Ten challenges in modeling bibliographic data for bibliometric analysis

Abstract

The complexity and variety of bibliographic data is growing, and efforts to define new methodologies and techniques for bibliometric analysis are intensifying. In this complex scenario, one of the most crucial issues is the quality of data and the capability of bibliometric analysis to cope with multiple data dimensions. Although the problem of enforcing a multidimensional approach to the analysis and management of bibliographic data is not new, a reference design pattern and a specific conceptual model for multidimensional analysis of bibliographic data are still missing. In this paper, we discuss ten of the most relevant challenges for bibliometric analysis when dealing with multidimensional data, and we propose a reference data model that, according to different goals, can help analysis designers and bibliographic experts in working with large collections of bibliographic data.

Social Cost-Benefit Analysis of HL-LHC

“…The benefits of publications are thus measurable looking at their impact on the scientific community as measured by the number of citations. See [22] for a theoretical background on these issues. If each publication written by LHC scientists (L0) has a value proportional to its production cost, the benefits of L0 publications cancel out with their cost of production, represented by the scientific personnel cost.…”

“…It is worth pointing out, that this second peak is rather the result of an increase of scientific publications after the availability of data rather than a breakthrough discovery, which is an event that cannot be predicted. See [23] and [22] for more details on the statistical analysis of scientific publications. For the CFS we extrapolate the value of the benefits in [1] that shows an exponential decaying trend.…”

Bibliographic Classification in the Digital Age: Current Trends & Future Directions

“…These issues require the urgent need for comprehensive ontologies for document publishing domain. (Ferrara & Salini, 2012) tossed 10 challenges for multiple dimensions of data in terms of bibliographic analysis. These challenges are: (i) analyzing bibliographic data in a multidimensional pattern; (ii) discovering and integrating data coming from diverse sources; (iii) detecting multiple references to the same item and cleaning, normalizing, and disambiguating bibliographic data records; (iv) analyzing multidimensional nature of bibliographic data through multivariate analysis for aggregating the data; (v) comparing different elements of bibliographic data and its ranking accordingly, (vi) aggregating indexes of different nature with respect to different parameters, dimensions, and elements of bibliographic data; (vii) dealing with multiple indexes for the same item with different values coming from different sources; (viii) extracting and indexing textual information from text corpus in support of text mining; (ix) analyzing textual data topic-wise and describing these topics for research and learning process and tracing different trends; and (x) combining multidimensional information for finding trends in bibliographic data collection.…”

The Artificial Immune Systems Domain: Identifying Progress and Main Contributors Using Publication and Co-Authorship Analyses

“…Social Network Analysis (SNA) has played major roles in many disciplines in the past few years [19,20]. Co-authorship Networks (CN) are social networks consisting of scientific collaborations and collaborators [21]. In CN, the authors are represented as nodes (or vertices) and collaborations as undirected edges.…”