iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data

TL;DR

The paper introduces iLCM, a SaaS-based research environment integrating text mining and reproducibility tools for large-scale qualitative data analysis in social sciences and digital humanities.

Contribution

It presents a novel integrated platform combining high-performance text mining with executable notebooks for reproducible research in social sciences.

Findings

Enables analysis of large qualitative datasets efficiently.

Supports reproducibility through integrated scripting environment.

Combines text mining with customizable research workflows.

Abstract

The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS). The research environment addresses requirements for the quantitative evaluation of large amounts of qualitative data with text mining methods as well as requirements for the reproducibility of data-driven research designs in the social sciences. For this, the iLCM research environment comprises two central components. First, the Leipzig Corpus Miner (LCM), a decentralized SaaS application for the analysis of large amounts of news texts developed in a previous Digital Humanities project. Second, the text mining tools implemented in the LCM are extended by an "Open Research Computing" (ORC) environment for executable script documents, so-called "notebooks". This novel integration allows to combine generic, high-performance methods to process large amounts of unstructured text data and with individual program scripts to address specific research requirements in computational social science and digital humanities.