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

iLCM项目将开发一个集成的研究环境，用于在软件即服务架构（SaaS）中分析结构化和非结构化数据。该项目解决了定量研究的定性数据与文本挖掘的要求，并在社会科学中的数据驱动的研究设计的再现性的要求。iLCM的研究环境是基于莱比锡语料库矿工（LCM），一个分散的SaaS应用程序，旨在分析大量的报纸文本，已开发的BMBF资助的政治学eHumanities项目。为了将LCM原型用于通用研究问题，它将扩展新的功能。此外，LCM的通用文本挖掘工具将补充一个开放式研究计算环境（ORC）的活动和可执行文件被称为笔记本电脑。该OCR-实现允许以灵活的方式将LCM从文本中提取的语义结构与其他数据链接起来。这允许开发来自项目特定要求的个人研究设计。分析工作流程可以组织和存储为笔记本，即其口头描述的脚本，并且可以与研究数据一起发布为活动和可执行的文档。全球科学信息系统作为研究数据存档服务提供者，将在本项目范围内为笔记本的执行、出版和存档开发一项中央服务。将笔记本沿着源数据和中间数据一起出版，将使研究结果和方法可复制、可分享和可重复使用。提案者希望在研究和教学中从iLCM中发展新兴的计算社会科学领域迈出一大步。

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.