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Data Wrangling from Socio-Academic Web-Space : Designing a Meta Model
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Data carpentry is an emerging field in the domain of LIS and has opened new possibilities for information professionals to survive in the age of data-intensive information services. However, library professionals face the challenges of information overload because of the free availability of data, both in terms of quantity and variety. The role of library professionals is moving from tech-savvy to data-savvy. This research discusses the possibilities of ODbL-based data sources that offer freely accessible data through API calls and proposes a meta-model for fetching and extracting datasets from these databases using an open-Source Data Wrangling Tool (OpenRefine). Further, it discusses the possible application of data wrangling techniques from diverse sources in libraries and how information professionals can take advantage of openly available data to provide value-added information services to users. The practical implications of an array of databases are projected through two case studies: Case Study I deals with measuring the productivity of individual institutions through different metrics, and Case Study II projects a coverage comparison among the ODbL-based citation and Altmetric databases. This meta-model will aid in understanding the potential application of data wrangling techniques to an array of library services.
Keywords
Data Wrangling, Data Carpentry, OpenRefine, ODbL Databases, Socio-Academic Data, REST/API.
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