iLCM - A virtual research infrastructure for large-scale qualitative data

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The project iLCM will develop an integrated research environment for the analysis of structured and unstructured data in a Software as a Service-architecture (SaaS). The project addresses the requirements of quantitative research in qualitative data with text mining and the requirements on the reproducibility of data-driven research designs in the social sciences. The research environment iLCM is based on the Leipzig Corpus Miner (LCM), a decentralized SaaS-application designed for the analysis of large amounts of newspaper texts which has been developed for a BMBF funded eHumanities project in political science. To use the LCM-prototype for generic research questions it will be extended with new functionalities. Additionally, the generic text mining tools of the LCM will be supplemented with an Open Research Computing-environment (ORC) for active and executable documents referred to as notebooks. This OCR-evironment allows to link the semantic structures which can be extracted from texts by the LCM with other data in a flexible way. This allows to develop individual research designs that derive from project specific requirements. Analysis workflows can be organized and stored as Notebooks, i.e. scripts of their verbal descriptions, and can be published in company with the research data as active and executable documents. GESIS as a service provider for archiving research data will develop a central service for the execution, publishing and the archiving of notebooks within the scope of this project. The publishing of notebooks along with source- and intermediate data will make research results and methodologies reproducible, shareable and reusable. The proposers expect a large step forward in the development of the emerging field of Computational Social Science from iLCM in research and teaching.