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This chapter will present lessons learned from CLARIN-D, the German CLARIN national consortium. Members of the CLARIN-D communities and of the CLARIN-D consortium have been engaged in innovative, data-driven, and community-based research, using language resources and tools in the humanities and neigh-bouring disciplines. We will present different use cases and users’ stories that demonstrate the innovative research potential of large digital corpora and lexical resources for the study of language change and variation, for language documentation, for literary studies, and for the social sciences. We will emphasize the added value of making language resources and tools available in the CLARIN distributed research infrastructure and will discuss legal and ethical issues that need to be addressed in the use of such an infrastructure. Innovative technical solutions for accessing digital materials still under copyright and for data mining such materials will be presented. We will outline the need for close interaction with communities of interest in the areas of curriculum development, data management, and training the next generation of digital humanities scholars. The importance of community-supported standards for encoding language resources and the practice of community-based quality control for digital research data will be presented as a crucial step toward the provisioning of high quality research data. The chapter will conclude with a discussion of impor-tant directions for innovative research and for supporting infrastructure development over the next decade and beyond.
Lexicography
(2008)
In this chapter, we discuss steps toward extending CMDI’s semantic interoperability beyond the Social Sciences and Humanities: We stress the need for an initial data curation step, in part supported by a relation registry that helps impose some structure on CMDI vocabulary; we describe the use of authority file information and other controlled vocabulary to help connecting CMDI-based metadata to existing Linked Data; we show how significant parts of CMDI-based metadata can be converted to bibliographic metadata standards and hence entered into library catalogs; and finally we describe first steps to convert CMDI-based metadata to RDF. The initial grassroots approach of CMDI (meaning that anybody can define metadata descriptors and components) mirrors the AAA slogan of the Semantic Web (“Anyone can say Anything about Any topic”). Ironically, this makes it hard to fully link CMDI-based metadata to other Semantic Web datasets. This paper discusses the challenges of this enterprise.