TY - GEN
T1 - Indicating Studies' Quality Based on Open Data in Digital Libraries
AU - Shakeel, Yusra
AU - Krüger, Jacob
AU - Saake, Gunter
AU - Leich, Thomas
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2018
Y1 - 2018
N2 - Researchers publish papers to report their research results and, thus, contribute to a steadily growing corpus of knowledge. To not unintentionally repeat research and studies, researchers need to be aware of the existing corpus. For this purpose, they crawl digital libraries and conduct systematic literature reviews to summarize existing knowledge. However, there are several issues concerned with such approaches: Not all documents are available to every researcher, results may not be found due to ranking algorithms, and it requires time and effort to manually assess the quality of a document. In this paper, we provide an overview of the publicly available information of different digital libraries in computer science. Based on these results, we derive a taxonomy to describe the connections between this information and discuss their suitability for quality assessments. Overall, we observe that bibliographic data and simple citation counts are available in almost all libraries, with some of them providing rather unique information. Some of these information may be used to improve automated quality assessment, but with limitations.
AB - Researchers publish papers to report their research results and, thus, contribute to a steadily growing corpus of knowledge. To not unintentionally repeat research and studies, researchers need to be aware of the existing corpus. For this purpose, they crawl digital libraries and conduct systematic literature reviews to summarize existing knowledge. However, there are several issues concerned with such approaches: Not all documents are available to every researcher, results may not be found due to ranking algorithms, and it requires time and effort to manually assess the quality of a document. In this paper, we provide an overview of the publicly available information of different digital libraries in computer science. Based on these results, we derive a taxonomy to describe the connections between this information and discuss their suitability for quality assessments. Overall, we observe that bibliographic data and simple citation counts are available in almost all libraries, with some of them providing rather unique information. Some of these information may be used to improve automated quality assessment, but with limitations.
KW - Citation counts
KW - Quality assessment
KW - Literature analysis
KW - Digital libraries
U2 - 10.1007/978-3-030-04849-5_50
DO - 10.1007/978-3-030-04849-5_50
M3 - Conference contribution
SP - 579
EP - 590
BT - Business Information Systems Workshops (BIS)
PB - Springer
ER -