Explore the vast range of titles available.

Although systematic reviews are intended to provide trusted scientific knowledge to meet the needs of decision-makers, their reliability can be threatened by bias and irreproducibility. To help decision-makers assess the risks in systematic reviews that they intend to use as the foundation of their action, we designed and tested a new approach to analyzing the evidence selection of a review: its coverage of the primary literature and its comparison to other reviews. Our approach could also help anyone using or producing reviews understand diversity or convergence in evidence selection. The basis of our approach is a new network construct called the inclusion network, which has two types of nodes: primary study reports (PSRs, the evidence) and systematic review reports (SRRs). The approach assesses risks in a given systematic review (the target SRR) by first constructing an inclusion network of the target SRR and other systematic reviews studying similar research questions (the companion SRRs) and then applying a three-step assessment process that utilizes visualizations, quantitative network metrics, and time series analysis. This paper introduces our approach and demonstrates it in two case studies. We identified the following risks: missing potentially relevant evidence, epistemic division in the scientific community, and recent instability in evidence selection standards. We also compare our inclusion network approach to knowledge assessment approaches based on another influential network construct, the claim-specific citation network, discuss current limitations of the inclusion network approach, and present directions for future work.

In the past 20 years, there have been increasingly more studies on sustainable technology innovation (STI), possessing a significance for sustainable development. This paper aims to provide a research landscape, since the systematic understanding of STI is still inadequate. Through bibliometric analysis, it explores the literature distribution characteristics and the literature citation network. Based on the relevant literature data in the Web of Science (WOS), the study visually analyzes the development trend, topic distribution, burst literature, and co-citation network of the research literature, and extracts the evolution path of literature citation by using the main path analysis method. Through the analysis of co-citation and main path, 13 clusters in the co-citation network are found, which are further extracted as the main path network containing 82 nodes. Furthermore, this paper summarized the bibliometric landscape and discussed the frontier STI research topics. The comprehensive framework contributes to the understanding of STI themes and identifying future research agenda.

Textual reuse is a fundamental characteristic of traditional Buddhist literature preserved in various languages. Given the sheer volume of preserved Buddhist literature and the often-unmarked instances of textual reuse, the thorough analysis and evaluation of this material without computational assistance are virtually impossible. This study investigates the application of computer-aided methods for detecting approximately similar passages within Xuanzang’s translation corpus and a selection of Abhidharma treatises preserved in Chinese translation. It presents visualizations of the generated network graphs and conducts a detailed examination of patterns of textual reuse among selected works within the Abhidharma tradition. This study demonstrates that the general picture of textual reuse within Xuanzang’s translation corpus and the selected Abhidharma texts, based on computational analysis, aligns well with established scholarship. Thus, it provides a robust foundation for conducting more detailed studies on individual text sets. The methods employed in this study to create and analyze citation network graphs can also be applied to other texts preserved in Chinese and, with some modifications, to texts in other languages.

The scientific literature on altmetrics published from 2005 to 2018 was analysed. The overall structure of the speciality's intellectual landscape is depicted through clusters of co-cited references, analysing journal and author co-citations. The 56,936 references cited in the 8,145 papers of all kinds retrieved from the Dimensions bibliographic database were included in the initial dataset used in the analysis. Pathfinder networks were generated with CiteSpace to determine the most prevalent journals and authors in the speciality. Conceptual structures were identified by co-citation clustering and latent semantic analysis. Open knowledge', altmetric collection', web indicator', assessing research', Research- Gate score', open data citation advantage', Google Scholar author citation', share data', academic tweet', Mendeley readership count' and social media metrics' were observed to be the lines of research presently favoured by specialists. Statistical indicators were calculated to determine the journals and contributors making the greatest impact.

This paper proposes a novel entitymetrics approach by exclusively focusing on citation sentences. Since citation sentences offer authors’ research interest, knowledge entities that appear in such sentences can be considered as key entities. To characterize such key entities, we focus on citation sentences that were extracted from full-text research articles collected from PubMed Central. We used “opioid” as our search query since it is an actively studied domain, which indicates that rigorous amounts of knowledge entities and entity pairs are available for examination. After which we construct two novel citation sentence-based networks, namely the Direct Citation Sentence (DCS) network and the Indirect Citation Sentence (ICS) network. The DCS network is built upon direct entity pairs that are captured within citation sentences. The ICS network, on the other hand, utilized indirect entity cooccurrences based on cited author information and section information. To do this, we propose a multi-anchor bipartite network that uses cited author information and section headings as a multi-anchor that is related to bio-entity nodes, namely the [author/section]-entity bipartite network. To demonstrate the usefulness of the DCS and ICS network, a conventional full-text network is formed for comparison analysis. In addition, during this process, MeSH tree structure is used to examine the bio-entity level characteristics. The results show that DCS and ICS network demonstrate distinct network characteristics and provide unobserved top-ranked bio-entity pairs when compared to traditional method. This indicates that our method can expand the base of entitymetrics and provide new insights for entity level bibliometrics analysis.

Authors and publishers use different metrics at various levels to estimate the impact of produced research, including the journal-level impact factor, the number of citations at an article-level and the H-index at an author-level. In this paper, we propose an approach to measure the Article Citation Impact (ACI) that will enable idenGEAtification of the impact of articles at their extended nearby citation network. We combine an article’s content with its bibliometrics to evaluate the citation impact of articles in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between two articles in the extended network. The articles’ similarity and bibliometric scores are then used to assess the impact of the article among their extended nearby citation network. In our empirical studies, we use two datasets to validate the efficiency of our approach to evaluate the impact of articles on improving article recommendation processes. The experimental results highlight the effectiveness of the proposed approach to optimize the overall recommendation quality, compared to other baseline approaches.

When the probability of being cited depends on the outcome of that study, this is called citation bias. The aim of this study is to assess the determinants of citation and how these compare across six different biomedical research fields. Citation network analyses were performed for six biomedical research questions. After identifying all relevant publications, all potential citations were mapped together with the actually performed citations in each network. As determinants of citation we assessed the following: study outcome, study design, sample size, journal impact factor, gender, affiliation, authority and continent of the corresponding author, funding source, title of the publication, number of references, and self-citation. Random effect logistic regression analysis was used to assess these factors. Four out of six networks showed evidence for citation bias. Self-citation, authority of the author, and journal impact factor were also positively associated with the probability of citation in all networks. The probability of being cited seems associated with positive study outcomes, the authority of its authors, and the journal in which that article is published. In addition, each network showed specific characteristics that impact the citation dynamics and that need to be considered when performing and interpreting citation analyses.

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers’ contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user input keywords. Our advantages comparing to the existing methods include: (1) Reaching a better recommendation results by taking the advantages of both content-based recommendation and citation-based recommendation and exploring much richer information of papers in one method; (2) Effectively solving the cold-start problem for new published papers by considering the vitality of papers and the impact factor of venues into the citation network; (3) Saving a large overhead in calculating the content-based similarity between papers and user input keywords by doing paper clustering based on the citation network. Extensive experiments on DBLP and Microsoft Academic datasets demonstrate that PubTeller improves the state-of-the-art methods with 4% in Precision and 4.5% in Recall.

An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.

Citation score has become a very important metric to assess the quality of a publication in the current global ranking scenario. In this context, the study of citation networks gains importance as it helps in understanding the citation process as well as in analyzing citation trends in the research world. Citation networks are modeled as directed acyclic graphs in which publications of the authors are considered as nodes and citations between the papers form the links. In this paper, we propose an additive Time-Stamp based Network Evolution(TNE) model for citation networks, extending Price’s preferential attachment model by including the recency effect on the citation process without neglecting the impact of classical papers. We propose a more meaningful definition of clustering coefficient for citation networks in terms of ’citation triangles’. Further, the network simulated by the TNE model with best-fit parameters is compared with the real-world(DBLP) citation network. The results of various significance tests show that the simulated network matches very well with the DBLP citation network in terms of several network properties.