A review of the characteristics of 108 author-level bibliometric indicators

An increasing demand for bibliometric assessment of individuals has led to a growth of new bibliometric indicators as well as new variants or combinations of established ones. The aim of this review is to contribute with objective facts about the usefulness of bibliometric indicators of the effects of publication activity at the individual level. This paper reviews 108 indicators that can potentially be used to measure performance on the individual author level, and examines the complexity of their calculations in relation to what they are supposed to reflect and ease of end-user application.Comment: to be published in Scientometrics, 201

Early identification of important patents through network centrality

One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926-2010) to test our ability to early identify a list of historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers.Comment: 14 page

Multiple Retrieval Models and Regression Models for Prior Art Search

This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend

Co-author weighting in bibliometric methodology and subfields of a scientific discipline

Collaborative work and co-authorship are fundamental to the advancement of modern science. However, it is not clear how collaboration should be measured in achievement-based metrics. Co-author weighted credit introduces distortions into the bibliometric description of a discipline. It puts great weight on collaboration - not based on the results of collaboration - but purely because of the existence of collaborations. In terms of publication and citation impact, it artificially favors some subdisciplines. In order to understand how credit is given in a co-author weighted system (like the NRC's method), we introduced credit spaces. We include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors.Comment: 11 pages, 1 figure, 4 table

Advancements in Road Safety Management Analysis

Road Safety Management (RSM) can be briefly defined as the tasks of preparing and implementing road safety policies. Many studies have been carried out on RSM, trying to identify success factors and reference best practice examples, but the complexity of the subject and the difficulty of quantitative data collection make it difficult a clear and comprehensive understanding. According to the EC-funded DACOTA research project, the weakest components of RSM systems in Europe are policy implementation and funding and the lack of knowledge-based road safety policy making. The main objective of the research, undertaken within the FERSI's working group on Road Safety Management (RSM), is to better investigate in several European countries those two RSM key functions: funding and research. Particularly the study aims at 1) exploring the existing structures, processes and factors affecting funding and research performances; 2) defining an assessment framework able to measure single country performances with reference to the efficiency and effectiveness of road safety funding and research, possibly shifting from a qualitative to a more quantitative approach. Based on the available knowledge on these two topics (research and funding), an assessment framework is defined and a set of qualitative and quantitative indicators for funding and research performance measurement is proposed. A desk analysis aiming at collecting available data useful to estimate the proposed indicators is conducted and a preliminary analysis with this subset of indicators is undertaken. A subset of research indicators (bibliometric) are used to estimate road safety research outputs performance of a country in terms of productivity and quality of research and international collaboration activities. Preliminary results show a positive correlation among them, even if the linear correlation turns to be not so strong. Countries are ranked on the basis of a composite index of all the three indicators

Influence, originality and similarity in directed acyclic graphs

We introduce a framework for network analysis based on random walks on directed acyclic graphs where the probability of passing through a given node is the key ingredient. We illustrate its use in evaluating the mutual influence of nodes and discovering seminal papers in a citation network. We further introduce a new similarity metric and test it in a simple personalized recommendation process. This metric's performance is comparable to that of classical similarity metrics, thus further supporting the validity of our framework.Comment: 6 pages, 4 figure