The Purdue University Research Repository (PURR) provides an online, collaborative working space and data-sharing platform to support Purdue researchers and their collaborators.
Mit der voranschreitenden Digitalisierung von Forschung und Lehre steigt die Zahl an Software-Lösungen, die an wissenschaftlichen Einrichtungen entstehen und zur Verarbeitung wissenschaftlicher Daten genutzt werden. Die unter dem Stichwort Open Science geforderte Zugänglichkeit und Nachnutzung von wissenschaftlichen Ergebnissen kann in vielen Fachgebieten nur sichergestellt werden, wenn Forschungsdaten und Programmcode offen zugänglich gemacht werden.
N. Micic, D. Neagu, I. Campean, and E. Habib Zadeh. (2017)Every industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity..