Take it personally - A Python library for data enrichment for infometrical applications

Like every other social sphere, science is influenced by individual characteristics of researchers. However, for investigations on scientific networks, only little data about the social background of researchers, e.g. social origin, gender, affiliation etc., is available. This paper introduces ”Take it personally - TIP”, a conceptual model and library currently under development, which aims to support the semantic enrichment of publication databases with semantically related background information which resides elsewhere in the (semantic) web, such as Wikidata. The supplementary information enriches the original information in the publication databases and thus facilitates the creation of complex scientific knowledge graphs. Such enrichment helps to improve the scientometric analysis of scientific publications as they can also take social backgrounds of researchers into account and to understand social structure in research communities

ORCID for Wikidata. Data enrichment for scientometric applications

Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientific articles, only 8.9% are entries describing a person and the number of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata. To fill this gap and to improve the content of Wikidata in general, we established a workflow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be transferred to other enrichments based on ORCID data. This is extension is beneficial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes

Früherkennung wissenschaftlicher Konvergenz im Hochschulmanagement

It is crucial for universities to recognize early signals of scientific convergence. Scientific convergence describes a dynamic pattern where the distance between different fields of knowledge shrinks over time. This knowledge space is beneficial to radical innovations and new promising research topics. Research in converging areas of knowledge can therefore allow universities to establish a leading position in the science community. The Q-AKTIV project develops a new approach on the basis of machine learning to identify scientific convergence at an early stage. In this work, we briefly present this approach and the first results of empirical validation. We discuss the benefits of an instrument building on our approach for the strategic management of universities and other research institutes

The Mitochondrial Ca(2+) Uniporter: Structure, Function, and Pharmacology.

Mitochondrial Ca(2+) uptake is crucial for an array of cellular functions while an imbalance can elicit cell death. In this chapter, we briefly reviewed the various modes of mitochondrial Ca(2+) uptake and our current understanding of mitochondrial Ca(2+) homeostasis in regards to cell physiology and pathophysiology. Further, this chapter focuses on the molecular identities, intracellular regulators as well as the pharmacology of mitochondrial Ca(2+) uniporter complex

Scaling the state: Egypt in the third millennium BC

Discussions of the early Egyptian state suffer from a weak consideration of scale. Egyptian archaeologists derive their arguments primarily from evidence of court cemeteries, elite tombs, and monuments of royal display. The material informs the analysis of kingship, early writing, and administration but it remains obscure how the core of the early Pharaonic state was embedded in the territory it claimed to administer. This paper suggests that the relationship between centre and hinterland is key for scaling the Egyptian state of the Old Kingdom (ca. 2,700-2,200 BC). Initially, central administration imagines Egypt using models at variance with provincial practice. The end of the Old Kingdom demarcates not the collapse, but the beginning of a large-scale state characterized by the coalescence of central and local models