly processed (interpreted) data available for public (education)
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11	Implications for Quality Control Increased pressure on investigators Increased pressure on peer review system Different data sharing configurations require differential approach to quality control Pressure on research funding mechanisms Big science networks/institutions: business as usual? ICT tools and skills and investments part of research infrastructure
12	Further Research Questions What are the limitations of this matrix in the analysis of other case studies? Can we see emerging interfaces between the actors? How are the actors developing qc mechanisms? Which dimensions of social relations such as trust are crucial in the different contexts? What role can be played by ICTs in qc of shared data? Which trade offs are being made?