Purpose

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

Design/methodology/approach

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

Findings

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Research limitations/implications

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

Practical implications

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

Social implications

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

Originality/value

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

中文翻译：

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OpenRank——一种使用客观和可公开验证的数据源对大学进行排名的新方法

目的

学术排名面临各种问题，包括使用不可公开验证的数据源、主观参数、狭隘地关注研究生产力和地区偏见等。这项研究工作旨在通过使用基于可公开验证的数据源的客观指标来提高排名过程的可信度。

设计/方法/途径

拟议的排名方法 - OpenRank - 从两个众所周知的可公开验证的数据存储库驱动客观指标：ArnetMiner 和 DBpedia。

发现

由此产生的学术排名反映了上海排名咨询公司（SRC）、Quacquarelli Symonds（QS）和泰晤士高等教育（THE）发布的国际学术排名的共同趋势。对拟议方法的评估提倡其有效性和快速可重复性，数据收集成本低。

研究局限性/影响

OpenRank 方法的实施面临着质量数据可用性的问题。未来，学术排名的准确性可以通过使用更多相关的公共数据源来进一步提高，例如 Microsoft Academic Graph、LinkedIn 存储库中可用的数百万毕业生个人资料以及计算机协会和 Scopus 维护的书目数据等。

实际影响

建议使用开放数据源将提供新的维度来评估高等教育机构 (HEI) 的学业表现，并全面了解高等教育中的催化剂因素。

社会影响

研究工作强调需要一个专门构建的、可公开验证的电子数据源来评估全球高等教育机构的绩效。这样一个全球数据库的可用性将有助于更好的学术规划、监测和分析。当然，使用理想的数据源可以生成更透明、更可靠、争议更少的学术排名。

原创性/价值

我们通过以下贡献提出了改进高等教育机构排名过程的令人满意的解决方案：（1）通过仅使用从可公开验证的数据源中提取的客观绩效指标来提高排名结果的可信度，（2）开发学术排名基于客观指标的方法，使用两个著名的数据存储库，DBpedia 和 ArnetMiner，以及 (3) 证明所提出的排名方法对真实数据源的有效性。