This study investigates how bilinguals use sublexical language membership information to speed up their word recognition process in different task situations. Norwegian-English bilinguals performed a Norwegian-English language decision task, a mixed English lexical decision task, or a mixed Norwegian lexical decision task. The mixed lexical decision experiments included words from the nontarget language that required a "no" response. The language specificity of the Bokmål (a Norwegian written norm) and English (non)words was varied by including language-specific letters ("smør", "hawk") or bigrams ("dusj", "veal"). Bilinguals were found to use both types of sublexical markedness to facilitate their decisions, language-specific letters leading to larger effects than language-specific bigrams. A cross-experimental comparison indicates that the use of sublexical language information was strategically dependent on the task at hand and that decisions were based on language membership information derived directly from sublexical (bigram) stimulus characteristics instead of indirectly via their lexical representations. Available models for bilingual word recognition fail to handle the observed marker effects, because all consider language membership as a lexical property only.

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

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Incremental sentence generation imposes special constraints on the representation of the grammar and the design of the formulator (the module which is responsible for constructing the syntactic and morphological structure). In the model of natural speech production presented here, a formalism called Segment Grammar is used for the representation of linguistic knowledge. We give a definition of this formalism and present a formulator design which relies on it. Next, we present an object-oriented implementation of Segment Grammar. Finally, we compare Segment Grammar with other formalisms.