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Database-Centric Guidelines for Building a Scholarly Metrics
Information System: A Case Study with ARIA Prototype
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Database-Centric Guidelines for Building a Scholarly Metrics Information System: A Case Study with ARIA Prototype

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Poster presenting during AROSIM 2018 Workshop. For more details, please visit www.altmetrics.ntuchess.com/AROSIM2018/

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Database-Centric Guidelines for Building a Scholarly Metrics Information System: A Case Study with ARIA Prototype

  1. 1. Database-Centric Guidelines for Building a Scholarly Metrics Information System: A Case Study with ARIA Prototype www.altmetrics.ntuchess.com I. BACKGROUND • Performance assessment of researchers is one of the key periodic functions in a university • Traditional indicators (bibliometrics) based on metrics such as publication count, citation count, journal impact factor, have been used for measuring research impact • Tools such as SciVal and inCites have been used for measuring research impact at university level Drawbacks of Traditional Metrics • Timeliness factor as most of these metrics can be measured only after an article is cited by another article • Restrictive nature of the measurement (no social media coverage) II. PREDESIGN PHASE A. Objectives of ARIA • SO1: To enable measuring research impact of researchers, universities, and research institutes • SO2: To enable cross-metric validation between traditional metrics and altmetrics for the hard sciences disciplines, the non-hard sciences disciplines, and innovation and commercialization B. Theoretical Design Framework C. Data Sources D. Design Considerations • Temporal Aggregation (e.g., Monthly, Quarterly, Yearly) • Structural Aggregation (e.g., School, Dept, Univ levels) • Statistical Aggregation (e.g., Top 5%, Bottom 5%, Mean) III. DATABASE DESIGN • Enterprise data warehouse (EDW) design methodology was used • Three database layers – (i) staging, (ii) data warehouse and (iii) data marts IV. DESIGN ISSUES • Handling High Volume of Periodic Data Extraction • Incorrect and Insufficient Metadata • Author-name Disambiguation This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Science of Research, Innovation and Enterprise programme (SRIE Award No. NRF2014-NRF-SRIE001-019). Any opinions, findings, conclusions and/or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Research Foundation Singapore Aravind Sesagiri Raamkumar (aravind002@ntu.edu.sg), Feiheng Luo ,Mojisola Erdt, Yin-Leng Theng

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