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Presentation . 2015
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New ICPSR tools for data discovery and classification

Authors: Ionescu, Sandra;

New ICPSR tools for data discovery and classification

Abstract

Capitalizing on rich, standardized DDI-XML metadata, ICPSR continues to develop its suite of tools for data discovery and analysis with new features and applications. Recently, ICPSR launched an innovative tool that enables linking individual variables with concepts to help increase granularity in the comparison of variables and/or questions across studies and series of studies. The tool allows users to create personalized concept lists and tag variables from multiple studies with these concepts; interactive crosswalks display the variable-concept associations to further assist in data analysis, comparison, and harmonization projects. In addition to personal concept lists, it is possible to create public lists so that an organization can apply its own authoritative tagging and make this resource publicly available. The concept tagging tool is integrated with ICPSR's variable search and comparison functions that have also been upgraded with a novel feature allowing retrieval of separate lists of variables measuring different concepts within the same study. We will present and discuss the tagging tool and the enhanced search features using live examples, and will also introduce the public ICPSR classification of the American National Election Studies and General Social Survey collections and the resulting crosswalk displaying the ANES Time Series and the GSS iterations by individual years.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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