An integrated view of data quality in Earth observation

Data quality is a difficult notion to define precisely, and different communities have different views and understandings of the subject. This causes confusion, a lack of harmonization of data across communities and omission of vital quality information. For some existing data infrastructures, data quality standards cannot address the problem adequately and cannot fulfil all user needs or cover all concepts of data quality. In this study, we discuss some philosophical issues on data quality. We identify actual user needs on data quality, review existing standards and specifications on data quality, and propose an integrated model for data quality in the field of Earth observation (EO). We also propose a practical mechanism for applying the integrated quality information model to a large number of datasets through metadata inheritance. While our data quality management approach is in the domain of EO, we believe that the ideas and methodologies for data quality management can be applied to wider domains and disciplines to facilitate quality-enabled scientific research

On the Scientific Maturity of Digital Forensics Research

In this paper we transfer a well-known grade schema of scientific maturity from the domain of software engineering into the domain of digital forensics research. On the basis of this maturity schema and its grades we classify the current state of maturity in the research field of digital forensics, and we argue for more efforts towards higher levels of scientificness in this still new field of research.http://link.springer.com/chapter/10.1007/978-3-642-41148-9_3mv201