Stodden, V. (2014). Enabling Reproducibility in Big Data Research: Balancing Confidentiality and Scientific Transparency. In J. Lane, V. Stodden, S. Bender, & H. Nissenbaum (Eds.), Privacy, Big Data, and the Public Good: Frameworks for Engagement (p. pp 112-132). Cambridge University Press. https://doi.org/10.1017/CBO9781107590205.007
In this chapter I outline what this digitization means for the independent verification of scientific findings from these data, and how the current legal and regulatory structure helps and hinders the creation and communication of reliable scientific knowledge. Federal mandates and laws regarding data disclosure, privacy, confidentiality, and ownership all influence the ability of researchers to produce openly available and reproducible research. Two guiding principles are suggested to accelerate research in the era of big data and bring the regulatory infrastructure in line with scientific norms: the Principle of Scientific Licensing and the Principle of Scientific Data and Code Sharing. These principles are then applied to show how intellectual property and privacy tort laws could better enable the generation of verifiable knowledge, facilitate research collaboration with industry and other proprietary interests through standardized research dissemination agreements, and give rise to dual licensing structures that distinguish between software patenting and licensing for industry use and open availability for open research.
In dem vom BMBF geförderten Projekt FeKoM werden Empfehlungen für forschungsethisches Handeln in der Kommunikations- und Medienwissenschaft systematisch erarbeitet, empirisch fundiert und der Scientific Community zur Verfügung gestellt.